Series Arc Fault Detection in a Low-Voltage Power System Based on CEEMDAN Decomposition and Sensitive IMF Selection

• Published in: Journal of sensors 2022-01, Vol.2022, p.1-14
• Main Authors: Fu, Guixia, Chen, Guizhen, Wang, Wei, Wang, Qinbing, Zou, Guofeng
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Decomposition; Electric potential; Fault detection; Fault diagnosis; Frequencies; Kurtosis; Neural networks; Noise; Signal processing; Support vector machines; Voltage; Wavelet transforms; Windows (intervals)
• ISSN: 1687-725X; 1687-7268
• DOI: 10.1155/2022/9453818

In the series arc fault detection of a low-voltage distribution network, the features of the fault current signal are easily submerged and arc fault features are difficult to be represented, which greatly increases the difficulty of fault arc detection based on current signals. To solve these problems, a series arc fault detection method combining CEEMDAN decomposition and sensitive IMF selection is proposed. In this paper, the CEEMDAN algorithm is first applied to complete decomposition of the arc current in series faults. Then, 12 feature indicators of the arc current are defined and the frequency band division of the IMF component is realized according to the kurtosis index and energy feature which are more sensitive. The time window-based feature calculation method is proposed to obtain the local features of the time scale of each high-frequency IMF component. Accurate selection of sensitive IMF components is realized by comparing feature indexes such as the variance and root mean square value. Finally, for the current feature set, the second dimension reduction is realized by the subspace transformation algorithm and the series arc fault detection is realized based on the SVM. The actual experiments show that the optimal detection accuracy of the proposed method is 91.67% and the average accuracy of 10 crossvalidation experiments is 88.33%. It shows that the proposed sensitive IMF selection method can effectively capture the fault component signals in the current and the proposed fault feature description method has good representation and discrimination ability.