EE

Thesis Defence of Mr. Unni V S
2022-11-18 20:00 - 21:00 (Asia/Kolkata)
Degree Registered: PhD (Engg).
Guide: Prof. Kunal Narayan ChaudhuryDate: November 18, 2022.Time: 2:30 pm.Venue: Online.Link: MS Teams link: https://tinyurl.com/yr96memmTitle: Efficient and Convergent Algorithms for High-Fidelity Hyperspectral Image Fusion.Abstract: Hyperspectral (HS) imaging refers to acquiring images with hundreds of bands corresponding to different wavelengths of light. HS imaging has a wide range of applications such as remote sensing, industrial inspection, environmental monitoring, etc. A fundamental consideration with multiband sensors is that the amount of incident energy is limited and this creates an intrinsic tradeoff between spatial resolution and the number of bands—current optical sensors can either generate images with high resolution but a small number of bands or images with a large number of bands but reduced resolution. For example, HS images have hundreds of bands but low spatial resolution, whereas the opposite is true for multispectral (MS) images. An extreme case is a panchromatic (PAN) image with very high spatial resolution but just a single band. Image fusion refers to techniques where multiband images with high spatial resolution are synthetically generated using image processing algorithms. It includes pansharpening (MS+PAN), hyperspectral sharpening (HS+PAN), and HS-MS fusion (HS+MS). Reconstructing a fused image from the observed images is ill-posed and needs regularization. Diverse regularization methods have been proposed over the years for general imaging problems, many of which perform very well for fusion. This includes vector total variation, sparsity and dictionary-based penalties, generalized Gaussian- and GMM-based priors, etc. This thesis proposes novel regularization models and algorithms that can outperform state-of-the-art image fusion techniques. We can broadly group these into two classes—explicit and implicit regularization.Explicit regularization refers to the design of hand-crafted penalty functions that impose desirable properties (e.g., smoothness) on the reconstruction; this is used along with the observed data for fusion. We propose a convex regularizer that is motivated by nonlocal patch-based methods for image restoration. Our regularizer accounts for long-distance correlations in hyperspectral images, considers patch variation for capturing texture information, and uses the higher resolution image for guiding the fusion process. Unlike local pixel-based methods, where variations along just horizontal and vertical directions are penalized, we use a wider search window in terms of nonlocality and directionality. This is shown to yield state-of-the-art results. The catch is that the resulting optimization problem is non-differentiable and we cannot use simple gradient-based algorithms. However, we show that by expressing patch variation as filtering operations and judiciously splitting the original variables and introducing latent variables, we develop a provably convergent iterative algorithm, where the subproblems can be solved efficiently using FFT-based convolution and soft-thresholding.In the implicit approach, we rely on a recent paradigm known as plug-and-play (PnP) regularization, where powerful off-the-shelf denoisers are used for regularization purposes. While this has been shown to give state-of-the-art results for general restoration tasks, it has not so much been explored for fusion. In fact, we faced few technical challenges in applying PnP for hyperspectral fusion. Firstly, existing denoisers are slow when applied to multiband images and we need to apply such denoisers several times with the PnP framework. Secondly, convergence is generally not guaranteed for PnP regularization since the mechanism is ad-hoc. Along with efficiency and good denoising performance, we need to come up with a denoiser with specific properties that can guarantee convergence. We proposed a couple of approaches to solve this problem. In the first approach, we have developed a high-dimensional kernel denoiser with low cost yet good denoising performance, which can guarantee PnP convergence. The overall algorithm is fast and competitive with state-of-the-art methods. In the second approach, we leverage the power of deep learning to develop a trained patch denoiser which has a couple of advantages over conventional end-to-end learning: (1) Unlike end-to-end networks which require excessive ground-truth data for training, we can be trained the denoiser from patches extracted from the observed images. For example, in HS+MS fusion, the MS image captures the same scene and has the same spatial resolution as the target image. We train the denoiser by sampling clean patches from the MS image and corrupting them with noise.(2) Compared to end-to-end learning, where the training is done with a fixed forward model, our method can be deployed for different forward models. This is possible thanks to the decoupling of the inversion (of the forward model) and denoising steps in PnP.We use the trained denoiser for PnP regularization and establish convergence of the PnP iterations under a technical assumption that we verify numerically. As far as the reconstruction quality is concerned, our method outperforms state-of-the-art variational and deep-learning fusion techniques.

Thesis Defence of Meshineni Deepchand
2022-11-14 16:00 - 17:00
Degree Registered: M.Tech(Res).
Guide: Dr. Rathna G N
Venue: MMCR EE, C 241
Date & Time: 14th November 10:30am
Teams meeting link: Click here to join the meeting
Title: Non contact Breathing and Heartbeat signals monitoring using FMCW radar
Abstract: Non-contact breathing and heartbeat signals monitoring are the tasks of extracting them without contact sensors. It became even more critical in COVID 19, and hence it is crucial to estimate them correctly. FMCW (Frequency Modulated Continuous Wave) radar is employed to estimate these two signals without contact. Radar captures chest displacement and body movement. Because of this, breathing and heartbeat signals are distorted. The reduction of false peaks and peak estimation is crucial for breathing rate calculation. So in this thesis, firstly, we propose a novel way for tracing body movement and eliminating the traced segment for breathing and heart rate calculation. In the second part, we efficiently reduced false peaks using maximal overlap discrete wavelet transform (MODWT) to decompose and reconstruct the filtered breathing signal for estimating breathing rate. The heartbeat signal is estimated using bandpass filtering of unwrapped phase. We also compared our algorithm with the task force monitoring (TFM) device as a reference and discussed its performance. Also, our proposed method for breathing rate estimation has an accuracy of 92.43% and heartrate estimation it is 85.16%.
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Lecture by Dr. Ganesh Sivararaman
2022-11-11 17:00 | EE, MMCR
Indian Institute of Science and
The IEEE Signal Processing Society, Bangalore Chapter
Cordially invites you to the following talk on
“Unsupervised adaptation in speech technologies”
(Click here for the poster.)
Speaker: Dr. Ganesh Sivaraman, Pindrop, Atlanta, USA
Date and Time: 11th November 2022 at 11:30am to 12:30pm
Venue: MMCR (Room No. C241), 1st Floor, Dept. of Electrical Engineering
Abstract: Unsupervised learning and adaptation techniques have taken center stage due to the exponential growth of unlabeled data. For many practical applications unsupervised learning helps in the adaptation of machine learning systems to mismatched train and test domains. Unsupervised adaptation can be performed by three broad approaches – 1) feature transformations in the test domain, 2) model adaptation to test domain, and 3) generation of synthetic test domain samples. This talk will outline these methods by showing three specific examples from speech processing. Unsupervised speaker adaptation for acoustic-to-articulatory speech inversion serves as an example of feature transformation-based adaptation. Adaptation of end-to-end ASR systems without manual transcriptions will be presented as an example of model adaptation. Finally, children’s speech simulation for zero-shot child speech classification using X-vectors will be presented as an example of synthetic data generation for the test domain.
Biography: Ganesh Sivaraman is a Senior Research Scientist at Pindrop, in Atlanta, USA. He received his M.S. (2013) and Ph.D. (2017) in Electrical Engineering from the University of Maryland College Park. His research experience and publications span several speech technologies like acoustic-to-articulatory inversion, ASR, speaker recognition, deepfake detection, and speech enhancement. During his PhD at Maryland, he was awarded the Future Faculty Fellowship, and the International Graduate Research Fellowship by the A. James Clark School of Engineering. Along with his official work, he is actively involved in teaching, mentoring, and collaborating with doctoral students at Maryland. Apart from research work, Ganesh is a fluent speaker of Sanskrit actively learning and teaching the language as a volunteer of Samskrita Bharati USA. He is passionate about creating computational tools for learning Sanskrit pronunciation.

Thesis Colloquium of Sayantan Das
2022-11-11 15:00 - 16:00
Thesis Title: Modeling of lightning attachment to aircraft and quantification of the influencing parameters
Guide: Prof. Udaya Kumar
Degree registered: Ph.D.
Date and Time: 11th November 2022, 9:30 AM
Meeting link: https://teams.microsoft.com/l/meetup-join/19%3ameeting_MDg5YzdhYWUtMTc3Zi00Yjg0LWE1ZTktYjgyY2I5Y2MyNDI4%40thread.v2/0?context=%7b%22Tid%22%3a%226f15cd97-f6a7-41e3-b2c5-ad4193976476%22%2c%22Oid%22%3a%227ef4df52-6005-46aa-bff3-d96db1a85b71%22%7d
Abstract: According to Air Transport Action Group (ATAG), 45 million aircraft took off worldwide in 2020, which translates to 1.5 lakh per day. Statistically, the aviation industry is found to double its fleet size every fifteen years. Lightning is considered one of the dreadful environmental threats to aircraft. Past incidents show that lightning strikes can lead to structural damage, operational interruption, and loss of lives. Field data suggest that, on average, an aircraft gets struck by lightning once or twice a year. Therefore, the threat due to lightning is considered a crucial safety aspect of an aircraft.
Design of suitable lightning protection for aircraft involves Zoning of its skin. It is intended to differentiate lightning attachment points, channel slipping regions, and regions that carry just the stroke current. The first step of Aircraft Zoning is to identify the initial attachment points. For the same, different methods like Laboratory experiments, similarity principle, Rolling Sphere Method (RSM), and Field-based approach are suggested in the standard ARP5414. In reality, the lightning strikes to aircraft can be of two modes, Aircraft-initiated and Aircraft-intercepted. In the former one, under the influence of a thundercloud or descending lightning leader, the aircraft initiates stable bipolar connecting leaders, upward and downward leader toward the ground. These leaders are deemed to propagate hundreds of meters to complete the lightning strike. In Aircraft-intercepted strikes, the aircraft intercepts a descending lightning leader and hence gets struck. The laboratory experiments on scaled aircraft models or isolated aircraft parts are inadequate to assess the initial attachment points. The similarity principle suggested in the standard is qualitative and can’t be extended to aircraft of any size and shape. The 25m Rolling Sphere Method (RSM) is routinely employed to determine the attachment points. This method doesn’t consider the connecting leader discharges from aircraft and therefore overestimates the possible attachment points. Most (90%) of the lightning strikes to aircraft are attributed to aircraft-initiated mode, which involves significant connecting leader activities. Therefore, it has to be traced accurately to assess attachment points.
In literature, it is difficult to find a model for bipolar leader discharges from aircraft. However, work on either negative or positive leader inception and propagation from laboratory gaps and their extension can be relatively found. Based on them, the present work aims to develop a suitable model for simulating bipolar leader discharges from aircraft. Additionally, the aircraft-intercepted mode of lightning strikes is also included. In summary, a novel model adapting the pertinent physical aspects of the leader discharges has been developed to accurately assess initial lightning attachment points to aircraft.
Using the model developed, the following practically important questions are addressed:

Dependency of the frequency of lightning strikes to aircraft on its shape and size.
Rate of lightning strikes to aircraft at different altitudes
For a given aircraft and its route, the number of times it gets struck by lightning
The average number of strikes to an aircraft per year

To present a quantitative assessment, two different aircraft models, McDonnell Douglas DC-10 and Standard Dynamic Model are considered.
In summary, a novel model based on physical grounds has been developed to assess the initial lightning attachment points on aircraft. Using the same, further methodologies are constructed to quantify the dependency of the strike rate on aircraft size, altitude, and possible average strike rate.
ALL ARE WELCOME

Lecture by Prof. Carlos Busso
2022-11-09 19:30 - 20:30 | EE, MMCR
Organised by
Indian Institute of Science and
The IEEE Signal Processing Society, Bangalore Chapter
Title: Robust Emotion Recognition (Click here for the poster)
Date and Time: 9th November 2022 at 2:00pm, Refreshments: 3:00pm
Venue: MMCR (Room No. C241, 1st Floor, Dept. of Electrical Engineering
Abstract of the talk: It is challenging to achieve robust and well-generalized models for tasks involving subjective concepts such as emotion. This tech talk will describe novel approaches to effectively develop robust speech emotion recognition (SER) systems. At the resource level, we will describe our effort to collect the MSP-Podcast corpus, which is a large, naturalistic emotional database. The data collection protocol combines machine-learning algorithms to retrieve recordings conveying balanced emotional content annotated with a cost-effective crowdsourcing protocol. To improve the temporal modeling of SER systems, this seminar will also discuss a novel dynamic chunking approach that maps the sequences of different lengths into a fixed number of chunks that have the same duration by adjusting their overlap. This simple chunking procedure creates a flexible framework, facilitating parallel computing. The approach can incorporate different feature extractions and sentence-level temporal aggregation approaches to cope, in a principled way, with a sequence-to-one SER task. Likewise, the seminar will discuss multimodal pre-text tasks that are carefully designed to learn better representations for predicting emotional cues from speech, leveraging the relationship between acoustic and facial features. Finally, the seminar will discuss our current effort to design multimodal emotion recognition strategies that effectively combine auxiliary networks, a transformer architecture, and an optimized training mechanism for aligning modalities, capturing temporal information, and handling missing features. These models offer principled solutions to increase the generalization and robustness of emotion recognitions systems.
Speaker Biography: Carlos Busso received his PhD degree (2008) in electrical engineering from the University of Southern California (USC), Los Angeles, in 2008. He is a professor at the Electrical Engineering Department of The University of Texas at Dallas (UTD). At UTD, he leads the Multimodal Signal Processing (MSP) laboratory [http://msp.utdallas.edu]. He is a recipient of an NSF CAREER Award. In 2014, he received the ICMI Ten-Year Technical Impact Award. In 2015, his student received the third prize IEEE ITSS Best Dissertation Award (N. Li). He also received the Hewlett Packard Best Paper Award at the IEEE ICME 2011 (with J. Jain), and the Best Paper Award at the AAAC ACII 2017 (with Yannakakis and Cowie). He received the Best of IEEE Transactions on Affective Computing Paper Collection in 2021 (with R. Lotfian) and in 2022 (with Yannakakis and Cowie). He is the co-author of the winner paper of the Classifier Sub-Challenge event at the Interspeech 2009 emotion challenge. His research interest is in human-centered multimodal machine intelligence and applications. His current research includes the broad areas of affective computing, multimodal human-machine interfaces, nonverbal behaviors for conversational agents, in-vehicle active safety system, and machine learning methods for multimodal processing. His work has direct implication in many practical domains, including national security, health care, entertainment, transportation systems, and education. He was the general chair of ACII 2017 and ICMI 2021. He is a member of ISCA, AAAC, and a senior member of ACM and IEEE.

Online Thesis Defence of Ashiq Muhammed P E
2022-11-07 20:00 - 22:00
Degree Registered: Ph.D.
Guide: Prof. Satish L and Prof. Udaya Kumar
Thesis Title: Improved Understanding of Standing Waves in Single Layer Coil and Elegant Methods to Estimate Transformer Winding Parameters
Click here to join the Meeting
Abstract: Analyzing the effect of impulse voltages (like lightning, switching) on transformer winding has occupied centerstage in core electrical engineering research for over a century. These investigations gather great significance and relevance as it eventually governs the design of insulation in the winding. Notwithstanding the colossal contribution this domain has witnessed from stalwarts in the past century, a closer scrutiny surprisingly reveals that there still exists tiny grey areas that demands attention. Pursuing this line of thought, the first part of this thesis aims to clearly describe what this grey area is and resolving it would provide a deeper insight about fundamental understanding of surge response in transformer windings – with special emphasis on its standing wave phenomenon. Following this, in the latter part, elegant procedures are stitched together to determine a few electrical parameters of the transformer winding equivalent circuit that have the potential to help in assessing mechanical status of windings. Objectives of the thesis are –
1. Formulate an analytical method to determine the exact shape of standing waves for all modes in a uniform single layer coil as a solution of its governing partial differential equation2. Estimate series capacitance of a uniform transformer winding from its measured driving point impedance3. Determine effective air-core inductance of an iron-core uniform winding as a function of its axial length from measured driving point impedance
First part of the thesis revisits a century-old classical theory of standing waves on uniform single layer coils. Accurate information about natural frequencies and shapes of the corresponding standing waves are essential for gaining a deeper understanding of the response of coils to impulse excitations. Analytical studies on coils have largely been based on the assumption that standing waves are sinusoids in both space and time. However, this contradicts the results from numerical circuit analysis and practical measurements. So, this thesis attempts to bridge this discrepancy by revisiting the classical standing wave phenomena in coils. It not only assesses the reason for the aforementioned inconsistency, but also makes a contribution by analytically deriving the exact mode shape of standing waves for both neutral open/short conditions. For this, the coil is modelled as a distributed network of elemental inductances and capacitances, while an exponential function describes the spatial variation of mutual inductance between turns. Initially, an elegant derivation of the governing partial differential equation (in terms of voltage as the variable instead of flux) for surge distribution is presented and to the best of our knowledge, for the first time, an analytical solution for the same has been found by the variable-separable method to find the complete solution (sum of time and spatial terms). Hyperbolic terms in the spatial part of the solution have always been neglected but are included here, thus, yielding the exact mode shapes. For verification, both voltage and current standing waves computed from the analytical solution were plotted and compared with PSPICE simulation results on a 100-section ladder network representing a uniform single-layer coil. Then, practical measurements were made on a tailor-made large-sized single layer coil with a length of 2.02 m, diameter of ~1 m and having 640 turns. It turns out that even in such simple single layer coils, the shape of standing waves of all modes deviates considerably from being sinusoidal. It was further observed that this deviation depends on spatial variation of mutual inductance, capacitive coupling, and order of the standing waves.
In the second part, an elegant method for determining the series capacitance (Cs) and air-core equivalent inductance of a uniform winding as a function of its axial length (termed as M0x in this thesis) of a uniform transformer winding, from its measured DPI magnitude, is discussed. Knowledge about the series capacitance of the winding is essential, which along with shunt capacitance, determines the initial impulse voltage distribution when a surge impinges on the winding. Unlike previously published approaches, the proposed method does not involve any cumbersome and time-consuming curve-fitting or running of optimization/search algorithms. Neither does it require winding geometry data. The proposed procedure for finding series capacitance relies on a property that is observable in the driving point impedance (DPI) function of a lossless winding with an open neutral condition, viz., the ratio of the product of squares of open circuit natural frequencies to the product of squares of short circuit natural frequencies bears a particular relation to the coefficients of the DPI function. A simple procedure involving a deft manipulation and combination of a few well-known properties that correlate the roots of a polynomial to its coefficients are then utilized for determining series capacitance.
Knowledge about equivalent air-core inductance distribution as a function of its axial length (i.e., M0x) is useful for localizing a minor/incipient mechanical fault in the winding. A physically realizable empirical relationship to estimate M0x is initially proposed. The corresponding constants of the empirical relationship are then calculated from the measured DPI. The proposed method requires three DPI measurements: one with neutral-end open and the other with neutral-end shorted. The third DPI is measured with a known external lumped capacitance connected between the neutral and ground. This method requires only the first few dominant natural frequencies observable in the first two of the DPIs.
Feasibility of proposed methods for estimating Cs and M0x was initially verified by simulation on an N-section ladder network and then by experiments on small-sized continuous-disk and interleaved-disk windings, and finally on a large-sized 33 kV, 3.5 MVA continuous-disk winding. Salient features of the proposed methods are – they are simple, elegant and involve minimum post-processing after measuring the DPI. Given its inherent simplicity and their relevance, the author is hopeful that industry will come forward to implement these procedures on an existing FRA measuring instruments – thus opening a new dimension to FRA measurements.
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EE Faculty Colloquium by Prof. A G Ramakrishnan
2022-10-21 21:30 - 22:30 | EE, MMCR
Title: Analyzing patterns in EEG: from biometrics to altered states of consciousness
Speaker: Prof. Ramakrishnan A. G., MILE Lab, Dept of Electrical Engineering, Indian Institute of Science
Venue: EE MMCR
Time: 4pm, 21 October 2022
Abstract: The electrical activity of brain, the key control organ of our body, carries a lot of information about our current activity, health, mental status and identity. We, in MILE Lab, have been focussing for the past few years on analysing the various patterns present in the electroencephalogram (EEG). When someone is in deep sleep, coma or under anesthesia, the level of consciousness is lower than that of waking state. When someone has locked-in syndrome, the level of consciousness is the same as that of waking state. On the other hand, during meditation, the consciousness level is higher than that of waking. Hypnosis is another completely different altered state of consciousness. The interrelationship between the different EEG channels is also distinctly different during inhalation, breath-hold and exhalation. We are studying the patterns in EEG under all the above conditions. While working on the above topics, we unexpectedly made a discovery that some measures based on the functional connectivity between the channels are distinct for each individual and can very well be used to identify people. Using other measures, we are also able to predict the word imagined by a person out of a set of words.Speaker bio: Ramakrishnan A. G. is a professor of Electrical Engineering and an associate faculty member at the Centre for Neuroscience. He obtained his Masters in Electrical Engineering and Ph D in Biomedical Engineering from the Indian Institute of Technology, Madras. He has graduated 19 Ph.D.s, 16 M.Tech.s by research, and guided over 100 M. Tech. projects at IISc. He is a Fellow of the Indian National Academy of Engineering, As the leader of a research consortium, he was instrumental in creating handwriting recognition technologies for eight Indian languages. He received Manthan award (South East Asia and Asia Pacific) twice for creating audio books for blind students through his OCR and TTS in Tamil and Kannada. His current areas of research include speech recognition in Indic languages, decoding of imagined words from EEG, brain functional connectivity analysis in modified states of consciousness and the study of the neural control and physiological mechanisms behind the health and therapeutic effects of deep breathing. For his earlier work on evoked potentials from leprosy patients, he had received Sir Watt Kay Young Researcher’s Prize from the Royal College of Physicians and Surgeons, Glasgow. He was a Senior Research Scientist at Hewlett Packard Research Labs, Bangalore India from May 2002 to August 2003. He is an invited member of the Senate of IIIT-Allahabad, Prayagraj and the Federation of Indian Chambers of Commerce and Industry – Indian Language Internet Alliance. He was a member of the Knowledge Commission, Government of Karnataka during 2017-2020. He is also one of the founder directors of RaGaVeRa Indic Technologies private limited recognized by Karnataka Government as one of the Elevate 2019 Startup winners. The Kannada TTS developed by RaGaVeRa has been evaluated to be better in quality than Google’s Wavenet TTS and Nuance’s Kannada TTS. He is also the Advisor-Neuroscience of Feedfront Technologies Pvt Ltd, Bengaluru.

EE and CPS Seminar by Prof. Ketan Savla
2022-10-21 19:45 - 21:00 | EE, MMCR
Title: Microscopic Traffic Flow Control
Date: 21 October
Time: 2:15pm
Venue: EE MMCR
Abstract: Design and performance evaluation of traffic control techniques such as ramp metering are typically based on macroscopic traffic flow models. These models, obtained by spatio-temporal averaging of microscopic vehicle-to-vehicle/infrastructure interactions, do not have sufficient resolution to model safety, or to study the impact of emerging paradigms of autonomy and connectivity. We present coordinated ramp metering algorithms that regulate entry into the freeway network at the vehicle level, based on information about state of vehicles in the network, but do not require information about travel demand. Under these algorithms, each on-ramp operates under cycles during which it does not release more vehicles than its queue size at the beginning of the cycle.
Additionally, the algorithms, dynamically, either introduce pause at the end of the cycle, or modulate the release rate during the cycle, or modulate safety distance for release during the cycle. Under standard safe vehicle-following and merging protocols, these algorithms are shown to keep the network undersaturated for maximal travel demand and result in lower travel time than known ramp metering algorithms.Biography of the speaker: Ketan Savla is an associate professor and the John and Dorothy Shea Early Career Chair in Civil Engineering at the University of Southern California. His current research interest is in distributed optimal and robust control, dynamical networks, state-dependent queuing systems, and mechanism design, with applications in civil infrastructure systems. His recognitions include NSF CAREER, George S. Axelby Outstanding Paper Award, and the Donald P. Eckman Award. He serve(d) as an associate editor of the IEEE Transactions on Control of Network Systems, IEEE Control Systems Letters, and IEEE Transactions on Intelligent Transportation Systems. He is also a co-founder and the chief science officer of Xtelligent, Inc.

Thesis Defence of Anwesha Roy
2022-10-19 21:30 - 22:30
Degree Registered: M. Tech. (Research)
Guide: Prof. Prasanta Kumar Ghosh
Date & Time: 19th October, 2022, Wednesday, 4:00 PM
Venue: Online link : https://tinyurl.com/3rystxed
Title: Improved air-tissue boundary segmentation in real-time magnetic resonance imaging videos using speech articulator specific error criterion
Abstract: Real-time Magnetic Resonance Imaging (rtMRI) is a tool used exhaustively in speech science and linguistics to understand the dynamics of the speech production process across languages and health conditions. rtMRI has two advantages over other methods which capture articulatory movement, like X-ray, Ultrasound and Electromagnetic articulography – it is non-invasive, and it captures a complete view of the vocal tract including pharyngeal structures. The rtMRI video provides spatio-temporal information of speech articulatory movements, which helps in modeling speech production. For this purpose, a common step is to obtain the air-tissue boundary (ATB) segmentation in all frames of the rtMRI video. The accurate estimation of ATBs of the upper airway of the vocal tract is essential for many speech processing applications like speaker verification, text-to-speech synthesis, visual augmentation for synthesized articulatory videos, and analysis of vocal tract movement. Thus, it is necessary to have an accurate air-tissue boundary segmentation in every frame of the rtMRI videos.
The best performance in ATB segmentation of rtMRI videos in speech production, in unseen subject conditions, is known to be achieved by a 3-dimensional convolutional neural network (3D-CNN) model. In seen subject conditions, both 3D-CNN and 2-dimensional deep convolutional encoder-decoder network (SegNet) show similar performance. However, the evaluation of these models, as well as other ATB segmentation techniques reported in literature, has been done using Dynamic Time Warping (DTW) distance between the entire original and predicted boundaries or contours. Such an evaluation measure may not capture local errors in the predicted contour. Careful analysis of predicted contours reveals errors in regions like the velum part and tongue base section, which are not captured in a global evaluation metric like DTW distance. In this thesis, we automatically detect such errors and propose a novel correction scheme for them. We also propose two new evaluation metrics for ATB segmentation, separately for each contour, to explicitly capture errors in these contours.
Moreover, the state-of-the-art models use overall binary cross entropy as the loss function during model training. However, such a global loss function does not give enough emphasis on regions which are more prone to errors. In this thesis, together with global loss, we explore the use of regional loss functions which focus on areas of the contours which have been analyzed as error prone in our analysis. Two different losses are considered in the regions around velum and tongue base – binary cross entropy (BCE) loss and dice loss. It is observed that dice-loss based models perform better than their BCE loss based counterparts.

Thesis Defense of Kiran Kumar Challa
2022-10-13 20:30 - 22:00 | EE, MMCR
Degree registered: PhD
Thesis Title: Algorithms and Testbed for Synchronous Generator Parameter Estimation
Guide: Prof. Gurunath Gurrala
Examiner: Prof. K. Shanti Swarup, IIT Madras
Date & TIme: 13th October 2022, Thursday, 3pm – 4.30pm
Mode: Hybrid mode, both Teams and Physical
Venue: MMCR, 1st floor C-wing, EE Department, IISc

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Abstract: The development of dynamic power system components models became increasingly important in the modern grids dominated by high penetration of renewables because of the increased dependency of planning and operational decisions on dynamic simulation studies. The parameters of synchronous machines and associated control models play significant role in the overall model of the grid, which need to be updated regularly by the utilities. So, the parameters of the power plants are calibrated/estimated either using off-line testing or online measurements from phasor measurement units (PMU) or digital fault recorders (DFR). Development of individual generator models is feasible only if the PMU/DFR data is available for each generator in a power plant. Otherwise, they can provide only aggregate model of a generating plant as PMU/DFRs are usually placed in substations. Digital protective relay (DPR) records are available for individual generators in any generating plant. This thesis explores the possibilities of utilizing DPR records of individual generators for parameter estimation. About 200 relay records have been collected from a hydro plant and a thermal plant in Karnataka. It is found that most of the records contain at the most 3 seconds data. Existing methods of parameter estimation using PMU/DFR data failed to work with the short duration records. There is no prior work reported in the literature which uses short relay records for parameter estimation of the synchronous generators. Constrained iterated unscented Kalman filter (CIUKF) and enhanced scattered search (eSS) algorithms are proposed for the parameter estimation using DPR records in this thesis. The parameters of a turbo alternator and its excitation system (210 MW) are estimated from the relay records collected using the proposed algorithms and the results are found be accurate. This is a first of its kind effort in the literature to the best of our knowledge. It is also found that the relay records should contain pre-fault data, during fault data and some post-fault data for accurate estimation. However, from the collected records only a small percentage of the records are found to be useful. To generate realistic data in the laboratory an experimental test bed development, replicating the field implementation aspects of the digital relays, is proposed in this thesis. A realistic scaled-down generalized substation model for translational research in smart grids is developed, which can be configured to operate in 7 widely used substation bus bar schemes with prevalent current transformer (CT) configurations. All the potential transformers (PT) and CT measurements, circuit breaker (CB), isolator and earth switch status signals are made available to configure any protection strategy like bus-bar protection, unit protection schemes, etc. precisely the same way they get implemented in the field. A systematic procedure for the development of an experimental scaled-down frequency-dependent transmission line model of a 230 kV transmission line is proposed. A lumped parameter frequency dependent transmission line model using modal transformation is derived for a 230 kV transmission line and scaled-down to 220 V. Clarke and inverse Clarke transformations are implemented using specially designed 1-phase transformers. The inductances of the scaled-down model are realized using amorphous cores. A new algorithm is proposed to fit a reduced-order R-L equivalent circuit to the frequency response of the modal impedances of the transmission lines. A close enough fitting is achieved with lesser number of passive elements using the proposed method compared to the widely used vector fitting algorithm. This kind of physical realization of a frequency dependent power transmission line model in the laboratory is first of its kind effort in the literature to the best of our knowledge.

Note: Know how generated from the implementation of the generalized substation panels and transmission line models has been licensed to MCore Technologies Pvt Ltd, Bangalore for commercialization.
Acknowledgements: This work is supported by Fund for Improvement of Science and Technology (FIST) program, DST, India, No.SR/FST/ETII-063/2015 (C) and (G) under the project “Smart Energy Systems Infrastructure – Hybrid Test Bed”. Acknowledge partial funding support from Robert Bosch Centre for Cyber Physical Systems (RBCCPS), IISc. Also acknowledge the Tata Trust Travel Grant.

Thesis Defence of Mr. Jerrin Thomas Panachakel
2022-10-11 17:30 - 18:30 | EE, MMCR
Degree Registered: Ph.D.

Date and Time: Oct. 11, 2022 (Tuesday) 12 Noon

Venue: MMCR, Hall No. C 241, II Floor, Dept. of Electrical Engineering.
MS Teams Link: https://teams.microsoft.com/l/channel/19%3a7nfRrQdIH7fPf5u003TVCBAp18cRM7dyFwv1eqJRGjY1%40thread.tacv2/General?groupId=53beb192-2a2f-4f20-8a0c-dbb67a65f532&tenantId=6f15cd97-f6a7-41e3-b2c5-ad4193976476

Title: Machine Learning for Decoding Imagined words and Altered State of Consciousness from EEG

Abstract: The thesis explores several architectures for accurately decoding the cognitive activity from EEG recorded during speech imagery and Rajayoga meditation. The major contributions of the thesis are listed below:

Neural Correlates of Phonological Category in Speech Imagery EEG

We have shown that neural correlates of phonological categories exist in the EEG recorded during speech imagery. These correlates lead to significant differences in the mean phase coherence (MPC) values of the EEG across several cortical regions.
We have also shown that MPC values can be used for accurately classifying the EEG recorded during speech imagery based on the phonological category of the prompts. The proposed architecture for this task has an accuracy of 84.9%.

Decoding Imagined Words from EEG

One of the challenges in designing systems for decoding imagined words from EEG is the limited availability of data. We have presented three architectures for decoding imagined words from EEG. All three architectures alleviate this problem of limited availability of data.
The transfer learning-based architecture employs MPC and magnitude-squared coherence values along with a ResNet50-based classifier. This architecture achieves an accuracy of 92.8% on a publicly available EEG dataset in classifying speech imagery.

Classification of Altered State of Consciousness from Resting State

We have presented three architectures for classifying the altered state of consciousness during Rajayoga meditation from the resting state.
Both CSP-LDA-LSTM (common spatial pattern-linear discriminant analysis-long short-term memory) and SVD-DNN (singular value decomposition-deep neural network) architectures are able to capture subject-invariant features.
The best intra-subject accuracy obtained is 98.2% and the best inter-subject accuracy is 96.4%.

Thesis Colloquium of Mr. Shreyas Ramoji @3pm
2022-10-07 20:30 - 21:30 | EE, MMCR
Date: October 7th, Friday, 3-4pm
Degree Registered: PhD
Venue: EE, MMCR [1st Floor, C241] and in Microsoft Teams at https://tinyurl.com/2rfbs7ke
Thesis Title: Supervised Learning Approaches for Language and Speaker Recognition
Abstract: In the age of artificial intelligence, one of the important goals of the research community is to get machines to automatically figure out who is speaking and in what language – a task that humans are naturally capable of. Developing algorithms that automatically infer the speaker, language, or accent from a given segment of speech are challenging tasks for machines and has been a topic of research for at least three decades. While most of the prior successes have been through the development of unsupervised embedding extractors, the main aim of this doctoral research is to propose novel supervised approaches for robust speaker and language recognition.
In the first part of this talk, we propose a supervised version of a popular embedding extraction approach called the i-vector. The i-vector is a popular technique for front-end embedding extraction in speaker and language recognition. In this approach, a database of speech recordings (in the form of a sequence of short-term feature vectors) is modeled with a Gaussian Mixture Model, called the Universal Background Model (GMM-UBM). The deviation in the mean components is captured in a lower dimensional latent space called the i-vector space using a factor analysis framework. In our work, we proposed a fully supervised version of the i-vector model, where each label class is associated with a Gaussian prior with a class-specific mean parameter. The joint prior (marginalized over the sample space of classes) on the latent variable becomes a GMM. The choice of prior is motivated by the Gaussian back-end, where the conventional i-vectors for each language are modeled with a single Gaussian distribution. With detailed data analysis and visualization, we showed that the supervised i-vector (s-vector) features yield representations succinctly capture the language (accent) label information and do a significantly better job distinguishing the various accents of the same language. We performed language recognition experiments on the NIST Language Recognition Evaluation (LRE) 2017 challenge dataset, which has test segments ranging from 3 to 30 seconds. With the s-vector framework, we observe relative improvements between 8% to 20% in terms of the Bayesian detection cost function, 4% to 24% in terms of EER, and 9% to 18% in terms of classification accuracy over the conventional i-vector framework. We also perform language recognition experiments showing similar improvements on the RATS dataset and Mozilla Common Voice dataset, and speaker classification experiments using LibriSpeech.
In the second part of the talk, we explore the problem of speaker verification, where a binary decision has to be made on a test speech segment as to whether it is spoken by a target speaker or not, based on a limited duration of enrollment speech. The state-of-the-art approach to speaker verification was to extract fixed-dimensional embeddings from speech of arbitrary duration and train a back-end generative model called the Probabilistic Linear Discriminant Analysis (PLDA) which was used to make decisions using a Bayesian decision framework. We proposed a neural network approach for back-end modeling, where the likelihood ratio score of the generative PLDA model is posed as a discriminative similarity function, and the learnable parameters of the score function are optimized using a verification cost. The proposed model, termed as neural PLDA (NPLDA), is initialized using the generative PLDA model parameters. The loss function for the NPLDA model is an approximation of the minimum detection cost function (DCF) used as one of the evaluation metrics in various speaker verification challenges. Further, we explore a fully neural approach where the neural model outputs the verification score directly, given the acoustic feature inputs. This Siamese neural network (SiamNN) model combines embedding extraction and back-end modeling into a single processing pipeline. The development of the single neural Siamese model allows the joint optimization of all the modules using a verification cost. We provide a detailed analysis of the influence of hyper-parameters, choice of loss functions, and data sampling strategies for training these models. Several speaker recognition experiments were performed using Speakers in the Wild (SITW), VOiCES, and NIST SRE datasets where the proposed NPLDA and SiamNN models are shown to improve over the state-of-art significantly.
We conclude the talk by highlighting some of the noteworthy approaches that were published during the course of this research work and identifying some important future directions that can be explored.
Bio: Shreyas Ramoji is a Ph.D. scholar at the Learning and Extraction of Acoustic Patterns (LEAP) Laboratory, Department of Electrical Engineering, Indian Institute of Science, Bengaluru. He obtained his Bachelor of Engineering degree from the Department of Electronics and Communication Engineering, PES Institute of Technology, Bangalore South Campus in 2016. He is a student member of the IEEE Signal Processing Society and ISCA. His research interests include Speaker Verification, Language and Accent Identification, Neuroscience, Machine learning, and Artificial Intelligence.
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​All are welcome.

Thesis Defence of Mr. Paawan Kirankumar Dubal
2022-10-06 20:30 - 21:30
Thesis Title: Cyber Attack Resilient Breaker Failure Protection Scheme Using Wide Area measurements
Name of the Advisor: Prof. Sarasij Das
Degree Registered: M.Tech-Research
Date and Time: 6th October, 2022, 3:00 PM IST Online
Meeting Link: https://teams.microsoft.com/l/meetup-join/19%3ameeting_ZWZkYzViOWQtNWIzYi00MWQ1LWE1ZTMtNzcwOTUxM2JkNGM0%40thread.v2/0?context=%7b%22Tid%22%3a%226f15cd97-f6a7-41e3-b2c5-ad4193976476%22%2c%22Oid%22%3a%2240bc61c0-cc62-49a2-80af-a06745a651ac%22%7d
Abstract: Breaker Failure Protection (BFP) is a backup protection that comes into play when a circuit breaker fails to isolate the fault. If the circuit breaker fails to clear the fault, the BFP scheme commands other required breakers to isolate the fault. BFP schemes are usually incorporated in microprocessor-based digital relays. Commonly employed BFP schemes use overcurrent element (50BF) and Breaker Failure Initiation (BFI) signals as inputs. The BFI signal is issued to the BFP relays from other digital relays. Line current sensed via current transformer is fed to the BFP relay for the overcurrent element (50BF). When both 50BF and BFI are high, it waits for a specified time for the primary protection to operate. The 50BF element is usually set much lower than the rated load currents. So, it will be high during the normal loading conditions. A cyber-attack can be launched by issuing false BFI or blocking a legitimate BFI signal to the BFP relay. Operation of BFP scheme usually leads to disconnection of a larger amount of loads. As a result, mal-operation of BFP schemes can cause major disturbances in power systems. There is a need to make the BFP schemes resilient to cyber-attacks for reliable operation of power systems. Currently, there is a lack of literature on the cyber-attack resilient BFP schemes.
Hence, this thesis proposes a Wide-Area Measurement-Based Cyber-Resilient Breaker Failure Protection Scheme. The scope of the work is to develop an algorithm that will ascertain if the BFI received by the BFP relay is genuine. Blocking a legitimate BFI will cause the backup protection to operate and clear the fault. The proposition assumes that the BFP relay is not compromised in any manner. However, a fake BFI can be issued by other digital relays, which may cause unwanted BFP operations. In the proposed algorithm, when the BFI is received. The BFP relay will communicate the receipt of BFI to the Phasor Data Concentrator (PDC). The proposed algorithm will run at the PDC, which has access to the time-stamped measurements of the adjacent substations and the substation that triggered the algorithm. The decision of the proposed algorithm is communicated back to the BFP relay, which will allow the tripping if the BFI is genuine. Hence, we also propose modifications in the BF scheme in the BFP relay to incorporate the algorithm’s decision in issuing the final trip. The proposal running at PDC is a two-layer algorithm. The first layer randomly samples the bus voltages at the adjacent substations considering different groups of digital relays. The relay which has issued the BFI may be compromised. It makes relays of the same make and family more susceptible to a cyber-attack exploiting the same vulnerabilities. Hence we propose grouping of relays by their make and relay families. The first layer is meant to determine if there is a fault in the vicinity of the BFP relay that issued the trigger. The second layer provides discrimination between fault and cyber-attack by measuring the impedance observed at the two ends of the perceived-faulted line. Since the proposed solution is computationally lightweight, it adheres to the timing requirement of the BFP. The proposition requires healthy communication between the PMUs and the PDC. Nevertheless, the proposed method is fail-safe. It will resort to the conventional BFP scheme in case of loss of communication with the PDC. The proposed solution mitigates n number of cyber-attacks in a no-fault scenario. Additionally, the proposed solution can detect one cyber-attack if the attacker times the cyber-attack during a fault condition. PSCAD simulations were performed to validate the proposition on IEEE 118 bus system. Furthermore, the hardware was developed emulating the PMU-PDC communication as per IEEE C37.118-2 standard, and the execution time of the proposal was verified to ensure adherence to the timing requirement of the BFP.

ALL ARE CORDIALLY INVITED

Thesis Defence of Mr. Ahmad Arfeen @ 11am
2022-09-26 16:30 - 17:30 | EE, MMCR
Title:  Data Efficient Domain Generalization
Faculty Advisor: Prof. Soma Biswas
Examiner: Prof. Venkatesh Babu R. (CDS, IISc)
Date: Monday, September 26, 2022
Time: 11:00 AM
Venue:  MMCR (EE dept)
Abstract: For the task of image classification, in general, the test data is assumed to come from the same distribution as the training data. But this may not always hold in real-life scenarios. For example, in night-time surveillance, we may need to classify images captured using NIR cameras, but the available model has been trained on RGB images. Domain generalization (DG) addresses the problem of generalizing classification performance across any unknown domain, by leveraging training samples from multiple source domains. In this thesis, we address two challenging scenarios for the DG task, with focus on data efficiency. Currently, the training process of majority of the state-of-the-art DG-methods is dependent on a large amount of labeled data. This restricts the application of the models in many real-world scenarios, where collecting and annotating a large dataset is an expensive and difficult task.
As the first contribution, we address the problem of Semi-supervised Domain Generalization (SSDG), where the training set contains only a few labeled data, in addition to a large number of unlabeled data from multiple domains. This is relatively unexplored in literature and poses a considerable challenge to the state-of-the-art DG models, since their performance degrades under such condition. To address this scenario, we propose a novel Selective Mixing and Voting Network (SMV-Net), which effectively extracts useful knowledge from the set of unlabeled training data, available to the model. Specifically, we propose a mixing strategy on selected unlabeled samples on which the model is confident about their predicted class labels to achieve a domain-invariant representation of the data, which generalizes effectively across any unseen domain. Extensive experiments on two popular DG-datasets demonstrate the usefulness of the proposed framework.
The second contribution of this thesis is a novel approach for the task of Zero-Shot Domain Generalization (ZSDG). This is very challenging since the query data can belong to an unseen class as well as unseen domain. For this task, we address the challenge of class imbalance by learning class specific classifier margins, which not only maintains the semantic relationship of the classes in the embedding space, but is also discriminative, and thus improves the classification performance on the test data. Extensive experiments on multiple datasets justify the effectiveness of the proposed approach.
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ALL ARE WELCOME

Online Lecture by Dr. Mohammad Hedayati @5pm
2022-09-21 22:30 - 23:30
Title: High Voltage DC Circuit Breakers (HVDC CB)
Speaker: Dr. Mohammad Hedayati, Dyson Technology, UK.
Time and Venue: 5 pm, MS Teams/Online mode (Link provided below)
Abstract: The main challenge of the DC meshed grid is the lack of suitable fault protection devices. The AC circuit breaker cannot be used in the DC system as in DC there is no zero crossing to extinguish the arc. Hence the DC circuit breakers need to create a virtual current zero crossing. There are different types of DC CB, and three of them (solid state, mechanical, hybrid circuit breaker) are explained in this talk. The advantages and disadvantages of each technology are then pointed out.
Speaker Bio: Dr. Mohammad Hedayati did his Master and PhD in department of Electrical Engineering, IISc between 2008 to 2016. Then he moved to University of Aberdeen as a postdoc, where he was working on DC CB for DC meshed grids in 2016. In 2018 he joined University of Bristol working on GaN Devices Reliability and health monitoring and switching characteristics. In 2021 he joined Dyson technology, during this time he was designing high speed (150kRPM) motor drive inverters for vacuum cleaner and personal care applications. In October 2022 he will be joining Jaguar Land Rover (owned by Tata group) to design EV motor drive inverters.
All are welcome.
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Thesis Colloquium of Anurenjan P. R.
2022-09-09 20:00 - 21:00 | EE, MMCR
Venue, Date and Time: MMCR, EE, IISc. 9-9-2022, 2.30-3.30pm.

Teams link – https://tinyurl.com/3bpmjxkx .
Title: Dereverberation of speech using frequency domain linear prediction
Faculty Advisor: Dr. Sriram Ganapathy
Abstract : The speech-based technologies are radically changing the way we interact with systems and how we access information. In many of these applications, the users prefer to interact with the system through a far-field microphone without the nuance of a handheld or body-worn device. Examples of such applications are automated meeting analysis, speech-based dictation systems, hands-free interfaces for controlling consumer-products, IoT, virtual assistants in mobile phones and smart speakers. The major challenge in capturing speech from the far-field is the degradation of the signal quality due to reverberation. Reverberation refers to the delayed and weighted summation of the direct component of the speech signal with the reflected versions. This talk is focused on developing methods for speech dereverberation, i.e., restoring the functional quality of reverberated speech, using the signal analysis technique of frequency domain linear prediction (FDLP).
The FDLP is the frequency domain dual of the conventional Time Domain Linear Prediction (TDLP). Just as the TDLP estimates the spectrum of a signal, the FDLP estimates the temporal envelopes of the signal using an autoregressive model. We apply the FDLP approach to the sub-bands of speech signal that are distributed in the mel scale.
This talk will describe two broad directions for addressing issues in the far-field speech using the FDLP approach. In the first part of the talk, we explore a front-end design for automatic speech recognition (ASR) applications that suppresses the reverberation artifacts in the FDLP envelope. In the second part of the thesis, we develop a speech enhancement model using the envelope and carrier decomposition given by the FDLP technique.
In the design of the ASR front end, I will discuss a novel approach for 3-D acoustic modeling framework, where the spatio-spectral features from all the sub-band channels are extracted. The features that are input to the 3-D CNN are extracted by modeling the signal peaks in the spatio-spectral domain using a multi-variate autoregressive modeling approach. In the subsequent part of this section, I will describe a neural model for speech dereverberation using the long-term sub-band envelopes of speech. The neural dereverberation model estimates the envelope gain, which when applied to reverberant signals, allows the suppression of the late reflection components. The de-reverberated envelopes are used for feature extraction in speech recognition. The key novelty in this model is the joint learning of the reverberation and the ASR system. In these ASR experiments using the proposed framework, we illustrate significant performance gains over previously proposed front ends.
The second part of the thesis deals with the FDLP based speech dereverberation for enhancement applications, where the goal is to restore the audible quality of the speech signal. For this task, we decompose the sub-band speech signal into the constituent envelope and carrier part. A dereverberation neural model is designed that attempts to enhance the envelope and carrier signals jointly. Further, joint learning of the speech enhancement model with the end-to-end ASR model is proposed with a single neural framework. The proposed model therefore can generate improved audio quality and provide robust representations for far-field ASR. Finally, I will illustrate the subjective quality improvement of the audio signal as well as the improvement in ASR performance obtained by the proposed envelope-carrier model.
Acknowledgement
This work was partly supported by project grants from Samsung Research India, Bangalore and the College of Engineering, Trivandrum, Kerala.
Bio: Mr. Anurenjan is a PhD student at the LEAP lab, Electrical Engineering, IISc. He is also currently working as Assistant Professor in College of Engineering, Trivandrum. Mr. Anurenjan completed his Bachelors in Technology from Government Engineering College, Barton Hill, Trivandrum, Kerala in 2006 and his Masters in Technology from College of Engineering, Trivandrum, Kerala in 2008. He joined the LEAP lab as a PhD candidate under AICTE-QIP program in the year 2017. He hails from Trivandrum district of Kerala. He is interested in signal processing, machine learning and speech processing. Mr. Anurenjan is a student member of IEEE SPS and the ISCA. During his free hours, Mr. Anurenjan likes to play badminton and swimming.
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All are invited. Coffee/Tea will be served before the talk.

Thesis Colloquium of Manish Tathode @3.30pm
2022-08-29 21:00 - 22:30 | EE, MMCR
Title: Fast and Compact Voltage Equalizer for Satellite Applications.Advisor: Prof. Vinod John.Date and Time: Monday, 29th August 2022, 3.30 pm.Location: MMCR, EE Department.Meeting Link: click here for Meeting Link
Abstract: Lithium-ion batteries have now become an critical constituent of Electrical Power System of solar-powered satellites due to their high energy density, wider operating temperature range and better radiation tolerance. For compact realization and better space utilization, the series-parallel connected Li-ion batteries are operated with currents close to the design limit of the cells, speeding up the increase in the inherent initial imbalance in the individual cell voltages in a series connected stack, demanding fast equalization. Active multicell-to-multicell equalization achieve fast equalization by efficient charge transfer among multiple cells in the series connected stack. PS-MAHB equalizer is a multicell-to-multicell equalizer, with its open-loop control maintains high equalization current throughout the equalization. Its soft-switched operation and modularization abilities make it an attractive choice for space applications. However, it lacks the necessary protective features and required redundancy essential for its use in space applications. Hence, a Modified PS-MAHB (MPS-MAHB) equalizer is developed by incorporating necessary protection features and redundancy in the PS-MAHB equalizer. The Failure Mode Impact Analysis of the MPS-MAHB equalizer reveals that during the most likely switch short circuit failure mode, the faulty part of the equalizer is disconnected by the protective device and the redundancy does not let the cell get out of the equalization.The existing static phase shift-based control of the equalizer causes direct dependency of the equalization currents on the cell voltages and limits the equalization current levels to lower than the design equalization current value when the cell voltages are lower. Thus, the control works with reduced rate of equalization, and causes the under-utilization of the equalizer hardware for significant duration of time in the charge-discharge cycle. A dynamic phase shift-based control is proposed to maximize the equalization current through the cells irrespective of the cell voltages which further increases the rate of equalization and improves the equalizer hardware utilization. In the simulation, a significant improvement in the equalization rate compared to the static phase shift control is verified with the proposed dynamic phase shift-based control.The compact hardware realization of the equalizer hardware and the voltage sensors addresses the space-volume constraints in satellite applications. The equalizer hardware is realized as 4-cell equalizer modules, and the compactness of the equalizer hardware is achieved by pushing the switching frequency to 1MHz reducing the values and sizes of the passive components. The challenges faced during the PCB design of the 4-cell equalizer module are addressed by the design. A non-isolated high-precision op-amp based voltage sensing scheme is developed to target the equalization band close to 10mV. The concept of easy-to-design motherboard-based interface is introduced, which does not require any changes in the design of 4-cell equalizer module and the voltage sensor board, irrespective of the cell connector geometry.The experimental results verify the operation of the equalizer showing the convergence of cell voltages from the initial imbalance of 300mV to the band of 10mV. The impact of the non-ideal dynamic response of the Li-ion cell voltage on the voltage-sensing-based control algorithm is discussed along with the necessary modifications incorporated in the control.We request your presence to the colloquium.All are welcome.

Thesis Colloquium of Ms. Mani Madhoolika Bulusu
2022-08-24 21:30 - 22:30 | EE, MMCR
Degree Registered: M.Tech.(Research)
Advisor: Prof. Chandra Sekhar Seelamantula
Title: Interpolation of Digital Elevation Models Using Generative Adversarial Networks
Venue: Multimedia Classroom (MMCR), Department of Electrical Engineering, IISc
Date & Time: August 24, 2022; 4 PM onward (coffee will be served during the talk)
Abstract: A digital elevation model (DEM) is a three-dimensional representation of elevation data of a terrain such as a lunar terrain acquired using a Chandrayaan rover or a terrestrial terrain acquired by a reconnaissance aircraft. Terrestrial DEMs are used in hydrological modeling, geomorphology, and glaciology. Lunar DEMs can be used to locate natural resources and to identify prospective landing sites for exploratory missions. Hence, high-quality, reliable DEMs are of great significance. DEMs are generally captured using LiDAR (Light Detection and Ranging), stereophotogrammetry, and time-of-flight cameras. However, a reliable DEM cannot be constructed without adequate landmark points/features. This is the case with smooth terrains, occlusions, and multiple voids. The measurements are nonuniform in general. Hence, there is a need for interpolation and void-filling techniques to estimate the elevation with a high accuracy.
Inverse distance weighting (IDW), De Launay triangulation, and Kriging are some of the popular benchmark algorithms for interpolating scattered and nonuniformly spaced data. Manual parameter tuning, inability to recover high-frequency information, and high computational complexity are some of the issues that these traditional interpolation techniques suffer from. Deep Learning (DL) has proven to be effective in providing excellent results in the field of image processing and computer vision, specifically in the tasks of super-resolution, image in-painting, extrapolation, and segmentation. With the massive success of DL in several image processing and computer vision applications, its applicability has been explored for solving the DEM interpolation problem as well. However, convolutions are not readily defined if the measurements are nonuniform. Hence, the recent DL based research on DEM interpolation has only focused only on regularly spaced data.
We address the realistic problem of DEM interpolation from irregularly spaced measurements, with the density of measurements varying spatially. This is a new and unexplored direction in the deep learning setting. We propose a new and robust DL architecture based on Generative Adversarial Networks (GANs) to perform interpolation and result in a uniform DEM with a user-specified resolution. The generator comprises three modules: Learnable Distance Weighting (LDW), DEM in-painting, and Continuous Convolution (CC). We designed the novel LDW module as a learnable counterpart to the popular IDW algorithm that operates on the distances between the measurements and grid locations. This reduces the problem to that of inpainting post the LDW transformation. The proposed method is evaluated on synthetically generated data and on standard publicly available NASA (LOLA LRO) datasets using the mean relative error and PSNR as performance metrics. Extensive experiments justify the effectiveness and accuracy of the proposed approach in comparison with the benchmark techniques. We conclude the presentation by discussing possible future directions for DL based DEM interpolation.
All are invited.

Lecture by Prof. Mainak Sengupta @4.00pm
2022-08-19 21:30 - 22:30 | EE, MMCR
Title: Induction heating : basic principles and power converters
Speaker: Prof. Mainak Sengupta, Professor, Department of Electrical Engineering, IIEST Shibpur
Venue: Multimedia Classroom (Hybrid Mode), Department of Electrical Engineering, Indian Institute of Science
Date and Time: Friday 19/08, 4:00 pm
Meeting Link: Click here to join the meeting
Abstract: Induction heating is a non-contact and “clean” electrical heating process. Induction Heating units and furnaces are used for melting (500Hz or so), surface heat treatment (10 s of kHz), induction welding, foil heating etc. and even cooking. The concepts involved are elementary yet interesting. The phenomenon of resonance may be used in an interesting manner (either parallel or series). The converters used might have a current source or a voltage source configuration. Some analytical and experimental studies shall be discussed.
Speaker Biography: Prof. Mainak Sengupta did his B.E (EE) from Jadavpur University in Calcutta in 1992. He did his MTech (Power Electronics and Machine Drives, Electrical Engg.) In January 1994 under Prof. K. Venkataratnam. In January 1994 he joined Ph D studies under Prof. K. Venkataratnam and Prof. Tapas K. Bhattacharya. He obtained his PhD degree in September 1999. In between, after completing the experimental work, he joined as a Lecturer in EE, in the then Bengal Engineering College (Deemed University) and is since teaching there. He has been a Professor since June 2010.

3rd talk of the "TCE Lecture Series on Power Systems"
2022-08-17 21:30 - 22:30 | EE, MMCR
Speaker: Prof Ramakrishna Gokaraju of University of Saskatchewan, Canada
Title: High-Speed Digital Relaying & Transient Stability Prediction/Controlled Islanding to Prevent Large-Scale Blackouts (Poster)
Abstract: Keeping the lights “on”, an axiom in power systems engineering has taken on a new level of complexity with increasing pressure on the existing
network to deliver more power over existing infrastructure. The first part of the presentation will discuss High-Speed Digital Relaying Scheme for EHV/UHV transmission systems (345 kV and above) with half-cycle operating times. The second part of the presentation will discuss a scheme
for real-time transient stability prediction in larger grids, and a Remedial Action Scheme (RAS) scheme applying intentional islanding to prevent large-scale blackouts. The proposed controlled islanding consists of two parts as “when” and “where” to island. The proposed methodology simplifies the communication between central “when” unit and each generator protection relay by using status flags communicated with IEC 61850 RGOOSE protocol. The proposed “when” methodology is combined with the “where” method based on graph theory to test the overall controlled islanding scheme.

Bio: Ramakrishna Gokaraju received his Bachelor of Engineering degree (with Distinction) in Electrical and Electronics Engineering from the National Institute of Technology, Trichy, India in April 1992. He received the M.Sc. and Ph.D. degrees in Electrical and Computer Engineering from the University of Calgary, Calgary, AB, Canada, in 1996 and 2000, respectively. From 1992-‘94 he worked with the Larsen & Toubro, Chennai, India as a graduate engineer and then later with the IIT, Kanpur & NIT, Rourkela as a Project Associate/Research Engineer. From 1999-2002, he was a Research Scientist with the Alberta Research Council and a Staff Software Engineer with IBM Toronto Lab. He joined the Department of Electrical & Computer Engineering at the University of Saskatchewan in 2003 and is currently a professor in the department. His current research works are in high speed digital relaying, controlled/active islanding in electric grids, wild fire mitigation due to electrical faults, and computer modelling of the new emerging nuclear-based Small Modular Reactors (SMRs) for electricity and other energy applications.

Thesis Defence of Shamibrota Kishore Roy @3.30pm
2022-08-16 21:00 - 22:30 | EE, MMCR
Title: Characterization and Modelling of Switching Dynamics of SiC MOSFETs
Name of the Student: Shamibrota Kishore Roy
Name of the Advisor: Dr Kaushik Basu
Degree Registered: PhD
Date and Time: 16 August 2022, 03:30 PM (IST)
Place: MMCR EE
Meeting Link: Click here to join the meeting
Abstract: Silicon Carbide MOSFETs (SiC MOSFETs) fall into the class of wide band gap (WBG) power devices. These devices are commercially available in the voltage range of 600-3300V and are superior to the state-of-the-art Si insulated gate bipolar junction transistors (IGBTs) due to their better electrical and thermal performances.

In power electronic converters, semiconductor devices operate as switches. They can be turned on or off using a control signal. Unlike ideal switches, practical devices require a finite amount of time to transit between on and off states. This is termed switching transient. Non-zero finite product of voltage and current during switching transient results in switching loss. Characterization and modelling of switching dynamics help gain insight into the switching process and estimate switching loss. It is useful over experimental measurement techniques like double pulse test (DPT) or calorimetric measurements in the early stages of power converter design. Estimated loss through the switching transient model can be used to determine switching frequency and selection of power devices. Also, switching dynamics is strongly impacted by the device and circuit parasitic. Insight into the switching process helps in the proper design of gate driver and power circuit layout. Superior material properties of SiC MOSFET lead to a smaller die size compared to the state-of-the-art Si-based power devices. It results in faster switching transients and lower switching loss. However, it excites device and circuit parasitic that may lead to prolonged oscillation, high device stress, spurious turn-on and EMI-related issues Etc. So, the benefit of using SiC MOSFET as power devices come with numerous design challenges resulting in slow commercial adaptation. It is predicted that the overall market share of WBG devices (SiC and GaN together) will be roughly 10% of the total market for power semiconductors by 2025.

To overcome the design challenges and fully utilize the benefits of fast switching SiC MOSFETs, a better understanding of dynamics is essential. However, the switching dynamics of SiC MOSFET is different compared to its Si counterpart. This is due to the highly non-linear device characteristics. Also, the fast-switching transient of SiC MOSFET excites the circuit parasitic and makes the switching dynamics highly involved. This work focuses on the characterization and modelling of switching transient of SiC MOSFET. Simulation and analytical modelling approaches are used to model the switching dynamics and estimate switching loss. The behavioral modelling approach is a widely used simulation-based approach (i.e., Spice simulation) and it can capture the switching transient with sufficient accuracy. This approach uses lumped parameter model (circuit model) of the device and external circuit and can be simulated in a circuit simulator like MATLAB/Simulink. This implies the numerical solution of a set of coupled non-linear differential equations. On the other hand, the analytical modelling approach is based on the simplified approximate solution of a set of coupled non-linear differential equations obtained from the behavioral model. In order to obtain the approximate solution, the entire switching process is divided into different modes with clearly defined transition conditions. Different approximations are used in each mode to arrive at analytical closed-form solutions or reduced order coupled non-linear differential equations. This model is computationally efficient and can be implemented easily in freely available programming platforms such as C or Python. Also, the parameters required for analytical models can be obtained from the device datasheet. This modelling approach is beneficial for the converter design when switching loss and junction temperature need to be evaluated over several operating points for many available devices from different manufacturers.

In the first part of the work, a behavioral model is developed to capture the switching transient of SiC MOSFET. It considers the detailed channel current model and captures the gradual transition effect from ohmic to saturation region. A piecewise non-linear model of gate-drain capacitance is used and a comprehensive non-linear model of drain-source and diode capacitances is considered. Also, the effect of the circuit parasitic are taken into account. A double pulse test (DPT) based experimental measurement is used for validation.

In the next part, analytical models to capture the switching dynamics (hard turn on and off) of SiC MOSFET are proposed. These models are based on the behavioral model developed in the first part of the work. In the existing literature, simplified modelling of channel current and device capacitances was used, resulting in underestimation of switching transition time and loss. On the contrary, the proposed approach considers the detailed non-linear model of channel current and device capacitances along with circuit parasitic. It accurately estimates transition time, switching loss, (dv=dt), (di=dt), and transient over-voltage. Double pulse test (DPT) based experimental measurement and behavioral simulations are used for validation.

In soft switched converters (i.e., DAB), hard turn-on is avoided by the converter operation, and the switching loss is solely dictated by the turn-off loss. The addition of external capacitance across the device prolongs the voltage rise period and reduces overlap between voltage and current during turn-off transient. This is termed zero voltage switching (ZVS). However, the selection of external capacitance is not straightforward. A large external capacitance reduces switching loss, (dv/dt), (di/dt), and transient over-voltage but may also result in higher dead-time loss and reduced switching frequency. Also, this may lead to partial soft switching for light load conditions if the dead-time is not sufficient. In this work, an analytical model to capture capacitor-assisted turn-off switching transient is also presented where the detailed non-linear modelling of the SiC MOSFET is used. This leads to a better estimation of switching transition time, actual loss, (dv/dt), (di/dt), and transient overvoltage. Also, a step-by-step design procedure for the optimal external snubber capacitor was proposed. It ensures the soft-switching condition is satisfied, and the maximum (dv/dt) rate is within a predefined limit for a specified DC bus voltage and range of load currents. This procedure also helps in the selection of proper dead-time to avoid partial soft-switching conditions. Double pulse test (DPT) based experimental measurement and behavioral simulations are used to validate the analytical model.
The fast switching transient of SiC MOSFET is significantly impacted by circuit parasitic. Circuit parasitic inductances are dependent on both device package (device lead, wire bond etc.) and circuit layout (PCB layout), whereas circuit parasitic capacitances are contributed solely by the circuit layout. Proposed switching transient models require circuit parasitic as input, and the values are not usually available in the device datasheet. Measurement is the only way to accurately estimate some device package-dependent circuit parasitic when the internal package geometry is unknown. In this context, a set of simple measurement techniques are proposed to determine important circuit parasitic necessary for switching dynamics study. The accuracy of the proposed technique is verified through behavioral simulation, and experimental results of the hard turn off and capacitor assisted soft turn off dynamics of SiC MOSFET over a range of operating conditions for two 1.2-kV discrete SiC MOSFET of different current ratings and two different PCB layouts. Measured circuit parasitic when used in switching transient model, correctly predicted both hard turn-off and capacitor assisted soft turn off switching dynamics over a wide range of operating conditions.
An interactive software based on the proposed analytical model is also developed in Python environment. The developed software takes device parameters and circuit parasitic as input and estimates transition time, switching loss, (dv/dt), (di/dt) and transient over-voltage as a function of load current.
ALL ARE CORDIALLY INVITED

Thesis Defence of Mr. Rupam Pal @ 11am
2022-08-03 16:30 - 17:30 | EE, MMCR
Degree Registered; PhD
Guide: Prof. Udaya Kumar
Thesis Title: Influence of soil’s electrical parameters on lightning stroke-current evolution and fields in the close range
Date: 3rd August 2022; Time: 11.00 AM
Mode: It will be in hybrid mode. To join online: Click here to join the meeting
Venue: MMCR Room, department of electrical engineering.
Abstract: The lightning return stroke forms one of the severest natural sources of electromagnetic interference for systems, both in the air and soil. Several physical fields govern this complex physical phenomenon, and most of the engineering applications resort to much simplifications. Several pertinent aspects are somewhat unclear, and it is not practical to conduct the field measurements to answer them. One such important aspect, which is of practical relevance, is the influence of soil’s electrical properties on the stroke current evolution and the fields in the close range. It is investigated in the present work.
Among different models for the lightning return stroke, only the ‘Self-consistent return stroke’ model is found to be suitable for the intended work. This model employs a macroscopic electrical representation of the underlying physical phenomenon to emulate the stroke current evolution. However, this model has considered only a perfectly conducting earth and relied on the time-domain thin-wire formulation to solve for the associated dynamic electromagnetic fields. On the other hand, a more realistic representation of the soil, including its dispersive nature and non-linearity, is required for the present work. This necessitated suitable adoption of the ‘Finite difference time domain’ (FDTD) method for modeling the channel and its corona sheath, soil-ionization, and soil-dispersion.
The developed FDTD formulation is used to investigate and ascertain the role of soil’s electrical properties on the stroke current evolution and the field in the soil. For the first time, it is shown that the soil’s electrical conductivity has some noticeable influence on the stroke current magnitude, and the ionization phenomenon in soil tends to reduce this influence. The dispersive nature of the soil’s conductivity, and permittivity to a lesser extent, significantly reduces the field in the soil. The current concentration near the surface, which is expected for the skin-effect phenomenon, is altered at later periods by the field produced by the channel current. Also, the normal field in the soil changes its polarity. The vertical stratification of the soil, which is expected near the water body-soil interface, influences the field in the soil quite significantly. A strike to a model mountain leads to an entirely different field structure beyond its base. Similarly, a strike to a tall tower produces a field in the soil, which is bipolar near the tower base. These are quite novel findings, and many of them are somewhat unexpected.
In summary, significant contributions have been made towards the FDTD formulations for modeling lightning phenomena and finding the role of soil’s electrical parameters on lightning stroke current evolution and the resulting field.

Thesis Colloquium of Mr. Shubham Rawat @ 2.00pm
2022-07-29 19:30 - 20:30 | EE, MMCR
Title: A Novel Passive Regenerative Snubber for the Phase-Shifted Full-Bridge Converter: Analysis, Design and Experimental Verification
Name of the Advisor: Dr Kaushik Basu
Degree Registered: MTech (Research)
Date and Time: 29 July 2022, 02:00 PM
Place: MMCR EE
Meeting Link: Click here to join the meeting
Abstract: The development of Wide Bandgap (WBG) devices has enabled power electronic converters to operate at much higher frequencies, voltages and power. Working at a higher switching frequency minimises the size of magnetics but results in significant switching losses and electromagnetic interference (EMI) noise. Thus, it necessitates the use of soft-switching techniques to reduce these losses. Phase-Shifted Full-Bridge (PSFB) Converter is the most widely used soft-switching topology in the high-voltage and high-power, unidirectional, DC-DC conversion. The phase shift PWM control utilises the converter parasitic to achieve zero voltage switching (ZVS) turn ON. The gating technique allows the magnetic energy stored in the leakage inductance of the isolation transformer to charge and discharge the output capacitances of the inverter leg.
However, the converter suffers from severe voltage overshoots across the rectifier bridge during the zero to the active state transition. The resonant circuit formed between the transformer leakage inductance and the parasitic diode capacitance of the rectifier is responsible for the high-voltage ringing. Many passive and active snubbers are presented in the literature to mitigate the high voltage overshoots across the diode bridge. While passive snubbers are relatively simple to implement than active snubbers, they are lossy. On the other hand, the active snubbers require additional gate driver circuitry and complex control.
The first part of the thesis proposes a novel passive regenerative snubber to overcome the mentioned drawbacks of the existing snubbers. The proposed snubber is ideally lossless with no control complexity. The work covers a detailed analysis of the PSFB operation with the proposed snubber while obtaining closed-form expressions for the converter state variables at the end of each topological stage. The study considers all the major converter parasitic, such as transformer leakage and magnetising inductances, and parasitic capacitances of the converter. Given the new snubber, the thesis also lays out a step-by-step PSFB design procedure utilising the analysis carried out in the first part of the work. The design aimed to develop a 100 kHz PSFB for an input voltage of 360-400 V and the output power range of 0.5-1.5 kW at a fixed output voltage of 48 V. The design approach focuses on the two design objectives. All inverter switches must achieve ZVS turn ON, and the converter gain must achieve the necessary gain to maintain desired constant output voltage for all possible operating conditions.
A hardware prototype is built and tested per the given specification. The experimental results validate the effectiveness of the snubber in reducing the voltage overshoot. Further, the analysis and design accuracy is verified using the measured state variables. Finally, the work presents the overall efficiency and the loss distribution among the converter components.

Lecture by Dr. Prasanth Venugopal @ 5.00pm
2022-07-27 22:30 - 23:30 | EE, MMCR
Title: SaRe ♫♪♪ Battery Electronics of the Future: Safe and Reliable
Speaker: Dr. Prasanth Venugopal, Assistant Professor, University of Twente
Date and Time: 27 July 2022, 5:00 pm
Venue: Multimedia Classroom (Hybrid Mode), Department of Electrical Engineering, Indian Institute of Science
Meeting Link: Click here to join the meeting

Abstract: Li-ion battery (LiB) has disrupted the world of energy storage thereby creating a pathway for a sustainable future relying on intermittent renewable energy sources. It is expected that the market for LiB will exceed 400 GWh per year from 2025 onwards. The talk will summarize an overview of the R&D program at the University of Twente in advanced battery power electronics and battery evaluation. This includes the development of a new generation of advanced BMS concepts based on advanced power electronics. In addition, an overview will be presented of performance and aging including implications on safety and second-life batteries.
Speaker Biography: Prasanth Venugopal received the B.Tech. degree in electrical and electronics engineering from Amrita Vishwa Vidyapeetham University, Coimbatore, India, in 2010, and the M.Sc. degree in electrical engineering and the Ph.D. degree from the Delft University of Technology, Delft, The Netherlands, in 2012, and 2018, respectively. His MSc. and Ph.D. theses were related to wireless charging of EVs. He worked in the semiconductor and passive components industry from 2016-2020 in Munich, Germany. From November 2016 to December 2018, he was with Qualcomm Halo, Munich, Germany, as a Senior Electrical Engineer in the field of power electronic systems and applications related to wireless charging of Electric Vehicles. He then went on to work at TDK Europe as a Technical Specialist/Manager for xEV applications until May 2020. From June 2020, he is appointed as a Tenured Assistant Professor in the Power Electronics and EMC group at U Twente.
He has so far published 29 papers with 6 journals and has filed 6 patents which are in various stages of acceptance. His current areas of interest are Electric Vehicles, Wireless Charging, Power Electronics Integration – Semiconductors, and Passive Component Technologies.

Lecture by Dr. Mathew Magimai Doss 3.00pm
2022-07-27 20:30 - 21:30 | EE, MMCR
Department of Electrical Engineering, IEEE Signal Processing Society Bangalore Chapter, and Indian Speech Communication Association (IndSCA) cordially invite you to the following lecture
Title: Towards speech-based biomarkers
Speaker: Dr. Mathew Magimai Doss, Idiap Research Institute and EPFL, Switzerland
Host: Prof. Chandra Sekhar Seelamantula, EE, IISc
Date and time: July 27, 2022; 3 PM (Coffee will be served during the talk.)
Venue: Multimedia Classroom, Department of Electrical Engineering, Indian Institute of Science
Abstract:Speech communication is an essential part of our lives, which can undergo short-term and long-term changes at paralinguistic level due to several reasons such as, emotion, mood, stress, drinking, speech pathology, neurological speech and language disorders (e.g., Parkinson’s Disease (PD), Alzheimer’s Diseases). So, there is a thrust to develop speech-based methods to detect such short-term and long-term changes for clinical applications. In this talk, I will present a few research and development activities that I am involved in in that direction.Specifically, I will talk about (a) pathological speech processing, (b) joint modeling of speech and physiological signals, and (c) developmentof closed-loop deep brain stimulation with PD patient’s symptoms in loop.
Biography of the Speaker: Dr. Mathew Magimai Doss received the Bachelor of Engineering (B.E.) in Instrumentation and Control Engineering from the University of Madras, India in 1996; the Master of Science (M.S.) by Research in Computer Science and Engineering from the Indian Institute of Technology, Madras, India in 1999; the PreDoctoral diploma and the Docteur ès Sciences (Ph.D.) from the Ecole polytechnique fédérale de Lausanne (EPFL), Switzerland in 2000 and 2005, respectively. He was a postdoctoral fellow at the International Computer Science Institute (ICSI),Berkeley, USA from April 2006 till March 2007. He is now a Senior Researcher at the Idiap Research Institute, Martigny, Switzerland. He is also a lecturer at EPFL. His main research interest lies in signal processing, statistical pattern recognition, artificial neural networks and computational linguistics with applications to speech and audio processing and multimodal signal processing. He is a Senior Area Editor of the IEEE Signal Processing Letters. He is also an Associate Editor of the IEEE/ACM Transactions on Audio, Speech, and Language Processing.
All are invited.

PhD Thesis Colloquium
2022-07-12 15:30 - 17:00 | EE, MMCR
Name of the student: Tanmay Mishra
Faculty Advisor: Dr. Gurunath Gurrala
Date : 12th July 2022
Time: 10AM – 11.30AM
Venue: MMCR, 1st floor C-wing, EE Department, IISc
Abstract: Studying the dynamic behavior of non-linear complex power systems in a laboratory is very challenging. Early experimental platforms used micro-alternators to emulate the behavior of fixed steam turbine models. The micro-alternator is a three-phase synchronous generator with similar electrical constants (in per unit on machine rating) as those typically found in alternators in large power stations. It is an electrical scaled-down model of machines up to 1000 MW rating and is rated between 1 to 10 kVA. Researchers used these micro-machines up to the 90s to study large electric generators’ transient and steady-state performance. The Department of electrical engineering at the Indian Institute of Science (IISc) was also very active in experimental research in power engineering. The department still retained two-three kVA and one ten kVA micro-machine sets, but the control panels of these machines became obsolete as the manufacturer of these machines Mawdsley, London, doesn’t exist anymore. Advancements in simulation software packages and real-time simulators have primarily replaced the experimental models of electric power systems worldwide. The push for green energy technologies worldwide due to climate concerns has increased the presence of power electronic converters in the power grids. Reduction of overall inertia, frequent occurrence of electromechanical oscillations, electromagnetic transients, and control interaction modes has become a concern for the power grid operators. The need for understanding the physical insights of the oscillatory modes introduced by fast-acting power electronic converters, the need for developing practically feasible control algorithms for mitigating the interaction modes, and the need for developing dispatchability and grid support features like conventional generation sources have triggered the development of laboratory-scale experimental power grids across the world in the past decade.
In this thesis, initially, an attempt is made to revive the existing three kVA alternator controls. An IGBT-based buck converter static excitation system has been developed for the micro-alternator. This exciter also incorporates several limiters which were non-existent in the old analog control panels. An under-excitation limiter, over-excitation limiter, and V/Hz limiter as per IEEE standard 421.5 have been designed to protect the micro-alternator during abnormal conditions such as overloading, overheating, and over-fluxing of the machine. The detailed tuning procedure of limiters and TCR is discussed to comply with IEEE STD 421.2 and IEEE STD 421.5. A digital time constant regulator (TCR) is incorporated to modify the micro-alternator’s field’s time constant to mimic large synchronous machines’ dynamics as micro-machine time constants are very small. A custom 5 kVA micro-alternator was manufactured through a local vendor having parameters like the Mawdsley machines to facilitate the creation of multiple short circuits in the testbed.
A single micro-alternator can represent only one large alternator dynamics, thereby limiting the platform’s scalability. Emulating machines of different ratings using a single micro-machine would undoubtedly boost the capabilities of experimental platforms for investigating conventional and nonconventional source interactions in laboratories. To the best of our knowledge, only one such attempt was made in the literature, where a model reference control algorithm is proposed to mimic any rating alternator dynamics using a doubly excited laboratory micro-alternator. However, doubly excited micro-alternators are non-existent today. A generalized experimental platform using a non-linear output matching controller based on output feedback linearization is developed in this thesis for emulation of large turbo-alternators of different ratings, IEEE STD 421.5 excitation systems, and standard turbine governor models in the laboratory using the 5 kVA micro-alternator. IEEE Model 1.1 synchronous machine model in per unit on machine MVA rating with associated excitation system and governor-turbine models has been used as a reference model to be emulated. A single machine infinite bus (SMIB) setup with the 5 kVA micro-alternator and a 50 km 220 kV scaled lumped parameter frequency-dependent transmission line model is used for experimental validation. Synchronous generators of ratings, 128 MVA, 247.5 MVA, and 1000 MVA have been physically emulated using the setup. The dynamic responses of the large machines with thermal turbines (reheat, non-reheat), hydro turbine, and excitation systems; DC1A, AC4A, and ST1C have been reproduced under small and large disturbances.
A systematic scaling approach has been proposed to emulate a multi-machine system in the laboratory. Unlike in the SMIB system, the power levels of generators in a multi-machine system should be scaled to the laboratory level for emulation. Hence, every power system component (generator, transmission lines, transformer, loads) is scaled to a uniform level so that the laboratory machines don’t get overloaded. The developed non-linear control strategy for emulation has been extended to multi-machine systems. The Western System Coordinating CouncilJ 3-generator 9-bus test system has been used to validate the proposed concept. The feasibility of replicating WSCC system dynamics in a laboratory as a scaled-down model has been verified through simulations under small and large disturbances. Emulating large machine dynamics with different types of turbines, governors, and excitation controls using a singly excited micro-alternator enabling a generalized synchronous machine emulation platform is a first-of-its-kind effort in the literature to the best of our knowledge.
Note: Know how generated from the Source Emulation has been licensed to MCore Technologies Pvt Ltd, Bangalore for commercialization.
Acknowledgments: This work is supported by Fund for Improvement of Science and Technology (FIST) program, DST, India, No.SR/FST/ETII-063/2015 (C) and (G) under the project “Smart Energy Systems Infrastructure – Hybrid Test Bed”.

EE Summer School 2022
2022-07-04 13:30 - 2022-07-08 22:30 | EE, IISc
Visit https://ee.iisc.ac.in/summerschool2022/ for details

2nd talk of the "TCE Lecture Series on Power Systems"
2022-07-01 15:30 - 16:30 | EE, MMCR
Title: Stability Modelling and Analysis of Converter Driven Power System
Speaker: Prof Bikash Pal
Date and Time: 1st July, 2022, 10 am
Venue: MMCR, EE
Abstract:The number of power electronics converters connected to electrical networks has been growing exponentially as they are part of all new generation connected to the grid. While the rapid control and fast electronic switching available with this technology offer flexibility in network operation, the dynamic interactions between several of them threaten the operational stability of the transmission grid is a concern. It is required to develop a methodology for identifying the risks associated with the stability and control interaction before a new power electronic device (e.g. Windfarm, interconnector, STATCOM) is introduced to the network.
The talk will focus on an analytical framework in impedance domain to quantify the interaction between the new plant and the rest of the network for setting additional grid connection study specifications which will include detail technical study to check and mitigate the risks associated with new power electronics interfaced generation. The framework developed is to support MMC technology, control delay, system strength and FRT capability of dynamic voltage support devices and windfarm through technical case study conducted at the research group of Bikash Pal at Imperial College London. Future research challenges and opportunities will be highlighted.
Speaker Bio: Bikash Pal is a Professor of Power Systems at Imperial College London (ICL). He is research active in power system stability, control, and estimation. Currently he is leading a six university UK-China research consortium on Resilient Operation of Sustainable Energy Systems (ROSES) as part of EPSRC-NSFC Programme on Sustainable Energy Supply. He led UK-China research consortium project on Power network stability with grid scale storage (2014-2017): He also led an eight- university UK-India research consortium project (2013-2017) on smart grid stability and control. His research is conducted in strategic partnership with ABB, SIEMENS, GE Grid Solutions, UK, and National Grid, UK. UK Power Networks. SIEMENS R&D collaborated with him to develop fast power flow and volt-var control tools in Spectrum Power, an advanced module for distribution management system solution from SIEMENS. This is now commissioned in distribution control centres in Columbia, Bosnia Norway and Azerbaijan serving 15 million customers in these countries. GE commissioned sequel of projects with him to analyse and solve wind farm HVDC grid interaction problems (2013-2019). Prof Pal was the chief technical consultant for a panel of experts appointed by the UNFCCC CDM (United Nations Framework Convention on Climate Change Clean Development Mechanism). He has offered trainings in Chile, Qatar, UAE, Malaysia and India in power system protections, stability and control topics. He has developed and validated a prize winning 68-bus power system model, which now forms a part of IEEE Benchmark Systems as a standard for researchers to validate their innovations in stability analysis and control design. He was the Editor-in-Chief of IEEE Transactions on Sustainable Energy (2012-2017) and Editor-in-Chief of IET Generation, Transmission and Distribution (2005-2012). He is Vice President, PES Publications (2019-). In 2016, his research team won the President’s outstanding research team award at Imperial College London (ICL). He is Fellow of IEEE for his contribution to power system stability and control. He is an IEEE Distinguished Lecturer in Power distribution system estimation and control. He has published about 125 papers in IEEE Transactions and authored four books in power system modelling, dynamics, estimations and control. He was Otto Monstead Professor at Denmark Technical University (DTU) (2019) and Mercator Professor sponsored by German Research Foundation (DFG) at University of Duisburg-Essen in 2011. He worked as faculty at IIT Kanpur, India. He holds a Visiting Professorship at Tsinghua University, China.

Talk by Prof Bikash Pal @10am
2022-07-01 15:30 - 16:30 | EE, MMCR
2nd talk of the “TCE Lecture Series on Power Systems”
Date and Time: 1st July, 2022 from 10 am
Venue: MMCR, EE
Title: Stability Modelling and Analysis of Converter Driven Power System
Abstract: The number of power electronics converters connected to electrical networks has been growing exponentially as they are part of all new generation connected to the grid. While the rapid control and fast electronic switching available with this technology offer flexibility in network operation, the dynamic interactions between several of them threaten the operational stability of the transmission grid is a concern. It is required to develop a methodology for identifying the risks associated with the stability and control interaction before a new power electronic device (e.g. Windfarm, interconnector, STATCOM) is introduced to the network
The talk will focus on an analytical framework in impedance domain to quantify the interaction between the new plant and the rest of the network for setting additional grid connection study specifications which will include detail technical study to check and mitigate the risks associated with new power electronics interfaced generation. The framework developed is to support MMC technology, control delay, system strength and FRT capability of dynamic voltage support devices and windfarm through technical case study conducted at the research group of Bikash Pal at Imperial College London. Future research challenges and opportunities will be highlighted.
Speaker Bio: Bikash Pal is a Professor of Power Systems at Imperial College London (ICL). He is research active in power system stability, control, and estimation. Currently he is leading a six university UK-China research consortium on Resilient Operation of Sustainable Energy Systems (ROSES) as part of EPSRC-NSFC Programme on Sustainable Energy Supply. He led UK-China research consortium project on Power network stability with grid scale storage (2014-2017): He also led an eight- university UK-India research consortium project (2013-2017) on smart grid stability and control. His research is conducted in strategic partnership with ABB, SIEMENS, GE Grid Solutions, UK, and National Grid, UK. UK Power Networks. SIEMENS R&D collaborated with him to develop fast power flow and volt-var control tools in Spectrum Power, an advanced module for distribution management system solution from SIEMENS. This is now commissioned in distribution control centres in Columbia, Bosnia Norway and Azerbaijan serving 15 million customers in these countries. GE commissioned sequel of projects with him to analyse and solve wind farm HVDC grid interaction problems (2013-2019). Prof Pal was the chief technical consultant for a panel of experts appointed by the UNFCCC CDM (United Nations Framework Convention on Climate Change Clean Development Mechanism). He has offered trainings in Chile, Qatar, UAE, Malaysia and India in power system protections, stability and control topics. He has developed and validated a prize winning 68-bus power system model, which now forms a part of IEEE Benchmark Systems as a standard for researchers to validate their innovations in stability analysis and control design. He was the Editor-in-Chief of IEEE Transactions on Sustainable Energy (2012-2017) and Editor-in-Chief of IET Generation, Transmission and Distribution (2005-2012). He is Vice President, PES Publications (2019-). In 2016, his research team won the President’s outstanding research team award at Imperial College London (ICL). He is Fellow of IEEE for his contribution to power system stability and control. He is an IEEE Distinguished Lecturer in Power distribution system estimation and control. He has published about 125 papers in IEEE Transactions and authored four books in power system modelling, dynamics, estimations and control. He was Otto Monstead Professor at Denmark Technical University (DTU) (2019) and Mercator Professor sponsored by German Research Foundation (DFG) at University of Duisburg-Essen in 2011. He worked as faculty at IIT Kanpur, India. He holds a Visiting Professorship at Tsinghua University, China.

PhD Colloquium of Sounak Nandi
2022-06-30 20:30 - 21:30
Thesis Title: Experimental and Theoretical Investigations on High Voltage Polymeric Insulators
Research Supervisor: Dr Subba Reddy B
Date and Time: Thursday 30th June 2022, 3.00 pm
Venue: Online. Click here to join the meeting
Abstract: High Voltage Ceramic and glass Insulators are widely used by various transmission and distribution utilities for several decades across the globe. Recently composite or silicone rubber insulators have evolved and are now replacing ceramic/glass insulators due to their improved advantages, however these Insulators suffer from degradation over a period in service.
The primary objective of the investigation relates to the performance of silicon rubber/polymer insulator under various climatic conditions, both experimentally and comprehend theoretically. Experimental studies were conducted to understand the degradation of insulators under different climatic conditions which prevail in the Country.
Studies on polymer insulators under sub-zero and under extreme high temperature conditions were attempted experimentally and their performance evaluated. During experimentation the leakage current was continuously monitored also material analysis which is very important factor and essential to correlate with the morphological changes of the insulator surface was studied. The experimental investigations demonstrate that there is a need to conduct multi-stress experimentation under specific climatic conditions before the Insulators are being installed in the field.
The next portion of the theses work deals with failure mechanism of Fibre Reinforced Plastic (FRP) Rod. Some portion of the work deals with mathematical analysis being extended to condition monitoring of dielectric surfaces and understanding the performance of FRP rods under high AC/DC voltages. Further, experimental Investigations are performed on FRP Rods to analyse the behaviour witnessed like the field failures reported on Silicon rubber Insulators.
Condition monitoring of dielectric surfaces is very important; hence it was felt necessary to analyse the field performance of transmission/distribution composite Insulators. To understand further a mathematical analysis based on Chaos has been evaluated for leakage current data and quantization of comparative degradation for a dielectric surface, later Empirical Mode Decomposition is also used for understanding leakage current and implied degradation under minimal data condition.
Subsequently, Surface electric field of insulators exposed to HVDC is studied considering the temporal boundary conditions which may arise due to the capacitive-resistive transients, some experimental investigations are also conducted to compare the simulated results.
The last portion of the thesis emphases on the study of bulk conductivity of polymer material. The Electric Field dependence of conductivity on the application of voltage and subsequent space charge distribution is attempted.
— All are Welcome —
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