NDLI: Application of Machine Learning for Fault Classification and Location in a Radial Distribution Grid

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Application of Machine Learning for Fault Classification and Location in a Radial Distribution Grid

Content Provider	MDPI
Author	Mamuya, Yordanos Dametw Lee, Yih-Der Shen, Jing-Wen Kuo, Cheng-Chien
Copyright Year	2020
Abstract	Fault location with the highest possible accuracy has a significant role in expediting the restoration process, after being exposed to any kind of fault in power distribution grids. This paper provides fault detection, classification, and location methods using machine learning tools and advanced signal processing for a radial distribution grid. The three-phase current signals, one cycle before and one cycle after the inception of the fault are measured at the sending end of the grid. A discrete wavelet transform (DWT) is employed to extract useful features from the three-phase current signal. Standard statistical techniques are then applied onto DWT coefficients to extract the useful features. Among many features, mean, standard deviation (SD), energy, skewness, kurtosis, and entropy are evaluated and fed into the artificial neural network (ANN), Multilayer perceptron (MLP), and extreme learning machine (ELM), to identify the fault type and its location. During the training process, all types of faults with variations in the loading and fault resistance are considered. The performance of the proposed fault locating methods is evaluated in terms of root mean absolute percentage error (MAPE), root mean squared error (RMSE), Willmott’s index of agreement (WIA), coefficient of determination ( $R^{2}$ ), and Nash-Sutcliffe model efficiency coefficient (NSEC). The time it takes for training and testing are also considered. The proposed method that discrete wavelet transforms with machine learning is a very accurate and reliable method for fault classifying and locating in both a balanced and unbalanced radial system. 100% fault detection accuracy is achieved for all types of faults. Except for the slight confusion of three line to ground (3LG) and three line (3L) faults, 100% classification accuracy is also achieved. The performance measures show that both MLP and ELM are very accurate and comparative in locating faults. The method can be further applied for meshed networks with multiple distributed generators. Renewable generations in the form of distributed generation units can also be studied.
Starting Page	4965
e-ISSN	20763417
DOI	10.3390/app10144965
Journal	Applied Sciences
Issue Number	14
Volume Number	10
Language	English
Publisher	MDPI
Publisher Date	2020-07-19
Access Restriction	Open
Subject Keyword	Applied Sciences Industrial Engineering Artificial Neural Network (ann) Extreme Learning Machine (elm) Multilayer Perceptron (mlp) Discrete Wavelet Transform (dwt) Fault Classification Fault Location
Content Type	Text
Resource Type	Article

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