EE

Colloquium On Investigation Of Direct Lightning Strike To High Voltage Transmission Lines
2024-04-10 09:30 - 11:30 (Asia/Kolkata) | HV seminar Hall
Name of the Candidate : Mr. Sukesh A
Degree Registered : Ph.D.
Title of the thesis : Investigation Of Direct Lightning Strike To High Voltage Transmission Lines
Time and date : 9.30 AM, 10th April 2024
Venue : High Voltage Laboratory Seminar Hall
Research supervisor : Udaya Kumar
ABSTRACT
Lightning continues to be the single largest natural cause of line outages. With high voltage lines spanning millions of kilometres around the globe, it has been a serious concern to transmission line engineers. Quite unfortunately, the lightning performance of ultra-high voltage lines falls far below that estimated from the available models. There are two reasons for this difference, namely, inadequacy in the present standards in evaluating the lightning attachment process and in ascertaining the surge response of these tall lines.
The recent progress in simulating the attachment process is dealt with in the latest CIGRE technical brochure. It is shown that the simplified model proposed over there can account for the observed differences between estimated using older models and the field data.
Different modelling approaches have been employed to evaluate lightning surge response. The most popular approach in power engineering is a lumped current source in parallel with an impedance. However, there is no consensus among researchers regarding the value of impedance to be used in such models. Based on observations over tall towers, it has been inferred that the peak value of the lightning current depends on the strike object. Importantly, a seven years long study based on measurements on heavily instrumented UHV lines in Japan has provided additional information that is not in line with expectations.
An alternative modelling approach for evaluating the lightning surge response of lines is to make use of the return stroke models. Among the different categories of return stroke models available, only the transmission line and electrodynamic models can, in some sense, be employed for modelling a direct strike to the transmission line. Even though current is not assumed in the transmission line models, the mode of propagation is assumed as TEM mode, which is not true on the channel, at least in the initial stages of current evolution. Even though the mode of propagation is not assumed in electrodynamic models, usually, a lumped excitation is assumed.
Therefore, a model that does not assume the excitation and the mode of propagation is essential. A self-consistent return stroke model recently proposed by the group would be an ideal tool for the required investigation. It basically emulates basic essential physical processes along with accurate tracing of dynamic electromagnetic fields using time-domain thin-wire formulation.
In order to apply this model to simulate a direct strike scenario to a transmission line, the field computation methodology had to be extended from the axisymmetric to a fully three-dimensional wire geometry. Further, wire junctions need to be considered. These were achieved and for the latter, spatial basic function is borrowed by the Numerical Electromagnetic Code.
Using this extended self-consistent return stroke model, detailed simulations are carried out for a direct strike to the transmission line. The peak current during a strike-to-phase conductor and strike-to-ground (or simplified ground wire-tower structure) are deduced. Using this consistent modelling approach, it has been clearly shown by modelling that the peak current value for the same conditions reduces to half of that for a strike to ground/ground wire-tower structure. The physical reason for this difference has also been identified. Furthermore, the mode of propagation of the stroke current along the phase conductor is also assessed. In summary, this work has delved into some of the fundamental aspects of lightning strikes.

Colloquium on Estimation of flashovers in the EHV/UHV lines on the east coast due to lightning produced by the Bay of Bengal cyclones
2024-04-12 09:30 - 11:00 | HV seminar Hall
Name of the Candidate: Anirban Chatterjee
Title of the Thesis: Estimation of flashovers in the EHV/UHV lines on the east coast due to lightning produced by the Bay of Bengal cyclones
Degree Registered: MTech (Res) in Electrical Engineering
Time and date: 9.30 AM, 12th April 2024
Venue: High Voltage Laboratory Seminar Hall of EE Department
Research Supervisor: Professor Udaya Kumar
Abstract
The Bay of Bengal produces a considerable number of cyclones. Many of them invade the east coast of India. They can cause structural damage to towers, substation flooding, conductor snapping, etc. In many cases, through lightning, they cause several flashovers on the EHV/UHV grid. However, there is no serious effort to estimate the possible number of flashovers caused by the lightning produced by such cyclones. The present work aims to fill this serious gap.
The estimation of such lightning-induced flashovers requires several aspects, both electrical and cyclone-related. The lightning stroke could be intercepted by the tower/ground-wire, or it can strike the phase conductor. The electro-geometric model(EGM), suggested in IEEE standards, is employed for assessing the normalized number of strokes striking the phase conductor and intercepted by the tower/ground-wire. The associated probabilities are also estimated for typical EHV and UHV lines.
The relation between the peak stroke current and the rise time is made based on the literature. Then, by modelling the lines in EMTP with a multi-story model for the tower, simulations are carried out to deduce the corresponding voltage rise. Using this information and the BIL of the line, the possibility of flashovers is assessed.
The trajectory of the cyclone and the speed, along with the number of lightning flashes produced by them are assimilated from different sources. Modelling the cyclone as a disc above ground, the line length shadowed as a function of time is calculated. In addition, equivalent ground flash density per square km per hour is also calculated. Combining all these information, the possible number of lighting-induced flashovers in the EHV/UHV grid along the east coast is estimated. Within three to four days the number of such flashover incidents can range from a few to few tens of flashovers within a time span of two to four days.

Talk On Making Machine Learning Models Safer and Better: Data and Model Perspectives
2024-04-12 16:00 - 17:30 | B306 , EE Dept
Abstract: As machine learning systems are increasingly deployed in real-world settings like healthcare, finance, and scientific applications, ensuring their safety and reliability is crucial. However, many state-of-the-art ML models still suffer from issues like poor out-of-distribution generalization, sensitivity to input corruptions, requiring large amounts of data, and inadequate calibration – limiting their robustness and trustworthiness for critical real-world applications.
In this talk, I will present a broad overview of different safety considerations for modern ML systems. I will then proceed to discuss our recent efforts in making ML models safer from two complementary perspectives – (i) manipulating data and (ii) enriching the model capabilities by developing novel training mechanisms. First, I will discuss our work on designing new data augmentation techniques for object detection followed by demonstrating how, in the absence of data from desired target domains of interest, one could leverage pre-trained generative models for efficient synthetic data generation. Next, I will introduce a new paradigm of training deep networks called model anchoring and show how one could achieve similar properties to an ensemble but through a single model. I will specifically discuss how model anchoring can significantly enrich the class of hypothesis functions being sampled and demonstrate its effectiveness through its improved performance on several safety benchmarks. Finally, I will present our efforts in proactively identifying samples on which a model would fail through a novel model counterfactual synthesis technique by leveraging foundation models (e.g., GPT family and CLIP). I will then conclude by highlighting exciting future research directions for producing robust ML models through leveraging multi-modal foundation models.

Bio: Kowshik Thopalli is a Machine Learning Scientist and a post-doctoral researcher at Lawrence Livermore National Laboratory. He is currently mentored by Dr. Jay Thiagarajan. His research focuses on developing reliable machine learning models that are robust under distribution shifts. He has published papers on a variety of techniques to address model robustness, including domain adaptation, domain generalization, and test-time adaptation using geometric and meta-learning approaches. His expertise also encompasses integrating diverse knowledge sources, such as domain expert guidance and generative models, to improve model data efficiency, accuracy, and resilience to distribution shifts. He received his Ph.D. in 2023 from Arizona State University under the mentorship of Dr. Pavan Turaga.
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All are welcome.

Colloquium on Development of Pulsed Power Systems and Tooling Coils for Electromagnetic Manufacturing Applications
2024-04-15 15:00 - 17:00 | HV seminar Hall
Title: Development of Pulsed Power Systems and Tooling Coils for Electromagnetic Manufacturing Applications
Speaker: DEEPAK KAUSHIK . of Ph.D. (Engg) in Electrical Engineering under Electrical Engineering
Date/Time: April 15 / 15:00:00
Location: HV Lab Seminar Hall
Research Supervisor: Joy Thomas M
Abstract:
Electromagnetic pulse manufacturing is an emerging non-conventional manufacturing technique used for the forming of workpieces at very high strain rates. The manufacturing assembly consists of the tooling coil (or actuator), the workpiece, a pulsed power source, and finally, a suitable die as per the required final shape that needs to be achieved. In this work, the author focuses on developing high voltage and high current pulsed power sources as well as the tooling coils required for the electromagnetic forming applications. The study encompasses theoretical simulations and practical experiments pertaining to electromagnetic forming applications involving workpieces made of sheet metal and tubular structures. Electromagnetic forming is a complex multi-physics process that involves electromagnetic, thermal, and mechanical fields which are strongly coupled to each other. Initially, the author focused on understanding the interaction between various physical fields involved and development of fast analytical methods to predict the deformation in the workpiece due to their interaction. Impact velocity and the pressure applied to the workpiece are critical aspects that determine the workpiece’s deformation. Based on the developed coupled models, the author has proposed techniques to control the impact velocity and the applied pressure on the workpiece and designed the Pulsed Power Systems to achieve the same. The tooling systems are the most vital components in the electromagnetic forming process. This is because they generate the necessary pressure on the workpiece to achieve the final desired shape. The author has designed and developed various tooling coil assemblies for sheet metal forming and operations on tubular workpieces. The author has developed a novel clamp-on type electromagnetic tooling coil for agile manufacturing of tubular components. The designed actuator offers several advantages over conventional helical tooling systems, including a pressure distribution which does not produce any end effects as opposed to the pressure on the workpiece dropping to 58% of the peak in a conventional helical actuator. It is also marginally less sensitive to standoff distance, where the reduction in the peak pressure is only 13% as compared to the conventional actuator, which shows a drastic drop of 56% as the standoff distance varies from 2 mm to 5 mm. The proposed clamp-on tooling coil is also robust to failure as the stress on the proposed actuator is compressive and reinforces it against the toroidal former. In contrast, the stress on the conventional helical actuator coil is repulsive, requiring external reinforcements. The proposed actuator is openable and can be reused easily for repeated applications. For sheet metal forming, the author optimized the uniform pressure actuator and proposed a high-efficiency dual-channel uniform pressure tooling coil (UPTC). The salient features of the designed dual-channel actuator are as follows. The designed tooling coil draws 6.2% more current and applies 24.9% more force than the conventional UPTC for the same pulsed power source parameters. The spatial distribution of the pressure is identical in both the tooling coils, but the magnitude of the pressure in the proposed dual-channel tooling coil is about 23% higher. In addition, the proposed tooling coil also has better capabilities for handling electromagnetic stress during the forming process. For the first time, the author has integrated attractive and repulsive sheet metal forming technology into a single assembly. The author introduces a novel design and analysis of a dual-mode universal uniform pressure tooling coil that integrates both forming techniques, simplifying the requirements for the pulsed power system. Finally, the author has studied the effect of tooling coil designs on the efficiency of the forming process. The study aims to find the effect of coil design on the forming efficiency and compares the performance of multi-turn and multi-layer coils over the existing tooling coils. The study has been validated using the development of multi-layered uniform pressure tooling coils with experiments performed on free bulging of AA-6061-T6 sheets. The author found that the multi-layered tooling coils outperformed the single-layered wire-wound coils for all values of the system capacitances used.
Meeting Link

Colloquium On Plasma activated/assisted solid wastes in diesel exhaust treatment: a case study with agricultural, industrial and composite wastes
2024-04-23 09:30 - 11:30 | HV seminar Hall
Name of the candidate: Ms. Apoorva Sahu
Research Supervisor: BS Rajanikanth
Degree Registered: PhD
Department: Electrical Engineering
Title of the Thesis: Plasma activated/assisted solid wastes in diesel exhaust treatment: a case study with agricultural, industrial and composite wastes
Time and Date: 9.30 AM, 23rd April 2024 (tuesday)
Venue: High Voltage building seminar hall
Synopsis
The consumption of crude oil is increasing every day particularly in developing countries like India which is the third largest consumer of crude oil in the world, utilizing on an average around 160 million litres per annum of which 30% constitutes diesel fuel. More than 50% of the NOx and hydrocarbons in air come from the diesel exhaust affecting the health of human beings, vegetation, and environment. While the solid particulate in the exhaust is taken care to a greater extent by the mechanical filters it is the gaseous pollutants such as oxides of nitrogen (NOx), oxides of carbon, hydrocarbons (HC) etc., that need to be addressed as they cause several health-related ailments in addition to acid rain, global warming, smog etc. It is timely to work on the development of economical and efficient pollution control strategies. On the other hand, issues that is affecting our country India currently are accumulation of wastes from utility industry, mariculture industry and agriculture industry. The prominent amongst them are fly ash, red mud, foundry sand, iron ore tailings, lignite ash, rice husk, wheat straw and sugarcane bagasse etc. Accumulation of these wastes are in several million tons per annum in India. Any proposition in recycling waste is a welcoming step.
Efforts are continuously being made for the past three decades to mitigate these gaseous pollutants, particularly NOx, at various levels by changing the fuel composition, engine modifications, pre-combustion techniques and post-combustion (aftertreatment) techniques. The existing post-combustion mode technique “catalyst-based converters and adsorbent based techniques” are becoming expensive owing to the short life, dependency on noble metals, more vulnerability to acidic coating, bulk usage of adsorbents etc. In this regard the application of non-thermal plasma or electric discharge plasma for pollution control aided by additional techniques is slowly gaining popularity in the past few years. Discharge plasma ionizes the atoms at normal temperature and atmospheric pressure (NTP) thus creating an oxidative environment resulting in chemical reactions such as reduction, oxidation, decomposition etc. However, among these reactions it was observed that oxidation was dominating, to a certain extent the oxidised by-products in the plasma appeared to be less hazardous to humans than to nature. This led to the redesigning of plasma reactors with the intention of enhancing the energy in the charged species favouring reduction reactions instead of oxidation ones but not without serious limitations with respect to the gas flow.
Application of electrical discharges for environmental purposes lies in the basic concept of treating the pollutants, particularly the gaseous ones, with plasma excited species. It is observed that plasma alone is insufficient for the successful treatment of any of the gaseous pollutants due to the oxidative environment prevailing in the discharge plasma at atmospheric conditions. This necessitated inclusion of additional treatment technique along with plasma leading to the origin of plasma catalysis/plasma adsorption methods where in the catalytic materials were kept inside the plasma environment (plasma catalysis) or the adsorbent materials are cascaded with plasma (plasma adsorption). It should be noted here that the total cost involved in the proposed technique, should be lower than that associated with conventional catalyst-alone or adsorbent-alone techniques so that the proposed ones can be a promising economic alternative to the conventional ones. That said, cascading commercially available catalysts/adsorbent with plasma can never be a cheaper alternative. Several research works, therefore, started by blending plasma with other lab made catalysts/adsorbents but the DeNOx efficiency was not significant.
Present work aimed at studying the NOx abatement in diesel engine exhaust at controlled laboratory condition using electrical discharges. The intention of the study is to provide not only an efficient but also an economical solution for reduction of the NOx pollutants. Keeping this in mind, it was decided to utilize electrical discharge technique in association with abundantly available solid wastes in India be it from industrial, mariculture or agriculture domains. Given the whole spectrum of parametric variations the thesis plan was carefully drawn to touch upon the following variations: type of corona electrodes, type of applied high voltage, type of solid wastes, type of gas treatment. Four types of electrodes were studied that include needle plate, metal film, helical wire and pipe type. Type of voltage involves fast rising repetitive pulses at 80 Hz, power frequency and high frequency AC. The solid wastes comprise of three categories namely (a) industrial/mariculture wastes that include namely iron tailings, lignite ash, red mud, foundry sand, waste tiles and copper slag, oyster shell (b) agricultural wastes including coffee husk, sugarcane waste, mulberry husk, rice husk, ragi husk, corn husk, wheat husk, pine, ground nut and areca nut husk and (c) composite wastes which include a blend of waste tiles + foundry sand, copper slag + red mud, iron tailings + waste tiles, red mud + waste tiles, foundry sand + red mud and foundry sand + iron tailings. The type of gas treatment involves treating the exhaust with only plasma/plasma catalysis/plasma adsorption/thermal catalysis utilizing catalytic properties of metal oxides present in the industry wastes or porous nature of the industry wastes. A comparison was also made by replacing the industrial wastes with commercial NOx catalysts. Important contribution of this research work can be summarized as: (a) with plasma catalysis approach the NOx removal efficiency gets enhanced by a factor of 5.3-6.7 compared to only plasma. (b) with plasma adsorption approach the NOx removal efficiency gets enhanced by a factor of 4-6 compared to only plasma. (c) Fe2O3/TiO2 present in red mud can act as photo catalysts in oxidizing NO through plasma generated ethyl nitrate in the plasma cascaded red mud adsorption process (d) Amongst the agricultural wastes, ground nut husk-based pellets exhibited 83% NOx removal efficiency (e) The newly developed metal film based DBD reactor enhances surfaces discharges far better than the helical wire reactor (f) commercial catalysts performed much better in NOx removal under plasma catalysis mode when compared to thermal catalysis mode. Further, plasma catalysis with industrial wastes such as iron tailings and oyster shell yielded better/similar DeNOx efficiency when compared to that with commercial NOx catalysts thus, justifying the usage of cheaper industrial wastes instead of expensive commercial ones.

Defense Talk on Investigating Neural Mechanisms of Word Learning and Speech Perception
2024-04-23 10:30 - 11:30 | Multi-Media Class Room (MMCR), EE Department (Hybrid mode)
Title
Investigating Neural Mechanisms of Word Learning and Speech Perception: Insights from Behavioral, Neural, and Machine Learning Perspectives
Speaker: Ms. Akshara Soman
Time: 1030am-1130am
Venue: MMCR, EE, C241 and on the Teams-Meeting-Link

Abstract
The process of language learning and speech perception is a remarkable feat of the human brain, involving complex neural mechanisms that allow us to understand and communicate with one another. By employing a multidisciplinary approach, this talk sheds light on the underlying processes involved in word learning and speech perception.
The talk begins by examining how imitation of sounds influences language learning and language discrimination using EEG signals. Results show that time-frequency features and phase in the EEG signal contain information for language discrimination. Further experiments confirm these findings and analyse improvements in pronunciation over time, identifying frontal brain regions involved.
The talk then discusses how learning patterns change when semantics are introduced. Participants learn Japanese words and undergo ERP analysis, revealing distinct EEG patterns for newly learned words. Notably, a delayed P600 component emerges, suggesting short-term memory processing. Based on the above neuro-behavioural experiments, a machine model is proposed to compare human-machine performances in audio-visual association learning. The model performs comparable to humans in learning with few examples, with slightly inferior generalisation abilities.
Moving to naturalistic stimuli, the talk analyses continuous speech perception using a deep learning model. This model achieves 93% accuracy in stimulus-response modelling on a speech-EEG dataset, surpassing previous efforts. It demonstrates the role of word-level segmentation during speech comprehension in the human brain. We further extend this study to investigate speech perception in complex listening environments where multiple speech streams are heard simultaneously. Our proposed model, based on Long Short-Term Memory (LSTM), reveals that the human brain prioritises understanding the semantics rather than the acoustics under such challenging listening conditions. The proposed model has potential applications in speech recognition, brain-computer interfaces, and attention studies.
Overall, the thesis enhances our understanding of language learning, speech comprehension, and the neural mechanisms involved. It provides insights into familiar and unfamiliar language processing, semantic effects, audiovisual learning, and word boundaries in sentence comprehension. These findings have implications for both human language learning and the development of machine systems aimed at understanding and processing speech.
Coffee/Tea will be served before the talk.

Open Ph.D. Thesis Defense of Mr. Ranashree Ram
2024-05-08 10:30 - 11:30 | High Voltage Lab Seminar Hall (Hybrid mode)
Ph.D. Thesis Open Defense
Student Name : Ranashree Ram
Thesis Title : Developmental Studies on a Multistage Induction Coilgun-Based Electromagnetic
Launcher
Place : High Voltage Lab Seminar Hall / MS Teams
Date & Time : 8th May 2024, 10.30 AM
Microsoft Teams Link
Abstract
The archetypal chemical propellant-based launchers (e.g., guns, missiles, spacecraft launchers, etc.) with their hot trailing plume has been widely deployed over the decades for various applications. However, because of certain disadvantages of these systems and the physical limitations associated with their designs, electromagnetic launchers (EMLs) seem to offer an alternative way forward as the next-generation hypervelocity (>3 km/s) launchers. The multistage induction coilgun is one such futuristic class of EMLs that works on the principle of electromagnetic induction between an array of coils (or drive coils), which are wound on a long barrel of appropriate length, and an electrically conducting projectile (or armature) placed inside the barrel. Previously charged high-voltage capacitor banks are sequentially discharged into the coils through solid-state switches leading to the generation and flow of very high pulsed currents (kA) through the coils. Time-varying magnetic flux thus produced by the pulsed currents through the coils interact with the projectile inside and induce a resultant current on it. The propulsive electromagnetic force exerted on the projectile is a product of the current through the coil, the induced current on the projectile, and the mutual inductance. The “turn on” and “turn off” of the coils of the various stages must be precisely and appropriately synchronized during the multistage operation to achieve a higher projectile velocity and this makes its successful design and operation a challenge. Owing to its high confidentiality in defense and space applications, not much can be known from the published works. In the present work a four-stage induction coilgun has been designed and developed in the laboratory. The research work presented in the thesis aims to understand the factors contributing to achieving a higher muzzle velocity for a projectile of a given mass while launching a payload with the coilgun. The projectile of a coilgun can be either sleeve-type (ring-shaped projectile) or solenoid-type (multi-turn projectile).
The author also designed and fabricated a high-speed infrared transmitter-receiver-based sensor (with 25 ns rise and fall time) to quickly sense the moving projectile (or armature) inside the barrel. The triggering instant of the subsequent stage coils of a multistage coilgun critically depends on the projectile’s position inside the barrel. The projectile will fail to achieve the highest muzzle velocity if the subsequent stage coils are not optimally triggered in a sequence. The fast-moving projectile through the barrel necessitates the fast sensing of its position inside the barrel. In addition, the author has also designed, developed, and fabricated a high-speed gate driver circuit with a peak 25 kV DC isolation for the signal circuit from the high voltage power circuit within a compact space of the printed circuit board (PCB) to trigger the high-voltage SCRs used for triggering the pulsed power source of each stage of the coilgun..
The large current flowing through each stage coil creates EMI problems in the coilgun. The EMI issues corrupt the sensor data, which prevents successful sensing of the projectile’s position. Also, EMI causes the SCRs to trigger the coils spuriously even when the projectile has not reached its optimal triggering position inside the coil. Synchronizing the triggering of stages by preventing the EMI issues is a significant challenge and is very important in successfully operating a multistage induction coilgun. The author could successfully synchronize the stages of the coilgun by preventing spurious triggering of the SCRs using appropriate EMI mitigation techniques.
The influence of the capacitance of the capacitor bank used in the high voltage pulsed power supply on the optimum triggering position of the projectile inside the drive coil of the coilgun has been analyzed. An empirical relationship between the projectile velocity and the charging voltage of the capacitor bank has been formulated for the first time in this thesis. The subject of the study presented in this thesis also focuses on analyzing the parameters on which the efficiency of an induction coilgun depends and how it can be optimized. Study has been performed to optimize the shape and dimensions of the projectile to achieve the highest muzzle velocity. The dependency of the projectile motion on the flow of induced current in the subsequent stages has been analyzed. The study also focuses on establishing an approach to choosing a proper distance between the stages in a multistage induction coilgun.
A comprehensive and explicit analysis has been performed to study and explain the reasons behind the differences in the optimum triggering positions of the projectile inside each stage coil and the achieved muzzle velocities for different arrangements of the drive coil current directions in a multistage induction coilgun.

PhD Colloquium
2024-05-20 11:00 - 13:00 | MMCR, Hall C 241, 1st floor, EE department
Title of Thesis: Frequent Episode Mining: Efficient Discovery Algorithms and Significance Analysis
Speaker: Mr. Santhosh B. Gandreti
Date/Time: Monday 20th May 2024
11:00 AM
Venue: MMCR, EE Department
Research Supervisor: Prof. P. S. Sastry
Abstract:
Frequent Pattern Mining is a popular area of data mining aimed at discovering interesting patterns that occur often in a given data. These patterns represent structures encapsulating correlations and dependencies among data elements. This thesis focuses on frequent episode mining which is aimed at discovering temporal patterns known as episodes in sequential data of event sequences.
Episodes are collections of event-types constrained by a partial order. An episode is frequent if its number of occurrences exceeds a user-defined threshold. Techniques for mining episodes employ either Breadth-First Search (BFS) or Depth-First Search (DFS) approaches to navigate the episode space. The talk begins by giving a brief introduction to frequent episode mining and various algorithms dealing with discovery of frequent episodes.
The talk next discusses a novel DFS algorithm for discovering injective general episodes and chain episodes, which are two broad subclasses of episodes. The proposed algorithms are more efficient compared to the state-of-art as demonstrated by empirical results. The talk next considers more complex patterns called episodes with simultaneous events, where the episodes contain multiple event-types at the same time instant. A novel BFS algorithm is presented for discovering serial episodes with simultaneous events. Through simulations on both synthetic and real data sets, the effectiveness and efficiency of the proposed algorithm is demonstrated.
The next part of the talk discusses significance analysis that assesses the statistical relevance of episodes and presents two novel approaches for significance analysis; one for non-overlapped occurrences under Markov-null hypothesis and the other for minimal occurrences under IID-null hypothesis. This analysis helps determine an episode-specific frequency threshold for the episode to be statistically significant, providing a more nuanced understanding of pattern relevance. For both the above mentioned cases, specialized Markov chains capturing the occurrences of interest are derived for a given episode, which help in computing episode-specific thresholds on frequency, to access its significance. Effectiveness of the proposed methods is confirmed through empirical studies.

Ph.D. Thesis Colloquium of Baby Sindhu A. V.
2024-05-24 10:00 - 12:00 | High Voltage Lab Seminar Hall (Hybrid mode)
Colloquium

Candidate’s Name : BABY SINDHU A V
Degree Registered : Ph.D.
Date & Time : 24th May 2024 @ 10.00 AM
Venue : Seminar Hall, High Voltage Lab and in Teams Link
Title : Developmental Studies on Polymeric Nano/Micro Composite
Insulation for Various High Voltage Power Applications

Abstract

The demand for electrical power is increasing day by day necessitating a higher voltage level for power transmission. Also the development of high speed rails , electric vehicles, more electric aircrafts and all electric ships call for improvement in electric motor capacity in those vehicles. Also the use of cast resin type dry transformers in high moisture area and confined area is increasing since it is more reliable in extreme conditions and also they require less maintenance. All these applications demand for better insulating materials which can address all the above issues cost effectively. In recent years, the use of polymeric insulating material in HV power apparatus is increasing. Hence this study focuses on the development of polymeric composite insulating material with better performance for various electrical power applications.
Silicone rubber is a preferred material for use as weathershed material in outdoor polymeric insulators used in high voltage power transmission lines. The tracking & erosion on the insulator surface due to the electrical discharges and corona cutting of the insulator surface are the main issues related to outdoor polymeric insulators and these are addressed in this study. Tracking and erosion performance of silicone rubber filled with nano/micro fillers of different loadings is analysed using Inclined Plane Test (IPT) as per IEC 60587. A computational study on the behavior of the samples subjected to tracking is also done and the same is verified with the experimental results obtained in this work. Corona ageing studies are done by ageing the samples in a corona chamber for 25 hours. Hydrophobicity changes, crack width formation and erosion performance after corona ageing are evaluated. An effort is made to correlate the value of leakage current to the eroded mass and a reliable online condition monitoring tool is also developed as a part of the thesis work.
Again, epoxy is extensively used in many electrical power apparatus such as ground wall insulation of the high voltage rotating machines, as spacer material in Gas Insulated Substations (GIS), as solid insulation in dry type transformers etc. Heat dissipation is an important area of concern when using epoxy as ground wall insulation in rotating machines and as an insulation in cast resin dry type transformer. The performance of epoxy filled with nano/ micron sized fillers are investigated in this study in terms of their heat removal capacity and at the same time retaining their dielectric properties. The improvement in thermal conductivity is correlated with the performance of various composites developed. The formation of track in the ground wall insulation and the failure of the machine is a major issue as far as rotating machines are considered. Hence the tracking time of various epoxy composites are observed and compared. The initiation of a faint track on the surface of the insulator is monitored with the help of a ratio of third harmonic component to the fundamental component. This ratio can be used as an efficient condition monitoring tool for rotating machines by measuring the leakage current online.
In summary polymeric composite insulating materials based on silicone rubber and epoxy with different fillers and loadings and having better electrical and thermal performance than the conventional materials are developed in this study.

Faculty Candidate Talk on Blind speaker separation from noisy speech mixtures
2024-06-04 15:00 - 17:00 | Multi-Media Class Room (MMCR), EE Department (Hybrid mode)
Faculty Candidate Talk
Title: Blind speaker separation from noisy speech mixtures
Date and Time: 3:00 PM: 4th JUNE, 2024.
Location: MMCR, EE dept (Online link)

Abstract:
Blind separation of speech mixtures into individual speaker signals is crucial for several speech processing applications, including teleconferencing. These applications require blind speech separation (BSS), i.e., without any additional information about the speakers in the mixture or their count, for both transcription and communication. This task becomes particularly difficult when the number of speakers in the mixture is unknown and recordings are made using a single microphone. In a recent work, we developed a deep-learning-based system for BSS from noisy single-channel mixtures, with an unknown number of speakers in the mixture. The work employs a transformer-based neural network architecture with an attractor generation scheme, allowing it to count the speakers and separate their signals simultaneously. In my presentation, I will share the results from experimental validation on simulated speech mixtures. Our findings show that the system can achieve 18 dB or more improvement in signal-to-distortion ratio and 99% accuracy in speaker counting for mixtures with up to three speakers. Additionally, I will also discuss the insights gained into the model’s internal mechanics, by examining the attention patterns computed in the transformers. We also observed that these findings apply universally across different transformer configurations used in other tasks, such as ambisonic-to-ambisonic and multi-channel speech separation.

Bio: Srikanth Raj Chetupalli received the Master of Engineering and Doctor of Philosophy degrees from the Division of Electrical Sciences, Indian Institute of Science (IISc.) Bengaluru, India, in 2011 and 2020, respectively. He is currently a Postdoctoral Researcher with the International Audio Laboratories Erlangen (a joint institution of the Friedrich-Alexander-Universität Erlangen-Nürnberg and Fraunhofer Institute for Integrated Circuits IIS), Erlangen, Germany. His research interests include speech processing, multimicrophone processing, spatial audio processing, and in particular, source extraction, speech dereverberation, acoustic parameter estimation, and speaker diarization. He was the recipient of the Tata Consultancy Services Research Scholarship from 2015 to 2019.

Thesis defense on Estimation of flashovers in the EHV/UHV lines on the east coast due to lightning produced by the Bay of Bengal cyclones
2024-06-10 11:00 - 13:00 | Multi-Media Class Room (MMCR), EE Department (Hybrid mode)
Name of the Candidate: Anirban Chatterjee
Title of the Thesis: Estimation of flashovers in the EHV/UHV lines on the east coast due to lightning produced by the Bay of Bengal cyclones
Degree Registered: MTech (Res) in Electrical Engineering
Time and date: 11.00 AM, 10th June 2024
Venue : MMCR Seminar Hall of EE Department
Meeting Link
Research Supervisor: Udaya Kumar
Abstract
The Bay of Bengal produces a considerable number of cyclones. Many of them invade the east coast of India. They can cause structural damage to towers, substation flooding, and conductor snapping. In many cases, lightning causes several flashovers on the EHV/UHV grid and they are much more in numbers than the former. However, no serious effort was made to estimate the possible number of flashovers caused by the lightning produced by such cyclones. The present work aims to fill this serious gap.
Estimating such lightning-induced flashovers requires several aspects, both electrical and cyclone-related. The lightning strike could be intercepted by the tower/ground wire, or it can strike the phase conductor. The electro-geometric model (EGM), suggested in IEEE standards, is employed to assess the normalized number of strokes striking the phase conductor and intercepted by the tower/ground wire. The associated probabilities are also estimated for typical EHV and UHV lines.
The simulations are carried out by modeling the lines in EMTP with a multi-story model for the tower and the voltage rise in the system is evaluated. Using this information and the BIL of the line, the possibility of flashovers is assessed.
The cyclone’s trajectory, the speed, and the number of lightning flashes produced by them are assimilated from different sources. Modeling the cyclone as a disc like structure,, the line length shadowed as a function of time is calculated. In addition, equivalent ground flash density per square km per hour is also calculated. Combining all this information, the possible number of lighting-induced flashovers in the EHV/UHV grid along the East Coast is estimated. It amounts to 100s of flashover in 400 kV lines and 1000s of flashover in 220kV line.
For engineering purposes, the maximum number of possible flashovers are required. Based on the maximum number of ground flashes per hour across the cyclones for five years, it was estimated. It amounts to a few to a few tens of flashovers in 765kV grid, a few tens to a few hundred in 400kV grid and that for 220kV grid, it amounted to a few hundred to a few thousands.

EE Talk on Advancements in Power Electronics for Sustainable and Resilient Energy Systems.
2024-06-13 16:00 - 17:30 | MMCR, Hall C 241, 1st floor, EE department
Title: Advancements in Power Electronics for Sustainable and Resilient Energy Systems.

Speaker: Prof Avik Bhattacharya
IIT Roorkee

Date 13/6/2024

Time: 4:00 pm

Venue: MMCR EE

Abstract: This talk delves into the pivotal role of power electronics in advancing the sustainability, reliability, and efficiency of modern energy systems. Key topics include sustainable microgrids, which utilize advanced power electronic converters to seamlessly integrate renewable energy sources and storage systems for resilient, self-sufficient power solutions. The presentation addresses power quality improvement techniques, leveraging power electronics to mitigate issues such as voltage sags, harmonics, and frequency variations, ensuring stable and high-quality power delivery. The development of multilevel solar inverters, which enhance photovoltaic system efficiency and reduce electromagnetic interference, will be explored, showcasing their design and operational benefits. The rapid expansion of electric vehicles (EVs) underscores the necessity for efficient fast charging infrastructure, where power electronics play a crucial role in reducing charging times and enhancing the reliability of charging networks. Lastly, the discussion highlights resilient space converters, emphasizing innovative power electronic designs that ensure robustness against extreme conditions and cyber threats. Integrating these cutting-edge power electronic technologies significantly advances the pursuit of a more sustainable, reliable, and resilient energy future.

PhD Thesis Colloquium of Nareddy Kartheek Kumar Reddy
2024-06-24 16:30 - 17:30 | MMCR, Hall C 241, 1st floor, EE department
Name of the Candidate: Mr. Nareddy Kartheek Kumar Reddy

Research Supervisor: Prof. Chandra Sekhar Seelamantula

Date and time: June 24, 2024; 4.30 PM
Coffee/tea will be served during the talk.

Venue: Multimedia Classroom (MMCR), EE Department, IISc.

Title: Tight Frames, Non-convex Regularizers, and Quantized Neural Networks for Solving Linear Inverse Problems

Abstract:
The recovery of a signal/image from compressed measurements involves formulating an optimization problem and solving it using an efficient algorithm. The optimization objective involves data fidelity, which is responsible for ensuring conformity of the reconstructed signal to the measurement, and a regularization term to enforce desired priors on the signal. More recently, the optimization based solvers have been replaced by deep neural networks.

This thesis considers three aspects of inverse problems in computational imaging: (i) Choice of data-fidelity term for compressed-sensing image recovery; (ii) Non-convex regularizers in the context of linear inverse problems; and (iii) Explainable deep-unfolded networks and the effect of quantization of model parameters.

Part-1: Tight-Frame-Based Data Fidelity for Compressed Sensing
The choice of the sensing matrix is crucial in compressed sensing. Random Gaussian sensing matrices satisfy the restricted isometry property, which is crucial for solving the sparse recovery problem using convex optimization techniques. However, tight-frame sensing matrices result in minimum mean-squared-error recovery given oracle knowledge of the support of the sparse vector. If the sensing matrix is not tight, could one achieve the recovery performance assured by a tight frame by suitably designing the recovery strategy? This is the key question addressed in this part of the thesis. We consider the analysis-sparse l1-minimization problem with a generalized l2-norm-based data-fidelity and show that it effectively corresponds to using a tight-frame sensing matrix. The new formulation offers improved performance bounds when the number of non-zeros is large. One could develop a tight-frame variant of a known sparse recovery algorithm using the proposed formalism. We solve the analysis-sparse recovery problem in an unconstrained setting using proximal methods. Within the tight-frame sensing framework, we rescale the gradients of the data-fidelity loss in the iterative updates to further improve the accuracy of analysis-sparse recovery. Experimental results show that the proposed algorithms offer superior analysis-sparse recovery performance. Proceeding further, we also develop deep-unfolded variants, with a convolutional neural network as the sparsifying operator. On the application front, we consider compressed sensing image recovery. Experimental validations on Set11, BSD68, Urban100, and DIV2K datasets show that the proposed techniques outperform the state-of-the-art techniques, where the performance is measured in terms of peak signal-to-noise ratio (PSNR) and structural similarity index metric (SSIM).

Part 2: Proximal Averaging Methods for Image Restoration and Recovery
Sparse recovery methods are iterative and most techniques typically rely on proximal gradient methods. While the commonly used sparsity promoting penalty is the l1-norm, which is convex, alternatives such as the minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD) penalty have also been employed to obtain superior results. Combining various penalties to achieve robust sparse recovery is possible, but the challenge lies in optimal parameter selection. Given the connection between deep networks and unrolling of iterative algorithms, it is possible to unify the unfolded networks arising from different formulations. We propose an ensemble of proximal networks for sparse recovery, where the ensemble weights are learnt in a data-driven fashion. The proposed network performs superior to or on par with the individual networks in the ensemble for synthetic data under various noise levels and sparsity conditions. We demonstrate an application to image denoising based on the convolutional sparse coding formulation.

Part 3: Deep Unfolded Networks, Quantization, and Explainability

Deep-unfolded networks (DUNs) have set new performance benchmarks in compressed sensing and image restoration. DUNs are built from conventional iterative algorithms, where an iteration is transformed into a layer/block of a network with learnable parameters. This work focuses on enhancing the explainability of DUNs by investigating potential reasons behind their superior performance over traditional iterative methods. Our findings reveal that the learned matrices in DUNs are unstable because their singular values exceed unity. However, the overall DUN gives rise to a recovery accuracy higher than the optimisation techniques. This goes to show that although the linear/affine components of the DUN are unstable, the overall network is stable, which leads us to conclude that it is the nonlinearities, more precisely, the activation functions, that are responsible for restoring stability. This study illustrates an intriguing property of deep unfolded networks, which is not observed in standard optimization schemes.

We also consider quantization of the network weights for efficient model deployment in resource-constrained devices. Quantization makes neural networks efficient both in terms of memory and computation during inference and also renders them compatible with low-precision hardware deployment. Our learning algorithm is based on a variant of the ADAM optimizer in which the quantizer is part of the forward pass. The gradients of the loss function are evaluated corresponding to the quantized weights while doing a book-keeping of the high-precision weights. We demonstrate applications for compressed image recovery and magnetic resonance image reconstruction. The proposed approach offers superior reconstruction accuracy and quality than state-of-the-art unfolding techniques, and the performance degradation is minimal even when the weights are subjected to extreme quantization.

Impact of the research: The novel techniques proposed in this thesis led to improved accuracy in linear inverse problems — sparse signals recovery, compressed image recovery, image deconvolution, and image denoising. The tight-frame based algorithms require fewer iterations to converge, thus reducing the reconstruction time. The quantized neural networks, on the other hand, improved the inference time and reduced the model footprint for efficient deployment on the edge. Analysis of deep-unfolded networks has shown that the learnt weights follow a Gaussian distribution suggesting more efficient initialisation schemes than weights derived from ISTA. We also identified potential local instabilities in a deep learning setting, which are avoided in a conventional optimization setting. The role of the nonlinearity is to restore stability. The analysis showed that while deep unfolded networks have potential instabilities, they can be useful for solving inverse problems.

Biography of the Candidate:
Nareddy Kartheek Kumar Reddy is a PhD student in the Spectrum Lab, Department of Electrical Engineering at the Indian Institute of Science (IISc). He received a Bachelor of Technology (Honors) degree from Indian Institute of Technology Kharagpur in 2016. Subsequently, he worked as a Senior Engineer at Honeywell Technology Solutions from 2016 to 2018, where he focused on developing device drivers for SD card and NAND Flash devices which went into production in Honeywell’s flagship weather radar RDR7000.

Kartheek joined IISc as a Masters student in Signal Processing, and subsequently upgraded to PhD after receiving the prestigious Prime Minister’s Research Fellowship in 2019. He is twice recipient of the Qualcomm Innovation Fellowship, once during 2020 & again in 2023. Kartheek enjoys traveling, reading books and manga, watching anime, and playing video games in his leisure time.

EE Talk on Analog Semiconductor innovations in the era of Artificial Intelligence
2024-06-25 10:00 - 17:00
Title: Analog Semiconductor innovations in the era of Artificial Intelligence

Speaker: Dr Sombuddha Chakraborty

Analog Design Manager, TI Kilby Labs (Power)
Date 25/6/2024

Time: 10:00 am

Venue: MMCR EE

Abstract: With the dramatic rise of computation related to the proliferation of AI, powering the GPU hardware poses new challenges and opportunities for power and analog semiconductor companies. This talk will discuss the various semiconductor innovations in development across the industry and how they are shaping data-centre power delivery solutions.
Bio:
Sombuddha Chakraborty (Senior Member, IEEE) received his M.S. and Ph.D. in electrical engineering from the University of Minnesota, Minneapolis, MN, USA, in 2003 and 2006, respectively. Before this, he received his BE from Bengal Engineering College in 2001.
Since 2014, he has been the Design Manager of Power Technology at Texas Instrument’s advanced product development team called Kilby Labs in Santa Clara, CA. His work and research interests include high-density AC/DC and DC/DC power management systems for computing, automotive, and industrial applications using leading-edge processes, package, integration, and circuit techniques to enhance power delivery efficiency.
Sombuddha holds around 30 US Patents and around like number of IEEE publications. He is involved in various IEEE consortiums and serves in IEEE editorial boards.

Ph.D. Thesis Colloquium
2024-07-23 10:00 - 12:00 | MMCR, Hall C 241, 1st floor, EE department
Colloquium Announcement

Title of the Thesis : Power Swing Blocking Protection in Presence of Large Scale Grid Following PV Generation
Name of the Student : Meenu Jayamohan
Name of the Advisor : Dr. Sarasij Das
Degree Registered : PhD (Eng.)
Date and Time : 23rd July, 2024, 10 AM
Location : C 241, MMCR, Electrical Engg Dept

Abstract:
The penetration of Inverter-Based Resources (IBRs) is increasing in power grids due to environmental concerns. The fault behaviour of IBR is quite different than that of Synchronous Generators (SGs). In addition, IBRs usually do not have inherent inertia. As a result, the existing protection schemes, which are traditionally developed for SG-dominated systems, can become ineffective. Stable power swings (SPS) and Unstable Power Swings (UPS) caused by oscillations generated during system disturbances may trigger undesired relay operations. Power swing Blocking (PSB) and Out-of-Step Tripping (OST) techniques have been employed to stop distance relays from malfunctioning during SPS and UPS, respectively. PSB schemes commonly use the magnitude of the rate of change of positive sequence impedance (|dZ/dt|) for SPS detection. This research work focuses on the PSB protection issues in the
presence of large-scale Grid-Following (GFOL) PV generation. A modified IEEE-39 bus system is used for all the studies presented in this thesis.

As the converter controls determine how PV generators behave during transients, the behaviour of SGs used in conventional power systems differs significantly from that of PVs. As a result, existing protection methods, including PSB methods, must be modified to protect the IBR-integrated power systems. This work examines how integrating GFOL PV generation affects power swing impedance (Z) trajectories and |dZ/dt|. The research reveals that the
GFOL PV systems can significantly alter the Z trajectories observed during power swings compared to that of an SG-dominated system. The results presented demonstrate that the penetration of GFOL PV may increase the speed of Z trajectories and, hence, |dZ/dt|, which may, in turn, cause maloperations of the PSB and OST functions. The findings emphasize the critical need to revisit and potentially adapt existing PSB and OST schemes to account for the growing presence of IBRs in power grids.

In the GFOL control strategy, the injected power is controlled with respect to the grid voltages measured at the terminal by the Phase-Locked Loop (PLL). Considering a PLL bandwidth in the range of 2−15 Hz for a weak grid, the PLL dynamics play a significant role in the power swing dynamics. In this work, the impact of various types and control parameters of PLLs on |dZ/dt| and Z trajectories are analyzed using mathematical analysis. Synchronous Reference Frame PLL with additional Low pass filter (LSRF PLL), Multiple Reference Frame (MRF) PLL and Dual Second-Order Generalized Integrator (DSOGI) PLL are used for the study. The impacts of varying penetration of PV and relay locations are also investigated. This study shows that the PLL parameters and bandwidth influence the operation/maloperation of the PSB during SPS.

During Fault Ride-Through (FRT), the PV system can provide additional reactive power to the grid to maintain the voltage at its terminals. This is achieved through the dynamic voltage or reactive power support and is provided in proportion to the drop in terminal voltage using the K-factor. The study also highlights the importance of considering the active power recovery rate to mitigate the oscillatory behaviour of IBR during the fault recovery process. The findings reveal that, following fault removal, the dynamic behaviour of inverters would be significantly influenced by both the K-factor and the active power recovery rate, which may affect the power swing characteristics. This work emphasizes the need for a comprehensive understanding of how dynamic voltage support features and active power recovery interact with the power swing dynamics and influence PSB operation.

Auto-Reclosing (AR) of a circuit breaker is a technique that attempts to re-energize the faulted line after a predetermined time delay. While IEEE Std C37.104-2012 provides guidelines for minimum AR dead time based on arc de-ionization, these may not be sufficient for grids with a high penetration of IBRs. This work explores how varying the three-phase AR dead time can influence the severity of power swings that may occur after consecutive Low-Voltage Ride-Through (LVRT) events in a GFOL PV plant. This finding highlights the potential need to revise
existing minimum AR dead time standards for grids with high IBR penetration levels to ensure reliable system operation.
The studies presented in the previous sections show that existing impedance-based PSB methods might fail in the presence of GFOL PV generation. The lack of inherent inertia of the GFOL PV is one of the reasons behind the increased |dZ/dt| which may cause maloperation of the existing impedance-based PSB schemes. Hence, a novel PSB method is proposed, which uses nodal inertia to re-evaluate the |dZ/dt| values. The effectiveness of the proposed method is verified for both the SG-dominated system and the GFOL PV-integrated system using PSCAD simulations.

Colloquium
2024-07-24 15:00 - 17:00 | Multi-Media Class Room (MMCR), EE Department (Hybrid mode)
Title: Modelling, Analysis and Control of Switched Reluctance Motors

Speaker: THIRUMALASETTY MOULI . of Ph.D. (Engg) in Electrical Engineering under Electrical Engineering
Date/Time: Jul 24 / 15:00:00
Location: Multi Media Class Room (MMCR), EE Department
Research Supervisor: Narayanan G
Abstract:
Switched reluctance machine (SRM) is known for many advantages such as permanent magnet-free operation, robust structure, low rotor inertia, low manufacturing cost, and excellent fault-tolerant capability. Hence, SRM has been adopted in many applications such as, electric vehicles, aerospace, and robotics. Nonlinear characteristics and pulsations in torque developed are well-known problems, rendering modelling and control of the SRM challenging. Hence this thesis focuses on the modelling, analysis and control of switched reluctance machines. Current, torque and speed control are all part of the scope of study. Conventionally rotors with laminations are used in SRM. However, in applications where shaft temperatures are very high, rotors made from a single piece of magnetic material are potential candidates. Solid-rotor and recently proposed slitted-rotor SRMs are prospective candidates for high temperature applications. Blocked rotor experiments and 3D finite element analyses reported show that the slitted-rotor SRM has lower core loss and higher torque density than the solid-rotor SRM. Further, mutually coupled winding connection is shown to enhance the torque output of both solid- and slitted-rotor SRMs, compared to conventional winding. Two new current control schemes are proposed in this research work. In the first part, an extended horizon model-based predictive current controller is proposed for SRM. An analytical equation is reported for real-time computation of the optimal duty ratio to minimize the RMS error between the future current references and predicted currents over a horizon. The proposed controller demonstrates lower RMS error in current tracking and robustness to parameter variations, with experimental validation on a laboratory prototype drive, over an existing dead-beat predictive controller. Further, a fixed-frequency, model-independent predictive current control for SRM is proposed. Unlike traditional approaches, this method does not require any pre-measured characteristics of the SRM. Instead, it only requires two constants: the optimal value of equivalent inductance and the moving average window period. Hence this method eliminates the need for time consuming characterization experiments, multi-dimensional lookup tables, and offline curve fitting to model the flux-linkage characteristics of the SRM for current control. A high-performance torque control scheme for SRMs is presented, incorporating a PI controller, feedforward compensation, high-frequency compensation, and optimized gating functions. This controller achieves significant reduction in pulsating torque and outperforms state-of-the-art techniques across various operating conditions. Further improvement in performance is achieved through a novel PWM-based optimal predictive direct torque control scheme. In this work, a cost function, encompassing the instantaneous torque error and the RMS values of phase currents is formulated to be minimized. An analytical expression for the optimal duty ratio towards this objective is derived resulting in improved computational efficiency. This controller delivers improved torque tracking, higher torque per ampere, and lower sound pressure levels compared to existing methods. An experimental method for determining the moment of inertia and frictional torque characteristics of SRMs is proposed. Using these identified parameters, a PI-based speed controller is designed and validated through simulations and experiments, demonstrating its effectiveness in enhancing the performance of SRM drives.
Meeting Link

Colloquium
2024-07-26 14:30 - 17:00
Investigations on Pulse Width Modulation Techniques for Split-Phase Induction Motor Drives
Speaker: LALGY GOPI . of Ph.D. (Engg) in Electrical Engineering under Electrical Engineering
Date/Time: Jul 26 / 14:30:00
Location: Multi Media Class Room (MMCR), EE Department Team Link
Research Supervisor: Narayanan G
Abstract:
A split-phase induction motor (SPIM) has two sets of identical three-phase windings separated by 30° in space, typically fed by two- level voltage-source inverters. This configuration reduces the voltage rating of the windings and the DC-link voltage of the inverters. SPIMs are advantageous for high-power variable-speed applications due to reduced torque pulsations, lower voltage-rated power converters, higher torque per ampere, and increased reliability. However, they may require more semiconductor switches and circuitry. Although the winding structure avoids harmonic torques of the order 6k (k=1, 3, 5…), small voltages of harmonic order 6h±1 (h=1,3,5…) can cause large currents and increase copper loss. Properly designed pulse width modulation (PWM) techniques can improve SPIM drive performance by reducing stator copper loss and pulsating torque. In triangle comparison (TC) based PWM techniques for SPIM drives, two sets of modulating signals, which are phase-shifted by 30° from each other, are compared against a common carrier wave. In contrast, space vector (SV) based PWM techniques use the reference voltage vector to calculate the dwell times of each voltage vector, which are then applied in a specific sequence. TC-based PWM techniques are renowned for their simplicity in implementation, whereas SV-based techniques offer improved performance but come with increased computational complexity. While the unified understanding of these two types of PWM techniques is well established for three-phase induction motors, this thesis aims to enhance the unified understanding between TC-based and SV-based PWM techniques for SPIM drives. Several SV-based PWM techniques have been introduced for SPIM drives to enhance harmonic performance. Four-dimensional 24-sector SVPWM techniques outperform 12-sector methods but are more complex to implement. This thesis performs a per-phase analysis of these techniques to develop an efficient implementation scheme. It evaluates and analyses the switching cycle averaged pole voltages to determine the common mode voltage (CMV) for each inverter, showing that CMV relates to the fundamental voltage of different phases in different sectors. This leads to understanding the offset voltages needed for the two sets of three-phase sinusoidal modulating signals to generate equivalent signals for the two inverters driving the SPIM. Adding the derived zero-sequence signal to the sinusoidal modulating signals results in a computationally efficient implementation method. Equivalent carrier waves are derived based on the switching sequences, requiring discontinuous carrier waves for improved harmonic performance. This carrier-based implementation significantly reduces computation time on a TMS320F28377S DSP platform. Experimental results of stator current waveforms under steady-state and various dynamic conditions from a 6kW, 200V, 50Hz SPIM drive are presented. A comparative study based on the stator flux ripple analysis is carried out to evaluate the stator current harmonic distortion and rms torque ripple. The comparison of analytically evaluated torque ripple factor, simulated instantaneous torque ripple and harmonic spectra of torque are presented to validate the performance of different PWM techniques. The proposed discontinuous PWM method offers lower total harmonic distortion (THD) of stator current at high speeds. The equivalent modulating signals of the discontinuous PWM technique helped to evaluate the switching loss of semiconductor devices used. A comparison based on switching loss factor is carried to demonstrate the reduced switching loss due to this PWM technique. The discontinuous PWM technique is shown to have reduced switching losses at high power factors than the continuous PWM techniques with significantly low computational effort. An indirect field-oriented control scheme has also been presented to demonstrate the dynamic performance of the proposed implementation scheme. Two advanced bus-clamping pulse width modulation (ABCPWM) techniques are proposed in this thesis for SPIM drives to enhance their performance further. These techniques employ special switching sequences which apply the null vector once and one of the active vectors twice in each sub-cycle. Stator flux ripple-based analysis brings out the superior performance of the proposed special sequences over the conventional sequences at high modulation indices. Switching loss factor-based analysis shows that the inverter switching loss gets significantly reduced with the proposed PWM techniques at high power factors. Simulations and experiments on a 6kW, 200V, 50Hz SPIM drive show that the THD of stator current is reduced significantly at high speeds by the proposed PWM techniques at the same average switching frequency. In particular, one of the proposed techniques improves the no-load stator current THD by 32% at rated frequency, compared to a state-of-the-art SVPWM technique.
Team Link

PhD Oral Examination of Subhas Chandra Das (ERP)
2024-07-29 16:00 - 17:00
Name of the Student

SUBHAS CHANDRA DAS

Programme / Degree

Ph.D. (Engg) in Electrical Engg

Name of the Department of IISc

Electrical Engineering

Name of the Research Supervisor

Narayanan G

Date & Time of the Oral Examination /Thesis Defense

29.07.2024 /16:00:00 /Scheduled

Venue / Mode

Multi Media Class Room (MMCR), EE Department (Hybrid mode)

Team Link
Abstract
Insulated gate bipolar transistors (IGBTs) are the dominant power semiconductor devices in high power applications, such as, locomotive traction and megawatt-level renewable energy systems. Power electronic converters in such applications are expected to have a long-life span of about 20-30 years. Hence, efficiency and reliability of these converters are very important. IGBT switching behavior has a direct influence on both power conversion efficiency and system reliability.
The various switching characteristics parameters of IGBTs, which are available in the respective device datasheets, are limited to certain operating conditions. For an example, the switching characteristic parameters are available for only one or two DC link voltages; however, in applications such as diesel-electric locomotives, IGBTs have to operate over a wide range of the DC link voltages. Similarly, the characteristic parameters are available at only one or two junction temperatures (e.g. 25 oC and 125 oC); but, the IGBTs in traction and wind energy systems have to operate over wide range of temperatures including sub-zero ambient temperatures.
In this work, switching behavior of IGBTs of four different makes are studied experimentally over a wide range of operating conditions. The load current is considered upto 1.667p.u., where 1 p.u corresponds to the rated current of the IGBTs. The range of DC link voltage considered is from 0.571 p.u. to 1.321p.u., where 1.0 p.u. is the nominal voltage of the application. The junction temperature range is considered from -35 oC to +125 oC. The following are the major highlights of the research work:
1. Generation of experimental data on switching behavior of IGBTs over wide range of operating conditions as mentioned above.
2. The experimental data, which are generated, complement the technical information available in device datasheets.
3. The experimental investigation are carried out on four traction-grade IGBTs of different makes and of comparable ratings to ensure that the findings of the study are applicable to reasonable cross-section of the available commercial devices.
4. Experimental study on the switching behavior of an IGBT converter leg, having top and bottom devices of two different makes, and its comparison with the switching behavior of a converter leg, having complementary devices of the same make.
5. Experimental study of the rise and fall times of the device switching voltages and currents, both during turn-on and turn-off, over the complete range of operating conditions.
6. Evaluation of turn-on and turn-off switching energy losses as functions of load current, DC link voltages and junction temperatures, which are valid over the complete operating range.
7. Experimental study of reverse recovery characteristics of anti-parallel diode of IGBTs with varying DC link voltage, load current and junction temperatures.
8. Experimental investigation on the effect of variations in DC link voltage, load current and junction temperatures on device peak stress parameters, namely, peak device voltage, peak device current, peak rate of change of device voltage, and peak rate of change of device current.
9. Experimental study of sub-intervals of the turn-on switching delay, turn-off switching delays and parameters related to the switching delay intervals over the complete operating range.
10. Correlation of the various turn-on and turn-off switching parameters with junction temperatures based on the experimental data generated.
11. Study of the consistency of the above correlations across different traction-grade devices of comparable ratings and different makes.
12. Critical review of various thermo-sensitive electrical parameters (TSEPs) already reported in literature.
13. Identification of new TSEPs that can be obtained from the measured gate-emitter voltage during switching delay times.
ALL ARE WELCOME

CBR/EE: Talk by Prof. Mathews Jacob
2024-07-30 11:30 - 12:30 | CBR Auditorium, CBR, IISc.
Talk on Model based deep Learning for inverse problems in MRI: Beyond Algorithm Unrolling

by Prof. Mathews Jacob, University of Iowa, USA.

on July 30th, from 11.30 AM to 12.30 PM.

Venue: CBR Auditorium, CBR, IISc.

Host: Prof. Chandra Sekhar Seelamantula, IISc

Abstract: The reconstruction of MR images from highly undersampled Fourier measurements is a problem that has received a lot of attention in the past decade. Compressed sensing algorithms have been extensively employed in MRI to overcome the challenges associated with the slow nature of MRI acquisition. These methods offer guaranteed uniqueness, fast convergence, and stability properties. Model-based deep learning methods that combine imaging physics with learned regularization priors have emerged as more powerful alternatives for MR image recovery in recent years. The talk will introduce different flavors of physics-based deep learning methods and discuss the unique challenges associated with these schemes in high-dimensional settings. Novel memory efficient iterative algorithms that possess guarantees similar to compressive sensing, while offering improved performance will be introduced. Energy models that allow sampling from the posterior distribution will also be discussed. The talk will draw upon our recent work, available at https://cbig.iibi.uiowa.edu/publications

Biography of the speaker: Mathews Jacob will be starting as a Professor in the Department of Electrical and Computer Engineering at the University of Virginia, starting August 2024. He is currently a professor in the Department of Electrical and Computer Engineering and is heading the Computational Biomedical Imaging Group (CBIG) at the University of Iowa. He obtained his B.Tech in Electronics and Communication Engineering from National Institute of Technology, Calicut, Kerala, and his M.E in signal processing from the Indian Institute of Science, Bangalore. He received his Ph.D. degree from the Biomedical Imaging Group at the Swiss Federal Institute of Technology. He was a Beckman postdoctoral fellow at the University of Illinois at Urbana Champaign.
Dr. Jacob is the recipient of the CAREER award from the National Science Foundation in 2009, the Research Scholar Award from American Cancer Society in 2011, and the Faculty Excellence Award for Research from University of Iowa in 2021. He is currently the associate editor of the IEEE Transactions on Medical Imaging and has served as the associate editor of IEEE Transactions on Computational Imaging from 2016-20. He was the senior author on two best paper awards (2015 & 2021) and one best machine learning paper award (2019) from IEEE ISBI. He was the general chair of IEEE International Symposium on Biomedical Imaging, 2020. He was elected as a Fellow of the IEEE (2022) for contributions to computational biomedical imaging.

PhD Thesis Defense by Apoorva Sahu
2024-08-06 10:30 - 13:00 | High Voltage Lab Seminar Hall (Hybrid mode)
Synopsis
Join conversation (microsoft.com)
The consumption of crude oil is increasing every day particularly in developing countries like India which is the third largest consumer of crude oil in the world, utilizing on an average around 160 million liters per annum of which 30% constitutes diesel fuel. More than 50% of the NOx and hydrocarbons in air come from the diesel exhaust affecting the health of human beings, vegetation, and environment. While the solid particulate in the exhaust is taken care to a greater extent by the mechanical filters it is the gaseous pollutants such as oxides of nitrogen (NOx), oxides of carbon, hydrocarbons (HC) etc., that need to be addressed as they cause several health-related ailments in addition to acid rain, global warming, smog etc. It is timely to work on the development of economical and efficient pollution control strategies. On the other hand, issues that is affecting our country India currently are accumulation of wastes from utility industry, mariculture industry and agriculture industry. The prominent amongst them are fly ash, red mud, foundry sand, iron ore tailings, lignite ash, rice husk, wheat straw and sugarcane bagasse etc. Accumulation of these wastes are in several million tons per annum in India. Any proposition in recycling waste is a welcoming step.

Efforts are continuously being made for the past three decades to mitigate these gaseous pollutants, particularly NOx, at various levels by changing the fuel composition, engine modifications, pre-combustion techniques and post-combustion (aftertreatment) techniques. The existing post-combustion mode technique “catalyst-based converters and adsorbent based techniques” are becoming expensive owing to the short life, dependency on noble metals, more vulnerability to acidic coating, bulk usage of adsorbents etc. In this regard the application of non-thermal plasma or electric discharge plasma for pollution control aided by additional techniques is slowly gaining popularity in the past few years. Discharge plasma ionizes the atoms at normal temperature and atmospheric pressure (NTP) thus creating an oxidative environment resulting in chemical reactions such as reduction, oxidation, decomposition etc. However, among these reactions it was observed that oxidation was dominating, to a certain extent the oxidised by-products in the plasma appeared to be less hazardous to humans than to nature. This led to the redesigning of plasma reactors with the intention of enhancing the energy in the charged species favoring reduction reactions instead of oxidation ones but not without serious limitations with respect to the gas flow.

Application of electrical discharges for environmental purposes lies in the basic concept of treating the pollutants, particularly the gaseous ones, with plasma excited species. It is observed that plasma alone is insufficient for the successful treatment of any of the gaseous pollutants due to the oxidative environment prevailing in the discharge plasma at atmospheric conditions. This necessitated inclusion of additional treatment technique along with plasma leading to the origin of plasma catalysis/plasma adsorption methods where in the catalytic materials were kept inside the plasma environment (plasma catalysis) or the adsorbent materials are cascaded with plasma (plasma adsorption). It should be noted here that the total cost involved in the proposed technique, should be lower than that associated with conventional catalyst-alone or adsorbent-alone techniques so that the proposed ones can be a promising economic alternative to the conventional ones. That said, cascading commercially available catalysts/adsorbent with plasma can never be a cheaper alternative. Several research works, therefore, started by blending plasma with other lab made catalysts/adsorbents but the DeNOx efficiency was not significant.

Present work aimed at studying the NOx abatement in diesel engine exhaust at controlled laboratory condition using electrical discharges. The intention of the study is to provide not only an efficient but also an economical solution for reduction of the NOx pollutants. Keeping this in mind, it was decided to utilize electrical discharge technique in association with abundantly available solid wastes in India be it from industrial, mariculture or agriculture domains. Given the whole spectrum of parametric variations the thesis plan was carefully drawn to touch upon the following variations: type of corona electrodes, type of applied high voltage, type of solid wastes, type of gas treatment. Four types of electrodes were studied that include needle plate, metal film, helical wire and pipe type. Type of voltage involves fast rising repetitive pulses at 80 Hz, power frequency and high frequency AC. The solid wastes comprise of three categories namely (a) industrial/mariculture wastes that include namely iron tailings, lignite ash, red mud, foundry sand, waste tiles and copper slag, oyster shell (b) agricultural wastes including coffee husk, sugarcane waste, mulberry husk, rice husk, ragi husk, corn husk, wheat husk, pine, ground nut and areca nut husk and (c) composite wastes which include a blend of waste tiles + foundry sand, copper slag + red mud, iron tailings + waste tiles, red mud + waste tiles, foundry sand + red mud and foundry sand + iron tailings. The type of gas treatment involves treating the exhaust with only plasma/plasma catalysis/plasma adsorption/thermal catalysis utilizing catalytic properties of metal oxides present in the industry wastes or porous nature of the industry wastes. A comparison was also made by replacing the industrial wastes with commercial NOx catalysts. Important contribution of this research work can be summarized as: (a) with plasma catalysis approach the NOx removal efficiency gets enhanced by a factor of 5.3-6.7 compared to only plasma. (b) with plasma adsorption approach the NOx removal efficiency gets enhanced by a factor of 4-6 compared to only plasma. (c) Fe2O3/TiO2 present in red mud can act as photo catalysts in oxidizing NO through plasma generated ethyl nitrate in the plasma cascaded red mud adsorption process (d) Amongst the agricultural wastes, ground nut husk-based pellets exhibited 83% NOx removal efficiency (e) The newly developed metal film based DBD reactor enhances surfaces discharges far better than the helical wire reactor (f) commercial catalysts performed much better in NOx removal under plasma catalysis mode when compared to thermal catalysis mode. Further, plasma catalysis with industrial wastes such as iron tailings and oyster shell yielded better/similar DeNOx efficiency when compared to that with commercial NOx catalysts thus, justifying the usage of cheaper industrial wastes instead of expensive commercial ones.

EE Talk 'Generic Modelling of Inverter Based Resources (IBRs) for Power System Planning Studies'
2024-08-08 16:00 - 17:00 | B308,2nd floor, EE Dept.
IEEE PES Student Branch Chapter, IISc, is organizing a series of technical talks on ‘Generic Modelling of Inverter Based Resources (IBRs) for Power System Planning Studies’ by Prof. Vijay Vittal, Arizona State University, USA.
The details of the first technical talk are as follows.
Venue: Conference Room B-303,EE 2nd Floor, Electrical Engineering Department
Date and Time: Thursday, 8th August 2024, 4 PM – 5 PM IST.
Mode: In-person
Speaker Biography:

Regents Professor Vijay Vittal is the Ira A. Fulton Chair Professor (2005) and ASU Foundation Professor in Electric Power Systems at Arizona State University. Prior to ASU, Vittal was an Anson Marston Distinguished Professor at the Iowa State University, Electrical and Computer Engineering Department. In addition, Vittal was a Murray and Ruth Harpole Professor and director of the university’s Electric Power Research Center and site director of the National Science Foundation IUCRC Power System Engineering Research Center. He also served as the program director for power systems for the National Science Foundation Division of Electrical and Communication Systems in Washington, D.C., from 1993 to 1994. He was the editor-in-chief of the IEEE Transactions on Power Systems from 2005 to 2011. Professor Vittal was elected to the U.S. National Academy of Engineering in 2004. In 2018, he received the IEEE Power and Energy Society Prabha. S. Kundur Power System Dynamics and Control Award and the Utility Variable-Generation Integration Group Achievement Award. In 2013, he was awarded the IEEE Herman Halperin Transmission and Distribution Technical Field Award. He also received the IEEE Power and Energy Society (PES) Outstanding Power Engineering Educator Award in 2000. He was elected an IEEE Fellow in 1997 and awarded the U.S. National Science Foundation Presidential Young Investigator Award in 1985.

Talk by SMA Solar India Pvt. Ltd
2024-08-09 15:30 - 17:00 | B-303,EE
Representative from SMA Solar India Pvt Ltd will be giving a talk today at 3:30 pm in the room B303. SMA is a leading global specialist in photovoltaic system technology headquartered in Niestetal. They are looking for interactions with faculties and students. In their talk, they will mostly explain the activities of their company.

PhD Colloquium on Image Reconstruction
2024-08-14 10:00 - 13:00
PhD Thesis Colloquium
Student : Deepak G Skariah
Advisor : Prof. Muthuvel Arigovindan
Title : Infimal Convolution Based Regularization for Image recovery
Date and Time: 14.08.2024 (Wednesday), 11 am.
Venue : MMCR, Department of Electrical Engineering

Abstract
The quality of image captured by acquisition devices has increased drastically over the years largely due to a revolution in imaging sensor capability. But, image acquisition under low illumination continues to be a bottleneck for imaging devices such as optical microscopes leading to blurred and noisy images. A potential solution to this limitation is a computational approach known as image restoration. An image restoration algorithm recovers an estimate of the original image from a noisy blurred observation while assuming a knowledge of the image degradation model. The restoration problem is even more challenging when it comes to a spatio-temporal signal as a good restoration scheme needs to be mindful of presence of motion in the measured signal. This means that in spatio-temporal signal restoration problem, the algorithm should ensure temporal regularity of restored signal in addition to spatial regularity. Regularization based image restoration attempts to pose image restoration problem as a regularized optimization problem from the measured signal. We propose to exploit the concept of infimal convolution from convex analysis to design effective and efficient restoration schemes for images and spatio-temporal images.
In our first work, we address the problem of regularization design. We propose a family of derivative based regularization which we call generalized unitary invariant regularization and it belongs to class of infimal convolution based functionals. We also design an algorithmic scheme to optimize the resultant optimization problem. We demonstrate the quality of proposed algorithm and restoration scheme through multiple experiments on simulated data.
In our second work, we address the restoration of spatio-temporal images measured from TIRF microscopes where a sequence of noisy blurred images are observed over time. We once again exploit the infimal convolution based approach to design a novel spatio-temporal regularizer that is tailor made for above class of signals. The proposed regularization was designed to ensure both spatial and temporal regularity of restored signal. The resultant regularization functional is defined as an optimization problem where the cost is a weighted sum of two constituent functions where the two functions play the role of promoting spatial and temporal regularity respectively. We also design an algorithm to optimize the resultant restoration problem using this regularization. We demonstrate the quality of the proposed algorithm by testing the restoration quality against spatio-temporal measurements collected from TIRF microscopes.
In the third and final work we address the problem of estimating the relative weights in spatio-temporal regularization functional designed based on infimal convolution formulation. We propose a renewed optimization model where the spatio-temporal signal is estimated together with the better quality image estimate by incorporating the weights as part of the optimization problem. We also design an iterative scheme to optimize the resultant joint optimization model. We demonstrate the effectiveness of this scheme against other joint optimization schemes for spatio-temporal signal estimation.
ALL ARE WELCOME

EE Talk 'Generic Modelling of Inverter Based Resources (IBRs) for Power System Planning Studies'
2024-08-23 16:00 - 17:00 | B-303,EE
IEEE PES Student Branch Chapter, IISc, is organizing a series of technical talks on ‘Generic Modelling of Inverter Based Resources (IBRs) for Power System Planning Studies’ by Prof. Vijay Vittal, Arizona State University, USA.
The details of the Third technical talk are as follows.
Venue: Conference Room B-303,EE 2nd Floor, Electrical Engineering Department
Date and Time: Friday, 23rd August 4 PM – 5 PM IST.
Mode: In-person
Speaker Biography:

Regents Professor Vijay Vittal is the Ira A. Fulton Chair Professor (2005) and ASU Foundation Professor in Electric Power Systems at Arizona State University. Prior to ASU, Vittal was an Anson Marston Distinguished Professor at the Iowa State University, Electrical and Computer Engineering Department. In addition, Vittal was a Murray and Ruth Harpole Professor and director of the university’s Electric Power Research Center and site director of the National Science Foundation IUCRC Power System Engineering Research Center. He also served as the program director for power systems for the National Science Foundation Division of Electrical and Communication Systems in Washington, D.C., from 1993 to 1994. He was the editor-in-chief of the IEEE Transactions on Power Systems from 2005 to 2011. Professor Vittal was elected to the U.S. National Academy of Engineering in 2004. In 2018, he received the IEEE Power and Energy Society Prabha. S. Kundur Power System Dynamics and Control Award and the Utility Variable-Generation Integration Group Achievement Award. In 2013, he was awarded the IEEE Herman Halperin Transmission and Distribution Technical Field Award. He also received the IEEE Power and Energy Society (PES) Outstanding Power Engineering Educator Award in 2000. He was elected an IEEE Fellow in 1997 and awarded the U.S. National Science Foundation Presidential Young Investigator Award in 1985.

EE Talk: “Is EMT Simulation Becoming More of a Necessity in Time-Domain Simulation
2024-08-28 16:00 - 17:30 | B-303,EE
EEE PES Student Branch Chapter, IISc, is organizing a series of technical talks on ‘Generic Modelling of Inverter Based Resources (IBRs) for Power System Planning Studies’ by Prof. Vijay Vittal, Arizona State University, USA.
The details of the Fourth technical talk are as follows.

Title: “Is EMT Simulation Becoming More of a Necessity in Time-Domain Simulation”
Venue: Conference Room B-303,EE 2nd Floor, Electrical Engineering Department
Date and Time: Wednesday, 28 August 2024, 4 PM – 5 PM IST.
Mode: In-person

Speaker Biography:

Regents Professor Vijay Vittal is the Ira A. Fulton Chair Professor (2005) and ASU Foundation Professor in Electric Power Systems at Arizona State University. Prior to ASU, Vittal was an Anson Marston Distinguished Professor at the Iowa State University, Electrical and Computer Engineering Department. In addition, Vittal was a Murray and Ruth Harpole Professor and director of the university’s Electric Power Research Center and site director of the National Science Foundation IUCRC Power System Engineering Research Center. He also served as the program director for power systems for the National Science Foundation Division of Electrical and Communication Systems in Washington, D.C., from 1993 to 1994. He was the editor-in-chief of the IEEE Transactions on Power Systems from 2005 to 2011. Professor Vittal was elected to the U.S. National Academy of Engineering in 2004. In 2018, he received the IEEE Power and Energy Society Prabha. S. Kundur Power System Dynamics and Control Award and the Utility Variable-Generation Integration Group Achievement Award. In 2013, he was awarded the IEEE Herman Halperin Transmission and Distribution Technical Field Award. He also received the IEEE Power and Energy Society (PES) Outstanding Power Engineering Educator Award in 2000. He was elected an IEEE Fellow in 1997 and awarded the U.S. National Science Foundation Presidential Young Investigator Award in 1985.

[EE-PhD Oral Defense] Dual Mode Operation of Grid-tied Inverters: Modeling, Stability Analysis, and Islanding Detection
2024-09-18 15:00 - 17:00 | Multi-Media Class Room (MMCR), EE Department (Hybrid mode)
Ph.D. Thesis Defense/ Oral examination
Name: Sugoto Maulik
Title of the Thesis: Dual Mode Operation of Grid-tied Inverters: Modeling, Stability Analysis, and Islanding Detection
Time and date: 3 PM , 18th September 2024
Venue: MMCR EE, IISc
Research Supervisor: Prof. Vinod John
Abstract: Increased penetration of renewable energy sources like solar PVs and wind is fundamentally altering the power flow dynamics in distribution networks. These localized forms of generation add redundancy to the power system and increase its load-handling capacity. However, these advantages come at the cost of reduced stability and altered protection requirements. These distributed forms of generation (DGs) are interfaced with the power grid via power electronic converters operating at high bandwidths compared to conventional sources. While these offer higher performance, but consequently lower the stability margins. An analytical framework is thus necessary for modeling and stability analysis of such systems. The dynamics involved in modeling a grid-tied DG system span a wide spectrum of frequencies. While simplified modeling can lead to inaccuracies, an all-inclusive model leads to complex and unintuitive models. This work proposes a systematic approach to model the behavior of 3-phase AC grid-tied DG systems using dynamic phasors. Dynamic phasors allow for a state-space representation of the relevant dynamics.
The developed state space model is used for the following:
1. Islanding detection
Islands are formed in 3-phase distribution networks when an active DG is disconnected from the grid. If undetected, the DG continues to energize its local loads, leading to safety concerns. In this work, a state-feedback approach is developed for islanding detection, which places a system pole in the right half plane (RHP). This ensures the destabilization of the islanded network and a zero non-detection zone. Methods for tuning of the control parameters to meet the system islanding detection requirements are proposed. The scheme is designed and implemented experimentally.
2. Transfer of Control
Post-island detection, the DG is required to disconnect from the grid while ensuring uninterrupted power flow to its local loads. A control scheme involving a voltage control loop and grid current feed-forward is developed to achieve a fast transfer from grid-following to grid-forming mode of operation. The introduced voltage control loop ensures that rated voltage is maintained across the loads, and the grid current feed-forward is used to minimize the transients during the transfer process. The method is designed and implemented in conjunction with the islanding detection scheme and verified experimentally with local loads.
3. Stability analysis of grid-tied DG systems
Owing to the formation of microgrids and weak grids in the distribution network, the stability assessment of such networks becomes essential. This assessment is performed by extending the dynamic phasor-based model for islanded systems to model grid-tied systems as well. The developed model includes the dynamics of the PLL, grid, DG current levels, and load. In addition to passive loads, considered in the relevant literature, the proposed model also incorporates the effect of constant power and constant current type power electronic loads. It is demonstrated, analytically and experimentally, that the presence of local loads has a stabilizing impact on the synchronization stability of a DG. Additionally, an upper limit on the bandwidth of power-electronic type constant power loads is derived, affirming the observation that high bandwidth loads lead to reduced system stability.
All the proposed methods are validated on hardware prototypes that have been developed as a part of the work.
The following online link can also be used to attend the Oral Exam:
Link

EE Faculty Colloquium: Development of Parallel Solvers for Bulk Power Systems Time Domain Simulations
2024-09-20 16:00 - 17:30
Title: Development of Parallel Solvers for Bulk Power Systems Time Domain Simulations
Speaker: Dr. Gurunath Gurrala, Associate Professor, Dept of Electrical Engineering, Indian Institute of Science
Venue: MMCR, EE
Time: 4 pm, Friday, 20 September 2024
Abstract:
This talk introduces two different time domain simulation paradigms, phasor domain, and electromagnetic transient simulations, typically used for bulk power systems simulation. It also introduces other simulation approaches explored in the literature and compares computational aspects. Motivates the audience about the need to converge these paradigms due to the penetration of renewable sources. Computational challenges that arise in this convergence will be discussed. Introduces the need for realistic modeling of the practical power systems for cascading failure analysis and associated computing challenges. The need for scalable parallel algorithms to speed up the time domain simulations will be discussed. Introduces efficient PARALLEL algorithms and modeling approaches developed by the PhD students of the Grid Control Automation and Modelling Lab (GridCAM), EE, IISc in the past 5 years.
Speaker’s Bio:
Gurunath Gurrala received B.Tech from S.V.H. College of Engineering, Machilipatnam, in 2001, M.Tech from J.N.T.U. College of Engineering, Anantapur, in 2003 and Ph.D from Indian Institute of Science, Bangalore, India, in 2010. He was an assistant professor in SSN college of engineering, Ongole during 2001-2002 and in Anil Neerukonda Institute of Technology and Sciences (ANITS), Visakhapatnam, during 2003-2005. He was a research engineer in GE Global research, Bangalore during 2010-2012. He was a post doctoral fellow at Texas A&M university, USA during 2012-2013 and at Oak ridge national lab, USA during 2014-2015. He is currently an associate professor at department of electrical engineering, Indian Institute of Science. He is the convener of the Power Grid Center of Excellence, FSID, IISc, funded by POWERGRID Corporation of India Ltd. He received SERB-STAR award 2020, IEEE PES Outstanding Engineer Award 2018, INAE Young Engineer Award 2015. He received Prof DJ Badkas medal for best PhD thesis from Electrical Engineering Department, IISc, Bangalore. His papers received best paper award in ICPS 2019, IEEE General meeting 2015, best poster awards in IEEE industrial society annual meeting 2015 and EEE PES T&D Conference and Exposition 2016. Four of his master students received POSOCO Power System Awards. He is senior member of IEEE and INAE young associate. His research interests include cyber physical system modelling in smart grids, development of realistic testbeds for power system operations, power system stability, grid integration of renewables, Microgrid control, high performance computing applications to power systems, nonlinear and adaptive control of power systems.

PhD Thesis Colloquium
2024-10-14 11:00 - 12:00 | Online, India
Title: Parallel Algorithms for Efficient Utilization of Multiprocessor Architectures for Transient Stability
Student: Francis C Joseph
Faculty Advisor: Dr. Gurunath Gurrala.
Date :14th October 2024
Time: 11 AM – 12 PM
ONLINE TEAMS LINK
ABSTRACT:
Computer hardware capabilities have been enormously increasing over the years. Multi-core processors, graphic processing units (GPUs), and field programmable gate array (FPGA) accelerators have grown significantly recently. They have opened new computational paradigms such as edge computing, fog computing, grid computing, distributed computing, cloud computing, and exascale supercomputing. However, efficiently utilising most of these computational paradigms in traditional engineering disciplines, such as power engineering, is challenging. In this thesis, efficient algorithms for multiprocessor-based high-performance computing and edge computing platforms for two power system applications are developed, power system stability assessment and power quality measurements respectively. Faster than real-time transient stability assessment of large power grids using time domain simulations with detailed models is computationally challenging. Today, the commercial tools used for this application in Energy Management Systems (EMS) worldwide rely on parallel batch processing methods, which don’t efficiently utilise the architecture of the computational paradigms. For transient stability simulations, this thesis explores a time parallel algorithm, Parareal in Time, which belongs to a class of temporal decomposition methods for time parallel solutions of differential equations. Two effective implementation approaches, Master Worker and Distributed, are analysed for large systems, and scaling tests are performed using a state space model with a Message Passing Interface (MPI) in a multiprocessor environment. One of the findings was that the performance of the Parareal depends on the accuracy and the computational cost of the coarse solver used for initialisation and subsequent correction steps. A potential coarse solver, Modified Euler (ME), a well-known solver for transient stability simulations even in commercial packages, has been explored to adapt its step size by controlling the Local Truncation Error (LTE) to achieve the desired accuracy. An LTE estimator using a Multistage Homotopy Analysis Method (MHAM), which gives an approximate solution to a set of non-linear equations in the form of a power series, is proposed to control the LTE at each integration step to enable adaptation of the ME step size. The proposed MHAM-assisted adaptive ME solver is faster and has comparable accuracy to the conventional fixed and adaptive Modified Euler solver for large systems’ transient stability simulations. Since MHAM is lighter than the ME solver and the LTE estimate is sufficient for step size adaptation, an adaptive MHAM coarse solver is proposed for the Parareal. However, MHAM provides a non-zero auxiliary parameter `c’ to select a family of solutions. Hence, an optimisation framework is also proposed to automatically select this parameter based on the system’s dynamics. Based on many case studies on test systems of different sizes, it is found that maintaining the LTE lower than the Parareal convergence tolerance improves the speedup of the Master-Worker paradigm; however, for the distributed implementation, maintaining LTE higher than the convergence tolerance gives improved speedup. An approach to include unscheduled events which arise in power system operation due to the operation of protective relays is also proposed for Parareal. The impact of frequency estimation on Parareal is evaluated using three estimation methods. It was found that the network admittance-based method has the lowest execution time. Many different types of disturbance types are performed on systems of different sizes and see that Parareal can maintain its performance. In Parareal implementation, each coarse time segment is assigned to one processor in the MPI environment. Multiple processors in a node can be assigned to a coarse time segment to improve speedup. Therefore, a shared memory-based space parallel transient stability solver is also considered for further performance enhancement. Space parallelisation of transient stability simulation involves breaking the network into subnetworks and solving each part independently while ensuring the original network’s convergence. Therefore, a Multi Area Thevenin Equivalent (MATE) based parallel solver implementation on a shared memory platform is proposed, and both space parallelisation and task parallelisation are explored. It is shown that the space parallelism can closely match the ideal speedup and can be exceeded by space + task parallelism while the network is well-partitioned. It can be further improved when combined with time parallelism. A hybrid time-space solver using OpenMP MATE, space + task parallelism, and MPI Parareal is proposed using two scheduling schemes: homogeneous and heterogeneous for both communication paradigms. The homogeneous scheduling enabled a faster-than-real time solution even for the PEGASE 13659 bus system and provided multiple combinations to achieve it. The heterogeneous can increase the performance of the hybrid solver when homogeneous scheduling is unavailable. A particular case for Hybrid Master with a single core worker was used to showcase the initialisation phase’s time reduction by reducing the coarse solver’s computational time. The current state-of-the-art chips also provide multicore architectures for edge computing applications. One such low-cost, open-source, heterogeneous, resource-constrained hardware platform is called Parallella. The unique hardware architecture of the Parallella provides many edge computing resources in the form of a Zynq SoC (dual-core ARM + FPGA) and a 16-core co-processor called Epiphany. This Parallella device was used as a measurement device for edge computing applications research in smart grids, and it could sample 3 voltages and four currents at a 32 kHz sampling rate. The thesis explores one application of such a device to measure the harmonics and compute various Power Quality (PQ) indices. A parallel implementation of multichannel FFT on Epiphany for the streaming data is developed in this regard. Epiphany 16-core architecture has very limited memory resources, and the order in which the cores are to be accessed significantly impacts the execution. Proper decomposition of the FFT algorithm tasks and scheduling the tasks for efficient core and memory usage are crucial, requiring a good understanding of the Epiphany architecture. The obtained PQ measurements from the proposed implementation are comparable to those of a commercial power analyser.

Acknowledgments:

SERB Science and Technology Award for Research (SERB-STAR) grant, File No: STR/2020/000019 titled Hybrid Parallel Solvers for Faster than Real-time Transient Stability Analysis of Large Power Grids.

Bosch Research and Technology Centre, Bangalore, India and by the Robert Bosch Centre for Cyber-Physical Systems, Indian Institute of Science, Bangalore, India (under Project E-Sense: Sensing and Analytics for Energy Aware Smart Campus)
DST Young Scientist Grant DST-YSS/2015/001371, India

PhD Thesis Colloquium on Solid-State Point-on-Wave Fault Creator with Adjustable Transient Recovery Voltage
2024-10-18 11:00 - 12:00 | MMCR, Hall C 241, 1st floor, EE department
Title: Solid-State Point-on-Wave Fault Creator with Adjustable Transient Recovery Voltage
Student: Rajesh K B
Faculty Advisor: Dr. Gurunath Gurrala.
Date : 18th October 2024
Time: 11 AM – 12 PM
Venue: MMCR, EE, IISc, 1st Floor
ABSTRACT:
The power grid is changing with the exponentially growing penetration of inverter-based resources (IBRs). Keeping the power grid stable, reliable, and secure, and delivering quality power has become increasingly important in recent years. As a result, many countries have devised grid codes for operating IBRs. Grid codes specify the requirements for IBRs to stay connected to the grid during various grid conditions, such as short circuit faults. As a result, a variety of devices are being developed to create grid short-circuit fault conditions. In addition to validating grid codes, devices capable of generating accurate and realistic fault transients are highly desirable for developing effective protection countermeasures, testing relay algorithms, studying fault characteristics in IBRs, and performing parameter estimation of power system components. Existing converter-based fault creators require high bandwidth control to provide an adjustable transient recovery voltage (TRV) feature. Providing this feature in the existing PWFCs utilizing FQSes is also difficult due to multiple over-voltage clamping circuits. The time series data generated during faults in power systems is essential for the development and validation of data-driven algorithms for power systems anomaly detection, classification, and mitigation.
This thesis explores the design of a point-on-wave fault creator (PWFC) with an adjustable transient recovery voltage feature. The developed PWFC can create all types of balanced and unbalanced faults at any desired angle on the voltage waveform (point-on-wave). Over-voltage protection is essential for solid-state switches in fault creators while clearing the fault. Existing fault creators using FQS, use one or two capacitors/surge protection devices per FQS for over-voltage protection. Hence fault creators with ‘N’ FQSes need ‘N’ or ‘2N’ capacitors, which increases the number of capacitors used in the fault creator, making it costly and bulky. In contrary to this, the PWFC topology in the author’s master thesis uses a single capacitor to protect all FQSes from overvoltage. A systematic analytical procedure to select the single capacitor value, adjustable transient recovery voltage feature, and a finite state machine (FSM) for PWFC control is developed in this thesis. The performance of the FQS and the PWFC are investigated under a wide range of test scenarios including thermal considerations and parasitic components. The ability of the selected capacitor to protect the FQSes during all types of fault clearances is experimentally validated by creating faults in an experimental test bed using the PWFC prototype. A novel FQS topology is proposed that can be realized using two commercially available half-bridge semiconductor modules. With this unique method of FQS realization, the lowest package count (Two), lowest on-state drop (one active switch plus one diode), modularity and scalability of the structure, gate control, and minimal package inter-connection length are simultaneously achieved. An adjustable TRV feature is achieved using a variable output voltage pre-charge circuit as a cost-effective solution. An algorithm is proposed to obtain the initial capacitor voltage required to limit the TRV to a specified target value. The experimental results demonstrated the ability of the PWFC to adjust the TRV for all fault configurations as per the test requirements.
A combined fault and power quality disturbance detection and classification method using symbolic dynamic filtering (SDF) is also developed. An SDF is constructed based on the symbolic encoding of time series data and finite state automata to generate steady-state probability distribution vectors (histograms) as signature patterns for different fault categories and power quality events. It provides an edge over existing methodologies as it compresses voluminous fault data into fixed-length probability distributions, which serve as the feature vectors for classifiers. Irrespective of the length of the time series data or the number of coefficients of the transformation used, the feature vector’s length is fixed in SDF. A new sinusoidally distributed partitioning (SDP) scheme is proposed for symbolic encoding. The proposed methodology can detect and classify low-impedance faults, high-impedance faults, and power quality disturbances. Support vector machines and k-nearest neighbor classifiers are explored for fault classification using the histograms. The proposed methodology is tested on two active distribution systems, the modified IEEE 33 and 13 bus systems. In addition to fault detection and classification, a data-driven method is proposed to identify the hardware signature of Intelligent Electronic Devices (IED) used in power grids. It utilizes test function data of the analog-to-digital converter (ADC) used in the IED. A credit card-sized Parallella board and ADC of a custom IED platform are utilized to obtain the hardware signature. A finite state machine is developed in the FPGA of the Parallella processor to control the ADC for generating different data sets to extract signature
Acknowledgments:

Fund for Improvement of Science and Technology (FIST) Program, Department of Science and Technology (DST), India, through the project “Smart Energy Systems Infrastructure Hybrid Test Bed,” under Grant SR/FST/ETII-063/2015 (C) and (G)
POWERGRID Center of Excellence in Cyber Security (PGCoE), IISc
Department of Science and Technology, India, under the Indo-Danish collaboration project, Data-Driven Control and Optimization for a Highly Efficient Distribution Grid (ID-EDGE), No. DST-1390-EED

Faculty Colloquium: Demystifying Large Language Models - Capabilities, Challenges and Opportunities
2024-10-18 16:00 - 17:30 | EE, MMCR
Title: Demystifying Large Language Models – Capabilities, Challenges and Opportunities
Speaker: Dr. Sriram Ganapathy, Associate Professor, Dept of Electrical Engineering, Indian Institute of Science
Venue: MMCR, EE
Team Link
Time: 4pm, 18 Oct 2024
Abstract:
In the last two years, large language models (LLMs) have taken giant leaps in tackling real world problems ranging from reasoning, coding, creative content generation and multimodal understanding. This has resulted in significant user growths in services like chatGPT and Gemini. In this talk, I will give a brief overview of i) what goes under the hood in developing these models, ii) what their current capabilities are, iii) who are the big players and iv) what are the potential challenges and blindspots. Along the way, I will also touch upon some of the theory that allows basic understanding of how the LLMs achieve their capabilities. The talk will end with a discussion of bias, safety and regulatory considerations in the development and deployment of these models.
Speaker’s Bio:
Sriram Ganapathy is an Associate Professor at the Electrical Engineering, Indian Institute of Science, Bangalore, where he leads the activities of the Learning and Extraction of Acoustic Patterns (LEAP) lab. He is also a visiting research scientist at Google Research India, Bangalore. Prior to joining the Indian Institute of Science, he was
a research staff member at the IBM Watson Research Center, Yorktown Heights, USA. He received his Doctor of Philosophy from the Center for Language and Speech Processing, Johns Hopkins University. He obtained
his Bachelor of Technology from College of Engineering, Trivandrum, India and Master of Engineering from the Indian Institute of Science, Bangalore. He has also worked as a Research Assistant in Idiap Research
Institute, Switzerland. Dr. Ganapathy currently serves as the IEEE Sigport Chief Editor, member of the IEEE Education Board, and functions as subject editor for Elsevier Speech Communication Journal. He is also a recipient of multiple awards including Department of Science and Technology (DST) Early Career Award in India, Department of Atomic Energy (DAE), India Young Scientist Award and Verisk AI Faculty Award.
