电器与能效管理技术Issue(3)：1-9,9.DOI:10.16628/j.cnki.2095-8188.2026.03.001
基于IBOA-RF的直流故障电弧诊断研究
Research on DC Fault Arc Diagnosis Based on IBOA-RF
摘要
Abstract
Aiming at the problem that the fault arc characteristics in low-voltage DC systems are weak and difficult to accurately identify under different operating conditions,a DC arc fault diagnosis method based on the improved butterfly optimization algorithm(IBOA)optimizing random forest(RF)is proposed.Firstly,the whale optimization algorithm(WOA)is employed to optimize and improve the parameters of the adaptive noise complete ensemble empirical mode decomposition(ICEEMDAN),and the current signal is decomposed to obtain multiple intrinsic mode functions(IMF).Secondly,the effective components are selected and the hierarchical weighted permutation entropy(HWPE)is extracted to construct the feature vector.Finally,the reverse learning mechanism and dynamic boundary adjustment strategy are introduced to improve the butterfly optimization algorithm,and the IBOA-RF diagnostic model is established for fault identification.The results show that the average recognition accuracy of this method reaches 97.92％under various typical working conditions.The research verifies the effectiveness of this method in the field of DC arc fault diagnosis.关键词
直流故障电弧/特征提取/层次加权排列熵/改进蝴蝶优化算法/故障诊断Key words
DC fault arc/feature extraction/hierarchical weighted permutation entropy/improved butterfly optimization algorithm/fault diagnosis分类
信息技术与安全科学引用本文复制引用
罗希元,刘树鑫,邢朝建,李艳凯..基于IBOA-RF的直流故障电弧诊断研究[J].电器与能效管理技术,2026,(3):1-9,9.基金项目
国家自然科学基金项目(51977132) （51977132）
辽宁省科技重大专项(2020JH1/10100012) （2020JH1/10100012）
辽宁省科技厅"揭榜挂帅"科技攻关专项(2022JH1/10800015) （2022JH1/10800015）
辽宁省教育厅高校基本科研项目(LJ212410142123) （LJ212410142123）
