Abstract
Transmission lines are the interconnecting power lines between the generating station and the distribution system. To detect and classify faults in transmission lines are very much important to restore the power supply to avoid power interruption, loss in economy to some extent and unnecessary wastage of time and energy of the workers. This article proposes an efficient machine learning classifier support vector machine (SVM) to detect and classify faults. The input features to the SVM are extracted by decomposing the fault signals using a signal processing approach empirical mode decomposition (EMD). Western System Coordinating Council (WSCC) 9-bus system is considered for simulation study. The proposed EMD-based SVM has the ability to classify the faults with reasonable higher accuracy.
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Das, S.R., Mallick, R.K., Nayak, P., mishra, S. (2022). Fault Classification in Transmission Line Using Empirical Mode Decomposition and Support Vector Machine. In: Mohanty, M.N., Das, S. (eds) Advances in Intelligent Computing and Communication. Lecture Notes in Networks and Systems, vol 430. Springer, Singapore. https://doi.org/10.1007/978-981-19-0825-5_17
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DOI: https://doi.org/10.1007/978-981-19-0825-5_17
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Online ISBN: 978-981-19-0825-5
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