EE

Thesis Colloquium of Mr. Debarpan Bhattacharya
2022-12-20 16:30 - 17:30 (Asia/Kolkata) | EE, MMCR
Degree Registered: MTech Resesarch
Title: A Learnable Distillation Approach For Model-agnostic Explainability With Multimodal Applications
Advisor: Prof. Sriram Ganapathy
Date and Time: Tuesday, Dec 20th , 11am
Venue: MMCR, Electrical Engineering, IISc.

Abstract: Deep neural networks are the most widely used examples of sophisticated mapping functions from feature space to class labels. In the recent years, several high impact decisions in domains such as finance, healthcare, law and autonomous driving, are made with deep models. In these tasks, the model decisions lack interpretability, and pose difficulties in making the models accountable. Hence, there is a strong demand for developing explainable approaches which can elicit how the deep neural architecture, despite the astounding performance improvements observed in all fields, including computer vision, natural language processing, generates the output decisions.

The current frameworks for explainability of model learning are based on gradients (eg. GradCAM, guided-gradCAM, Integrated gradients etc) or based on locally linear assumptions (eg. LIME). Some of these approaches require the knowledge of the deep model architecture, which may be restrictive in many applications. Further, most of the prior works in the literature highlight the results on a set of small number of examples to illustrate the performance of these XAI methods, often lacking statistical evaluation.

This talk proposes a new approach for explainability based on mask estimation approaches, called the Distillation Approach for Model-agnostic Explainability (DAME). The DAME is a saliency-based explainability model that is post-hoc, model-agnostic, and applicable to any architecture/domain. The DAME is a student-teacher modeling approach, where the teacher model is the original model for which the explainability is sought, while the student model is the mask estimation model. The input sample is augmented with various data augmentation techniques to produce numerous samples in the immediate vicinity of the input. Using these samples, the mask estimation model is learned to learn the saliency map of the input sample for predicting the labels. A distillation loss is used to train the DAME model, and the student model tries to locally approximate the original model. Once the DAME model is trained, the DAME generates a region of the input (either in space or in time-domain for images and audio samples, respectively) that best explains the model predictions.

We also propose an evaluation framework, for both image and audio tasks, where the XAI models are evaluated in a statistical framework on a set of held-out of examples with the Intersection-over-Union (IoU) metric. We have validated the DAME model for vision, audio and biomedical tasks. Firstly, we deploy the DAME for explaining a ResNet-50 classifier pre-trained on ImageNet dataset for the object recognition task. Secondly, we explain the predictions made by ResNet-50 classifier fine-tuned on Environmental Sound Classification (ESC-10) dataset for the audio event classification task. Finally, we validate the DAME model on the COVID-19 classification task using cough audio recordings. In these tasks, the DAME model is shown to outperform existing benchmarks for explainable modeling.

The talk will also illustrate results on various multimodal datasets and conclude with a discussion on the limitations of the DAME approach along with the potential future directions.

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All are welcome,

Invited Talk by Prof Anjan Bose
2022-12-30 16:30 - 17:30 | EE, MMCR
Title: Maintaining Reliability and Resiliency while Decarbonizing the Power Grid
Abstract: The world has focused strongly, through the Paris Accords, on ridding the electricity generation mix of fossil fuels and replacing that with sustainable non-carbon resources. The emerging new generation mix, however, is changing the characteristics of the grid thus requiring the modification of the ways we plan, design, operate and control the grid. The new engineering tools and procedures must be ready as the penetration of renewable energy resources keeps increasing. These methods to provide highly reliable electricity to society were developed over decades but now must be updated more urgently. In this address we outline where these threats are coming from and how we maintain the highly reliable systems despite the changes. In addition, the increase in extreme weather events is necessitating more efficient ways to recover from grid damage thus increasing the resiliency of the system in addition to maintaining reliability.
Author’s Bio: Prof Anjan Bose (Life Fellow, IEEE) received the B.Tech. degree from IIT Kharagpur, Kharagpur, India, the M.S. degree from the University of California, Berkeley, CA, USA, and the Ph.D. degree from Iowa State University, Ames, IA, USA. He has worked for industry, academe, and government for 40 years in electric power engineering. He is currently a Regents Professor and holds an Endowed Distinguished Professor in power engineering at Washington State University, Pullman, WA, USA, where he also served as the Dean for the College of Engineering and Architecture, from 1998 to 2005. He is a member of the U.S. National Academy of Engineering and a Foreign Fellow of the Indian National Academy of Engineering. He received the Herman Halperin Award and the Millennium Medal from the IEEE and was recognized as a Distinguished Alumnus by IIT Kharagpur and Iowa State University

Workshop on Protection and Stability of Renewable Dominated Power Grids
2023-01-02 13:30 - 2023-01-04 22:30
Click here for the poster.
Topics that will be covered in the workshop:

Overview of Photovoltaic and Wind Generations
Converter Controls for Renewables
Grid Connection Requirements
Impact of Renewables on Fault Analysis and Protection
Impact of Renewables on System Stability
Training on Renewable Modelling in PSCAD
AC Microgrids
DC Microgrids
Case Studies

The list of speakers: (Click here for the schedule.)

Prof Sukumar Brahma, Clemson University, USA
Prof Prasad Enjeti, Texas A&M University, USA
Dr Ritwik Majumder, Mathworks
Prof Vinod John, IISc
Prof Kaushik Basu, IISc
Prof Gurunath Gurrala, IISc
Dr. Vishnu Mahadeva Iyer
Prof Sarasij Das, IISc

Thesis Defence of Mr. Ruturaj Gavaskar
2023-01-02 16:30 - 17:30
Title: On Plug-and-Play Regularization using Linear Denoisers.
Degree Registered: PhD
Guide: Prof Kunal Narayan Chaudhury
Date: Jan 2, 2023.
Time: 11:00 am.
Venue: Online.
MS Teams: https://tinyurl.com/bdz95wmw
(meeting ID: 483 231 041 151; Passcode: [REDACTED]
Abstract: The problem of inverting a given measurement model comes up in several computational imaging applications. For example, in CT and MRI, we are required to reconstruct a high-resolution image from incomplete noisy measurements, whereas in superresolution and deblurring, we try to infer the ground truth from low-resolution or blurred images. Traditionally, this is done by minimizing f + φ, where f is a data-fidelity (or loss) function that is determined by the acquisition process, and φ is a regularization (or penalty) function that is based on a subjective prior on the target image. The solution is obtained numerically using iterative algorithms such as ISTA or ADMM.
While several forms of regularization and associated optimization methods have been proposed in the imaging literature over the last few decades, the use of denoisers (aka denoising priors) for image regularization is a relatively recent phenomenon. This has partly been triggered by advances in image denoising in the last 20 years, leading to the development of powerful image denoisers such as BM3D and DnCNN. In this thesis, we look at a recent protocol called Plug-and-Play (PnP) regularization, where image denoisers are deployed within iterative algorithms for image regularization. PnP consists of replacing the proximal map — an analytical operator at the core of ISTA and ADMM — associated with the regularizer φ with an image denoiser. This is motivated by the intuition that off-the-shelf denoisers such as BM3D and DnCNN offer better image priors than traditional hand-crafted regularizers such as total variation. While PnP does not use an explicit regularizer, it still makes use of the data-fidelity function f. However, since the replacement of the proximal map with a denoiser is ad-hoc, the optimization perspective is lost — it is not clear if the PnP iterations can be interpreted as optimizing some objective function f + φ. Remarkably, PnP reconstructions are of high quality and competitive with state-of-the-art methods. Following this, researchers have tried explaining why plugging a denoiser within an inversion algorithm should work in the first place, why it produces high-quality images, and whether the final reconstruction is optimal in some sense.
In this thesis, we try to answer such questions, some of which have been the topic of active research in the imaging community in recent years. Specifically, we consider the following questions.
1. Fixed-point convergence: Under what conditions does the sequence of iterates generated by a PnP algorithm converge? Moreover, are these conditions met by existing real-world denoisers?
2. Optimality and objective convergence: Can we interpret PnP as an algorithm that minimizes f + φ for some appropriate φ? Moreover, does the algorithm converge to a minimizer of this objective function?
3. Exact and robust recovery: Under what conditions can we recover the ground truth exactly via PnP? And is the reconstruction robust to noise in the measurements?
While early work on PnP has attempted to answer some of these questions, many of the underlying assumptions are either strong or unverifiable. This is essentially because denoisers such as BM3D and DnCNN are mathematically complex, nonlinear and difficult to characterize. A first step in understanding complex nonlinear phenomena is often to develop an understanding of some linear approximation. In this spirit, we focus our attention on denoisers that are linear. In fact, there exists a broad class of real-world denoisers that are linear and whose performance is quite decent; examples include kernel filters (e.g. NLM, bilateral filter) and their symmetrized counterparts. This class has a simple characterization that helps to keep the analysis tractable and the assumptions verifiable. Our main contributions lie in resolving the aforementioned questions for PnP algorithms where the plugged denoiser belongs to this class. We summarize them below.
• We prove fixed-point convergence of the PnP version of ISTA under mild assumptions on the measurement model.
• Based on the theory of proximal maps, we prove that a PnP algorithm in fact minimizes a convex objective function f + φ, subject to some algorithmic modifications that arise from the algebraic properties of the denoiser. Notably, unlike previous results, our analysis applies to non-symmetric linear filters.
• Under certain verifiable assumptions, we prove that a signal can be recovered exactly (resp. robustly) from clean (resp. noisy) measurements using PnP regularization. As a more profound application, in the spirit of classical compressed sensing, we are able to derive probabilistic guarantees on exact and robust recovery for the compressed sensing problem where the sensing matrix is random. An implication of our analysis is that the range of the linear denoiser plays the role of a signal prior and its dimension essentially controls the size of the set of recoverable signals. In particular, we are able to derive the sample complexity of compressed sensing as a function of distortion error and success rate.
We validate our theoretical findings numerically, discuss their implications and mention possible future research directions.

Lecture by Prof. Manoj Saranathan
2023-01-02 21:30 - 22:30 | EE, MMCR
Department of Electrical Engineering, Indian Institute of Science
and
IEEE Signal Processing Society, Bangalore Chapter
cordially invite you to a lecture on
Advances in imaging and segmentation of thalamic nuclei with applications
by
Prof. Manoj Saranathan, Professor of Radiology at UMass Chan Medical School in Worcester, Massachusetts
Date and time: January 2, 2023, 4 PM
Venue: Multimedia Classroom, Department of Electrical Engineering (EE), IISc.
Coffee will be served at 3.45 PM.
Abstract: The thalamus is a subcortical deep brain structure increasingly implicated in a number of neurodegenerative and neuropsychiatric conditions. It is subdivided into regions called nuclei which are linked to specific cortical and sensory regions of the brain. However, thalamic nuclei have largely been ignored in most imaging studies as they are mostly invisible in conventional MRI. In this talk, I will present our work on MRI methods to improve visualization of thalamic nuclei as well as cutting edge thalamic nuclei segmentation methods. I will then show examples of characterization of atrophy of specific thalamic nuclei in neurodegenerative diseases as well as for cutting edge neurosurgical treatment of epilepsy and essential tremor.
Biography of the speaker: Manoj Saranathan is an MRI physicist with over twenty-five years of experience in MR physics, pulse sequence development, image reconstruction, and image processing spanning industry and academia. His current research interests are focused on ultra high-resolution imaging and segmentation of deep brain structures like thalamus and hippocampus and the specificity of their involvement in pathology such as alcoholism, multiple sclerosis, Alzheimer’s disease, and essential tremor. Another area of interest is high spatio-temporal resolution dynamic contrast enhanced MRI for quantification of physiologic function and cancer. One of his methods, DISCO, is now a product available on all GE MRI scanners since 2017, and is widely used for prostate and breast imaging worldwide. He has a PhD in Bioengineering from the University of Washington, Seattle and is currently a Professor of Radiology at UMass Chan Medical School in Worcester, Massachusetts.
Hosts: Prof. P. S. Sastry and Prof. Chandra Sekhar Seelamantula, EE, IISc
All are invited.

Seminar by Prof. Niladri Chakraborty
2023-01-06 15:30 - 16:30 | EE, MMCR
Title: Emulator-based Microgrid studies vis a vis Some Indian Renewable interconnected Microgrids.
Time: 10 am 6/1/23
Venue: EE MMCR
Abstract: Mini / Micro Grid in Stand Alone or Grid integrated forms are coming up very fast in the conventional power system. Many a time, these are renewable sources integrated. Design considerations, Plant automation, Remote monitoring and O & M strategies play important roles in their operational exploitation. However, for economics and systematic, technically sound energy businesses, it is prudent to have emulator-based studies done before these systems are commercially realized. Attempts will be made to showcase an emulator-based microgrid available in the Department of Power Engineering, Jadavpur University, Kolkata. At the same time, generalized Design considerations, Plant automation, Remote monitoring, O & M strategies and System studies will be briefly highlighted. Introduction of different types of renewable integrated Mini / Microgrids encountered during consultancy services or for technical studies available elsewhere in the pan-Indian domain will be made perceptible to the audience.
Bio: Prof Niladri Chakraborty was born in Kolkata on 27/08/1964. He received his Bachelor’s (1986) and Masters’s Degree (1989) in Electrical Engineering from Jadavpur University, Kolkata, India. He was the recipient of the prestigious Commonwealth Scholarship in the United Kingdom and obtained the Diploma of Imperial College and Doctor of Philosophy from the University of London in 1999. He was elected as the youngest Dean of the Faculty of Engineering and Technology at Jadavpur University in 2010. He has served twice as Head of the Department of Power Engineering and as Joint Director of the School of Energy Studies at Jadavpur University. He was often elected to the Executive Council and The Court, the highest policy-making academic body of Jadavpur University. He has also received Scholarships from the Royal Society (UK)-DST and Abdul Kalam Award, besides many other awards from numerous academic activities. Dr Chakraborty has served as one of the Lead Scientists in India’s National Communication to United Nations on Climate Change (NATCOM, India, UNFCCC) and a Specialized Energy Expert in formulating the Futuristic Energy Action Plan for the Government of West Bengal, India. He has also completed many research projects obtained from different funding agencies and earned revenue for Jadavpur University as a consultant.
Prof. Chakraborty’s recent research interests include Energy Economics, Renewable integrated Microgrid, Environmental Measurement and Material Modelling. Dr Chakraborty has in his credit to be the author of about 75 referred journal publications and nearly 130 International Conference Publications. Prof. Chakraborty has guided 13 PhDs (4 more are in writing-up status) and 41 Masters of Engineering Thesis.

Lecture by Prof. Anubha Gupta
2023-01-11 22:30 - 23:30 | EE, MMCR
Department of Electrical Engineering, Indian Institute of Science

and

IEEE Signal Processing Society, Bangalore Chapter

cordially invite you to a lecture on

Proper Definition of Dirichlet Conditions and Convergence of Fourier Representations

By

Prof. Anubha Gupta

Indraprastha Institute of Information Technology -Delhi

Date and time: January 11, 2023, 5 PM

Venue: Multimedia Classroom, Department of Electrical Engineering (EE), IISc.

Coffee will be served during the talk.

Abstract:  Fourier theory is the backbone of Signal Processing (SP) and Communication Engineering. It has been widely used in almost all branches of science and engineering in numerous applications since its inception. However, Fourier representations such as Fourier series (FS) and Fourier transform (FT) may not exist for some signals that fail to fulfil a predefined set of Dirichlet conditions (DCs). We note a subtle gap in explaining these conditions as available in the popular SP literature. For example, the original DCs require a signal to have bounded variations over one time period for the convergence of FS, where there can be at most countably infinite number of maxima and minima, and at most countably infinite number of discontinuities of finite magnitude. However, a large body of the literature replaces this statement with the requirements of a finite number of maxima and minima over one time period, and a finite number of finite discontinuities over one time period. Due to the latter, some signals fulfilling the original DCs are incorrectly perceived as not having convergent FS representation. A similar problem holds in the description of DCs for the FT. This talk is based on our recent lecture notes published in IEEE Signal Processing Magazine (Sep 2022 issue), wherein we provide the required clarifications and a lucid but precise description and explanation on the DCs along with a lot of suitable examples.

Brief Bio:

Anubha Gupta (anubha@iiitd.ac.in) received her B.Tech and M.Tech from Delhi University, India in 1991 and 1997 in Electronics and Communication Engineering. She received her PhD. from Indian Institute of Technology (IIT), Delhi, India in 2006 in Electrical Engineering. She did her second master’s as a full-time student from the University of Maryland, College Park, USA from 2008-2010 in Education. She worked as Assistant Director with the Ministry of Information and Broadcasting, Govt. of India (through Indian Engineering Services) from 1993 to 1999 and, as faculty at NSUT-Delhi (2000-2008) and IIIT-Hyderabad (2011-2013), India. Currently, she is working as a Professor at IIIT-Delhi, where she served as the Dean, Academic Affairs from June 2019 to June 2020. She has authored/co-authored more than 100 technical papers in scientific journals and conferences. She received SERB POWER Fellowship, 2021 from DST. Govt. of India. She has published research papers in both engineering and education. A lot of exciting work is being taken up in her lab: SBILab (Lab: http://sbilab.iiitd.edu.in/index.html

SBILab – Indraprastha Institute of Information Technology, Delhi
Signal Processing and Biomedical Imaging Lab. Led By Prof. Anubha Gupta of IIIT-Delhi (Started in Jan 2014) SBILab focuses on Signal Processing areas including applications of Wavelet Transforms, Machine (Deep) Learning, and Compressed Sensing, Sparse Reconstruction, fMRI/EEG/MRI/DTI Signal and Image Processing, Genomics Signal Processing, Signal Processing for Communication Engineering, and …
sbilab.iiitd.edu.in

). Her research interests include applications of machine learning in cancer genomics, cancer imaging, biomedical signal and image processing including fMRI, MRI, EEG, ECG signal processing, and Wavelets in deep learning. Dr. Gupta is a senior member of IEEE Signal Processing Society (SPS) and a member of IEEE Women in Engineering Society. She is a technical committee member of BISP committee of IEEE SPS Society for Jan 2022– Dec 2024.

Host: Prof. Chandra Sekhar Seelamantula, EE, IISc.

All are invited.

Thesis Colloquium of Sayan Paul
2023-01-12 20:30 - 21:30 | EE, MMCR
Thesis Title: Pulse Width Modulation Techniques of Inverter Fed Split-Phase Machine Drive in Linear and Overmodulation Regions
Degree registered: PhD
Guide: Prof. Kaushik Basu
Date and Time: 12th January 2023, 03:00 PM
Place: MMCR EE,
Meeting Link: Teams Meeting Link
Multi-phase machines (MPMs) have more than three windings in their stator, rotor, or both. With the broader adoption of power-electronic converters for efficient driving of the machines, MPMs are gaining attention in different applications due to their certain advantages over three-phase machines. One such advantage is higher fault tolerance due to higher phase redundancy, which makes it suitable for safety-critical applications like electric vehicles (EVs), ship propulsions, electric aircraft, etc. Another advantage is that MPMs allow power splitting across multiple phases. Hence, the power rating per phase drive unit becomes low, making it suitable for high-power applications like railway traction, pumps, compressors, etc. Recent literature also proposes using the same multi-phase converter fed MPM, otherwise used for propulsion, as an onboard battery charger; it substantially reduces space, weight, and cost. During charging mode, the leakage inductance of the machine provides the required inductance for the grid connection, and MPM’s higher degrees of freedom are used to lock the rotor electronically. An asymmetrical six-phase machine (ASPM) or split-phase machine is one such MPMs and is very common in EVs. This thesis aims to devise the pulse-width modulation (PWM) techniques of a two-level six-phase inverter fed ASPM to improve the overall drive efficiency.
ASPM has two sets of balanced three-phase windings, which are spatially shifted by 30 degrees (electrical angle). In one of the popular configurations, the two three-phase winding sets are connected in star fashion with two isolated neutral points. This machine is conventionally analyzed in two two-dimensional (2D) orthogonal subspaces. One of these subspaces is associated with electromagnetic energy transfer and torque production. The other subspace doesn’t transfer energy through the air gap and the equivalent circuit in this plane, consisting of winding resistance and leakage inductance, provides a low impedance. Therefore, excitation of this non-energy-transferring subspace causes a large current and associated copper loss. Any PWM technique of ASPM aims to synthesize the desired voltages in the energy-transferring plane and minimize the applied voltage in the non-energy-transferring subspace.
Linear modulation techniques (LMTs) of ASPM apply zero average voltage in the non-energy-transferring subspace and synthesize the desired voltages in the energy-transferring plane on an average over a switching cycle. It is expected that these LMTs should avoid more than two switching transitions of an inverter leg within a carrier period to limit the instantaneous switching loss. Through an innovative approach, our work finds a way to account for all possible infinitely many LMTs that follow the rule of at most two transitions per leg. But each of them results in a different current ripple performance. Ripple current is inevitable in PWM converters and should be minimized through modulation to reduce the associated copper loss. The total ripple current RMS of ASPM is contributed by both energy-transferring and non-transferring planes. One machine parameter also impacts this performance: the ratio of high-frequency inductances in these two subspaces. For all reference voltage vectors and the whole feasible range of the machine parameter, our work finds the techniques with minimum current ripple (RMS) among the above infinite possible LMTs through numerical optimization. A hybrid PWM strategy is proposed with these optimal techniques, which outperforms all existing techniques regarding current ripple performance.
Overmodulation (OVM) techniques of ASPM attain higher voltage gain in energy-transferring subspace than LMTs by applying non-zero average voltage in the non-energy transferring subspace. This operation doesn’t cause any torque ripple, but the applied voltage in non-energy transferring subspace should be minimised to reduce unwanted current and associated loss. The existing OVM technique in the literature minimizes this average voltage from the space-vector perspective with a pre-defined set of four active vectors. To find the best technique, one needs to perform the above minimization problem with all possible sets of active vectors, which can give higher voltage gain. So, this requires the evaluation of a large number of cases. In this thesis, we have formulated the above minimization problem in terms of average voltage vectors of two three-phase inverters, where active vectors need not be specified beforehand. Thus, the analysis is more general. Following the above analysis, eight switching sequences in one part and two in another part of the OVM region are derived, which attain the minimum average voltage injection in the non-energy transferring subspace.
Although the above OVM sequences apply the same average voltages in the two subspaces, they have different high-frequency ripple currents due to different switching strategies. The current ripple study of the OVM techniques of ASPM is missing in the literature. Hence, one of our works in the thesis studies the current ripple performances of the above optimal PWM sequences in the OVM region, which apply minimum average voltage in the non-energy-transferring subspace. We find the sequence with the best switching current ripple performance for a given reference vector in the OVM region and the machine parameter. After that, a PWM technique is proposed, which substantially improves the high-frequency current ripple performance (RMS) compared to two existing OVM techniques for a given machine parameter value.
Finally, simple carrier-comparison-based implementation methods of the proposed LMTs and OVM sequences are found. The six-phase inverter is split into two three-phase inverters, and the proposed strategy implements the PWM sequences per three-phase inverter basis. In carrier-based implementations, the duty signal of the top switch of an inverter leg is compared with a triangular carrier. The bottom switch’s gating pulse complements the top switch’s pulse with a fixed dead time. The duty signal of the top switch of any leg has two components- a modulation signal and a common-mode signal. Two 180-degree phase-shifted carrier signals are required to implement the proposed sequences. The energy-transferring plane of ASPM is divided into twenty-four equivalent sectors; the carrier signals and the expressions of modulation and common-mode signals differ from one sector to another. Henceforth, a sector-independent algorithm is proposed in this thesis to derive these duty signals that substantially reduce the computational burden.
The proposed techniques are validated through simulation in MATLAB/Simulink and experiments on a hardware prototype at a power level of 4 kW.

EE Faculty Colloquium by Prof. Kaushik Basu
2023-01-20 21:30 - 22:30 | EE, MMCR
Venue: MMCR, EE
Time: 4pm, 20 January 2023
Abstract:Silicon Carbide MOSFETs (SiC MOSFETs) fall into wide band gap (WBG) power devices. These devices are commercially available in the voltage range of 600-3300V and compete with the state-of-the-art Si-insulated gate bipolar junction transistors (IGBTs). Superior material properties of SiC MOSFET lead to smaller die sizes. This 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. So, the benefit of using SiC MOSFET as a power device comes 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 utilise the benefits of fast-switching SiC MOSFETs, a better understanding of switching dynamics is essential. However, the switching dynamics of SiC MOSFET are different compared to its Si counterpart due to the highly non-linear device characteristics and participation of circuit parasitic in the process. In this talk, we will discuss our recent work on developing an analytical model of the switching dynamics for hard and soft transitions of SiC MOSFET by simplifying the complex non-linear dynamics predicted by the behavioural model. The developed model, given the device-related parameters extracted from the data sheet, estimated or measured circuit parasitic, can predict lost switching energy, rate of change of device voltage etc., necessary for a successful power converter design through computation with sufficient accuracy. Based on this model a Python based interactive software tool has been developed. The results of this work are applied to two WBG-based advanced power converter development: A 3kW onboard charger for 2 Wheelers and a 200kW SiC-based inverter for high-bandwidth power amplifiers.Speaker’s Bio:Kaushik Basu received the BE. Degree from the Bengal Engineering and Science University, Shibpore, India, in 2003, the M.Sc. degree in electrical engineering from the Indian Institute of Science (IISc), Bangalore, India, in 2005, and the PhD degree in electrical engineering from the University of Minnesota, Minneapolis, in 2012, respectively. He was a Design Engineer with Cold Watt India in 2006 and an Electronics and Control Engineer with Dynapower Corporation USA from 2013-to 15. He is an Associate Professor in the Department of Electrical Engineering at the IISc. He served as the Technical Program Committee Vice-Chair of IEEE ECCE 2019 and 2022. In 2019, he received the Prof. Priti Shankar Teaching Award from IISc. As a co-author, he received the Second Best Prize Paper Award from IEEE Transactions on Transportation Electrification in 2021. He is IEEE senior member and is the founding chair of both IEEE PELS and IES Bangalore Chapter. He is an Associate Editor of IEEE Transactions on Power Electronics, IEEE Transactions on Industrial Electronics, and Springer Journal of Power Electronics. His research interests include most aspects of Power Electronic converter design from a few kW to a few MW for applications ranging from space, grid integration of renewables and storage to fast charging of electric vehicles.

Ph.D thesis Defense of Mr.Sounak Nandi
2023-01-24 16:30 - 17:30
Title of Thesis: Experimental and Theoretical Investigations on High Voltage Polymeric Insulators.
Research Supervisor: Subba Reddy B
Date and Time: Tuesday 24th Jan 2023, 11am
Venue: ON Line: Meeting link:
Abstract
High Voltage Ceramic and glass Insulators have been 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 of service.
The Primary objective of the investigation relates to the study of silicon rubber/polymer insulators under various climatic conditions. Exhaustive 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 extremely high-temperature conditions were attempted experimentally to evaluate their performance. During experimentation, the leakage current was continuously monitored. Later, material analysis, which is a very important aspect and essential to correlate with the morphological changes of the insulator surface, was examined. The experimental investigations demonstrate that there is a need to conduct multi-stress experimentation under specific climatic conditions before the Insulators are installed in the field.
The next portion of the thesis work deals with the failure mechanism of a 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 voltages. Further, experimental investigations are performed on FRP Rods to analyze the behaviour witnessed, as the field failures reported on Silicon rubber Insulators, interesting results are reported.
Condition monitoring of dielectric surfaces is very important; hence it was felt necessary to analyze 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 is presented. Later, Empirical Mode Decomposition is also used for understanding leakage current and implied degradation under minimal data conditions.
Subsequently, the 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. In short, the thesis work reports some new findings on the experimental, simulation and theoretical studies pertaining to the high voltage polymeric insulators used for EHV/UHV Transmission.

All are welcome

PhD Thesis Defense of Dhruv Jawali
2023-01-25 16:30 - 17:30 | EE, MMCR
Advisors: Prof. Chandra Sekhar Seelamantula (EE) & Prof. Supratim Ray (CNS)

Examiner: Prof. Vikram M. Gadre (EE), IIT Bombay
Title of the thesis: Learning Filters, Filterbanks, Wavelets, and Multiscale Representations

Date & Time: January 25, 2023; 11:00 AM onward (Coffee will be served during the defense)
Venue: Multimedia Classroom (MMCR), Department of Electrical Engineering, IISc
Abstract:

The problem of filter design is ubiquitous. Frequency selective filters are used in speech/audio processing, image analysis, convolutional neural networks for tasks such as denoising, deblurring/deconvolution, enhancement, compression, etc. While traditional filter design methods use a structured optimization formulation, the advent of deep learning techniques and associated tools and toolkits enables the learning of filters through data-driven optimization. In this thesis, we consider the filter design problem in a learning setting in both data-dependent and data-independent flavors. Data-dependent filters have properties governed by a downstream task, for instance, filters in a convolutional dictionary used for the task of image denoising. On the contrary, data-independent filters have constraints imposed on their frequency responses, such as lowpass, having diamond-shaped support, satisfying perfect reconstruction property, ability to generate wavelet functions, etc.
The contributions of this thesis are four-fold: (i) the formulation of filter, filterbank, and wavelet design as regression problems, allowing them to be designed in a learning framework; (ii) the design of contourlet-based scattering networks for image classification; (iii) the design of a deep unfolded network using composite regularization techniques for solving inverse problems in image processing; and (iv) a multiscale dictionary learning algorithm that learns one or more multiscale generator kernels to parsimoniously explain certain neural recordings.We begin by developing learning approaches for designing filters having data-independent specifications, for instance, filters with a specified frequency response, including an ideal filter. The problem of designing such filters is formulated as a regression problem, using a training set comprising cosine signals with frequencies sampled uniformly at random. The filters are optimized using the mean-squared error loss, and generalization bounds are provided. We demonstrate the applicability of our approach for filters such as lowpass, bandpass, and highpass in 1-D, and diamond, fan and checkerboard support filters in 2-D. We then show how the methodology extends easily for designing 1-D and 2-D cosine modulated filterbanks.
Second, we consider the problems of 1-D filterbank and wavelet design through learning. Wavelets have proven to be highly successful in several signal and image processing applications. Wavelet design has been an active field of research for over two decades, with the problem often being approached analytically. We draw a parallel between convolutional autoencoders and wavelet multiresolution approximation and show how the learning angle provides a coherent computational framework for solving the design problem. We design data-independent wavelets by interpreting the corresponding perfect reconstruction filterbanks as autoencoders (what we refer to as “filterbank autoencoders”), which precludes the need for customized datasets. In fact, we show that it is possible to design them efficiently using high-dimensional Gaussian vectors as training data. Generalization bounds show that a near-zero training loss implies that the learnt filters satisfy the perfect reconstruction property with a very high probability. We show that desirable properties of a wavelet such as orthogonality, compact support, smoothness, symmetry, and vanishing moments can all be incorporated into the proposed framework by means of architectural constraints or by introducing suitable regularization functionals to the MSE cost. Notably, our approach not only recovers the well-known Daubechies family of orthogonal wavelets and the Cohen-Daubechies-Feauveau (CDF) family of symmetric biorthogonal wavelets, which are used in JPEG-2000 compression, but also learns new wavelets outside these families.
Third, we extend the ideas used for 1-D filterbank and wavelet learning to 2-D filterbank and wavelet design. A variety of efficient representations of natural images, such as wavelets and contourlets can be formulated as corresponding filterbank design problems. The design constraints on the continuous-domain wavelets have corresponding filter-domain manifestations. While most learning problems require specialized datasets, we employ 2-D random Gaussian matrices as training data and optimize filter coefficients considering the MSE loss. Design specifications such as orthogonality of the filterbank, perfect reconstruction property, symmetry, and vanishing moments are enforced through an appropriate parameterization of the convolutional units. We demonstrate several examples of learning biorthogonal and orthogonal filterbanks and wavelets having a specified number of vanishing moments, both point vanishing moments and directional vanishing moments, and symmetry constraints. Sparse recovery via composite regularization is an interesting approach proposed recently in the literature. One could design non-convex regularizers through a convex combination of sparsity-promoting penalties with known proximal operators. We develop a new algorithm, namely, convolutional proximal-averaged thresholding algorithm (C-PATA) for {it composite-regularized} convolutional sparse coding (CR-CSC) based on the recently proposed idea of proximal averaging. We develop an autoencoder structure based on the deep-unfolding of C-PATA iterations into neural network layers, which results in the composite-regularized neural network (CoRNet) architecture. The convolutional learned iterative soft-thresholding algorithm becomes a special case of CoRNet. We demonstrate the efficacy of CoRNet considering applications to image denoising and inpainting, and compare the performance with state-of-the-art techniques such as BM3D, convolutional LISTA, and fast and flexible convolutional sparse coding (FFCSC).The data-independent filter design technique is employed to learn a contourlet transform used within a hybrid scattering network. Hybrid scattering networks are convolutional neural networks (CNNs) where the first few layers implement a fixed windowed scattering transform, while the rest of the network is learned. Scattering networks outperform state-of-the-art deep learning models for limited-data classification tasks although the performance gains are not much for large datasets. The 2-D Morlet filterbank used in Mallat’s scattering network is replaced by a contourlet filterbank, which provides sparser representations and better frequency-domain directional separation. The contourlet transform comprises a multiresolution pyramidal filterbank cascaded with directional filters. We construct directional filters using diamond-shaped quincunx filterbanks and consider two pyramidal filter variants — square-shaped, and filters with radially isotropic frequency domain support. The performance of all variants is evaluated for natural image classification tasks on CIFAR-10 and ImageNet datasets. We show that the radial contourlet variant achieves competitive performance compared with the Morlet scattering transform on large-dataset classification tasks while performing better for the limited-dataset scenario.We then switch over to the problem of learning data-dependent filters for sparse recovery by employing a combination of sparsity promoting regularizers. Sparse recovery via such composite regularization approaches is an interesting framework proposed recently in the literature. One could design non-convex regularizers through a convex combination of sparsity-promoting penalties with known proximal operators. We developed a new algorithm, namely, convolutional proximal-averaged thresholding algorithm (C-PATA) for composite-regularized convolutional sparse coding (CR-CSC) based on proximal averaging. We develop an autoencoder structure based on the deep-unfolding of C-PATA iterations into neural network layers, which results in the composite-regularized neural network (CoRNet) architecture. The convolutional learned iterative soft-thresholding algorithm becomes a special case of CoRNet. We demonstrate the efficacy of CoRNet considering applications to image denoising and inpainting and compare the performance with state-of-the-art techniques such as BM3D, convolutional LISTA, and fast and flexible convolutional sparse coding (FFCSC).Finally, we conclude by developing a data-dependent method to learn filters generating a multiscale convolutional dictionary. First, the multiscale convolutional dictionary learning (MCDL) algorithm is proposed to extract a representative waveform shape from a given dataset. The proposed algorithm is based on the popularly used convolutional dictionary learning formulation with a crucial difference — we assume that the learned atoms are scaled versions of a single generator kernel. We evaluate kernel recovery for synthetic data under noiseless and noisy data conditions. A smoothness regularizer on the learned atom is used to aid better kernel recovery under noisy conditions. Kernel recovery is shown to be robust to model choices of scales and the assumed support size of the kernel without any restrictive assumptions. The proposed approach is applied to visualizing the typical patterns present within human electrocorticogram (ECoG) measurements. The validation is carried out using publicly available ECoG data recorded from a single Parkinson’s disease patient.This thesis thus presents a cogent framework for learning filters, filterbanks, wavelets, convolutional and multiscale dictionaries.
Biography of the candidate: Dhruv Jawali received the Bachelor of Technology (B. Tech) degree from the Department of Computer Science and Engineering, National Institute of Technology Goa, India, in 2014. He worked as a software developer at the Samsung Research Institute, Bangalore during 2014-2015. He enrolled into the PhD program at the IISc Mathematics Initiative (IMI) Department, Indian Institute of Science (IISc) in August 2015, and has been working at the Spectrum Lab, Department of Electrical Engineering ever since. His research interests include wavelet theory, deep neural networks, and sparse signal processing. He is currently employed as an instructor at Scaler Academy specializing in Data Science and Machine Learning.

All are invited.

Towards optimal design of lithium-ion batteries through physics-based modelling by Dr. Krishnakumar Gopalakrishnan
2023-02-15 22:30 - 23:30 | EE, MMCR
Abstract
For online participants: Meeting Link

Increased driving range and enhanced fast charging capabilities are acknowledged as the immediate goals of transport electrification. However, these two objectives are at loggerheads with each other, since they place demands on improving two contrasting aspects of vehicular pouch-cell design, viz. their energy and power densities. By varying the number of layers versus the volume of active electrode material, bespoke pouch-cell designs targeting either of these goals can be obtained. Attempting this design trade-off through iterative empirical testing of layer choices is expensive and often leads to sub-optimal designs. This talk presents the author’s research towards developing a computational framework that employs a model-based methodology for determining the optimal number of electrochemical layers. The modelling objective is to maximise the usable energy whilst satisfying specific acceleration and fast charging targets.

Currently, the model developed thus far is able to handle the critical need to avoid lithium plating during fast charging and accounts for a range of thermal conditions. The modelling framework also takes into account the electrochemical and kinetic phenomena at the micro-scale using a hybrid Finite Element (FE)-spectral scheme, whilst propagating the results upwards to higher length scales for cell-level system design. Drawing upon inferences from his recent research into Hierarchical Multi-Scale Modelling (HMM) of composite materials, the author shall conclude the talk by presenting preliminary results from his hypothesis that coupling the micro-scale FE quadrature points to nano-scale phenomena shall, for the first time, help to quantify the influence of electrode cracking on cell capacity degradation.

Speaker’s bio:

Dr Krishnakumar Gopalakrishnan is a Senior Research Software Engineer at the Dept of Advanced Research Computing (ARC), University College London (UCL) in the UK where he works on high performance scientific computing (HPC) applications across a range of computational modelling research projects. Prior to this, he held a 2-year post doctoral research fellowship in scientific computing at the Centre for Computational Science (CCS) at University College London (UCL), UK, and has served as a visiting researcher at the University of Konstanz, and Rutherford Appleton Laboratories (RAL), UK.He holds a BTech degree in Electrical and Electronics Engineering from College of Engineering, Thiruvananthapuram, an MS degree in Electrical Engineering (power electronics systems & control) from the Center for Power Electronics Systems (CPES) labs at Virginia Tech. Later, he won a US Dept of Energy GATE fellowship to complete a graduate certificate program in electric drivetrain automation. Dr Gopalakrishnan received his PhD degree in Mechanical Engineering (mathematical modelling of lithium ion batteries) from Imperial College London. He was formerly employed at ABB Innovation Labs (Bangalore, India) and ABB Corporate Research Center (Baden-Dättwil, Switzerland). He has also served as a power management algorithms and systems engineer at Qualcomm Inc. (San Diego, USA) where he successfully filed corporate patents on novel Battery Management Systems (BMS) designs. He was awarded the President’s PhD Scholarship at Imperial College London and is a Mathworks Certified Matlab Associate.

Dr Gopalakrishnan has over a decade of teaching experience at various levels. At UCL, he currently teaches the University’s scientific computing with C++ course and is leading the course development effort and teaching plan for UCL ARC’s first Massively Open Online Course (MOOC) on the FutureLearn platform. He has also had the privelege of teaching Imperial College London’s first MOOC on Mathematics Essentials (for business majors) hosted on the EdX platform. He has also served as a teaching fellow at Imperial College London’s Computational Methods hub, wherein he was the lead instructor for several scientific computing courses taught to a campus-wide audience.

Dr Gopalakrishnan has published several well-cited research articles in peer-reviewed journals (including in Nature Computational Science) and technical whitepapers, and has presented at UK and international conferences. His research software engineering interests include heterogeneous and GPU computing, parallel and threaded programming, linear algebra libraries, low-latency network communications, and Unix systems administration. His scientific research interests include computational modelling of dynamic systems, power electronics and control, energy storage, non-linear optimisation, feedback control, signal processing, numerical methods and state estimation.

EE Faculty Colloquium on Learning from unreliable data
2023-02-17 21:30 - 22:30 | EE, MMCR
Speaker: Prof. P.S. Sastry, Dept of Electrical Engineering, Indian Institute of Science
Abstract:
Supervised learning of classifiers is widely used in many applications of AI/ML. The deep networks used in such applications today need a large training set. Creating a labelled data set where one can have high confidence in the labels is both expensive and time consuming. Data sets created through crowd sourcing or automatic labelling methods normally have many random labelling errors. There is considerable amount of empirical evidence to show that standard algorithms are likely to do poorly when there is significant amount of label noise in the data. Hence it is interesting to ask whether one can design classifier learning algorithms that are robust to different types of random labelling errors (in the training data). Over the years this problem has been investigated by many researchers and many interesting ideas and algorithms for such robust learning are proposed. In this talk we present an overview of the problem of learning with noisily labelled training set and review some of the approaches proposed for tackling the problem. We concentrate mainly on risk minimization schemes. We discuss what are called symmetric loss functions and their role in robust risk minimization. We will also briefly discuss approaches based on sample selection and weighted risk minimization and present a sample selection algorithm based on batch statistics. The discussion would be biased towards some work done in our lab.Speaker’s Bio:P.S. Sastry obtained BSc in Physics from IIT, Kharagpur, and BE from ECE dept and PhD from EE dept at IISc. He has been a faculty member of dept EE, IISc, for more than 35 years now. His research interests include Pattern Recognition, Machine Learning, Data Mining, and Computational Neuroscience.

PhD Oral Defense, of Mr. Bidhan Biswas
2023-02-20 15:30 - 18:30 | HV seminar Hall
Title of the thesis: Short Circuit and Open Circuit Natural Frequencies of 3-Φ Transformers.
Research Supervisor: Prof. L. Satish
Meeting link : Click here to join the meeting
Abstract
Frequency Response Analysis (FRA) method is perhaps the most sensitive tool that can detect even the slightest of winding/core movements. High sensitivity, non-invasiveness, non-destructiveness, and on-site capability are some of its salient features – making it an ideal monitoring and detection tool. The existence of Standards (IEEE, IEC, and CIGRE) is ample testimony of its global acceptance and superior detection capabilities. The principle of detection is based on observance of a deviation between two measured FRAs which implies a possible fault. Naturally, the next logical step is to analyse these deviations to determine the type of fault, estimate the extent of damage and its severity, and as a bonus, predict its location, if possible. However, even after three decades of existence, arriving at these inferences is still at the research level. Even though there is a consensus among all the standards on FRA test/measurement procedures, best-suited terminal connections, cable layout, grounding practices, etc., they remain largely silent regarding interpretation and diagnostics.
A detailed analysis of literature compiled in Chapter 1 reveals that perhaps lack of a mathematical foundation might be one reason for the present plight of FRA. So, developing a generic mathematical-based approach for interpretation and location of incipient mechanical winding damages in actual 3-Φ transformer windings, using measured FRA, is imperative. Development of a such generic method necessitates derivation of closed-form expressions which can provide a direct link between measured FRA quantities to the electrical parameters of the winding. For assessing damage severity, the challenge is to identify a quantity which is not only extractable from measured FRA, but also be sensitive, monotonic, and traceable to the fault. Driven by this philosophy this thesis aims to address the following –

Propose a unified and general approach to derive closed-form analytical expressions (for each multiphase winding) to link the measured open and short circuit natural frequencies to electrical parameters of the winding, and valid for any condition of the neutral
Define a quantity calculable from the measured FRA’s peak/trough frequencies which is physically related to mechanical damage in the winding, and capable of yielding some physical insight about damage
Develop novel methods using the derived analytical expressions to identify an incipient, discrete, and localized axial and/or radial displacement in any multiphase winding, and applicable for any condition of the neutral

In the second chapter, a generic and unified analytical method is developed (applicable to any 1-Φ or 3-Φ winding) starting from the basic mutually coupled lossless ladder network model to derive equations which relate the harmonic sum of squares of short circuit natural frequencies (SCNF) and open circuit natural frequencies (OCNF) to the elemental winding inductances and capacitances. Complete details of the derivation are discussed, and all the derived formulae were cross verified by extensive numerical circuit simulations.
Each one of these derived expressions has a strikingly similar structure and possesses a unique property viz., the contribution of series capacitances and ground capacitances are decoupled. This important property paves way for estimating a physical quantity that is directly responsible for the winding resonances, viz., the effective air-core inductance (Leff). This estimation requires multiple FRA measurements. Chapter 3 presents complete details of the concept, its derivation, measurements, and experimental results for all 1-Φ and 3-Φ windings.
Loss of clamping pressure in a winding is not directly identifiable by any means, other than an FRA measurement. But, this damage cannot be judged by merely comparing two FRAs. So, a clamping pressure measurement experiment was carried out on a single isolated winding to ascertain the sensitivity and monotonicity afforded by the quantity, Leff, to a change in clamping pressure. Driven by the promising results, author proceeds to build a method based on Leff to find the location of a discrete and localized axial displacement (AD) in any 3-Φ winding configuration. Details of this method, experimental results, and measurement steps are presented in Chapter 4.
Proceeding further, Chapter 5 discusses concept of a new method, measurement steps and experimental results to identify presence of a Radial Displacement (RD) in a 3-Φ star winding with neutral-open, as well as, in a delta connected winding. Driven by success, the concept was extended to identify the simultaneous occurrence of a discrete and localized AD and RD in one phase of a 3-Φ star winding, with neutral-open. Preliminary experimental results proved the method can successfully identify faulted phases that contained AD and RD.
All experiments reported in the thesis were carried out on transformer windings rated at 33 kV, 3.5 MVA. The results are encouraging and the author believes that true potential of the proposed methods can be judged when implemented on actual transformers.
In summary, this thesis presents, perhaps for the first time, a mathematical basis for identifying and diagnosing axial and radial displacements in 1-Φ and 3-Φ windings using the peak/trough frequency data from the measured FRA. The author believes that this is a small step forward in advancing FRA as a diagnostic tool.
ALL ARE INVITED

An Introduction to Neuromorphic Computing with Intel Loihi
2023-02-20 21:30 - 2023-02-21 00:30 | EE, MMCR
Speaker: Ashish Rao Mangalore, Doctoral resident at Intel Labs, Munich, Germany
Abstract:

Neuromorphic computing is a new paradigm of computing inspired by the organization and functioning of neurons in the mammalian brain. The architectural features derived from this inspiration result in orders of gain in the time and energy to solution for various classes of problems. Over the years, there have been neuromorphic platforms of different types, e.g., IBM TrueNorth, SpiNNaker, DYNAPs, and BrainChip Akida to name a few. In this talk, we shall focus on the state-of-the-art digital CMOS-based neuromorphic research platform, Loihi, developed by Intel Corporation. We shall then go through the basic operating principles of Loihi, problems that Loihi is best suited for, and the current main algorithmic research verticals being pursued by the neuromorphic research community. After this, there shall be a special emphasis on mathematical optimization on Loihi, one of the most promising and viable applications on Loihi. The talk will end with pointers on how groups can get involved to address open problems and contribute to neuromorphic research in general.

Biography of the speaker:

Ashish Rao Mangalore currently works as a doctoral resident at Intel Labs, Munich, Germany under the supervision of Prof. Alin Albu-Schäffer at the German Aerospace Center/DLR & TU Munich. His main research focus lies in the development & implementation of control algorithms for robotics and mathematical optimization problems on neuromorphic computers, specifically, Loihi. During a prior research stint at the Indian Institute of Science (IISc), Bengaluru, he conducted research on 3-D reconstruction techniques with neuromorphic cameras. He obtained his masters in Neuroengineering from the Technical University of Munich (TUM) and bachelors in Electrical & Electronics Engineering from R.V. College of Engineering (RVCE), Bengaluru.

Thesis Defense of Tanmay Mishra
2023-02-22 14:30 - 16:30 | EE, MMCR
Title: Development of A Reconfigurable Synchronous Machine Emulation Platform
Faculty Advisor: Prof. Gurunath Gurrala.
Meeting Link:Click here to join the meeting
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 Council 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”.

International Workshop on Planar Magnetic Technology
2023-02-23 - 2023-02-25 | IISc

Open Day 4th March 2023
2023-03-04 14:30 - 22:30 | EE, IISc
Events are to be held in the Electrical Engineering Department on an open day.
SPECTRUM LAB DEMOS
Generative Models, XAI for Optical Coherence Tomography, AI Assisted Wireless-Capsule Endoscopy Analysis, Neuromorphic Cameras, Foreign Object Debris (FOD) Detection, Real Time Human Pose Detection,How to get into IISc?, AI-assisted echocardiography Spectrum Lab at Open Day
COMPUTER VISION
Image Analysis and Computer Vision Laboratory IACV_Lab_Poster
CONTROL SYSTEM
Reinforcement Learning Aided Efficient and Distributed Planning for Multi-Agent Systems cns-pt_open-day-2023 (3)
DIGITAL SIGNAL PROCESSING
INDIAN SIGN LANGUAGE, AI VS HUMAN, RPS GAME, NON-CONTACT MEASUREMENT DSP Open Day Poster
Power SYSTEM
POWER SYSTEM OPERATION AND CONTROL FIST_Lab_Open_Day23_Poster
Signal Processing Interpretation and Representation (SPIRE) Lab

Watch Me Speak Demo1
Make computer speak in Indian Languages Demo2
Wanna be a sound engineer? Demo3
Can your breath sound reveal your gender?Mystery Demo4
Speed up to launch up! Demo5
Mimic your favorite actor Demo6
Do you hear what they say? Demo7
Visualize your vocals Demo8
Can you recognize correct pronunciation of an English word? Demo9.
Language engineering and processing Lab
Auditory perception Illusions LEAP_openday_BAI_2023
Who spoke when in a conversation? openday_diarization_poster
Multimodal Conversational Emotion Recognition open_day2023_soumya
Sound Based COVID-19 Diagnosis open_day_poster2023_COSWARA
Automatic Speech Recognition Introduction_To_ASR_Srikanth

Thesis Defense of Shubham Rawat
2023-03-09 14:30 - 2023-03-10 04:30 | Online, India
Title: A Novel Passive Regenerative Snubber for the Phase-Shifted Full-Bridge Converter: Analysis, Design and Experimental Verification.
Research Supervisor: Prof. Kaushik Basu
Meeting Link

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 as 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.

MTech (Research) Colloquium of Vishwabandhu Uttam
2023-03-10 16:30 - 2023-03-13 18:30 | EE, MMCR
Title: A Unified Modeling Approach for Design and Performance Improvement of Triple Active Bridge Converter

Research Supervisor: Vishnu Mahadeva Iyer

Meeting link

Abstract:Triple Active Bridge (TAB) converter is a multi-port DC-DC converter. This converter is an extension of the popular Dual Active Bridge converter. It features desirable traits of the DAB converter, such as high power density, galvanic isolation, and bi-directional power flow between any of the ports. As in other multi-port converters, redundant power conversion is minimized through component sharing among the ports in a TAB converter. All the switches in a TAB converter can undergo soft-switching over a wide range of operating points, reducing switching losses and the size of auxiliary components. The multiple degrees of freedom in modulating a TAB converter offer several design and operational flexibilities.However, this converter has yet to come into the limelight despite these advantages. One of the reasons is the lack of a unified analytical framework for the design and operation of this converter. The existing models for the TAB converter are limited in scope and cannot be easily used for the design and operational optimization of the converter. This work focuses on developing simple, unified models for analyzing the TAB converter.Firstly, the popular Fundamental Harmonic Approximated (FHA) large-signal and small-signal models are evaluated to understand their limitations. Improved large-signal and small-signal Generalised Harmonic Models (GHM) are developed by incorporating the impact of higher-order harmonics. While the GHM is shown to be superior for small-signal analysis of the converter, it is not suitable to analyze the soft-switching bounds of the TAB converter. To overcome the limitations of GHM, a Unified Model that incorporates the impact of the magnetising inductance of the three-winding transformer is proposed. The Unified Model can accurately predict the AC port currents at the switching instants and is used to study the soft-switching bounds of the TAB converter. The GHM and Unified Model are validated through extensive switching circuit simulations and experimental results from a 1 kW hardware prototype developed in the laboratory.Further, a new design algorithm for the TAB converter is proposed. The proposed algorithm leverages the FHA model’s simplicity and the Unified Model’s accuracy. Finally, a new modulation scheme based on Penta Phase Shift with five degrees of freedom is proposed to achieve soft-switching across the operational range of the TAB converter.
We request your presence at the colloquium.
All are welcome.

Thesis Defense of Mr. Manish Tathode
2023-03-27 21:00 - 23:00 | EE-B304, EE Department.
Title: Fast and Compact Voltage Equalizer for Satellite Applications.
Advisor: Prof. Vinod John.Date and Time: Monday, 27th March 2023, 3.30 pm.Location: EE-B304, EE Department.Meeting Link:
Abstract:Lithium-ion batteries have now become an inevitable constituent of the Electrical Power System of solar-powered satellites due to their high energy density, wider operating temperature range, and better radiation tolerance. For the 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 to avoid underutilization and reduced lifetime. Active multicell-to-multicell equalization achieves fast equalization by efficient simultaneous charge transfer among multiple cells in the series connected stack. PS-MAHB equalizer is one such multicell-to-multicell equalizer with the ability to maintain higher equalization currents irrespective of decreasing differences in the cell voltages. Its open loop, soft-switched operation, and modularization abilities make it an attractive choice for space applications. However, it needs to be modified with the necessary protective features and required redundancy essential for its use in space applications. Hence, the Modified PS-MAHB (M-PS-MAHB) equalizer is developed by incorporating necessary protection features and redundancy in the PS-MAHB equalizer. The flow of development of M-PS-MAHB equalizer is discussed. The Failure Mode Impact Analysis of the M-PS-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. Simulation results of FMIA considering transient and steady state impact of the failure are discussed.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 a reduced rate of equalization and causes the under-utilization of the equalizer hardware for a 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 sensor board addresses the space-volume constraints put by 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 of the passive components. A non-isolated high-precision op-amp-based voltage sensing scheme is developed to target the equalization band close to 10mV. The concept of an easy-to-design motherboard-based interface is introduced, which does not require any changes in the design of the 4-cell equalizer module and the voltage sensor board, irrespective of the cell connector geometry.The experimental results verify the operation of the equalizer demonstrating 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 to a step change in current and its impact on the voltage-sensing-based control algorithm is discussed along with the necessary modifications brought in the control to reduce the impact.We request your presence at the thesis defence.

Thesis colloquium of Mr. Anoop C. S.
2023-04-10 21:00 - 23:00
Advisor : Prof. A. G. Ramakrishnan

Date and Time: 10 April 2023 (Monday) 3:30 PM

meeting link:

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TITLE: Automatic speech recognition for low-resource Indian languages

Building good models for automatic speech recognition (ASR) requires large amounts of annotated speech data. Most Indian languages are low-resourced and lack enough training data to build robust and efficient ASR systems. However, many have an overlapping phoneme set and a strong correspondence between their character sets and pronunciations. In this thesis, we exploit such similarities among the Indian languages to improve speech recognition in low-resource settings.

Significant contributions of the thesis:

Exploiting the pronunciation similarities across multiple Indian languages through shared label sets:

We propose the use of a common set of tokens across multiple Indian languages and analyze their performance in mono and multilingual settings.

We find that the Sanskrit Library Phonetic Encoding (SLP1) tokens, which exploit the pronunciation-based structuring of character Unicodes in Indian languages, perform better than some other grapheme-to-phoneme (G2P) based tokens in monolingual ASR settings.
Syllable-based sub-words perform better than the character-based token units in monolingual speech recognition. However, character-based SLP1 tokens perform better in cross-lingual transfer.

Strategies for improving the performance of ASR systems in low-resource scenarios (target languages) exploiting the annotated data from high-resource languages (source languages):

We study three different low-resource settings:

A) Labelled audio data is not available in the target language. Only a limited amount of unlabeled data is available. We adopt the unsupervised domain adaptation (UDA) schemes popular in image classification problems to tackle this case.

The adversarial training with gradient reversal layers (GRL) and domain separation networks (DSN) provides word error rate (WER) improvements of 6.71% and 7.32% in Sanskrit compared to a baseline hybrid DNN-HMM system trained on Hindi.
The UDA models outperform multi-task training with language recognition as the auxiliary task.
Selection of the source language is critical in UDA systems.

B) Target language has only a small amount of labeled data and has some amount of text data to build language models. We try to benefit from the available data in high-resource languages through shared label sets to build unified acoustic (AM) and language models (LM).

Unified language-agnostic AM + LM performs better than monolingual AM + LM in cases where (a) only limited speech data is available for training the acoustic models and (b) the speech data is from domains different from that used in training.
In general, multilingual AM + monolingual LM performs the best.

C) There are N target languages with limited training data and several source languages with large training sets. Here, we establish the usefulness of model-agnostic meta-learning (MAML) pre-training in Indian languages and propose improvements with text-similarity-based loss-weightings.

MAML beats joint multilingual pretraining by an average of 5.4% in CER and 20.3% in WER.
With just 25% of the data, MAML performance matches joint multilingual models trained on the whole target data.
Similarity with the source languages impacts the target language’s ASR performance.
We use text-similarity measured through cosine and Mahalanobis distances to weigh the losses during MAML pretraining. It yields a mean absolute improvement of 1% in WER.

ALL ARE WELCOME ONLINE!

Ph.D. Thesis colloquium of Ms. Ritika Jain
2023-04-17 21:00 - 23:00 | MMCR, Hall C 241, 1st floor, EE department
Advisor: Prof. A. G. Ramakrishnan

TITLE: Multimodal sleep staging and diagnosis of sleep disorders
MS Teams link

Sleep staging is a tedious and time-consuming process carried out manually by clinicians in which they annotate overnight polysomnograph recordings. An automated sleep scoring system can perform faster and objective sleep staging. Methods are proposed to classify the sleep EEG data into multiple stages by utilizing temporal, spectral, time-frequency, non-linear, and statistical features and random undersampling with boosting technique (RUSBoost) on a decision tree classifier. The role of data augmentation and temporal context on classifier performance is evaluated for healthy controls and clinical populations. This work also attempts to classify different sleep disorders using single-channel EEG and evaluate the role of individual sleep stages in that task.

Significant contributions of the thesis:

Binary classification of sleep and wake states for healthy individuals and clinical population:

For this two-class classification problem, we explored the performances of different modalities such as EEG, EOG & EMG.

We also performed ensemble empirical mode decomposition and Poincare plot analysis of the signal for identifying sleep and wake states.

Multi-class classification of sleep stages using single channel EEG:

Utilising the knowledge from earlier works on binary classification, we considered different sets of features and evaluated the performance of RUSBoost classifier on unseen test subjects. This work reports the performance of different n-class (n=2,3,4,5,6) classification problems on three publicly available datasets of overnight polysomnography recordings.

Multi-modal classification of sleep stages using a hierarchical model

In this work, a six-level hierarchical model (HM) has been designed. The aim is to improve the sleep staging accuracy by breaking down the 5-class classification problem into six binary classification problems, while also reducing the misclassifications among N1, REM, and wake stages.

Introducing data augmentation (DA) and temporal context (TC) in the proposed hierarchical model to further improve sleep staging performance. We validated the results of DA and TC on healthy as well as clinical populations from seven publicly available datasets.

Diagnosis of different sleep disorders using a single EEG channel

This work aims to classify seven different sleep disorders and healthy controls using light gradient boosting model with a single-channel EEG.

We examined the importance of different features in distinguishing various pathological groups and healthy individuals.

We also evaluated the role of individual sleep stages in distinguishing the different disorders.

ALL ARE WELCOME
Publications based on this Thesis

Journals
1. Ritika Jain and Angarai Ganesan Ramakrishnan. Electrophysiological and neuroimaging studies–during resting state and sensory stimulation in disorders of consciousness: a review. Frontiers Neurosc., 14:987, 2020
2. Ritika Jain and Ramakrishnan A G. Reliable sleep staging of unseen subjects with fusion of multiple EEG features and RUSBoost. Biomed. Sig. Proc. Control, 70:103061, 2021

Conferences
1. Ritika Jain and Ramakrishnan Angarai Ganesan. Assessment of submentalis muscle activity for sleep-wake classification of healthy individuals and patients with sleep disorders.
In 44th IEEE EMBC 2022. IEEE, 2022
2. Ritika Jain and Ramakrishnan Angarai Ganesan. Single EOG channel performs well in distinguishing sleep from wake state for both healthy individuals and patients. In 44th
IEEE EMBC. IEEE, 2022
3. Ritika Jain and Ramakrishnan Angarai Ganesan. Poincar ́e plot analysis for sleep-wake classification of unseen patients using a single EEG channel. In 17th IEEE Int. Symp.
Med. Meas. Applns. IEEE, 2022
4. Ritika Jain and Ramakrishnan Angarai Ganesan. Classifying sleep-wake states of patients by training on single EEG or EOG channel data from normal subjects. In 2022 IEEE Region 10 Symposium (TENSYMP), pages 1–5. IEEE, 2022
5. Ritika Jain and Ramakrishnan Angarai Ganesan. An efficient sleep scoring method using visibility graph and temporal features of single-channel EEG. In 43rd Ann. Int. Conf IEEE EMBC, pages 6306–6309. IEEE, 20216. Ramakrishnan A G and Ritika Jain. Binary state prediction of sleep or wakefulness using EEG and EOG features. In 17th India Council Int. Conf (INDICON), pages 1–7. IEEE, 20207. Ritika Jain and Angarai Ganesan Ramakrishnan. Sleep-awake classification using EEG band-power-ratios and complexity measures. In 2020 IEEE 17th India Council International Conference (INDICON), pages 1–6. IEEE, 2020Manuscripts under Review

• Ritika Jain and Ramakrishnan A G. Modality-specific feature selection, data augmentation, and temporal context for superior performance in sleep staging. IEEE Jl. Of Biomedical & Health Informatics, 2023.

ALL ARE WELCOME – People outside IISc can join through the MS Teams link given.

EE Seminar: High voltage aviation electrical system EMC challenges and opportunities
2023-04-24 16:00 - 18:00 | MMCR, Hall C 241, 1st floor, EE department
Dear all,

I cordially invite you to attend this talk by Dr. Cong Li from GE Aerospace Research, Niskayuna, NY, USA. The details are given below.

Date & Time: 24/04/2023, 10.30 am – 11.30 am

Venue: MMCR, Dept. of Electrical Engineering (Hybrid Mode)

For online participants:Meeting Link

Title: High voltage aviation electrical system EMC challenges and opportunities

Abstract: High voltage aviation electrical systems have unique design challenges to meet ultrahigh power density and reliability requirements under extreme operation conditions. One critical aspect is the Electromagnetic Compatibility (EMC), such as emission and susceptibility, etc. This talk will explain the fundamental EMC challenges for wide band gap (WBG) based high voltage aviation electrical system, and share an effective “SOLVE” EMC design process for meeting stringent aviation EMC requirements.

Speaker Bio: Dr. Cong Li (S’09-M’15-SM’19) received the Ph.D. degree in electrical engineering specializing in power electronics from The Ohio State University, Columbus, OH, USA, in 2014. He joined GE Aerospace Research at Niskayuna, NY, USA as a Research Engineer in 2014 and is currently a Senior Power Electronics Engineer and EMC Lead. His research interests include aviation high voltage, high power, high density wide band gap (WBG) power electronics systems, and EMI mitigation techniques. He is currently leading multiple flight demo EMC projects at GE Research. He has authored more than dozens of technical papers, and patent applications in the area of power electronics and EMC. He is a voting member of commercial aviation DO-160 EMI standard working group.

He has been given EMI webinars and tutorials at multiple IEEE conferences such as ECCE, APEC, EMC Symposium, etc. He is currently a Senior Member of IEEE, and Associate Editor at IEEE Open Journal of Power Electronics. He is serving as secretary of IEEE-EMCS-SC5 Power Electronics EMC, as well as Technical Committee member of IEEE APEC, ECCE, ITEC, EATS conferences.

Regards,
Vishnu

EE seminar: Advances in Power Electronics and Power Semiconductors for E-mobility Applications
2023-04-24 20:30 - 22:30 | B 303 (Old 311), Dept. of Electrical Engineering (Hybrid Mode)
Dear all,

I cordially invite you to attend this talk by Dr. Ajay Poonjal Pai from Infineon Technologies AG, Neubiberg, Germany. The details are given below.

Date & Time: 24/04/2023, 3 pm – 4 pm

Venue: B 303 (Old 311), Dept. of Electrical Engineering (Hybrid Mode)

For online participants: Meeting Link

Title of the Talk: Advances in Power Electronics and Power Semiconductors for E-mobility Applications

Abstract: E-mobility has emerged as an interesting application for power electronics and power semiconductors. Not only is this market demonstrating an exponential growth, but is also filled with interesting challenges. This is where Power semiconductors and power electronics can pitch in to facilitate adoption of electric cars. This talk gives an overview of the most interesting power electronic applications in electric Vehicles, with a main focus on the traction inverter application, which is, by far, the most important from a power semiconductor point of view. The latest trends in the application and power semiconductor technologies is discussed. Special focus will be given to Silicon Carbide Mosfet technology which offers significant benefits in terms of power density, switching behaviour, conduction behaviour etc. Results of power loss measurements are shown and discussed.

Speaker Bio: Dr. Ajay Poonjal Pai obtained his B.Tech in Electrical Engineering from NITK Surathkal, India and M.Sc. in Electrical Power Engineering from RWTH Aachen University, Germany. He then pursued his PhD focusing on Silicon Carbide at the Friedrich Alexander University, Erlangen-Nuremberg, Germany. Since 2015, he is working at Infineon Technologies AG, Neubiberg, Germany as a Principal Application Engineer responsible for next-generation Silicon Carbide technologies and Power Modules for electric vehicles. His research interests include e-mobility, Silicon Carbide semiconductors and power electronics. He enjoys following new technologies and understanding trends in the power electronics and automotive markets. He has contributed to several conferences and journals, and has delivered numerous lectures and tutorials around the world.

Regards,
Vishnu

IISc-TU Delft Talk by Prof Marjan Popov 26th April 11 am
2023-04-26 16:30 - 18:30 | MMCR, Hall C 241, 1st floor, EE department
Dear All,
Prof Marjan Popov of TU Delft will be visiting IISc on 26th April 2023. He will be giving a talk in the MMCR of the Electrical Engineering Department, from 11 am on “Power System Protection Essentials – research activities”. He will mostly give an overview of the research activity carried out by his lab. This talk is a part of an IISc-TU Delft joint project.
All are invited.

PhD Oral Examination of Kapil Upamanyu @ 10 am on Thursday, May 11, 2023
2023-05-11 15:30 - 17:00 | Multi-Media Class Room (MMCR), EE Department (Hybrid mode)
PhD Oral Examination

Name of the candidate: Kapil Upamanyu
Programme: PhD (Regular)
Department: Electrical Engineering

Title:
Modelling, Stabilization Methods and Power Amplification for Power Hardware-in-Loop Simulation with Improved Accuracy

Supervisor: G Narayanan

Date: May 11, 2023 (Thursday)
Time: 10 am – 11:30 am
Venue: Multi-Media Class Room (MMCR), EE Department (Hybrid mode)
Video link: Please find below:

All are welcome

Meeting link

Join conversation
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Thesis title: Modelling, Stabilization Methods and Power Amplification for Power Hardware-in-Loop Simulation with Improved Accuracy
Abstract:

Simulations of physical systems are extensively conducted for research and design purpose. Potential of a simulation can be extended significantly by conducting it in real-time. Real-time simulation allows a part of the mathematical model of the system to be replaced by a physical hardware; the real-time simulator (RTS) and the physical hardware interact with each other through sensors and power amplifier (PA). When the operating power level of the PA is considerably higher than that of the sensor signals, the simulation is called as power hardware in loop (PHIL) simulation. PHIL simulation is a good alternative to the conventional simulation where a part of the system cannot be adequately represented by a mathematical model. Unlike conventional simulation, PHIL simulation allows the testing of a hardware, in a safe and controlled environment, without the rest of the system being available. But several factors, such as the computation time delay and sampling effects of RTS, the dynamics of PA and the transport lag of signals, are part of a PHIL simulation but not that of the actual system. As a result, the response of the PHIL simulation of a system can differ from that of the actual system. The inaccuracy can be so significant that the PHIL simulation of a system can be unstable even though the actual system is stable, and vice versa. An unstable PHIL simulation can be stabilized by employing compensation algorithms. This work proposes novel PA for accurate response, stability analysis methodology for the accurate estimation of instability, and compensation algorithms for stable response of PHIL simulation.
Conventional switched-mode PAs have limited dynamic response due the presence of passive filter. These PAs employed in a PHIL simulation are unable to accurately replicate the fast transients of the system. An output filter-less voltage source inverter is proposed as a power amplifier suitable to be interfaced with inductive loads (e.g., most of the power system loads). Such a PA has a reference tracking bandwidth comparable to the switching frequency. Unlike the output of the conventional PAs, the output of the proposed PA is completely unaffected by the sudden changes in the current drawn by the loads. The proposed PA is utilized to emulate the transients of synchronous generator, along with the fast transient corresponding to the field excitation controller, while feeding a passive linear load. With a proposed improvement in the emulation method, accurate responses for unbalanced and non-linear loads are also obtained for the emulated generator. With further proposed techniques, the applicability of the proposed PA is extended for it to be interfaced to PWM converters. The PA is utilized for emulating unbalanced and harmonic (up to 23rd order) grid voltages while testing the control of a PWM rectifier. Accurate current responses are also obtained when the step changes in the grid voltage and the rectifier dc bus reference are considered.
Conventionally, stability analysis of PHIL simulation is evaluated in continuous-time domain. Since, a PHIL simulation consists of discrete-time sampling, a discrete-time domain modelling approach is proposed for more accurate stability analysis. The proposed approach is also used to accurately estimate the stability of a PHIL simulation utilizing compensation algorithms, such as feedback current filtering method. Novel compensation algorithms, based on lag compensator and cross coupled compensator, are proposed for stabilizing those PHIL simulations which cannot be stabilized using existing algorithms. PHIL simulation of a single generator infinite bus power system, which is originally unstable without and with existing compensation algorithms, is successfully conducted using the novel cross-coupled compensator.
PA sourced from a PWM rectifier can be used as 4-quadrant PA. A simple input voltage sensor-less vector control of PWM rectifier is proposed. While the performance of the proposed method, in terms of THD and power factor, is comparable to the sensor-based method and existing sensor-less methods, its computation time requirement is much lower than those for these methods. A discrete-time domain modelling of the PI-controlled current loop of PWM converters is presented. The model is used to derive closed-form time-domain expressions of the current for step changes in the current reference and the disturbance voltage, for a given set of controller and hardware parameters. Based on the derived expressions, a pre-filter is proposed to achieve the dead-beat response with the PI-controlled current loop, while having a disturbance rejection settling time of just ten switching cycles.
ALL ARE WELCOME

[Online] Faculty Candidate Talk Friday 12th May 9:30 am
2023-05-12 15:00 - 18:00
Dr. Avinash Kumar has applied for a faculty position at the EE Dept IISc. His online talk is scheduled on 12th May 9:30 am. Please find his talk details below.

Topic: Islanding Detection Methods for Inverter Interfaced Distributed Generator (IIDG)

Continuous disturbance injection-based islanding detection of an inverter-interfaced distributed generator (IIDG) has been a general trend in reported literature work. The major issues that persist in previous islanding detection methods are large Non-Detection Zone (NDZ), poor Power Quality (PQ), high implementation cost, and lack of real-time validation. Furthermore, the selection of a generic threshold is not considered for general applications and depends on the system’s rating. Various types of islanding detection methods are reported in the literature as passive, active, and hybrid methods.
In this talk, a local voltage and current measurement-based hybrid islanding detection method for IIDGs will be presented in detail. The parameters are estimated from the fundamental phasors of voltage and current at the Point of Common Coupling (PCC) of IIDG using Space Vector Rotation (SVR). The proposed disturbance in IIDG is injected only after disturbance detection by SVR parameters to mitigate the impact on PQ. Disturbance identification is easy due to the combined PCC voltage and current effects on parameter estimation. The disturbance injection is controlled and self-decaying. Further, the talk will discuss the validation of the proposed approach on Real-Time Digital Simulator (RTDS) and Controller Hardware In the Loop (CHIL) setup. The talk will also highlight the advantage of the proposed method in terms of real-time efficacy (low islanding detection time) with no NDZ and negligible impact on PQ for the detection of different islanding events.
At the end of the talk, a brief overview of recently reported work on “Dynamic-State-Estimation-Based Cyber Attack Detection for Inverter-Based Resources” will be delivered.

The link of the talk is:

Team Link

Ph. D. Thesis Colloquium of Mr. Pradeep K. G.: 11 am Monday, 15 May: EEG correlates of non-ordinary states of consciousness and slow-paced breathing
2023-05-15 16:30 | MMCR, Hall C 241, 1st floor, EE department
Title: EEG correlates of non-ordinary states of consciousness and slow-paced breathing

Name of the student: Pradeep Kumar G

Advisor: Prof. A. G. Ramakrishnan

Date and Time: 15 May 2023 (Monday) 11:00 AM

Venue: Hybrid: MMCR, Hall C 241, 1st floor, Department of EE
Meeting Link

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TITLE: EEG correlates of non-ordinary states of consciousness and slow-paced breathing

Studies on the non-ordinary states of consciousness (NSCs) induced by meditation, hypnosis, and trance are gaining visibility due to their potential efficacy in treating various clinical conditions. Slow-paced breathing at six cycles per minute (cpm) has been labelled as coherent breathing since it has been suggested to induce synchronous resonance frequency in various physiological signals. These self-regulatory or guided processes are practiced primarily to reduce stress and manage emotions and mental health. However, the underlying mechanisms for the health benefits of these practices still need to be fully understood. Electroencephalography (EEG), a non-invasive electrophysiological tool to investigate brain’s electrical activity, is used to study the changes in brain dynamics during different NSCs.

Significant contributions of the thesis:

Changes in EEG coupling during eyes-open meditation.

The interdependencies between brain signals clustered in different groups across the hemispheres were studied using bivariate functional connectivity (FC) methods.

Changes in the FC between EEG electrode pairs were investigated during the meditation practiced by long-term Brahmakumaris Rajyoga meditators with open eyes and during listening to music by controls as the comparable task.

Common and distinct patterns were observed in distinct frequency bands in meditators and control groups. Node-degree strength was consistently higher in meditators than controls in theta band.

Synergy and redundancy of the brain during different non-ordinary states of consciousness.

This is a multicentric study on three different NSCs: Rajyoga meditation (RM), hypnosis, and self-induced cognitive trance (SICT).

Synergistic and redundant information measures were used to compare and contrast the higher-order interactions during three NSCs.

The synergy of the brain increased during RM and decreased during hypnosis and SICT, and redundancy decreased during RM.

The pattern of changes observed in the synergy and redundancy values of each NSC is defined by the phenomenology of the NSC, including changes in the sense of self, environmental awareness, altered sensory perception, and selective attention.

Respiration-entrained brain oscillations during slow-paced breathing.

Coherence between the cortical activity (EEG) and respiration were analyzed during baseline and slow-paced breathing at six cpm guided by visual metronome.

Significant coherence between respiration and EEG was observed, with no common localization across subjects. However, the coherence further increased during the slow-paced breathing at six cpm.

Phase-amplitude coupling showed distinct patterns during baseline and slow-paced breathing in specific EEG frequency bands.

The modulation index increased during slow-paced breathing compared to baseline, supporting the link between respiration and brain activity and providing possible insight into the benefits of therapeutic breathing exercises like pranayama.

—————— ALL ARE WELCOME —————

Research Admission 2023
2023-05-22 13:30 - 2023-05-27 22:30 | EE, IISc
Info_EE_ResInterview_2023
Choice of research areas of 2023

Eateries:- Click here(Nesara), Click here (Sarvam)
