Issue |
Int. J. Metrol. Qual. Eng.
Volume 15, 2024
|
|
---|---|---|
Article Number | 3 | |
Number of page(s) | 10 | |
DOI | https://doi.org/10.1051/ijmqe/2024001 | |
Published online | 08 March 2024 |
Research article
Low-voltage AC series arc fault detection based on Fisher-mutual information feature selection
1
Shandong University of Technology, Zibo City 255000, Shandong, China
2
Electric Power Research Institute of State Grid Hubei Electric Power Co., Ltd., Hubei Wuhan 430077, China
* Corresponding author: wwsdut@163.com
Received:
23
September
2023
Accepted:
12
February
2024
The detection of multi-feature fusion is a crucial approach to address the issue of series arc fault detection. Effective feature selection plays a vital role in enhancing the accuracy of the classifier and reducing system complexity. In this study, a feature selection algorithm based on Fisher-mutual information is proposed to tackle the problem of feature selection in multi-feature fusion detection. This algorithm utilizes the characteristics of arc fault voltage source to construct a feature pool. The Fisher-score algorithm and mutual information algorithm are employed to construct an optimal feature subset. The feature subset undergoes rough selection by retaining key features of the classifier and fine selection by eliminating redundant features. Experimental results and comparisons with related methods demonstrate that the proposed feature selection method significantly enhances the classifier's recognition accuracy, reduces classification and recognition time, diminishes the feature dimension, and outperforms other existing methods.
Key words: Series arc fault / Fisher-score / mutual information / GA-BP neural network / feature selection
© B. Qin et al., Published by EDP Sciences, 2024
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