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X-ORIGINAL-URL:https://ee.iisc.ac.in
X-WR-CALDESC:Events for EE
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TZID:Asia/Kolkata
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TZOFFSETFROM:+0530
TZOFFSETTO:+0530
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DTSTART:20230101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20230215T223000
DTEND;TZID=Asia/Kolkata:20230215T233000
DTSTAMP:20260528T222624
CREATED:20230214T015525Z
LAST-MODIFIED:20230214T015525Z
UID:240425-1676500200-1676503800@ee.iisc.ac.in
SUMMARY:Towards optimal design of lithium-ion batteries through physics-based modelling by Dr. Krishnakumar Gopalakrishnan
DESCRIPTION:                                                                                                                             Abstract \nFor online participants: Meeting Link  \n\nIncreased driving range and enhanced fast charging capabilities are acknowledged as the immediate goals of transport electrification. However\, these two objectives are at loggerheads with each other\, since they place demands on improving two contrasting aspects of vehicular pouch-cell design\, viz. their energy and power densities. By varying the number of layers versus the volume of active electrode material\, bespoke pouch-cell designs targeting either of these goals can be obtained. Attempting this design trade-off through iterative empirical testing of layer choices is expensive and often leads to sub-optimal designs. This talk presents the author’s research towards developing a computational framework that employs a model-based methodology for determining the optimal number of electrochemical layers. The modelling objective is to maximise the usable energy whilst satisfying specific acceleration and fast charging targets.\n\nCurrently\, the model developed thus far is able to handle the critical need to avoid lithium plating during fast charging and accounts for a range of thermal conditions. The modelling framework also takes into account the electrochemical and kinetic phenomena at the micro-scale using a hybrid Finite Element (FE)-spectral scheme\, whilst propagating the results upwards to higher length scales for cell-level system design. Drawing upon inferences from his recent research into Hierarchical Multi-Scale Modelling (HMM) of composite materials\, the author shall conclude the talk by presenting preliminary results from his hypothesis that coupling the micro-scale FE quadrature points to nano-scale phenomena shall\, for the first time\, help to quantify the influence of electrode cracking on cell capacity degradation.\n\n\nSpeaker’s bio:\n\n\n\nDr Krishnakumar Gopalakrishnan is a Senior Research Software Engineer at the Dept of Advanced Research Computing (ARC)\, University College London (UCL) in the UK where he works on high performance scientific computing (HPC) applications across a range of computational modelling research projects. Prior to this\, he held a 2-year post doctoral research fellowship in scientific computing at the Centre for Computational Science (CCS) at University College London (UCL)\, UK\, and has served as a visiting researcher at the University of Konstanz\, and Rutherford Appleton Laboratories (RAL)\, UK.He holds a BTech degree in Electrical and Electronics Engineering from College of Engineering\, Thiruvananthapuram\, an MS degree in Electrical Engineering (power electronics systems & control) from the Center for Power Electronics Systems (CPES) labs at Virginia Tech. Later\, he won a US Dept of Energy GATE fellowship to complete a graduate certificate program in electric drivetrain automation. Dr Gopalakrishnan received his PhD degree in Mechanical Engineering (mathematical modelling of lithium ion batteries) from Imperial College London. He was formerly employed at ABB Innovation Labs (Bangalore\, India) and ABB Corporate Research Center (Baden-Dättwil\, Switzerland). He has also served as a power management algorithms and systems engineer at Qualcomm Inc. (San Diego\, USA) where he successfully filed corporate patents on novel Battery Management Systems (BMS) designs. He was awarded the President’s PhD Scholarship at Imperial College London and is a Mathworks Certified Matlab Associate.\n\nDr Gopalakrishnan has over a decade of teaching experience at various levels. At UCL\, he currently teaches the University’s scientific computing with C++ course and is leading the course development effort and teaching plan for UCL ARC’s first Massively Open Online Course (MOOC) on the FutureLearn platform. He has also had the privelege of teaching Imperial College London’s first MOOC on Mathematics Essentials (for business majors) hosted on the EdX platform. He has also served as a teaching fellow at Imperial College London’s Computational Methods hub\, wherein he was the lead instructor for several scientific computing courses taught to a campus-wide audience.\n\n\nDr Gopalakrishnan has published several well-cited research articles in peer-reviewed journals (including in Nature Computational Science) and technical whitepapers\, and has presented at UK and international conferences. His research software engineering interests include heterogeneous and GPU computing\, parallel and threaded programming\, linear algebra libraries\, low-latency network communications\, and Unix systems administration. His scientific research interests include computational modelling of dynamic systems\, power electronics and control\, energy storage\, non-linear optimisation\, feedback control\, signal processing\, numerical methods and state estimation.
URL:https://ee.iisc.ac.in/event/towards-optimal-design-of-lithium-ion-batteries-through-physics-based-modelling-by-dr-krishnakumar-gopalakrishnan/
LOCATION:EE\, MMCR
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20230217T213000
DTEND;TZID=Asia/Kolkata:20230217T223000
DTSTAMP:20260528T222624
CREATED:20230213T033242Z
LAST-MODIFIED:20230217T055545Z
UID:240420-1676669400-1676673000@ee.iisc.ac.in
SUMMARY:EE Faculty Colloquium on Learning from unreliable data
DESCRIPTION:Speaker: Prof. P.S. Sastry\, Dept of Electrical Engineering\, Indian Institute of Science \nAbstract: \nSupervised learning of classifiers is widely used in many applications of AI/ML. The deep networks used in such applications today need a large training set. Creating a labelled data set where one can have high confidence in the labels is both expensive and time consuming. Data sets created through crowd sourcing or automatic labelling methods normally have many random labelling errors. There is considerable amount of empirical evidence to show that standard algorithms are likely to do poorly when there is significant amount of label noise in the data. Hence it is interesting to ask whether one can design classifier learning algorithms that are robust to different types of random labelling errors (in the training data). Over the years this problem has been investigated by many researchers and many interesting ideas and algorithms for such robust learning are proposed. In this talk we present an overview of the problem of learning with noisily labelled training set and review some of the approaches proposed for tackling the problem. We concentrate mainly on risk minimization schemes. We discuss what are called symmetric loss functions and their role in robust risk minimization. We will also briefly discuss approaches based on sample selection and weighted risk minimization and present a sample selection algorithm based on batch statistics. The discussion would be biased towards some work done in our lab.Speaker’s Bio:P.S. Sastry obtained BSc in Physics from IIT\, Kharagpur\, and BE from ECE dept and PhD from EE dept at IISc. He has been a faculty member of dept EE\, IISc\, for more than 35 years now. His research interests include Pattern Recognition\, Machine Learning\, Data Mining\, and Computational Neuroscience.
URL:https://ee.iisc.ac.in/event/learning-from-unreliable-data/
LOCATION:EE\, MMCR
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20230220T153000
DTEND;TZID=Asia/Kolkata:20230220T183000
DTSTAMP:20260528T222624
CREATED:20230213T034941Z
LAST-MODIFIED:20230213T034941Z
UID:240423-1676907000-1676917800@ee.iisc.ac.in
SUMMARY:PhD Oral Defense\, of Mr. Bidhan Biswas
DESCRIPTION:Title of the thesis: Short Circuit and Open Circuit Natural Frequencies of 3-Φ Transformers. \nResearch Supervisor: Prof. L. Satish \nMeeting link : Click here to join the meeting \nAbstract \nFrequency Response Analysis (FRA) method is perhaps the most sensitive tool that can detect even the slightest of winding/core movements. High sensitivity\, non-invasiveness\, non-destructiveness\, and on-site capability are some of its salient features – making it an ideal monitoring and detection tool. The existence of Standards (IEEE\, IEC\, and CIGRE) is ample testimony of its global acceptance and superior detection capabilities. The principle of detection is based on observance of a deviation between two measured FRAs which implies a possible fault. Naturally\, the next logical step is to analyse these deviations to determine the type of fault\, estimate the extent of damage and its severity\, and as a bonus\, predict its location\, if possible. However\, even after three decades of existence\, arriving at these inferences  is still at the research level. Even though there is a consensus among all the standards on FRA test/measurement procedures\, best-suited terminal connections\, cable layout\, grounding practices\, etc.\, they remain largely silent regarding interpretation and diagnostics. \nA detailed analysis of literature compiled in Chapter 1 reveals that perhaps lack of a mathematical foundation might be one reason for the present plight of FRA. So\, developing a generic mathematical-based approach for interpretation and location of  incipient mechanical winding damages in actual 3-Φ transformer windings\, using measured FRA\, is imperative. Development of a such generic method necessitates derivation of closed-form expressions which can provide a direct link between measured FRA quantities to the electrical parameters of the winding. For assessing damage severity\, the challenge is to identify a quantity which is not only extractable from measured FRA\, but also be sensitive\, monotonic\, and traceable to the fault. Driven by this philosophy this thesis aims to address the following – \n\nPropose a unified and general approach to derive closed-form analytical expressions (for each multiphase winding) to link the measured open and short circuit natural frequencies to electrical parameters of the winding\, and valid for any condition of the neutral\nDefine a quantity calculable from the measured FRA’s peak/trough frequencies which is physically related to mechanical damage in the winding\, and capable of yielding some physical insight about damage\nDevelop novel methods using the derived analytical expressions to identify an incipient\, discrete\, and localized axial and/or radial displacement in any multiphase winding\, and applicable for any condition of the neutral\n\nIn the second chapter\, a generic and unified analytical method is developed (applicable to any 1-Φ or 3-Φ winding) starting from the basic mutually coupled lossless ladder network model to derive equations which relate the harmonic sum of squares of short circuit natural frequencies (SCNF) and open circuit natural frequencies (OCNF) to the elemental winding inductances and capacitances. Complete details of the derivation are discussed\, and all the derived formulae were cross verified by extensive numerical circuit simulations. \nEach one of these derived expressions has a strikingly similar structure and possesses a unique property viz.\, the contribution of series capacitances and ground capacitances are decoupled. This important property paves way for estimating a physical quantity that is directly responsible for the winding resonances\, viz.\, the effective air-core inductance (Leff). This estimation requires multiple FRA measurements. Chapter 3 presents complete details of the concept\, its derivation\, measurements\, and experimental results for all 1-Φ and 3-Φ windings. \nLoss of clamping pressure in a winding is not directly identifiable by any means\, other than an FRA measurement. But\, this damage cannot be judged by merely comparing two FRAs. So\, a clamping pressure measurement experiment was carried out on a single isolated winding to ascertain the sensitivity and monotonicity afforded by the quantity\, Leff\, to a change in clamping pressure. Driven by the promising results\, author proceeds to build a method based on Leff to find the location of a discrete and localized axial displacement (AD) in any 3-Φ winding configuration. Details of this method\, experimental results\, and measurement steps are presented in Chapter 4. \nProceeding further\, Chapter 5 discusses concept of a new method\,  measurement steps and experimental results to identify presence of a Radial Displacement (RD) in a 3-Φ star winding with neutral-open\, as well as\, in a delta connected winding. Driven by success\, the concept was extended to identify the simultaneous occurrence of a discrete and localized AD and RD in one phase of a 3-Φ star winding\, with neutral-open. Preliminary experimental results proved the method can successfully identify faulted phases that contained AD and RD. \nAll experiments reported in the thesis were carried out on transformer windings rated at 33 kV\, 3.5 MVA. The results are encouraging and the author believes that true potential of the proposed methods can be judged when implemented on actual transformers. \nIn summary\, this thesis presents\, perhaps for the first time\, a mathematical basis for identifying and diagnosing axial and radial displacements in 1-Φ and 3-Φ windings using the peak/trough frequency data from the measured FRA. The author believes that this is a small step forward in advancing FRA as a diagnostic tool. \nALL ARE INVITED
URL:https://ee.iisc.ac.in/event/phd-oral-defense-of-mr-bidhan-biswas/
LOCATION:HV seminar Hall
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20230220T213000
DTEND;TZID=Asia/Kolkata:20230221T003000
DTSTAMP:20260528T222624
CREATED:20230219T225005Z
LAST-MODIFIED:20230219T225107Z
UID:240445-1676928600-1676939400@ee.iisc.ac.in
SUMMARY:An Introduction to Neuromorphic Computing with Intel Loihi
DESCRIPTION:Speaker: Ashish Rao Mangalore\, Doctoral resident at Intel Labs\, Munich\, Germany\nAbstract:\n\nNeuromorphic computing is a new paradigm of computing inspired by the organization and functioning of neurons in the mammalian brain. The architectural features derived from this inspiration result in orders of gain in the time and energy to solution for various classes of problems. Over the years\, there have been neuromorphic platforms of different types\, e.g.\, IBM TrueNorth\, SpiNNaker\, DYNAPs\, and BrainChip Akida to name a few. In this talk\, we shall focus on the state-of-the-art digital CMOS-based neuromorphic research platform\, Loihi\, developed by Intel Corporation. We shall then go through the basic operating principles of Loihi\, problems that Loihi is best suited for\, and the current main algorithmic research verticals being pursued by the neuromorphic research community. After this\, there shall be a special emphasis on mathematical optimization on Loihi\, one of the most promising and viable applications on Loihi. The talk will end with pointers on how groups can get involved to address open problems and contribute to neuromorphic research in general. \n\nBiography of the speaker:\n\nAshish Rao Mangalore currently works as a doctoral resident at Intel Labs\, Munich\, Germany under the supervision of Prof. Alin Albu-Schäffer at the German Aerospace Center/DLR & TU Munich. His main research focus lies in the development & implementation of control algorithms for robotics and mathematical optimization problems on neuromorphic computers\, specifically\, Loihi. During a prior research stint at the Indian Institute of Science (IISc)\, Bengaluru\, he conducted research on 3-D reconstruction techniques with neuromorphic cameras. He obtained his masters in Neuroengineering from the Technical University of Munich (TUM) and bachelors in Electrical & Electronics Engineering from R.V. College of Engineering (RVCE)\, Bengaluru.
URL:https://ee.iisc.ac.in/event/an-introduction-to-neuromorphic-computing-with-intel-loihi/
LOCATION:EE\, MMCR
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20230222T143000
DTEND;TZID=Asia/Kolkata:20230222T163000
DTSTAMP:20260528T222624
CREATED:20230215T224825Z
LAST-MODIFIED:20230215T224825Z
UID:240431-1677076200-1677083400@ee.iisc.ac.in
SUMMARY:Thesis Defense of Tanmay Mishra
DESCRIPTION:Title: Development of A Reconfigurable Synchronous Machine Emulation Platform \nFaculty Advisor: Prof. Gurunath Gurrala. \nMeeting Link:Click here to join the meeting \nABSTRACT: \nStudying the dynamic behavior of non-linear complex power systems in a laboratory is very challenging. Early experimental platforms used micro-alternators to emulate the behavior of fixed steam turbine models. The micro-alternator is a three-phase synchronous generator with similar electrical constants (in per unit on machine rating) as those typically found in alternators in large power stations. It is an electrical scaled-down model of machines up to 1000 MW rating and is rated between 1 to 10 kVA. Researchers used these micro-machines up to the 90s to study large electric generators’ transient and steady-state performance. The Department of electrical engineering at the Indian Institute of Science (IISc) was also very active in experimental research in power engineering. The department still retained two-three kVA and one ten kVA micro-machine sets\, but the control panels of these machines became obsolete as the manufacturer of these machines Mawdsley\, London\, doesn’t exist anymore. Advancements in simulation software packages and real-time simulators have primarily replaced the experimental models of electric power systems worldwide. The push for green energy technologies worldwide due to climate concerns has increased the presence of power electronic converters in the power grids. Reduction of overall inertia\, frequent occurrence of electromechanical oscillations\, electromagnetic transients\, and control interaction modes has become a concern for the power grid operators. The need for understanding the physical insights of the oscillatory modes introduced by fast-acting power electronic converters\, the need for developing practically feasible control algorithms for mitigating the interaction modes\, and the need for developing dispatchability and grid support features like conventional generation sources have triggered the development of laboratory-scale experimental power grids across the world in the past decade. \n In this thesis\, initially\, an attempt is made to revive the existing three kVA alternator controls. An IGBT-based buck converter static excitation system has been developed for the micro-alternator. This exciter also incorporates several limiters which were non-existent in the old analog control panels. An under-excitation limiter\, over-excitation limiter\, and V/Hz limiter as per IEEE standard 421.5 have been designed to protect the micro-alternator during abnormal conditions such as overloading\, overheating\, and over-fluxing of the machine. The detailed tuning procedure of limiters and TCR is discussed to comply with IEEE STD 421.2 and IEEE STD 421.5. A digital time constant regulator (TCR) is incorporated to modify the micro-alternator’s field’s time constant to mimic large synchronous machines’ dynamics as micro-machine time constants are very small. A custom 5 kVA micro-alternator was manufactured through a local vendor having parameters like the Mawdsley machines to facilitate the creation of multiple short circuits in the testbed. \nA single micro-alternator can represent only one large alternator dynamics\, thereby limiting the platform’s scalability. Emulating machines of different ratings using a single micro-machine would undoubtedly boost the capabilities of experimental platforms for investigating conventional and nonconventional source interactions in laboratories. To the best of our knowledge\, only one such attempt was made in the literature\, where a model reference control algorithm is proposed to mimic any rating alternator dynamics using a doubly excited laboratory micro-alternator. However\, doubly excited micro-alternators are non-existent today. A generalized experimental platform using a non-linear output matching controller based on output feedback linearization is developed in this thesis for emulation of large turbo-alternators of different ratings\, IEEE STD 421.5 excitation systems\, and standard turbine governor models in the laboratory using the 5 kVA micro-alternator. IEEE Model 1.1 synchronous machine model in per unit on machine MVA rating with associated excitation system and governor-turbine models has been used as a reference model to be emulated. A single machine infinite bus (SMIB) setup with the 5 kVA micro-alternator and a 50 km 220 kV scaled lumped parameter frequency-dependent transmission line model is used for experimental validation. Synchronous generators of ratings\, 128 MVA\, 247.5 MVA\, and 1000 MVA have been physically emulated using the setup. The dynamic responses of the large machines with thermal turbines (reheat\, non-reheat)\, hydro turbine\, and excitation systems; DC1A\, AC4A\, and ST1C have been reproduced under small and large disturbances. \nA systematic scaling approach has been proposed to emulate a multi-machine system in the laboratory. Unlike in the SMIB system\, the power levels of generators in a multi-machine system should be scaled to the laboratory level for emulation. Hence\, every power system component (generator\, transmission lines\, transformer\, loads) is scaled to a uniform level so that the laboratory machines don’t get overloaded. The developed non-linear control strategy for emulation has been extended to multi-machine systems. The Western System Coordinating Council 3-generator 9-bus test system has been used to validate the proposed concept. The feasibility of replicating WSCC system dynamics in a laboratory as a scaled-down model has been verified through simulations under small and large disturbances. Emulating large machine dynamics with different types of turbines\, governors\, and excitation controls using a singly excited micro-alternator enabling a generalized synchronous machine emulation platform is a first-of-its-kind effort in the literature to the best of our knowledge. \n Note: Know how generated from the Source Emulation has been licensed to MCore Technologies Pvt Ltd\, Bangalore for commercialization. \nAcknowledgments: This work is supported by Fund for Improvement of Science and Technology (FIST) program\, DST\, India\, No.SR/FST/ETII-063/2015 (C) and (G) under the project “Smart Energy Systems Infrastructure – Hybrid Test Bed”.
URL:https://ee.iisc.ac.in/event/thesis-defense-of-tanmay-mishra/
LOCATION:EE\, MMCR
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20230223
DTEND;VALUE=DATE:20230226
DTSTAMP:20260528T222624
CREATED:20230207T233153Z
LAST-MODIFIED:20230208T002600Z
UID:240387-1677130200-1677302999@ee.iisc.ac.in
SUMMARY:International Workshop on Planar Magnetic Technology
DESCRIPTION: 
URL:https://ee.iisc.ac.in/event/planar-magnetic-technology/
LOCATION:IISc
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