Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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    Power Quality Event Classification Using Transfer Learning on Images
    (Institute of Electrical and Electronics Engineers Inc., 2019) Manikonda, S.K.G.; Santhosh, J.; Sreckala, S.P.K.; Gangwani, S.; Gaonkar, D.N.
    Given the ever-increasing complexity of the electrical grid system, power quality events have been surging in frequency with each passing day. Due to their potential to cause massive losses for a wide variety of customers, it is crucial that such events are detected and classified immediately for appropriate response. in this paper, a novel approach has been developed wherein Transfer Learning techniques have been employed to classify power quality events using image classification. More specifically, the VGG16 model has been utilized to classify five distinct power quality issues by using scalograms as input images. 489 scalograms were generated via feature extraction using wavelet transforms. The VGG16 model has then been trained and tested using the same. Thereafter, the model performance has been evaluated, and the results have been discussed. © 2019 IEEE.
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    Power Quality Event Classification Using Long Short-Term Memory Networks
    (Institute of Electrical and Electronics Engineers Inc., 2019) Manikonda, S.K.G.; Santhosh, J.; Sreckala, S.P.K.; Gangwani, S.; Gaonkar, D.N.
    Due to the increased frequency of power quality events and complexity of modern electric grids, there is a growing need to classify such events. In this paper, a novel approach to the above problem has been explored, wherein Long Short-Term Memory networks have been employed to fulfil the power quality event classification task. Given the sheer size of the input dataset, feature extraction was carried out by deriving important statistical features from the data. The Long Short-Term Memory model used was then trained and tested on these extracted features. Following this, the model performance has been evaluated, wherein the model was shown to perform remarkably well. © 2019 IEEE.
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    Design of a New Single-Phase 15-Level Inverter with Minimized Components
    (Institute of Electrical and Electronics Engineers Inc., 2023) Nageswar Rao, B.N.; Yellasiri, Y.; Shiva Naik, B.S.; Aditya, K.; Karunakaran, E.; Kumar, M.V.
    Multilevel inverters (MLI) provide a number of challenges, the most significant of which is the requirement for a high number of power semiconductors and separate dc supplies to assimilate renewable energy into a grid successfully. Because of this, reducing the number of components used in these kinds of inverters is quite important. Because transformer-based multilevel inverters (TBMIs) have become more commonplace, the use of many dc supplies in the cascaded inverter is no longer necessary for the device to function. Based on the outcomes of this study, a new transformer-based MLI with fifteen levels (15L) and eight switches can be built with only one dc source required. The suggested MLI consists of three isolated transformers. The suggested MLI structure has many unique benefits, including the use of fewer switching components and the availability of self-galvanic isolation. The MATLAB simulation results are carried out to evaluate the effectiveness of the suggested TBMLI. In addition, a comparison of the suggested structure to other recent configurations is presented. © 2023 IEEE.
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    A Zig-Zag Multiwinding Transformer Based AC-DC Converter for EV Battery Charger Using Interleaved Buck DC-DC Converter
    (Institute of Electrical and Electronics Engineers Inc., 2024) Sinha, S.; Kalpana, R.
    Electric vehicles (EVs) are depending on reliable and efficient battery charging infrastructure. This work provides a 2 set of three phase ac-dc converter-based power-quality (PQ) compliant IEEE-519 battery charging system. The zigzag multi-winding transformers (ZZMWTs) will play a role in reducing total harmonic distortion (THD) without the use of any power factor correctors or filter circuits. The secondary side star and delta arrangement of the ZZMWTs will have a phase shift of ± 150. This paper focuses on designing, optimizing, and managing a DC-DC interleaved buck converter for EV battery charging in both constant current (CC) and constant voltage (CV) modes. The planned EV charger performance is assessed using the MATLAB/Simulink environment in terms of THD and PF. A laboratory prototype hardware arrangement of the suggested battery charger is used to validate the results while providing controlled feedback. To support the theoretical analysis, further experimental findings from the lab prototype are presented. © 2024 IEEE.