Conference Papers

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

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Author: search.filters.author.Manikonda, S.K.G.Has files: No

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    A New Islanding Detection Method Using Transfer Learning Technique
    (IEEE Computer Society help@computer.org, 2018) Manikonda, S.K.G.; Gaonkar, D.N.
    The increasing need for energy in the recent times is unprecedented, which is driving the penetration of renewable sources in distribution system in a big way. The increasing number of renewable sources in a system has made the operation, control and protection of the system very complex. One of the key issues in seamless interconnection of renewable energy sources to a system is islanding. This paper proposes a new method to detect islanding in an efficient way by employing transfer learning based technique for image classification. The results show that the proposed method can successfully classify islanding events with a good accuracy. © 2018 IEEE.
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    A Novel Islanding Detection Method Based on Transfer Learning Technique Using VGG16 Network
    (Institute of Electrical and Electronics Engineers Inc., 2019) Manikonda, S.K.G.; Gaonkar, D.N.
    The escalating need for energy in the recent times is unprecedented, which is driving the penetration of renewable energy sources in distribution system in a big way. The growing number of renewable sources in a system has made the control, operation and protection of the system very complex. Among others, one of the key issues in seamless interconnection of renewable energy sources to a system is islanding. This paper proposes a new and efficient islanding detection method that employs transfer learning based technique. The results show that the proposed method can successfully classify islanding events with a good accuracy. © 2019 IEEE.
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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 Convolutional Neural Networks on Images
    (Institute of Electrical and Electronics Engineers Inc., 2019) Manikonda, S.K.G.; Gangwani, S.; Sreckala, S.P.K.; Santhosh, J.; Gaonkar, D.N.
    There is an increasing need to detect powerquality events due to the surge of such disturbances and the losses they can cause. Detection and classification of such events allow for an immediate response. In this paper, a novel approach to this problem has been detailed, wherein Convolutional Neural Networks have been used to classify power quality events using image classification. These Convolutional Neural Network models use scalograms as input images to carry out the power quality classification task. Scalograms are generated by feature extraction using the wavelet transform. The model is then trained and tested on the same. The model performance has then been evaluated, wherein it was shown to perform well on the validation data set. © 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.