Abstract
This paper provides the understanding of features and method used for classifying the root cause of the fault event using those unique features. In particular, the paper focuses on identification of fault caused by the animal, lighting, tree, equipment and vehicle events. Different features are extracted to provide the input to neural network. For extracting features voltage and current samples collected during the fault at the monitoring, stations were analyzed. Artificial neural network with multilayer perceptron model is trained for the classification. Features were calculated using 154 real-world fault events and applied to the classifier. It estimated to be having 76.47% of classification rate.
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Vaghera, P., Kumar, D., Kothari, N., Niamatullah, S. (2020). Identification of the Source of Power Quality Degradation Using Signature Extraction from Voltage Waveforms. In: Mehta, A., Rawat, A., Chauhan, P. (eds) Advances in Electric Power and Energy Infrastructure. Lecture Notes in Electrical Engineering, vol 608. Springer, Singapore. https://doi.org/10.1007/978-981-15-0206-4_19
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DOI: https://doi.org/10.1007/978-981-15-0206-4_19
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