EE

PhD Thesis Defense of Mr. Siddarth Asokan
2023-09-15 09:30 - 11:00 (Asia/Kolkata) | MMCR, Hall C 241, 1st floor, EE department
Name of the Candidate: Mr. Siddarth Asokan
Ph.D. Supervisor: Prof. Chandra Sekhar Seelamantula (EE)

External Examiner: Prof. Santanu Chaudhury (Director, IIT Jodhpur; Professor, IIT Delhi)

Title of the Thesis: On the Optimality of Generative Adversarial Networks — A Variational Perspective

Date & Time: September 15, 2023; 9.30 AM (Coffee will be served during the defense)
Venue: Multimedia Classroom (MMCR), Department of Electrical Engineering, IIScAbstract:Generative adversarial networks are a popular generative modeling framework, where the task is to learn the underlying distribution of data. GANs comprise a min-max game between two neural networks, the generator and the discriminator. The generator transforms noise, typically Gaussian distributed, into a desired output, typically images. The discriminator learns to distinguish between the target samples and the generator output. The objective is to learn the optimal generator — one that can generate samples that perfectly confuse the discriminator. GANs are trained to either minimize a divergence function or an integral probability metric (IPM) between the data and generator distributions. Common divergences include the Jensen-Shannon divergence in the standard GAN (SGAN), the chi-squared divergence in least-squares GAN (LSGAN) and f-divergences in f-GANs. Popular IPMs include the Wasserstein-2 metric or the Sobolev metric. The choice of the IPM results in a constraint class over which the discriminator is optimized, such as Lipschitz-1 functions in Wasserstein GAN (WGAN) or functions with bounded energy in their gradients as in the case of Sobolev GAN. While GANs excel at generating realistic images, their optimization is not well understood. This thesis focuses on understanding the optimality of GANs, viewed from the perspective of Variational Calculus. The thesis is organized into three parts.In Part-I, we consider the functional analysis of the discriminator in various GAN formulations. In f-GANs, the functional optimization of the loss coincides with pointwise optimization as reported in the literature. We extend the analysis to novel GAN losses via a new contrastive-learning framework called Rumi-GAN, in which the target data is split into positive and negative classes. We design novel GAN losses that allow for the generator to learn the positive class while the discriminator is trained on both classes. For the WGAN IPM, we propose a novel variant of the gradient-norm penalty, and show by means of Euler-Lagrange analysis, that the optimal discriminator solves the Poisson partial differential equation (PDE). We solve the PDE via Fourier-series approximations and involving radial basis function (RBF) expansions. We extend the approach to image generation by means of latent-space matching in Wasserstein autoencoders (WAE). We also present generalizations to higher-order gradient penalties for the LSGAN and WGAN losses, and show that the optimal discriminator can be implemented by means of a polyharmonic spline interpolator, giving rise to the name PolyGANs. PolyGANs, implemented by means of an RBF discriminator whose weights and centers are evaluated in closed-form, results in superior convergence of the generator.In Part-II, we tackle the issue of choosing the input distribution of the generator. We introduce Spider GANs, a generalization of image-to-image translation GANs, wherein providing the generator with data coming from a closely related/“friendly neighborhood” source dataset accelerates and stabilizes training, even in scenarios where there is no visual similarity between the source and target datasets. Spider GANs can be cascaded, resulting in state-of-the-art performance when trained with StyleGAN architectures on small, high-resolution datasets, in merely one-fifth of the training time. To identify “friendly neighbors” of a target dataset, we propose the “signed Inception distance” (SID), which employs the PolyGAN discriminator to quantify the proximity between datasets.In Part-III, we extend the analysis performed in Part-I to GAN generators. In divergence-minimizing GANs, the optimal generator matches the gradient of its push-forward distribution with the gradient of the data distribution (known as the score), linking GANs to score-based Langevin diffusion. In IPM-GANs, the optimal generator performs flow-matching on the gradient-field of the discriminator, thereby deriving an equivalence between the score-matching and flow-matching frameworks. We present implementations of flow-matching GANs, and develop an active-contour-based technique to train the generator in SnakeGANs. Finally, we leverage the gradient field of the discriminator to evolve particles in a Langevin-flow setting, and show that the proposed discriminator-guided Langevin diffusion accelerates baseline score-matching diffusion without the need for noise conditioning.
Biography of the Candidate: Siddarth Asokan received a Bachelor of Engineering (B.E.) degree in 2017 with a specialization in Electronics and Communication Engineering from M.S. Ramaiah Institute of Technology, Bangalore. During 2016–2017, he worked in Robert Bosch Centre for Cyber-Physical Systems (RBCCPS) as a Project Intern on the Smart Cities Project. Subsequently, he joined RBCCPS as a direct PhD student in 2017 working under the guidance of Prof. Chandra Sekhar Seelamantula, and has since been with the Spectrum Lab, Department of Electrical Engineering. He received the Microsoft Research Fellowship in 2018, the Qualcomm Innovation Fellowship in 2019, 2021, 2022, and 2023 and the RBCCPS PhD Fellowship in 2020 and 2021. He is also a recipient of the Best Presenter Award at the AI/ML track of the IISc EECS Symposium 2023, and has been selected to present his PhD research at the Doctoral Consortium at the British Machine Vision Conference, 2023. His research interests are in signal processing, image processing and machine learning, focusing on building mathematical foundations of generative learning frameworks.
All are invited.

Cancelled [Talk] Prof Neetesh Saxena Cardiff University UK
2023-08-25 15:00 - 17:00 | MMCR, Hall C 241, 1st floor, EE department
Cancelled *

Due to some unavoidable circumstances, we are cancelling the talk by Prof Neetesh Saxena of Cardiff University scheduled today. Sorry for any inconvenience this may cause.

Title: Cyber-physical Smart Grid Situational Awareness

Speaker:

Prof Neetesh Saxena
Senior Lecturer
School of Computer Science and Informatics
Cardiff University, UK

Date: 25th August 2023, 3 pm

Venue: MMCR, EE Dept, IISc

Abstract:
In recent years, the impact evaluation of cyber-physical security of the smart grid has become highly notable and extremely important and critical research direction due to several recent cyber-attacks attempts in different countries. The smart grid is vulnerable to cyber-attacks due to its integration with communication and control technologies. The cyber-attacks can affect operations and decision-making at the energy management system or effectively destroy the critical components, even shut down the power operations and can disrupt service for its customers. Inaccurate information leads to triggering inappropriate actions by the operators. The cyber-attacks either can directly target a power component of the smart grid system or can be triggered through the communication network to the power system. In this talk, I will discuss cyber security issues with a case study to explore the cyber-physical situational awareness.

Biography:
Prof Neetesh is a Senior Lecturer with the School of Computer Science and Informatics at Cardiff University, UK having 16+ years of professional experience. Previously, he had affiliations with the Bournemouth University (UK), Georgia Institute of Technology (USA), Stony Brook University (USA) and SUNY Korea. He was a DAAD Scholar at Bonn-Aachen International Center for Information Technology (B-IT), Rheinische-Friedrich-Wilhelms Universität, Bonn (Germany) and was also a TCS Research Scholar. His current research interests include cyber security and critical infrastructure security, including cyber-physical system security: smart grid, V2G and cellular communication networks.

EE PhD Colloquium on Imaging Inverse problems
2023-08-23 11:00 - 13:00 | MMCR, Hall C 241, 1st floor, EE department
Title : Improved Derivative based Regularizations for Imaging Inverse problems
Student : Manu GhulyaniAdvisor : Prof. Muthuvel Arigovindan
Date and Time: 23.08.2023 (Wednesday), 11 am.
Venue : MMCR, Department of Electrical Engineering
Abstract:
Images undergo degradation during the capturing process due to physical limitations inherent to the capturing devices. Addressing this degradation and recovering high-quality images constitute the image recovery problem, a crucial concern with diverse applications across various fields such as biology, astronomy, and medicine. The enhancement of captured image resolution significantly influences these disciplines. Examples of this problem include tasks like reconstructing computed tomography images, magnetic resonance imaging, image deconvolution, and microscopic image reconstruction.
Image recovery is frequently approached using regularization techniques, with derivative-based regularizations being popular due to their ability to exploit image smoothness, yielding interpretable results devoid of introduced artifacts. Total Variation regularization (TV), proposed by Rudin, Osher, and Fatemi, is a seminal approach for image recovery. TV involves the norm of the image’s gradient, aggregated over all pixel locations. As TV encourages minimal values in the derivative norm, it leads to piece-wise constant solutions, resulting in what is known as the “staircase effect.” To mitigate this effect, the Hessian Schatten norm regularization (HSN) employs second-order derivatives, represented by the pth norm of eigenvalues in the image hessian vector, summed across all pixels. HSN demonstrates superior structure-preserving properties compared to TV. However, HSN solutions tend to be overly smoothed. To address this, we introduce a non-convex shrinkage penalty applied to the Hessian’s eigenvalues, deviating from the convex lp norm. While the analytical form of this penalty was unknown, we derived the algorithm using proximal operations. We established that the proposed regularization adhered to restricted proximal regularity, ensuring the algorithm’s convergence. The images recovered by this regularization were sharper than the convex counterparts.
In the subsequent work, we extend the concept of the Hessian-Schatten norm. By encompassing Schatten norms of the Hessian and introducing a smoothness constraint, we broaden the scope of Hessian Schatten norm. The resulting regularization can be derived as a Lagrange dual of the Hessian Schatten norm, akin to the total generalized variation. Furthermore, we present an efficient variable splitting scheme for solving image restoration challenges.
Total Generalized Variation (TGV) represents an important generalization of Total Variation. TGV involves multiple orders of derivatives, with higher-order TGV leading to improved recovered image quality. This enhancement has been validated through numerical experiments in image denoising. Consequently, a demand arises for an algorithm capable of solving TGV for any order. While various methods address TGV regularization, many are confined to second-order TGV, and only a few explore orders greater than three for image recovery with TGV regularization. To our knowledge, no algorithm resolves image recovery challenges employing TGV regularization for orders exceeding three under a general forward model. This challenge arises from the intricate nature of TGV representation. We surmount this obstacle by presenting two simple matrix based representations of TGV: the direct and compact forms. We prove the equivalence of both forms with the original TGV definition. Leveraging the compact representation, we propose a generalized ADMM-based algorithm to solve TGV regularization for any order.
ALL ARE WECOME.

[Talk]: Prof Ramakrishna Gokaraju University of Saskatchewan, Canada
2023-08-18 16:00 - 17:30 | MMCR, Hall C 241, 1st floor, EE department
Talk Title: Future Clean Power and Energy Systems: Co-generation with New Nuclear Based Small Modular Reactors (SMRs) and Renewable Energy for Electricity and Energy Applications

Speaker:

Prof Ramakrishna Gokaraju, PhD, PEng
Professor, Department of Electrical & Computer Engineering
Associate Dean, Graduate Studies & Special Projects, College of Engineering
University of Saskatchewan, Canada

Date: 18th August 2023, 4 pm

Venue: MMCR, EE Dept, IISc

Abstract:
Small modular reactors (SMRs)—a fast-emerging nuclear power plant technology—and renewables hold significant promise for the development of future clean energy systems. They are suitable for large grids as well as remote load centers and offer load following and frequency response capabilities. This talk will first provide a background of this technology and recent developments related to this technology. Followed by this it will describe a dynamic model of an integral pressurized water reactor (iPWR)-type SMR and studies assessing the contribution of the reactor to the electrical side dynamics. SMRs with their faster response rates, along with intermittent renewable energy sources (RESs) could be effectively used to develop sustainable clean energy systems. The talk will also describe a dynamic simulation model developed in our lab showing how SMRs along with renewable energy could be used for electricity and district heating.

Biography:
Prof. Ramakrishna Gokaraju received his Bachelor of Engineering degree (with Distinction) in Electrical and Electronics Engineering from the National Institute of Technology, Trichy, India in April 1992. He received the M.Sc. and Ph.D. degrees in electrical and computer engineering from the University of Calgary, Canada, in 1996 and 2000, respectively. He worked with IBM Toronto Lab from 2000-‘22. Prof. Gokaraju joined the Electrical & Computer Engineering Department, University of Saskatchewan in 2003 and is a professor in the department of electrical & computer engineering. He is also currently the Associate Dean of Graduate Studies and Special Projects in the College of Engineering. His current research works are on power system protection, smart grids and nuclear-based “SMRs” for power and energy applications. He currently serves the Natural Sciences and Engineering Council of Canada (NSERC) Evaluation Group in Electrical and Computer Engineering. His research is funded by the NSERC Discovery Grant and the Canadian Nuclear Safety Commission (CNSC).

[Talk] Prof Nando Ochoa of University of Melbourne 17th Aug 4 PM MMCR EE
2023-08-17 16:00 - 17:30 | MMCR, Hall C 241, 1st floor, EE department
Title: “Smart Meter-Driven Approaches for PV-Rich Low Voltage Network Modelling, Operation and Planning”

Speaker: Prof Nando Ochoa Pizzali

Affiliation: University of Melbourne, Australia

Venue: MMCR, EE Dept

Date: 17th Aug 2023

Time: 4:00 PM

Abstract: Residential solar PV is installed behind the meter of mainly single-phase customers connected to three-phase low voltage (LV) feeders (e.g., 400V line-to-line). This means that for distribution companies to adequately quantify the impacts from reverse power flows due to excess solar PV generation, the corresponding electrical models are required. These models are critical when calculating voltages given the non-linear and unbalance nature of LV feeders. However, the task of producing electrical models of thousands of LV feeders is already a significant challenge for distribution companies around the world, which, in turn, makes the operation and planning of PV-rich LV networks even more challenging. It is in this context that the exploitation of historical smart meter data can not only help distribution companies with their modelling tasks but also provide radical alternatives to how they operate and plan future PV-rich LV networks.

This talk presents and discusses the findings of three advanced smart meter-driven approaches using realistic case studies from Victoria, Australia. The first enhances LV models. Using simplified three-phase voltage drop equations and multiple linear regression, it is able to estimate three-phase and single-phase line impedances which, in turn, allows for the quick and accurate calculation of customer voltages for operational purposes. The second, a more radical approach, goes model-free. It demonstrates that neural networks can be trained to capture the physics of three-phase LV feeders with dozens of single-phase customers; making it possible to have fast and accurate voltage calculations. The last one, from a planning perspective, also demonstrates that regression techniques and data from early solar PV penetrations can be used to quickly estimate the hosting capacity of LV networks without the need for complex and detailed network studies.

Bio: Prof Nando is a Professor of Smart Grids and Power Systems at The University of Melbourne, Australia, and Chief Scientist & Co-Founder at VoltMind. He is an IEEE PES Distinguished Lecturer, an Editorial Board Member of the IEEE Power and Energy Magazine, and an IEEE Senior Member. Previously, from 2011 to 2021, he was full and part-time with The University of Manchester, UK. From 2007 to 2010 he was a Research Fellow in Energy Systems at the University of Edinburgh, UK. In 2010, he also undertook an industrial secondment with the Edinburgh-based company Psymetrix Ltd (part of GE). He hold a Bachelor’s degree in Mechanical and Electrical Engineering received from UNI (Peru), and a Research MSc and a PhD in Electrical Power Engineering, both received from UNESP Ilha Solteira (Brazil).

Ph.D. Thesis Oral Defense of Mr. Anoop C. S.: Automatic speech recognition for low-resource Indian languages
2023-08-11 15:00 - 17:00 | MMCR, Hall C 241, 1st floor, EE department
Name of the student: ANOOP C. S.

Advisor: Prof. A. G. Ramakrishnan & Dr. G. N. Rathna

External examiner: Prof. Umesh S, Dept of EE, IIT Madras

Date and Time: 11 August 2023 (Friday) 3:00 PM

Venue (hybrid): MMCR, C 241, First Floor, Dept. of EE
AND

Microsoft Teams meeting link:

Join conversation
teams.microsoft.com

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. This thesis exploits 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:

The use of a common set of tokens is proposed across multiple Indian languages and their performance analyzed in mono and multilingual settings.

It is found that the Sanskrit Library Phonetic Encoding (SLP1) tokens, which exploit the pronunciation-based structuring of character Unicodes in Indian languages, perform better than 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):

Three different low-resource settings have been studied:

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

The adversarial training with gradient reversal layers (GRL) and domain separation networks (DSN) provide word error rate (WER) improvements of 6.71% and 7.32%, respectively, on 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 speech data and has some amount of text data to build language models. In this case, available data in high-resource languages is used 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 test 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. In this case, the usefulness of model-agnostic meta-learning (MAML) pre-training is established for Indian languages and improvements are proposed 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.
Text-similarity measured through cosine and Mahalanobis distances is used to weigh the losses during MAML pretraining. It yields a mean absolute improvement of 1% in WER.

ALL ARE WELCOME ONLINE!
Meeting Recording

https://ee.iisc.ac.in/wp-content/uploads/2023/08/Anoop-PhD-Viva-20230811_150431-Meeting-Recording_Cut.mp4

PhD Colloquium - 8 August: Resource-Aware State-Triggered Control of Networked Control Systems
2023-08-08 11:00 - 12:00 | MMCR, Hall C 241, 1st floor, EE department
Speaker: Anusree RajanSupervisor: Pavankumar TallapragadaDate and Time: Tuesday, 8 August 2023, at 11amVenue: MMCR (C241), EE DepartmentTitle: Resource-Aware State-Triggered Control of Networked Control SystemsAbstract:Networked control systems are very popular nowadays, with different fields of applications such as environmental monitoring, industrial automation, military surveillance, and disaster management. Event- or self-triggered control is a commonly used control method in the field of networked control systems owing to its advantage of efficient utilization of resources while simultaneously achieving control objectives. In these control methods, the communication times are opportunistic and implicitly determined by a triggering rule. Thus, understanding inter-event times generated by a triggering rule is necessary for higher level planning and scheduling for control over shared or constrained resources as well as for analytical quantification of the usage of communication or other resources compared to a time-triggered controller.In the first part of this thesis, we provide a systematic way to analyze the evolution of inter-event times in planar linear systems, under a general class of scale-invariant event triggering rules. We provide a sufficient condition for the convergence or non-convergence of inter-event times to a steady state value. We also provide a sufficient condition for the asymptotic average inter-event time to be a constant for all non-zero initial states of the system. Then, under a special case, we comment on the asymptotic behaviour of the inter-event times, including on whether the inter-event times converge to a periodic sequence. Later, we extend our analysis of inter-event times to linear systems under region-based self-triggered control. In this control method, the state space is partitioned into a finite number of conic regions and each region is associated with a fixed inter-event time. We provide several necessary conditions and sufficient conditions for the local convergence of inter-event times to a constant or to a given periodic sequence.In the second part of this thesis, we consider a design problem. Most of the existing event- or self-triggered controllers are designed using sampled-data zero-order-hold (ZOH) control input. However, many communication protocols used in networked control systems, such as TCP and UDP, have a minimum packet size. So, ZOH control may lead to under-utilization of each packet while also increasing the number of communication instances. On the other hand, use of non-ZOH control leads to better utilization of the minimum payload of each packet while also reducing the overall number of communication instances. With these motivations, we propose a new control method called event-triggered parametrized control (ETPC). In this control method, between two consecutive events, each control input to the plant is a linear combination of a set of linearly independent scalar functions. At each event, the coefficients of the parameterized control input are chosen to minimize the error in approximating a continuous time control signal and then they are communicated to the actuator. We, first, showcase this method by focusing on the specific problem of stabilization of linear systems. We design two event-triggering rules that guarantee global asymptotic stability of the origin of the closed loop system under some conditions on the model uncertainty. Later, we use a similar idea to propose an event-triggered polynomial control method for trajectory tracking of unicycle robots. We design an event-triggered parametrized controller for trajectory tracking by a unicycle robot and provide guarantees for uniform ultimate boundedness of the tracking error.Due to time limitations, the colloquium will focus on the first part of the thesis.——————————- All are Welcome —————————-

Orientation Program 2023
2023-08-03 15:30 - 17:30 | B308,2nd floor, EE Dept.
Dear All
A warm welcome to all the new Masters and Research students!
The department of EE is flourishing with a broad spectrum of research activities and multiple master’s programs. Being a proud member of the department, you may be eager to know more about the department and the courses.
For this, a Welcome/Orientation program is planned for the 3rd (Thursday) at 3:30 pm. The venue for the program is classroom 308 on the second floor. It will start with a brief history and current activities. Followed by a presentation by the DCC Chair about the course requirement for the masters and research students. Then we wish to know about you!
Coffee & Tea will be served at the end.
Please attend the event without fail.

Technical Talk by Dr. Amristanshu Pandey, University of Vermont, USA
2023-08-03 15:00 - 17:00 | MMCR, Hall C 241, 1st floor, EE department
IEEE PES Student Branch Chapter, IISc has organized a technical talk on 3rd August 2023 at 3 PM IST. Please find the talk details as below.

Title: Homotopy-based Circuit-theoretic Optimizations for Power Grids

Speaker: Dr. Amritanshu Pandey, University of Vermont, USA

Date and Time: 3rd August 2023, 3 PM ISTVenue: MMCR Electrical Engineering Department, IISc, Bengaluru.

Abstract: Optimizations are at the center of many power grid analyses. With rapid decarbonization and electrification, the scale and complexity of these optimizations are increasing. Current state-of-the-art tools for nonconvex optimization are not always robust at solving these emerging power grid problems. In this talk, I will discuss a novel domain-focused circuit-theoretic optimization method for power grids. In this approach, the first-order optimality conditions for an optimization problem are mapped into a set of equivalent circuits, the response for which represents a problem’s stationary point. As these equivalent circuits are large and highly nonlinear, we developed and used a novel homotopy method: Incremental Model Building (IMB). I will cover this homotopy in this talk and show that this method can also be extended to optimize for discrete control variables in the grid. I will conclude the talk by showing pertinent results and comparisons against a state-of-the-art method that won the recent ARPA-E grid optimization challenge.

Speaker Bio: Amrit Pandey is an Assistant Professor in the Electrical and Biomedical department at the University of Vermont with a courtesy appointment in the Electric and Computer Engineering and Engineering and Public Policy departments at Carnegie Mellon University. His overarching research goal is to develop electric energy system technologies to help combat climate change while modernizing the underlying system. In the past, he and his team developed a novel circuit-theoretic simulation and optimization framework for power grids. The project culminated in a new grid analytics tool: Simulation of Unified Grid Analysis and Renewables (SUGAR), which Pearl Street Technologies, Inc. has since commercialized. This work has won several best paper awards, including two best-of-the-best paper awards at IEEE PES General Meeting in 2017 and 2021. His current research focuses on developing computational methods that address problems in the space of large-scale grid simulation and optimization, grid cybersecurity, and energy inequity.

Time Forecasting of COVID-19 Signals: Challenges and Model Development
2023-07-31 16:00 - 17:30 | MMCR, Hall C 241, 1st floor, EE department
Title: Real-Time Forecasting of COVID-19 Signals: Challenges and Model Development
Speaker: Dr. Aniruddha Adiga, Research Scientist, Biocomplexity Institute at the University of Virginia
Host Faculty: Prof. Chandra Sekhar Seelamantula, EE, IISc
Venue: Multimedia Classroom (MMCR), Department of Electrical Engineering, Indian Institute of Science
Date & Time: July 31, 2023; 4 PM onward (Coffee will be served during the talk)
Abstract:
COVID-19 is the largest pandemic the world has seen with approximately 700 million confirmed cases, 8 million confirmed deaths pandemic to date, and unprecedented social, economic, and political impact. During the pandemic, we also observed an extensive development of computational and mathematical models to aid policymakers and response efforts. An essential use of such models is in early warning systems and forecasting of COVID-19 signals. Real-time forecasting of COVID-19 signals is a challenging problem due to data quality issues, nonstationarity of time series, evolving targets, behavioral adaptations, etc. It has been observed that under such circumstances, ensemble models consisting of a diverse set of model classes are a better choice than individual models.
In this talk, I will discuss our efforts toward the development of an ensemble model consisting of statistical, deep learning, and compartmental models and our participation in national-level collaborative forecasting efforts. Through these efforts we have observed that all classes of models are important, however, different model classes performed differently during various phases of the pandemic. Armed with this understanding, I will present a modification to the ensembling method to employ this phase information and use different weighting schemes for different phases to produce improved forecasts. However, predicting the phases of the time series is another challenge, especially when behavioral and immunological adaptations govern the evolution of the time series. I will discuss a phase prediction algorithm that employs auxiliary datasets and transfer entropy techniques. We evaluate our model’s performance with other models in the collaborative effort.
Biography of the speaker:

Aniruddha Adiga is a research scientist at the Biocomplexity Institute at the University of Virginia. His interests are in signal processing and machine learning with a current focus on the development of forecasting models. From May 2018 to May 2019, he was a postdoctoral associate at North Carolina State University. He received his PhD from the Department of Electrical Engineering at the Indian Institute of Science. Aniruddha has published in top venues such as KDD, AAAI, IJCAI, BigData, etc. His paper in IEEE BigData 22 received the “Best Paper” award. His work also supports public health agencies such as the US CDC, EU CDC, and the Virginia Department of Health.

Technically co-sponsored by IEEE Signal Processing Society, Bangalore Chapter

[Thesis Defense Talk - Shreyas Ramoji, 28/7 @430pm, MMCR, EE] - "Supervised Learning Approaches for Language and Speaker Recognition"
2023-07-28 16:30 - 17:30 | MMCR, Hall C 241, 1st floor, EE department
Thesis Defense Talk

Venue: MMCR, EE
Date: 28/7/2023
Time: 4:30pm [High Tea at 4:15pm]
Speaker: Shreyas Ramoji

Title: Supervised Learning Approaches for Language and Speaker Recognition

Abstract:
In the age of artificial intelligence, one of the important goals of the speech processing research community is to enable machines to automatically recognize who is speaking and in what language.

In the first part of this talk, I will discuss our efforts towards a supervised version of the generative model based embedding extractor for speaker and language recognition. We call the embeddings from this supervised approach as s-vectors. In this approach, a database of speech recordings (in the form of a sequence of short-term feature vectors) is modeled with a Gaussian Mixture Model, called the Universal Background Model (GMM-UBM). The deviation in the mean components is captured in a lower dimensional latent space, called the i-vector space, using a factor analysis framework. In our research, we propose a fully supervised version of the i-vector model, where each label class is associated with a Gaussian prior with a class-specific mean parameter. The joint prior (marginalized over the sample space of classes) on the latent variable becomes a GMM. With detailed data analysis and visualization, we show that the s-vector features yield representations that succinctly capture the language (accent) label information and also perform significantly improved the recognition of various accents of the same language.

In the second part of the talk, I will discuss our efforts for the problem of fully supervised end-to-end speaker verification, where a binary decision has to be made whether a pair of recordings belong to the same speaker or not. We proposed a neural network approach for back-end modeling, where the likelihood ratio score of the generative probabilistic discriminative analysis (PLDA) model is posed as a discriminative similarity function, and the learnable parameters of the score function are optimized using a verification cost. The proposed model, termed as neural PLDA (NPLDA), is initialized using the generative PLDA model parameters. The loss function for the NPLDA model is an approximation of the minimum detection cost function (DCF) used as one of the evaluation metrics in various speaker verification challenges. The speaker recognition experiments using the NPLDA model are performed on the speaker verification task in the VOiCES datasets as well as the SITW challenge dataset. Further, we explore a fully neural approach where the neural model outputs the verification score directly, given the acoustic feature inputs. This Siamese neural network (E2E-NPLDA) model combines embedding extraction and back-end modeling into a single processing pipeline. Several speaker recognition experiments were performed on benchmark datasets where the proposed N E2E-NPLDA models are shown to improve significantly over the then state-of-art system.

I will conclude the talk by highlighting some of the noteworthy approaches that were published during the course of this research work, and identifying some important research directions related to this thesis that can be pursued in the future.

Bio:
Shreyas Ramoji is a Research Associate at the Learning and Extraction of Acoustic Patterns (LEAP) Laboratory, Department of Electrical Engineering, Indian Institute of Science, Bengaluru. He obtained his Bachelor of Engineering degree from the Department of Electronics and Communication Engineering, PES Institute of Technology, Bangalore South Campus. His research interests include speaker and language recognition, diarization, representation learning for multilingual and conversational speech, ML/AI applied to healthcare and the environment, natural language processing, explainability and interpretability of neural networks, and neuro-symbolic AI.

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EE PhD Thesis Colloquium -- Francis C Joseph -- 28th July, 4 PM
2023-07-28 16:00 - 17:30 | B 303 (Old 311), Dept. of Electrical Engineering (Hybrid Mode)
Title: Parallel Algorithms for Efficient Utilization of Multiprocessor Architectures in Power System ApplicationsSpeaker: FRANCIS C JOSEPH . of Ph.D. (Engg) in Electrical Engineering under Electrical EngineeringDate/Time: Jul 28 / 16:00:00Location: Room 303, 2nd Floor, EEResearch Supervisor: Dr. Gurunath GurralaAbstract:Computer hardware capabilities have been enormously increasing over the years. Multi-core processors, graphic processing units (GPUs), and field programmable gate array (FPGA) accelerators have seen significant growth in recent years. They have opened new computational paradigms such as edge computing, fog computing, grid computing, distributed computing, cloud computing, and exascale supercomputing. However, efficient utilization of most of these computational paradigms in traditional engineering disciplines such as power engineering is very challenging. In this thesis, we develop efficient algorithms for multiprocessor-based high-performance computing and edge computing platforms for two power system applications, power system stability assessment and power quality measurements respectively.Faster than real-time transient stability assessment of large power grids using time-domain simulations with detailed models is comp utationally challenging. Today the commercial tools being used for this application in Energy Management Systems (EMS) across the world rely on parallel batch processing methods which don’t utilize the architecture of the computational paradigms efficiently. In this thesis for transient stability simulations, we explore a time parallel algorithm, Parareal in Time, which belongs to a class of temporal decomposition methods for time parallel solutions of differential equations. Two effective implementation approaches, Master Worker and Distributed, are analysed for large systems, and scaling tests are performed using a state space model with a Message Passing Interface (MPI) in a multiprocessor environment. One of the findings was that the performance of the Parareal depends on the accuracy and the computational cost of the coarse solver used for initialization and subsequent correction steps. A potential coarse solver, Modified Euler (ME), a well-known solver for transient stability simulations even in commercial packages, has been explored to adapt its step size by controlling the Local Truncation Error (LTE) to achieve the desired accuracy. An LTE estimator using a Multistage Homotopy Analysis Method (MHAM), which gives an approximate solution to a set of non-linear equations in the form of a power series, is proposed to control the LTE at each integration step to enable adaptation of the ME step size. The proposed MHAM-assisted adaptive ME solver is found to be faster with comparable accuracy when compared to the conventional fixed and adaptive Modified Euler solver for large systems transient stability simulations. Since MHAM is lighter than the ME solver and LTE estimate is sufficient for step size adaptation, an adaptive MHAM coarse solver is proposed for the Parareal. However, MHAM provides a non-zero auxiliary parameter `c’ to select a family of solutions. Hence, an optimisation framework is also proposed to select this parameter based on the system’s dynamics automatically. Based on many case studies on test systems of different sizes, it is found that maintaining the LTE lower than the Parareal convergence tolerance improves the speedup of the Master-Worker paradigm, however for the distributed implementation maintaining LTE higher than the convergence tolerance gives improved speedup. An approach to include unscheduled events which arise in power system operation due to the operation of protective relays is also proposed for Parareal.In Parareal implementation, each coarse time segment is assigned to one processor in the MPI environment. In order to improve speedup, multiple processors in a node is to be assigned to a coarse time segment. Therefore, a shared memory-based space parallel transient stability solver is also considered for further performance enhancement. Space parallelisation of transient stability simulation involves breaking the network into subnetworks and solving each part independently while ensuring the original network’s convergence. Therefore, a Multi Area Thevenin Equivalent (MATE) based parallel solver implementation on a shared memory platform is proposed in which both the space parallelisation and task parallelisation are explored. It is shown that the ideal speedup can be closely matched by the space parallelism and can be exceeded by space + task parallelism while the network is well-partitioned and it can be further improved when combined with time parallelism. The current state-of-the-art chips provide multicore architectures for edge computing applications also. One such low-cost, open-source, heterogeneous, resource-constrained hardware platform is called “Parallella”. The unique hardware architecture of the Parallella provides many edge computing resources in the form of a Zynq SoC (dual-core ARM + FPGA) and a 16-core co-processor called Epiphany. This Parallella device was used as a measurement device for edge computing applications research in smart grids which could sample 3 voltages and 4 currents at 32 kHz sampling rate. One application of such a device to measure the harmonics and compute various Power Quality (PQ) indices is explored in the thesis. In this regard, we have developed a parallel implementation of multichannel FFT on Epiphany for the streaming data. Epiphany 16-core architecture has very limited memory resources and the order in which the cores are to be accessed has a significant impact on the execution. Proper decomposition of the FFT algorithm tasks and scheduling of the tasks for efficient core and memory usage are crucial which requires a good understanding of the Epiphany architecture. The obtained PQ measurements from the proposed implementation are found to be comparable to commercial power analyser measurements.Acknowledgements: This work is funded by the • SERB Science and Technology Award for Research (SERB-STAR) grant, File No: STR/2020/000019 titled Hybrid Parallel Solvers for Faster than Real-time Transient Stability Analysis of Large Power Grids. • Bosch Research and Technology Centre, Bangalore, India and by the Robert Bosch Centre for Cyber-Physical Systems, Indian Institute of Science, Bangalore, India (under Project E-Sense: Sensing and Analytics for Energy Aware Smart Campus) • DST Young Scientist Grant DST-YSS/2015/001371, IndiaMeeting Link :

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[Colloquium EE 27 Jul 2023] Dual Mode Operation of Grid-tied Inverters: Modeling, Stability Analysis, and Islanding Detection
2023-07-27 10:30 - 12:30
Dual Mode Operation of Grid-tied Inverters: Modeling, Stability Analysis, and Islanding DetectionSpeaker: SUGOTO MAULIK . of Ph.D. (Engg) in Electrical EngineeringDate/Time: Jul 27 / 10:30:00 amLocation: MMCR EE, IIScResearch Supervisor: Vinod JohnTeams link.Abstract:Increased penetration of renewable energy sources like solar PVs and wind is fundamentally altering the power flow dynamics in distribution networks. These localized forms of generation add redundancy to the power system and increase its load-handling capacity. However, these advantages come at the cost of reduced stability and altered protection requirements. These distributed forms of generation (DGs) are interfaced with the power grid via power electronic converters operating at high bandwidths compared to conventional sources. While these offer higher performance, but consequently lower the stability margins. An analytical framework is thus necessary for modeling and stability analysis of such systems. The dynamics involved in modeling a grid-tied DG system span a wide spectrum of frequencies. While simplified modeling can lead to inaccuracies, an all-inclusive model leads to an over-complicated and unintuitive model. This work proposes a systematic approach to model the behavior of 3-phase grid-tied DG systems using dynamic phasors. Dynamic phasors allow for a state-space representation of the relevant dynamics. The developed state space model is then used for the following:1. Islanding detection: Islands are formed in 3-phase distribution networks when an active distributed generation (DG) is disconnected from the grid. If undetected, the DG continues to energize its local loads leading to safety concerns. In this work, a state-feedback approach is developed for islanding detection, which places a system pole in the right half plane (RHP). This ensures the destabilization of the islanded network and a zero non-detection zone. The scheme is designed and implemented experimentally.2. Transfer of Control: Post-island detection, the DG is required to disconnect from the grid while ensuring uninterrupted power flow to its local loads. A control scheme involving a voltage control loop and grid current feed-forward is developed to achieve a fast transfer from grid-following to grid-forming mode of operation. The introduced voltage control loop ensures that rated voltage is maintained across the loads, and the grid current feed-forward is used to minimize the transients during the transfer process. The method is designed and implemented in conjunction with the islanding detection scheme and verified experimentally with local loads.3. Stability analysis of grid-tied DG systems: Owing to the formation of microgrids and weak grids in the distribution network, the stability assessment of such networks becomes essential. This assessment is performed by extending the dynamic phasor-based model for islanded systems to model grid-tied systems as well. The developed model includes the dynamics of the PLL, grid, DG current levels, and load. In addition to passive loads, considered in the relevant literature, the proposed model also incorporates the effect of constant power and constant current type power electronic loads. It is demonstrated, analytically and experimentally, that the presence of local loads has a stabilizing impact on the synchronization stability of a DG. Additionally, an upper limit on the bandwidth of power-electronic type constant power loads is derived, affirming the observation that high bandwidth loads lead to reduced system stability.All the proposed methods are validated on hardware prototypes that have been developed as a part of the work.Meeting Link :

Join conversation
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PhD Thesis Colloquium of student, Kamisetti Prasad
2023-07-26 09:00 - 23:00 | MMCR, Hall C 241, 1st floor, EE department
PhD Thesis Colloquium
Title: “Modeling, Design and Control of Power-Electronic-Actuated Electromagnetic Bearings”
Speaker: Kamisetti N V Prasad,
Department: Electrical Engineering
Supervisor: Prof. G. Narayanan
Date and Time: 26 July 2023 (Wednesday), 9 am – 10 am
Venue: MMCR, EE Department
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Abstract
Many practical electrical machines, turbines, and compressors operate in speeds, ranging from tens of thousands of rpm to hundreds of thousands of rpm, and also, handling a significant amount of power. While high-speed operation reduces the machine dimensions for a given power rating, its challenges include high bearing loss, reduced bearing life, and high viscous drag. Contactless bearings, such as gas, oil, or electromagnetic bearings (EMB), offer longer life than conventional bearings in high-speed applications. In addition to being contactless, an electromagnetic bearing (EMB) is lubrication-free; hence this is suited for both clean conditions (e.g., food and pharmaceutical industry) and hazardous applications (e.g., petroleum and chemical industry). This thesis presents the modelling, analysis, design and control of power-electronic-actuated EMBs. The scope of thesis includes both radial EMB and axial (or thrust) EMB, which handle the radial and axial forces, respectively, acting on the rotor assembly.
Drawing from the switched reluctance machine (SRM) literature, a flux linkages-based modelling approach is proposed for radial and axial EMB. The flux-linkage characteristics can be obtained through either numerical simulation or measurement, and can be used to generate the force vs current vs displacement characteristics of the bearing. Such modelling includes the effects of magnetic saturation, leakage flux and fringing. An improved design procedure is proposed, which guarantees linear force characteristics along with the desired maximum force. A radial EMB and an axial EMB are designed for load capacities of 180 N and 1600 N, respectively, using the improved design procedure and are validated using finite element analysis tools. Further, a modified geometry of the thrust bearing is proposed to reduce the thrust disc diameter (and thereby, to cater to higher rotational speeds), while maintaining the same load capacity. A systematic PID design procedure is presented for the position control of the EMB, guaranteeing the required stability margins. The performance of this controller is validated through simulations using detailed models of EMB.
Position control of the EMB, which is an unstable system, require high-bandwidth control of the EMB coil currents. This, in turn, requires high-switching-frequency power amplifiers to feed the coils. An SiC device-based asymmetric H-bridge converter of 300V, 10 A, with a switching frequency of 50 kHz, is designed and tested. Further, the current controller is designed, and its reference tracking capability is validated experimentally for different types of current references that are expected during the EMB operation. Further, this thesis proposes a novel test rig for thrust-bearing characterization. This test rig can characterize the given thrust bearing under static and dynamic conditions (under rotation and varying loading).
—————— ALL ARE WELCOME —————

[PhD Colloquium Talk by Prachi Singh] - 21-7 in MMCR, EE @ 330-430pm {Graph Clustering Approaches for Speaker Diarization of Conversational Speech}
2023-07-21 11:00 - 13:00 | MMCR, Hall C 241, 1st floor, EE department
Dear All,

We are pleased to invite you to the following PhD colloquium talk,

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Who: Ms. Prachi Singh, PhD candidate, EE.

When: 21/7/2023 at 11AM [Note the updated the time]. High Tea at 1045am

Where: MMCR, EE, IISc and in the Teams Link

What: Graph Clustering Approaches for Speaker Diarization of Conversational Speech

Abstract
In this era of advanced machine intelligence, real-world speech applications need to be equipped to deal with conversations involving multiple speakers. An essential first step in speech information extraction from conversational speech is the task of finding “who spoke when”, also referred to as speaker diarization. The focus of this talk is to describe our efforts in investigating graph clustering techniques for this problem. While graph models have been used in several other domains, its application to temporal segmentation of speech is the first of its kind.

The talk is divided into three main parts. In the first part of this talk, I will describe a novel proposal on self-supervised learning to perform joint representation learning and clustering, called self-supervised clustering (SSC) for diarization. On the learned representations, we explore path integral clustering (PIC), a graph-based clustering algorithm. The PIC is an agglomerative graph clustering method that performs clustering based on the edge connections of a node, called path integral. The proposed SSC with path integral clustering (SSC-PIC) is shown to achieve state-of-the-art performance for benchmark datasets.

The second part of the talk is an extension of SSC-PIC to incorporate metric learning. We design a neural version of the probabilistic linear discriminant analysis (PLDA) approach with learnable parameters to compute a log-likelihood score between embeddings from two segments of the recording. We propose a joint self-supervised representation learning and metric learning approach called Selfsup-PLDA-PIC.

In the third part of the talk, we introduce an end-to-end supervised graph clustering approach. We develop a supervised learning setup using labeled conversational data for training this model. In this setting, we propose a supervised clustering approach called Supervised HierArchical gRaph Clustering (SHARC) for speaker diarization. This approach uses Graph Neural Networks (GNN) to capture the similarity between the speaker embeddings and perform hierarchical clustering. An extension of this work is the joint training of the speaker embedding extractor along with the GNN module, referred to as end-to-end SHARC (E-SHARC). To incorporate overlapped speech detection, I will illustrate how to extend the E-SHARC model for diarization of overlapped speech recordings.

The talk will conclude with a summary of our key contributions, while highlighting the pros and cons of using graph-based models for speaker diarization.

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All are welcome

A 3 Day workshop on electric vehicle power train design on 20 21 22 July 2023
2023-07-20 09:00 - 2023-07-22 17:30
A 3 Day workshop on electric vehicle power train design on 20 21 22 July 2023 at Dept of EE IISc.
The link for workshop :
http://www.nwevtech.com
Poster: IISc EE EV workshop poster

Ph.D. Thesis Colloquium
2023-07-19 14:30 - 17:00
Colloquium Announcement

Candidate’s Name : BABY SINDHU A V
Degree Registered : Ph.D.
Date & Time : 19th July 2023 @ 2.30 PM
Venue : Seminar Hall, High Voltage Lab and in the Teams Link
Title : Development of Polymeric Nano/Micro Composite Insulation with
Better Performance for Various High Voltage Power Applications

Abstract

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

All are welcome

Meeting link

[PhD Colloquium of Akshara Soman, EE on 14/7, 11AM] {Investigating Neural Encoding of Word Learning and Speech Perception}
2023-07-14 11:00 - 13:00 | MMCR, Hall C 241, 1st floor, EE department
Dear All,
Inviting you to the PhD Thesis Colloquium talk with the following details.

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Speaker: Ms. Akshara Soman

Title: Investigating Neural Encoding of Word Learning and Speech Perception

Date & Time : 14-7-2023, 11:00AM
Venue : MMCR (C241), EE, IISc

Research Supervisor: Prof. Sriram Ganapathy, EE.

=================================================================ABSTRACT
Language learning and speech perception are remarkable feats performed by the human brain, involving complex neural mechanisms that allow us to understand and communicate with one another. Unravelling the mysteries of these mechanisms has far-reaching implications, from theories of human cognition to developing effective language learning strategies and advancing speech technology. By employing a multidisciplinary approach encompassing neural investigations using EEG signals, behavioral analyses, and machine learning perspectives, this talk sheds light on the underlying processes involved in word learning and speech perception.

The talk is divided into three parts. The first part begins by examining how an imitation based learning of foreign sounds is captured in the EEG signals. In this listen and reproduce setting, subjects were introduced to words from a foreign language (Japanese), and English. The subjects were also asked to articulate the words. The results show that time-frequency features and phase in the EEG signal contain information for language discrimination. Further analysis showed that speech production improved over time, and the frontal brain regions were involved in language learning. These findings suggest the potential of EEG for personalized language exercises and for assessing learners’ abilities.

The next part of the talk investigates how learning patterns change when semantics are introduced and presented in a sentence context. The participants listen to Japanese words in an English sentence, once before understanding the semantics of these words and later with the semantic exposure. We quantify the learning patterns in the EEG signal. Notably, a delayed P600 component emerges for Japanese words, suggesting short-term memory processing unlike the N400 typically seen for incongruent words in the known language. The brain regions associated with semantic learning are also identified in this study using the EEG data.

In the final part of the talk, we analyze the neural mechanisms of human speech comprehension using a match-mismatch classification of the continuous speech stimulus and the neural response (EEG). We make three major contributions on this front – i) Illustrate the role of word-boundaries in continuous speech comprehension for the first time, ii) Elicit the encoding of speech data (acoustics) as well as the text data (semantics) in the EEG signal, and, iii) Increased signature of semantic content (text) in the EEG data in acoustically challenging environments of dichotic listening. The findings have potential applications for understanding speech recognition in noise, brain-computer interfaces, and attention studies.

In summary, the talk will attempt to enhance our understanding of language learning, speech comprehension, and the neural mechanisms involved.
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ALL ARE WELCOME

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[Oral Examination Talk] - A Learnable Distillation Approach For Model-agnostic Explainability With Multimodal Applications, {Debarpan, EE} [MMCR, EE, 11:00AM, July 12]
2023-07-12 11:00 - 13:00
Title: A Learnable Distillation Approach For Model-agnostic Explainability With Multimodal Applications
Venue: MMCR, C241, EE, IISc, and also in the Teams Link

Date and Time: July 12, 11:00AM.

Speaker: ​ Debarpan Bhattacharya, MTech (Res) EE, 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 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/ViT 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.

​
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All are welcome

M.Tech.(Res.) Thesis Defense of Vishwabandhu Uttam
2023-07-11 15:00 - 17:00 | MMCR, Hall C 241, 1st floor, EE department
Title: A Unified Modeling Approach for Design and Performance Improvement of Triple Active Bridge Converter
Name of the Student: VISHWABANDHU UTTAM, M.Tech (Res) in Electrical Engineering

Research Supervisor: Vishnu Mahadeva Iyer
External Examiner: Anirudh Guha, Assistant Professor, IIT Palakkad

Date/Time: 11th July 2023, Tuesday at 3:00 PM
Location: MMCR (Electrical Engineering)

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.
The popular First 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 thesis defense.
All are welcome.

IEEE PES Talk: Prof. Satyajayant Misra, 10-07-2023, Monday 3PM - 4PM, MMCR, EE
2023-07-10 15:00 - 17:00 | MMCR, Hall C 241, 1st floor, EE department
Title: An Information-Centric Network Architecture for DDoS Protection in the Smart Grid

Time and Time: 3 PM – 4 PM, Monday, 10 July 2023

Venue: MMCR, EE, IISc

Abstract: With the proliferation of differently-abled and heterogeneous devices in the smart grid Denial of Service (DoS) is becoming an even more potent attack vector than it was before. We demonstrate the ease with which an adversary can orchestrate DoS and distributed DoS (DDoS) attacks on the grid. In this talk, we will discuss our proposed architecture iCAD–an information-centric network architecture, and our prior architecture iCAAP, on which iCAD is built. We discuss our architecture in detail and demonstrate the architecture and the mitigation technique’s effectiveness in mitigating significant DoS/DDoS attacks.

Bio: Dr. Satyajayant Misra (Jay) is a professor of computer science and electrical and computer engineering at New Mexico State University (NMSU). He is also the associate dean of research for the College of Engineering. His research expertise is in cybersecurity and computer networking and his recent research interests are in edge computing, future Internet, the smart grid, cryptocurrencies, and decentralized finance. He has over 100 peer-reviewed publications in several prestigious venues, such as ACM CCS, IEEE/ACM Transactions on Networking and Mobile Computing, IEEE/ACM Supercomputing Conference, and IEEE Transactions on Intelligent Transportation Systems. His research has garnered over 7700 international citations and he has an h-index of 26 and an i-10 index of 60. His research has been supported by Intel Labs, US NSF, DoD, DoE, DoEd, and the FAA, and national labs such as Sandia National Lab, LANL, and Idaho National Lab.

[EE Seminar] - Prof. Saikat Chatterjee, KTH - {Data-driven Non-linear State Estimation of Model-free Process in Unsupervised Learning}, Friday, June 30th, 11am, MMCR, EE.
2023-06-30 11:00 - 13:00
The IEEE Signal Processing Society, Bangalore Chapter, and the Electrical Engineering, IISc are happy to host the following talk,

Venue : MMCR (C241), EE, IISc
Time : 11am-12noon
Date : 30-June-2023
Speaker : Prof. Saikat Chatterjee (KTH)

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Title: DANSE: Data-driven Non-linear State Estimation of Model-free Process in Unsupervised Learning Setup
Abstract:
We address the tasks of Bayesian state estimation and forecasting for a model-free process in an unsupervised learning setup. In the seminar, we discuss our new method called DANSE – Data-driven Nonlinear State Estimation method. DANSE provides a closed-form posterior of the state of the model- free process, given linear measurements of the state. In addition it provides a closed-form posterior for forecasting. We show how data-driven recurrent neural networks (RNNs) are used in the DANSE to provide closed-form prior of the state and posterior. The training of DANSE, mainly learning the parameters of RNN, is executed in an unsupervised learning approach. In unsupervised learning, we have access to a training dataset consisting of only a set of measurement data trajectories, but we do not have any access to the state trajectories. Therefore, DANSE does not have access to state information in training data and can not use supervised learning. Using simulated linear and non- linear process models (Lorenz attractor and Chen attractor), we evaluate the unsupervised learning- based DANSE. We show that the proposed DANSE, without knowledge of the process model and without supervised learning, provides a competitive performance against model-driven methods, such as Kalman filter (KF), extended KF (EKF) and unscented KF (UKF), and a recently proposed hybrid method called KalmanNet.
Preprint of the paper: https://arxiv.org/abs/2306.03897
Bio:
Saikat Chatterjee is associate professor at School of Electrical Engineering and Computer Science, KTH-Royal Institute of Technology, Sweden. He received a Ph.D. degree from Indian Institute of Science, India. His website: https://www.kth.se/profile/sach

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​All are welcome,

[CPRI Chair Talk] Prof Sukumar Brahma on 26th June 2023 at 11 am at MMCR, EE Dept
2023-06-26 16:30 - 18:30 | MMCR, Hall C 241, 1st floor, EE department
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Title: Source-Agnostic, Inherently Directional Time-Domain Distance Relay
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Abstract:
There have been reported instances of legacy numerical distance relays failing to identify fault direction when they are fed by an inverter based resource (IBR) – solar, wind or storage. This is largely because the response to fault from an inverter is radically different than the fault response of a traditional synchronous machine. This presentation will explain the reasons for misoperations and introduce design, implementation, testing and validation of a distance relay designed in time domain which avoids polarization problems in numerical legacy relays that operate in phasor domain. It will also report superior performance of the proposed relay when compared to the only time-domain distance relay in the market that operates based on travelling waves.

Bio:
Sukumar Brahma received his Bachelor of Engineering from Gujarat University in 1989, Master of Technology from Indian Institute of Technology, Bombay in 1997, and PhD in from Clemson University in 2003; all in Electrical Engineering. He joined Clemson university as the Dominion Energy Distinguished Professor of Power Engineering in August 2018. He also serves as the director of the industry-funded Clemson University Electric Power Research Association (CUEPRA). Before joining Clemson he was William Kersting Endowed Chair Professor at New Mexico State University, USA. Dr. Brahma has chaired IEEE Power and Energy Society’s Power and Energy Education Committee, Life Long Learning Subcommittee and Distribution System Analysis Subcommittee. He is a member of the Power System Relaying and Control Committee (PSRCC), where he has been contributing to and leading working groups that produce reports, guides and standards in the area of power system protection. He has been an editor for IEEE Transactions on Power Delivery, and served as Guest Editor-in-Chief for the Special Issue on Frontiers of Power System Protection for the journal. His research, widely published and funded by the National Science Foundation, US Department of Energy, utilities, and other government agencies has focused on different aspects of power system modeling, analysis, and protection. Fundamentally, it spans across diverse areas of electrical engineering and computer science, integrating signal processing, machine learning, control and communication to holistically approach the emerging problems in the power and energy domain. Current research, funded by the US Department of Energy, investigates and addresses protection and fault location issues in integration of renewables with power systems and develops new paradigms in protection of smart grid, at both transmission and distribution levels.

Dr. Brahma is a Distinguished Lecturer of the IEEE and CPRI Visiting Chair Professor at IISC in 2022-23. He has been elected IEEE Fellow “for contributions to power system protection with distributed and renewable generation”.

Provisional Research Admission Results 2023
2023-06-21 16:30 - 2023-07-06 22:30
Provisional research admission results 2023
Provisinal Result reserch 2023

Ph. D. Colloquium - Ms. Anwesha Mukhopadhyay: 10.30 am Wed, 21 June 2023: Reduced Electrolytic Capacitor-Based Single-Phase Converters: Topologies, Control, and Stability
2023-06-21 16:00 - 18:30 | MMCR, Hall C 241, 1st floor, EE department
Ph. D. Thesis ColloquiumStudent: Anwesha MukhopadhyayAdvisor: Prof. Vinod JohnDegree: PhDDate and Time: 10:30 AM, 21st June 2023Place: MMCR EE, IISc.=======================================================Title: Reduced Electrolytic Capacitor-Based Single-Phase Converters: Topologies, Control, and StabilityAbstract: Single-phase power converters find wide applications as inverters for grid integration of solar photovoltaics, fuel cells, front-end converters for consumer electronics, battery chargers for electric vehicles, etc. Applications ranging from a few hundred Watts for household solar micro-inverters, to multi-Megawatt levels for electric traction powertrain, single-phase converters are adopted worldwide.In single-phase power conversion, there is always a mismatch between the instantaneous input and output power, producing a second-harmonic ripple in the dc link current. Electrolytic capacitors are conventionally deployed for filtering the second-harmonic ripple due to their low cost and excellent energy density. However, their frequent premature failures often compromise the lifespan of the converters. Therefore, in applications demanding higher reliability, electrolytic capacitors are minimised or eliminated completely. In recent technologies demanding high power density, active filters have minimised the electrolytic capacitors in the circuit. However, the cost, efficiency, and power density trade-offs need scrutiny before adopting an active filter topology.Among the reported active filters (AF) for second-harmonic ripple mitigation, series capacitor stacked buffer (SSB) topology has emerged as a popular choice owing to its high efficiency and compactness. The use of low VA-rated switching devices enables achieving the high efficiency equivalent to passive filters. Owing to these benefits, the use of SSB is proposed in two-terminal active capacitors and active inductors and pulsed power applications.Despite its prospective utility in a range of applications, the model of the SSB, essential for implementing functional engineering control strategies under a wide range of operating conditions, is not discussed in existing literature. In the first part of the work, the plant model for controlling the buffer converter in voltage control mode as well as current control mode is developed. Using the proposed model, a closed-loop control scheme is developed, which ensures a fixed-frequency switching of the buffer converter. A step-by-step controller design procedure is elaborated, and the controller gain limit is identified to ensure closed-loop stability. The stability limit and the filtering performance are verified experimentally on a hardware prototype.Based on the developed SSB model, an average current mode control is implemented in the second part of the work. Unlike the existing methods of current mode control, in the proposed scheme, the current reference is estimated without using the dc-link current sensor, which is verified experimentally.The SSB-based existing topologies, though promising for many applications, are not realised with minimum switch counts. As opposed to four switch H-bridge-based buffer converter, two switch-based series capacitor stacked buffer converter topologies are synthesised in this part of the work. The generalised topology synthesis procedure and control challenges are identified. One of the proposed two-switch-based topologies named Series Capacitor Boost Hybrid Filter (SC-BOHF) is implemented and verified experimentally.Apart from the active solutions, an alternative dc bus filter structure, consisting of a combination of an inductor (L) and capacitor (C), tuned at the second harmonic (2ω) frequency, reduces the capacitance requirement, enhancing the likelihood of deployment of film capacitors. The proposed solid-state tuning restorer (SSTR) offers consistent filtering performance of the LC filter under frequency and parameter variations. As per the tuning requirement of the LC filter, SSTR acts as an electronic inductor or capacitor. It also ensures a graceful degradation in the filter characteristics during SSTR converter failure modes. The evolution of the SSTR configuration, analysis of its VA rating, and control requirements are studied in this work.The realisation that SSTR requires to behave as an electronic inductor and capacitor as per the sense of LC filter detuning motivated this part of the work, where a unified active capacitor and inductor (UACI) is proposed and implemented without using any dc capacitor. Conventionally, an H-bridge-based active capacitor or inductor requires large dc capacitances to ensure satisfactory current THD. In the proposed configuration, a dc capacitor-less three-leg converter topology is proposed to emulate a two-terminal unified active capacitor and inductor. Based on the current reference, the proposed configuration emulates inductive or capacitive characteristics and smoothly transits from one characteristic to another. The operation of the proposed UACI is studied, and a closed-loop control scheme is developed.All the proposed methods are validated on hardware prototypes that have been developed as a part of the work. —————— ALL ARE WELCOME —————

Summer School 2023
2023-06-20 08:00 - 2023-07-07 17:00
Summer School 2023 Website Link
summer school | EE (iisc.ac.in)

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

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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 —————

[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

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
teams.microsoft.com

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
