Kurtosis-Skewness Scanning and Machine Learning-based Discrimination of Fault Location in Radial Power Distribution Network

Chattopadhyay, Surajit; Roy, Bhaskar; Bera, Animesh; Humne, Gaurang; Alam, Md Sanir; Bandyopadhyay, Gopal

doi:10.1109/icpee54198.2023.10060396

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2024

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“…Reference [17] makes a statistical treatment of Kurtosis-Skewness analysis after the wavelet data extraction, and then trains different machine learning tools to achieve fault location. The best results were obtained by the Decision Tree Machine Learning.…”

Section: Introductionmentioning

confidence: 99%

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2024

Adv. Electr. Comp. Eng.

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Every power system is vulnerable to fault occurrences. Under permanent fault conditions, maintenance crew has the duty to detect the problem, repair the defect and recover the power supply system. If the fault is previously located, the repair can be performed faster. The common methods to locate the fault in electrical distribution systems use the final user information or some heuristics with fuse coordination and the loss of loads. In this paper, a new algorithm for fault location in distribution power systems is presented. Using computational simulations, travelling waves theory, wavelet transform, a new data preprocessing feature, and artificial neural networks, this new algorithm tries to approximate the fault location using data provided by only one measurement point at the beginning of the feeder.

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Section: Introductionmentioning

confidence: 99%

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2024

Adv. Electr. Comp. Eng.

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Kurtosis-Skewness Scanning and Machine Learning-based Discrimination of Fault Location in Radial Power Distribution Network

Cited by 1 publication

References 22 publications

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