电器与能效管理技术Issue(2)：21-27,55,8.DOI:10.16628/j.cnki.2095-8188.2024.02.004
基于鲸鱼优化算法改进随机森林的电弧故障检测方法
Arc Fault Detection Method Based on Whale Optimization Algorithm to Improve Random Forest
摘要
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.关键词
电弧故障/鲸鱼优化算法/特征提取/改进随机森林Key words
arc fault/whale optimization algorithm(WOA)/feature extraction/improved random forest分类
信息技术与安全科学引用本文复制引用
朱海..基于鲸鱼优化算法改进随机森林的电弧故障检测方法[J].电器与能效管理技术,2024,(2):21-27,55,8.基金项目
《基于功率平衡和区域惯量分布的高渗透率新能源电网自适应失步解列方法研究》(20220101252JC) （20220101252JC）
