Low-voltage series arc fault detection based on ECMC and VB-SCN

Abstract

The currents of low-voltage series arc faults are non-stationary and nonlinear, so the selection of fault features and the adaptability of the detection algorithm will affect the detection results. In response to these issues, firstly, a feature selection method combining Euclidean distance, classifier criterion, max-relevance min-redundancy and clustering index is proposed, ECMC. This method can filter out weak correlation features and redundant information in the time, frequency and time–frequency domains. The optimal feature data set constructed by ECMC can fully reflect the characteristics of arc faults. Then, a stochastic configuration network (SCN) based on variational Bayesian (VB) optimization, VB-SCN, is proposed. This method uses VB to optimize the output weight and scale function of SCN iteratively. VB-SCN can improve the quality of hidden-layer nodes and the generalization ability of the network while ensuring the adaptive learning of current features, making arc fault detection fast and stable. Finally, the experiments based on the self-built sample set prove that the optimal feature dataset constructed by ECMC has perfect separability. VB-SCN can adaptively realize the accurate and fast detection of arc faults. The detection accuracy is stable between 98.2506% and 100%, which has better robustness and stability.