机械与电子2024，Vol.42Issue(3)：60-64,70,6.
基于粒子群差分进化极限学习机的电力系统故障诊断模型
Power System Fault Diagnosis Model Via Particle Swarm Differential Evolution-based Extreme Learning Machine
摘要
Abstract
Rapid diagnosis for faults occurring in power systems is of extraordinary significance for timely restoration of power supply and reduction of fault impact.In order to effectively deal with the uncer-tainty in the operation of protective relays and circuit breakers during power system faults,this paper pro-poses an extreme learning machine-based fault diagnosis model based on particle swarm differential evolu-tion algorithm with multiple random variants(MRPSODE).The MRPSODE is used to determine the opti-mal number of nodes in the hidden layer of extreme learning machine to achieve efficient fault diagnosis.A cross-validation method is used to reduce the influence of noise on the original samples to improve the di-agnosis performance.Simulation results of actual fault cases show that the proposed method can success-fully diagnose complex faults and is competitive compared with other methods.关键词
故障诊断/极限学习机/进化算法/交叉验证Key words
fault diagnosis/extreme learning machine/evolutionary algorithm/cross-validation分类
信息技术与安全科学引用本文复制引用
张耀,姚瑶,陈卓,袁子霞,熊国江..基于粒子群差分进化极限学习机的电力系统故障诊断模型[J].机械与电子,2024,42(3):60-64,70,6.基金项目
国家自然科学基金资助项目(51907035) （51907035）
