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Energies 2018, 11(9), 2328; https://doi.org/10.3390/en11092328

Distribution Grids Fault Location employing ST based Optimized Machine Learning Approach

1
Electrical Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia
2
Electrical Engineering Department, Menoufia University, Shebin El-Kom 32511, Egypt
*
Author to whom correspondence should be addressed.
Received: 11 July 2018 / Revised: 12 August 2018 / Accepted: 14 August 2018 / Published: 4 September 2018
(This article belongs to the Section Electrical Power and Energy System)
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Abstract

Precise information of fault location plays a vital role in expediting the restoration process, after being subjected to any kind of fault in power distribution grids. This paper proposed the Stockwell transform (ST) based optimized machine learning approach, to locate the faults and to identify the faulty sections in the distribution grids. This research employed the ST to extract useful features from the recorded three-phase current signals and fetches them as inputs to different machine learning tools (MLT), including the multilayer perceptron neural networks (MLP-NN), support vector machines (SVM), and extreme learning machines (ELM). The proposed approach employed the constriction-factor particle swarm optimization (CF-PSO) technique, to optimize the parameters of the SVM and ELM for their better generalization performance. Hence, it compared the obtained results of the test datasets in terms of the selected statistical performance indices, including the root mean squared error (RMSE), mean absolute percentage error (MAPE), percent bias (PBIAS), RMSE-observations to standard deviation ratio (RSR), coefficient of determination (R2), Willmott’s index of agreement (WIA), and Nash–Sutcliffe model efficiency coefficient (NSEC) to confirm the effectiveness of the developed fault location scheme. The satisfactory values of the statistical performance indices, indicated the superiority of the optimized machine learning tools over the non-optimized tools in locating faults. In addition, this research confirmed the efficacy of the faulty section identification scheme based on overall accuracy. Furthermore, the presented results validated the robustness of the developed approach against the measurement noise and uncertainties associated with pre-fault loading condition, fault resistance, and inception angle. View Full-Text
Keywords: distribution grid; extreme learning machine; fault location; feature extraction; multilayer perceptron neural network; s-transform; support vector machine distribution grid; extreme learning machine; fault location; feature extraction; multilayer perceptron neural network; s-transform; support vector machine
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Shafiullah, M.; Abido, M.A.; Abdel-Fattah, T. Distribution Grids Fault Location employing ST based Optimized Machine Learning Approach. Energies 2018, 11, 2328.

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