Power line fault detection and classification system based on HHO-SVM

doi:10.3969/j.issn.2097-0706.2023.03.001

Abstract

Abstract:

In order to accurately detect power line faults and make proper classification， a power fault detection and classification system based on artificial intelligence is designed and implemented. Its core module works based on the support vector machine（SVM），a classical machine learning algorithm. To improve the accuracy of the model， its parameters are optimized by Harris hawks optimization（HHO）. And the experimental results show that the SVM with the optimal parameters offers high accuracy for two open data sets. The system not only realizes the accurate power line fault detection and fault classification， but also streamlines and visualizes the upload， analysis and processing functions for fault data sets.

Key words: power line, fault, detection and classification, AI, support vector machine, Harris hawks optimization

CLC Number:

TM721

WEI Wei, GAO Ciwei, SONG Meng, MING Hao. Power line fault detection and classification system based on HHO-SVM[J]. Integrated Intelligent Energy, 2023, 45(3): 1-8.

Figures/Tables 15

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G C B A	故障类型	样本数	说明
0 0 0 0	No fault	2 365	无故障
1 0 0 1	LG fault	1 129	A相和接地之间故障
0 1 1 0	LL fault	1 004	C相和B相之间故障
1 0 1 1	LLG fault	1 134	A相、B相和接地之间故障
0 1 1 1	LLL fault	1 096	所有三相之间故障
1 1 1 1	LLLG fault	1 133	三相之间对称故障