Abstract
The detection of high impedance faults (HIFs) on a power distribution system has been a subject of concern for many decades. This poses a very unique challenge to the protection engineers, as it seems to be invisible to be detected by conventional protection schemes. The major concern about HIFs is that they pose a safety risk, as these faults are associated with arcing which may be dangerous for the surroundings. In this work, we propose a technique, which uses feature extraction, classification and a locating algorithm. Discrete wavelet transform (DWT) is used to extract meaningful information, support vector machine (SVM) is used as a classifier and a support vector regression (SVR) scheme is used as a fault location estimator. The technique is tested on a network of a power utility.
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Moloi, K., Jordaan, J.A., Hamam, Y. (2018). Support Vector Machine Based Method for High Impedance Fault Diagnosis in Power Distribution Networks. In: Yin, H., Camacho, D., Novais, P., Tallón-Ballesteros, A. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2018. IDEAL 2018. Lecture Notes in Computer Science(), vol 11314. Springer, Cham. https://doi.org/10.1007/978-3-030-03493-1_2
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DOI: https://doi.org/10.1007/978-3-030-03493-1_2
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