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TZID:Asia/Kolkata
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DTSTART:20220101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20221107T200000
DTEND;TZID=Asia/Kolkata:20221107T220000
DTSTAMP:20260529T053001
CREATED:20221102T034842Z
LAST-MODIFIED:20221102T042355Z
UID:240082-1667851200-1667858400@ee.iisc.ac.in
SUMMARY:Online Thesis Defence of Ashiq Muhammed P E
DESCRIPTION:Degree Registered:              Ph.D.\nGuide:               Prof. Satish L and Prof. Udaya Kumar\nThesis Title:     Improved Understanding of Standing Waves in Single Layer Coil and Elegant Methods to Estimate Transformer Winding Parameters \nClick here to join the Meeting \nAbstract: Analyzing the effect of impulse voltages (like lightning\, switching) on transformer winding has occupied centerstage in core electrical engineering research for over a century. These investigations gather great significance and relevance as it eventually governs the design of insulation in the winding. Notwithstanding the colossal contribution this domain has witnessed from stalwarts in the past century\, a closer scrutiny surprisingly reveals that there still exists tiny grey areas that demands attention. Pursuing this line of thought\, the first part of this thesis aims to clearly describe what this grey area is and resolving it would provide a deeper insight about fundamental understanding of surge response in transformer windings – with special emphasis on its standing wave phenomenon. Following this\, in the latter part\, elegant procedures are stitched together to determine a few electrical parameters of the transformer winding equivalent circuit that have the potential to help in assessing mechanical status of windings. Objectives of the thesis are – \n1.    Formulate an analytical method to determine the exact shape of standing waves for all modes in a uniform single layer coil as a solution of its governing partial differential equation2.    Estimate series capacitance of a uniform transformer winding from its measured driving point impedance3.    Determine effective air-core inductance of an iron-core uniform winding as a function of its axial length from measured driving point impedance \nFirst part of the thesis revisits a century-old classical theory of standing waves on uniform single layer coils. Accurate information about natural frequencies and shapes of the corresponding standing waves are essential for gaining a deeper understanding of the response of coils to impulse excitations. Analytical studies on coils have largely been based on the assumption that standing waves are sinusoids in both space and time. However\, this contradicts the results from numerical circuit analysis and practical measurements. So\, this thesis attempts to bridge this discrepancy by revisiting the classical standing wave phenomena in coils. It not only assesses the reason for the aforementioned inconsistency\, but also makes a contribution by analytically deriving the exact mode shape of standing waves for both neutral open/short conditions. For this\, the coil is modelled as a distributed network of elemental inductances and capacitances\, while an exponential function describes the spatial variation of mutual inductance between turns. Initially\, an elegant derivation of the governing partial differential equation (in terms of voltage as the variable instead of flux) for surge distribution is presented and to the best of our knowledge\, for the first time\, an analytical solution for the same has been found by the variable-separable method to find the complete solution (sum of time and spatial terms). Hyperbolic terms in the spatial part of the solution have always been neglected but are included here\, thus\, yielding the exact mode shapes. For verification\, both voltage and current standing waves computed from the analytical solution were plotted and compared with PSPICE simulation results on a 100-section ladder network representing a uniform single-layer coil. Then\, practical measurements were made on a tailor-made large-sized single layer coil with a length of 2.02 m\, diameter of ~1 m and having 640 turns. It turns out that even in such simple single layer coils\, the shape of standing waves of all modes deviates considerably from being sinusoidal. It was further observed that this deviation depends on spatial variation of mutual inductance\, capacitive coupling\, and order of the standing waves. \nIn the second part\, an elegant method for determining the series capacitance (Cs) and air-core equivalent inductance of a uniform winding as a function of its axial length (termed as M0x in this thesis) of a uniform transformer winding\, from its measured DPI magnitude\, is discussed. Knowledge about the series capacitance of the winding is essential\, which along with shunt capacitance\, determines the initial impulse voltage distribution when a surge impinges on the winding. Unlike previously published approaches\, the proposed method does not involve any cumbersome and time-consuming curve-fitting or running of optimization/search algorithms. Neither does it require winding geometry data. The proposed procedure for finding series capacitance relies on a property that is observable in the driving point impedance (DPI) function of a lossless winding with an open neutral condition\, viz.\, the ratio of the product of squares of open circuit natural frequencies to the product of squares of short circuit natural frequencies bears a particular relation to the coefficients of the DPI function. A simple procedure involving a deft manipulation and combination of a few well-known properties that correlate the roots of a polynomial to its coefficients are then utilized for determining series capacitance. \nKnowledge about equivalent air-core inductance distribution as a function of its axial length (i.e.\, M0x) is useful for localizing a minor/incipient mechanical fault in the winding. A physically realizable empirical relationship to estimate M0x is initially proposed. The corresponding constants of the empirical relationship are then calculated from the measured DPI. The proposed method requires three DPI measurements: one with neutral-end open and the other with neutral-end shorted. The third DPI is measured with a known external lumped capacitance connected between the neutral and ground. This method requires only the first few dominant natural frequencies observable in the first two of the DPIs. \nFeasibility of proposed methods for estimating Cs and M0x was initially verified by simulation on an N-section ladder network and then by experiments on small-sized continuous-disk and interleaved-disk windings\, and finally on a large-sized 33 kV\, 3.5 MVA continuous-disk winding. Salient features of the proposed methods are – they are simple\, elegant and involve minimum post-processing after measuring the DPI. Given its inherent simplicity and their relevance\, the author is hopeful that industry will come forward to implement these procedures on an existing FRA measuring instruments – thus opening a new dimension to FRA measurements. \n* * * * * * * * ALL ARE CORDIALLY INVITED * * * * * * * *
URL:https://ee.iisc.ac.in/event/thesis-defence-of-ashiq-muhammed-p-e/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20221109T193000
DTEND;TZID=Asia/Kolkata:20221109T203000
DTSTAMP:20260529T053001
CREATED:20221103T232724Z
LAST-MODIFIED:20221103T233801Z
UID:240097-1668022200-1668025800@ee.iisc.ac.in
SUMMARY:Lecture by Prof. Carlos Busso
DESCRIPTION:Organised by\nIndian Institute of Science and\nThe IEEE Signal Processing Society\, Bangalore Chapter\nTitle: Robust Emotion Recognition (Click here for the poster)\nDate and Time: 9th November 2022 at 2:00pm\, Refreshments: 3:00pm\nVenue: MMCR (Room No. C241\, 1st Floor\, Dept. of Electrical Engineering \nAbstract of the talk: It is challenging to achieve robust and well-generalized models for tasks involving subjective concepts such as emotion. This tech talk will describe novel approaches to effectively develop robust speech emotion recognition (SER) systems. At the resource level\, we will describe our effort to collect the MSP-Podcast corpus\, which is a large\, naturalistic emotional database. The data collection protocol combines machine-learning algorithms to retrieve recordings conveying balanced emotional content annotated with a cost-effective crowdsourcing protocol. To improve the temporal modeling of SER systems\, this seminar will also discuss a novel dynamic chunking approach that maps the sequences of different lengths into a fixed number of chunks that have the same duration by adjusting their overlap. This simple chunking procedure creates a flexible framework\, facilitating parallel computing. The approach can incorporate different feature extractions and sentence-level temporal aggregation approaches to cope\, in a principled way\, with a sequence-to-one SER task. Likewise\, the seminar will discuss multimodal pre-text tasks that are carefully designed to learn better representations for predicting emotional cues from speech\, leveraging the relationship between acoustic and facial features. Finally\, the seminar will discuss our current effort to design multimodal emotion recognition strategies that effectively combine auxiliary networks\, a transformer architecture\, and an optimized training mechanism for aligning modalities\, capturing temporal information\, and handling missing features. These models offer principled solutions to increase the generalization and robustness of emotion recognitions  systems. \nSpeaker Biography: Carlos Busso received his PhD degree (2008) in electrical engineering from the University of Southern California (USC)\, Los Angeles\, in 2008. He is a professor at the Electrical Engineering Department of The University of Texas at Dallas (UTD). At UTD\, he leads the Multimodal Signal Processing (MSP) laboratory [http://msp.utdallas.edu]. He is a recipient of an NSF CAREER Award. In 2014\, he received the ICMI Ten-Year Technical Impact Award. In 2015\, his student received the third prize IEEE ITSS Best Dissertation Award (N. Li). He also received the Hewlett Packard Best Paper Award at the IEEE ICME 2011 (with J. Jain)\, and the Best Paper Award at the AAAC ACII 2017 (with Yannakakis and Cowie). He received the Best of IEEE Transactions on Affective Computing Paper Collection in 2021 (with R. Lotfian) and in 2022 (with Yannakakis and Cowie). He is the co-author of the winner paper of the Classifier Sub-Challenge event at the Interspeech 2009 emotion challenge. His research interest is in human-centered multimodal machine intelligence and applications. His current research includes the broad areas of affective computing\, multimodal human-machine interfaces\, nonverbal behaviors for conversational agents\, in-vehicle active safety system\, and machine learning methods for multimodal processing. His work has direct implication in many practical domains\, including national security\, health care\, entertainment\, transportation systems\, and education. He was the general chair of ACII 2017 and ICMI 2021. He is a member of ISCA\, AAAC\, and a senior member of ACM and IEEE.
URL:https://ee.iisc.ac.in/event/lecture-by-prof-carlos-busso/
LOCATION:EE\, MMCR
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20221111T150000
DTEND;TZID=Asia/Kolkata:20221111T160000
DTSTAMP:20260529T053001
CREATED:20221106T232123Z
LAST-MODIFIED:20221106T233337Z
UID:240104-1668178800-1668182400@ee.iisc.ac.in
SUMMARY:Thesis Colloquium of Sayantan Das
DESCRIPTION:Thesis Title:  Modeling of lightning attachment to aircraft and  quantification of the influencing parameters \nGuide: Prof. Udaya Kumar \nDegree registered:          Ph.D. \nDate and Time:              11th November 2022\, 9:30 AM\nMeeting link:                  https://teams.microsoft.com/l/meetup-join/19%3ameeting_MDg5YzdhYWUtMTc3Zi00Yjg0LWE1ZTktYjgyY2I5Y2MyNDI4%40thread.v2/0?context=%7b%22Tid%22%3a%226f15cd97-f6a7-41e3-b2c5-ad4193976476%22%2c%22Oid%22%3a%227ef4df52-6005-46aa-bff3-d96db1a85b71%22%7d \nAbstract: According to Air Transport Action Group (ATAG)\, 45 million aircraft took off worldwide in 2020\, which translates to 1.5 lakh per day. Statistically\, the aviation industry is found to double its fleet size every fifteen years. Lightning is considered one of the dreadful environmental threats to aircraft. Past incidents show that lightning strikes can lead to structural damage\, operational interruption\, and loss of lives. Field data suggest that\, on average\, an aircraft gets struck by lightning once or twice a year. Therefore\, the threat due to lightning is considered a crucial safety aspect of an aircraft. \nDesign of suitable lightning protection for aircraft involves Zoning of its skin. It is intended to differentiate lightning attachment points\, channel slipping regions\, and regions that carry just the stroke current. The first step of Aircraft Zoning is to identify the initial attachment points. For the same\, different methods like Laboratory experiments\, similarity principle\, Rolling Sphere Method (RSM)\, and Field-based approach are suggested in the standard ARP5414. In reality\, the lightning strikes to aircraft can be of two modes\, Aircraft-initiated and Aircraft-intercepted. In the former one\, under the influence of a thundercloud or descending lightning leader\, the aircraft initiates stable bipolar connecting leaders\, upward and downward leader toward the ground. These leaders are deemed to propagate hundreds of meters to complete the lightning strike. In Aircraft-intercepted strikes\, the aircraft intercepts a descending lightning leader and hence gets struck. The laboratory experiments on scaled aircraft models or isolated aircraft parts are inadequate to assess the initial attachment points. The similarity principle suggested in the standard is qualitative and can’t be extended to aircraft of any size and shape. The 25m Rolling Sphere Method (RSM) is routinely employed to determine the attachment points. This method doesn’t consider the connecting leader discharges from aircraft and therefore overestimates the possible attachment points. Most (90%) of the lightning strikes to aircraft are attributed to aircraft-initiated mode\, which involves significant connecting leader activities. Therefore\, it has to be traced accurately to assess attachment points. \nIn literature\, it is difficult to find a model for bipolar leader discharges from aircraft. However\, work on either negative or positive leader inception and propagation from laboratory gaps and their extension can be relatively found. Based on them\, the present work aims to develop a suitable model for simulating bipolar leader discharges from aircraft. Additionally\, the aircraft-intercepted mode of lightning strikes is also included. In summary\, a novel model adapting the pertinent physical aspects of the leader discharges has been developed to accurately assess initial lightning attachment points to aircraft.  \nUsing the model developed\, the following practically important questions are addressed:  \n\nDependency of the frequency of lightning strikes to aircraft on its shape and size.\nRate of lightning strikes to aircraft at different altitudes\nFor a given aircraft and its route\, the number of times it gets struck by lightning\nThe average number of strikes to an aircraft per year\n\nTo present a quantitative assessment\, two different aircraft models\, McDonnell Douglas DC-10 and Standard Dynamic Model are considered. \nIn summary\, a novel model based on physical grounds has been developed to assess the initial lightning attachment points on aircraft. Using the same\, further methodologies are constructed to quantify the dependency of the strike rate on aircraft size\, altitude\, and possible average strike rate. \nALL ARE WELCOME
URL:https://ee.iisc.ac.in/event/thesis-colloquium-of-sayantan-das/
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DTSTART;TZID=Asia/Kolkata:20221111T170000
DTEND;TZID=Asia/Kolkata:20221111T170000
DTSTAMP:20260529T053001
CREATED:20221107T014902Z
LAST-MODIFIED:20221107T025832Z
UID:240109-1668186000-1668186000@ee.iisc.ac.in
SUMMARY:Lecture by Dr. Ganesh Sivararaman
DESCRIPTION:Indian Institute of Science and\nThe IEEE Signal Processing Society\, Bangalore Chapter\nCordially invites you to the following talk on\n“Unsupervised adaptation in speech technologies”\n(Click here for the poster.) \nSpeaker: Dr. Ganesh Sivaraman\, Pindrop\, Atlanta\, USA\nDate and Time: 11th November 2022 at 11:30am to 12:30pm\nVenue: MMCR (Room No. C241)\, 1st Floor\, Dept. of Electrical Engineering \nAbstract: Unsupervised learning and adaptation techniques have taken center stage due to the exponential growth of unlabeled data. For many practical applications unsupervised learning helps in the adaptation of machine learning systems to mismatched train and test domains. Unsupervised adaptation can be performed by three broad approaches – 1) feature transformations in the test domain\, 2) model adaptation to test domain\, and 3) generation of synthetic test domain samples. This talk will outline these methods by showing three specific examples from speech processing. Unsupervised speaker adaptation for acoustic-to-articulatory speech inversion serves as an example of feature transformation-based adaptation. Adaptation of end-to-end ASR systems without manual transcriptions will be presented as an example of model adaptation. Finally\, children’s speech simulation for zero-shot child speech classification using X-vectors will be presented as an example of synthetic data generation for the test domain. \nBiography:  Ganesh Sivaraman is a Senior Research Scientist at Pindrop\, in Atlanta\, USA. He received his M.S. (2013) and Ph.D. (2017) in Electrical Engineering from the University of Maryland College Park. His research experience and publications span several speech technologies like acoustic-to-articulatory inversion\, ASR\, speaker recognition\, deepfake detection\, and speech enhancement. During his PhD at Maryland\, he was awarded the Future Faculty Fellowship\, and the International Graduate Research Fellowship by the A. James Clark School of Engineering. Along with his official work\, he is actively involved in teaching\, mentoring\, and collaborating with doctoral students at Maryland. Apart from research work\, Ganesh is a fluent speaker of Sanskrit actively learning and teaching the language as a volunteer of Samskrita Bharati USA. He is passionate about creating computational tools for learning Sanskrit pronunciation.
URL:https://ee.iisc.ac.in/event/lecture-by-dr-ganesh-sivararaman/
LOCATION:EE\, MMCR
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20221114T160000
DTEND;TZID=Asia/Kolkata:20221114T170000
DTSTAMP:20260529T053001
CREATED:20221102T044553Z
LAST-MODIFIED:20221102T045520Z
UID:240090-1668441600-1668445200@ee.iisc.ac.in
SUMMARY:Thesis Defence of Meshineni Deepchand
DESCRIPTION:Degree Registered:              M.Tech(Res).\nGuide:                                    Dr. Rathna G N\nVenue:                                    MMCR EE\, C 241\nDate & Time:                         14th November 10:30am \nTeams meeting link: Click here to join the meeting \nTitle:    Non contact Breathing and Heartbeat signals monitoring using FMCW radar \nAbstract: Non-contact breathing and heartbeat signals monitoring are the tasks of extracting them without contact sensors. It became even more critical in COVID 19\, and hence it is crucial to estimate them correctly. FMCW (Frequency Modulated Continuous Wave) radar is employed to estimate these two signals without contact. Radar captures chest displacement and body movement. Because of this\, breathing and heartbeat signals are distorted. The reduction of false peaks and peak estimation is crucial for breathing rate calculation. So in this thesis\, firstly\, we propose a novel way for tracing body movement and eliminating the traced segment for breathing and heart rate calculation. In the second part\, we efficiently reduced false peaks using maximal overlap discrete wavelet transform (MODWT) to decompose and reconstruct the filtered breathing signal for estimating breathing rate. The heartbeat signal is estimated using bandpass filtering of unwrapped phase. We also compared our algorithm with the task force monitoring (TFM) device as a reference and discussed its performance. Also\, our proposed method for breathing rate estimation has an accuracy of 92.43% and heartrate estimation it is 85.16%. \n* * * * * * * * ALL ARE CORDIALLY INVITED * * * * * * * *
URL:https://ee.iisc.ac.in/event/thesis-defence-of-meshineni-deepchand/
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20221118T200000
DTEND;TZID=Asia/Kolkata:20221118T210000
DTSTAMP:20260529T053001
CREATED:20221109T234238Z
LAST-MODIFIED:20221109T234314Z
UID:240117-1668801600-1668805200@ee.iisc.ac.in
SUMMARY:Thesis Defence of Mr. Unni V S
DESCRIPTION:Degree Registered: PhD (Engg). \nGuide: Prof. Kunal Narayan ChaudhuryDate: November 18\, 2022.Time: 2:30 pm.Venue: Online.Link: MS Teams link: https://tinyurl.com/yr96memmTitle: Efficient and Convergent Algorithms for High-Fidelity Hyperspectral Image Fusion.Abstract: Hyperspectral (HS) imaging refers to acquiring images with hundreds of bands corresponding to different wavelengths of light. HS imaging has a wide range of applications such as remote sensing\, industrial inspection\, environmental monitoring\, etc. A fundamental consideration with multiband sensors is that the amount of incident energy is limited and this creates an intrinsic tradeoff between spatial resolution and the number of bands—current optical sensors can either generate images with high resolution but a small number of bands or images with a large number of bands but reduced resolution. For example\, HS images have hundreds of bands but low spatial resolution\, whereas the opposite is true for multispectral (MS) images. An extreme case is a panchromatic (PAN) image with very high spatial resolution but just a single band. Image fusion refers to techniques where multiband images with high spatial resolution are synthetically generated using image processing algorithms. It includes pansharpening (MS+PAN)\, hyperspectral sharpening (HS+PAN)\, and HS-MS fusion (HS+MS). Reconstructing a fused image from the observed images is ill-posed and needs regularization. Diverse regularization methods have been proposed over the years for general imaging problems\, many of which perform very well for fusion. This includes vector total variation\, sparsity and dictionary-based penalties\, generalized Gaussian- and GMM-based priors\, etc. This thesis proposes novel regularization models and algorithms that can outperform state-of-the-art image fusion techniques. We can broadly group these into two classes—explicit and implicit regularization.Explicit regularization refers to the design of hand-crafted penalty functions that impose desirable properties (e.g.\, smoothness) on the reconstruction; this is used along with the observed data for fusion. We propose a convex regularizer that is motivated by nonlocal patch-based methods for image restoration. Our regularizer accounts for long-distance correlations in hyperspectral images\, considers patch variation for capturing texture information\, and uses the higher resolution image for guiding the fusion process. Unlike local pixel-based methods\, where variations along just horizontal and vertical directions are penalized\, we use a wider search window in terms of nonlocality and directionality. This is shown to yield state-of-the-art results. The catch is that the resulting optimization problem is non-differentiable and we cannot use simple gradient-based algorithms. However\, we show that by expressing patch variation as filtering operations and judiciously splitting the original variables and introducing latent variables\, we develop a provably convergent iterative algorithm\, where the subproblems can be solved efficiently using FFT-based convolution and soft-thresholding.In the implicit approach\, we rely on a recent paradigm known as plug-and-play (PnP) regularization\, where powerful off-the-shelf denoisers are used for regularization purposes. While this has been shown to give state-of-the-art results for general restoration tasks\, it has not so much been explored for fusion. In fact\, we faced few technical challenges in applying PnP for hyperspectral fusion. Firstly\, existing denoisers are slow when applied to multiband images and we need to apply such denoisers several times with the PnP framework. Secondly\, convergence is generally not guaranteed for PnP regularization since the mechanism is ad-hoc. Along with efficiency and good denoising performance\, we need to come up with a denoiser with specific properties that can guarantee convergence. We proposed a couple of approaches to solve this problem. In the first approach\, we have developed a high-dimensional kernel denoiser with low cost yet good denoising performance\, which can guarantee PnP convergence. The overall algorithm is fast and competitive with state-of-the-art methods. In the second approach\, we leverage the power of deep learning to develop a trained patch denoiser which has a couple of advantages over conventional end-to-end learning: (1) Unlike end-to-end networks which require excessive ground-truth data for training\, we can be trained the denoiser from patches extracted from the observed images. For example\, in HS+MS fusion\, the MS image captures the same scene and has the same spatial resolution as the target image. We train the denoiser by sampling clean patches from the MS image and corrupting them with noise.(2) Compared to end-to-end learning\, where the training is done with a fixed forward model\, our method can be deployed for different forward models. This is possible thanks to the decoupling of the inversion (of the forward model) and denoising steps in PnP.We use the trained denoiser for PnP regularization and establish convergence of the PnP iterations under a technical assumption that we verify numerically. As far as the reconstruction quality is concerned\, our method outperforms state-of-the-art variational and deep-learning fusion techniques.
URL:https://ee.iisc.ac.in/event/thesis-defence-of-mr-unni-v-s/
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20221123T153000
DTEND;TZID=Asia/Kolkata:20221123T163000
DTSTAMP:20260529T053001
CREATED:20221115T032528Z
LAST-MODIFIED:20221115T032641Z
UID:240120-1669217400-1669221000@ee.iisc.ac.in
SUMMARY:Thesis Colloquium of Mr  Bidhan Biswas
DESCRIPTION:Degree  Registered:   Ph.DThesis Title:                Short Circuit and Open Circuit Natural Frequencies of 3-Φ Transformers: Derived Analytical Expressions and its ApplicationsGuide:                          Prof. L. Satish\nDate & Time:               Wednesday\, 23 November 2022\, at 10:00 AM\nVenue:                         Hybrid Mode\, on MS TEAMS and held in HV Seminar Hall\n\n\nAbstract: Frequency Response Analysis (FRA) method is perhaps the most sensitive tool that can detect even the slightest of winding/core movements. High sensitivity\, non-invasiveness\, non-destructiveness\, and on-site capability are some of its salient features – making it an ideal monitoring and detection tool. The existence of Standards (IEEE\, IEC\, and CIGRE) is ample testimony of its global acceptance and superior detection capabilities. The principle of detection is based on observance of a deviation between two measured FRAs which implies a possible fault. Naturally\, the next logical step is to analyse these deviations to determine the type of fault\, estimate the extent of damage and its severity\, and as a bonus\, predict its location\, if possible. However\, even after three decades of existence\, arriving at these inferences  is still at the research level. Even though there is a consensus among all the standards on FRA test/measurement procedures\, best-suited terminal connections\, cable layout\, grounding practices\, etc.\, they remain largely silent regarding interpretation and diagnostics.\n\n\n\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\n\n\n•   Propose a unified and general approach to derive closed-form analytical expressions (for each multiphase winding) to link the measured open and short circuit natural frequencies to electrical parameters of the winding\, and valid for any condition of the neutral•   Define a quantity calculable from the measured FRA’s peak/trough frequencies which is physically related to mechanical damage in the winding\, and perhaps yield some physical insight about damage•   Develop novel methods using the derived analytical expressions to identify an incipient\, discrete\, and localized axial and/or radial displacement in any multiphase winding\, and applicable for any condition of the neutral\n\n\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.\n\n\n\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 are for all 1-Φ and 3-Φ windings.\n\n\n\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.\n\n\n\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.\n\n\n\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.\n\n\n\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.\n\n* * * * * * * * * * ALL ARE CORDIALLY INVITED * * * * * * * * *
URL:https://ee.iisc.ac.in/event/thesis-colloquium-of-mr-bidhan-biswas/
LOCATION:HV seminar Hall
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