机电工程技术2024，Vol.53Issue(7)：246-250,259,6.DOI:10.3969/j.issn.1009-9492.2024.07.052
基于IWOA优化LSSVM的煤矿变压器故障诊断研究
Research on Fault Diagnosis of Coal Mine Transformer Based on IWOA Optimized LSSVM
摘要
Abstract
In order to quickly identify transformer fault types and improve the accuracy of fault diagnosis,an improved Whale Algorithm(IWOA)optimized least squares support vector machine(LSSVM)transformer fault diagnosis model is proposed.Using kernel principal component analysis(KPCA)to reduce the dimensionality of complex and diverse data and reduce invalid features.The Whale Algorithm by using heuristic probability,dynamic weights optimized by fusing sine functions,and optimized proportional coefficients are improved to enhance its optimization ability.Performance tests are conducted and the algorithm with whale algorithm(WOA)and particle swarm optimization(PSO)are compared to verify its effectiveness.Using the improved whale algorithm to optimize and solve the relevant hyperparameters of LSSVM,premature problems in the algorithm and improving the accuracy of transformer fault diagnosis are avoided.And simulation experiments are conducted on the model,and the simulation results show that the accuracy reaches93.33%,which increases by6.66% and10% compared to the WOA-LSSVM model and PSO-LSSVM model,respectively,showing good fault diagnosis performance.关键词
变压器/故障诊断/核主成分分析/鲸鱼优化算法/最小二乘支持向量机Key words
transformer/fault diagnosis/kernel principal component analysis/whale optimization algorithm/least squares support vector machine分类
矿业与冶金引用本文复制引用
郭志强,呼成林,张宗瑞..基于IWOA优化LSSVM的煤矿变压器故障诊断研究[J].机电工程技术,2024,53(7):246-250,259,6.基金项目
国家自然科学基金资助项目(51974151) （51974151）
