Arc Fault Detection Method Based on Whale Optimization Algorithm to Improve Random Forest

doi:10.16628/j.cnki.2095-8188.2024.02.004

Abstract

Abstract:

Based on the wide variety of appliances, it is difficult to detect similar current waveforms when different appliances experience a fault arc, which can easily lead to misoperation and rejection of protection. A arc fault detection method based on whale optimization algorithm (WOA) to improve random forest (RF) algorithm is proposed. According to the national standard GB14287.4—2014, a arc fault experimental platform is independently designed and built to collect fault arc signals and extract feature values. Introducing an improved WOA to intelligently optimize and solve RF parameters. Comparing the experimental results of the classical random forest algorithm, a total of 320 sets of normal fault data from seven load combinations are collected for experiments. The experimental results show that the recognition effect of the optimized model is better than that of the classical random forest algorithm, and it can effectively diagnose arcs fault.

Key words: arc fault, whale optimization algorithm(WOA), feature extraction, improved random forest

CLC Number:

TM501.2

ZHU Hai. Arc Fault Detection Method Based on Whale Optimization Algorithm to Improve Random Forest[J]. LOW VOLTAGE APPARATUS, 2024, 0(2): 21-27.

Figures/Tables 11

References 26

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负载类别	负载名称
线性负载	电水壶1 600 W
线性负载	电吹风1 000 W
非线性负载	电磁炉1 300 W
	调压电路800 W
	计算机100 W
混合负载	计算机(支路电弧)+水壶
混合负载	计算机(干路电弧)+水壶

批次	经典算法准确率/%	改进算法准确率/%	批次	经典算法准确率/%	改进算法准确率/%
1	90.2	97.0	6	87.9	95.8
2	87.8	96.2	7	91.3	97.5
3	79.5	96.1	8	92.5	97.8
4	83.5	97.6	9	91.1	98.4
5	89.4	99.3	10	92.1	97.6