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.
Highlights
The detailed information regarding the faulty area and fault location, plays a vital role in expediting the restoration process in electric utilities after being subjected to any kind of fault.there is a growing research interest to locate faults and identify faulty sections in distribution grids efficiently, to reduce customer minute losses and revenue losses for the utilities.The available fault location techniques for distribution grids can be categorized into three major groups namely the impedance, the traveling wave, and the knowledge-based techniques [1,2,3].The impedance-based technique, evaluates fault location using voltage and current measurements available at the substation end, as well as technical information of the distribution grids, including grid topology, load, and line data
The FT loses the temporal information and provides erroneous results for non-stationary signals. In response to this deficiency, Dennis Gabor (1946) employed a small sampling window of the regular interval to map a signal into a two-dimensional function of time and frequency, and the adaptation is known as short time Fourier transform (STFT) [25]
The proposed fault location/faculty section identification techniques based on the Stockwell transform (ST) and machine learning tools (MLT), MLT, were tested on two different test distribution feeders
Summary
The detailed information regarding the faulty area and fault location, plays a vital role in expediting the restoration process in electric utilities after being subjected to any kind of fault. The impedance-based technique, evaluates fault location using voltage and current measurements available at the substation end, as well as technical information of the distribution grids, including grid topology, load, and line data. This research proposes the hybrid fault location and faulty section identification approach, for power distribution grids, combining the Stockwell transform and different machine learning tools. The proposed approach starts with the extraction of useful features from the ST decomposed faulty current signals, collected from different branches of the grids It fetches the extracted characteristic features, as inputs of the different machine learning tools, including the MLP-NN, SVM, and ELM to get decisions on fault location and faulty section. The presented results, demonstrated the independence of the proposed approach in the presence of measurement noise, pre-fault loading condition, fault resistance, and inception angle
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