Abstract

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.

Highlights

  • In the low-voltage distribution network, arc fault is caused by line insulation damage and loose terminal

  • Experimental results show that the combination of CEEMDAN decomposition and sensitive intrinsic mode functions (IMF) selection strategy, as well as the local feature construction method based on the time window, can effectively capture the discriminant features of the series arc, which realize the reliable detection of the arc fault

  • In order to realize arc detection of series faults accurately and efficiently, a detection algorithm based on CEEMDAN decomposition and sensitive IMF selection is proposed

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Summary

Introduction

In the low-voltage distribution network, arc fault is caused by line insulation damage and loose terminal. Experimental results show that the combination of CEEMDAN decomposition and sensitive IMF selection strategy, as well as the local feature construction method based on the time window, can effectively capture the discriminant features of the series arc, which realize the reliable detection of the arc fault. (1) To obtain complete decomposition results of fault current signals, the CEEMDAN decomposition algorithm is first applied to current signal decomposition (2) To extract the strongest discriminative IMF component, a method of frequency division and an accurate selection method of sensitive IMF are proposed (3) To overcome the difficulty in fault feature representation, a local feature calculation method based on the time window is proposed and 12 feature indexes are defined to express fault features (4) To better improve the effectiveness of series arc fault features and fault detection accuracy, this paper adopts subspace transformation for feature compression and SVM for fault detection.

CEEMDAN Algorithm
Feature Calculation of the Current Signal
Selection of the Sensitive IMF Component and Series Arc Fault Detection
Experiment and Analysis
Conclusions
Findings
Conflicts of Interest

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