Abstract
Incipient faults which occur due to the electrical arc occurrence within the facility cables which have insulation problems are hard to detect by normal protective relays, and with passing time can become a permanent fault within the system. Employing Radial Basis Function Neural Network (RBFNN) method, the paper puts forward the method to detect the faults and finding out the efficiency and advantages of RBFNN method over other methods within the facility grid. The result proposed in this paper is based on the differentiation between the wavelet transform method and RBFNN method of the measured voltage and fundamental component of the measured voltage, evaluated by RBFNN computation within the sending end of the cable during the fault using which the incipient fault is detected. This method uses neurons to train the model and increase its accuracy so that with new data set, it can produce super accurate and faster results.
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Mishra, D.P., Biswal, P., Sahu, S.S., Dash, S., Giri, N.C. (2023). Radial Basis Function Neural Network with Wavelet Transform for Fault Detection in Transmission Line. In: Rani, A., Kumar, B., Shrivastava, V., Bansal, R.C. (eds) Signals, Machines and Automation. SIGMA 2022. Lecture Notes in Electrical Engineering, vol 1023. Springer, Singapore. https://doi.org/10.1007/978-981-99-0969-8_9
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DOI: https://doi.org/10.1007/978-981-99-0969-8_9
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