机电工程技术2024，Vol.53Issue(11)：240-244,305,6.DOI:10.3969/j.issn.1009-9492.2024.00043
基于INGO优化LSSVM的矿用变压器故障诊断方法
Mining Transformer Fault Diagnosis Method Based on INGO Optimized LSSVM
摘要
Abstract
In order to improve the diagnostic accuracy of mining transformers,an improved northern goshawk optimization(INGO)optimized least squares support vector machine(LSSVM)fault diagnosis model for mining transformers is proposed.Firstly,KPCA is used to reduce the dimension of the obtained transformer fault data to reduce the impact of invalid features.Then,using the strategy of Logistic chaotic mapping,Cauchy mutation,and random difference perturbation,the traditional Northern goshawk optimization is improved to improve its optimization ability.Compared with NGO and particle swarm optimization,it is proved that its convergence speed and solution accuracy can be improved.Finally,INGO is used to optimize the relevant hyperparameters in the solution set to improve the classification accuracy of LSSVM.The fault data processed by KPCA is input into the INGO-LSSVM diagnostic model for fault diagnosis simulation research.The simulation results show that the accuracy of the INGO-LSSVM model reaches 94.17%,which is 7.5%and 11.67%higher than the NGO-LSSVM and PSO-LSSVM models,demonstrating that the method can effectively improve the transformer diagnosis performance.关键词
矿用变压器/故障诊断/核主成分分析/北方苍鹰算法/最小二乘支持向量机Key words
mining transformer/fault diagnosis/kernel principal component analysis/northern goshawk optimization/least squares support vector machine分类
信息技术与安全科学引用本文复制引用
俎少杰,张宗瑞..基于INGO优化LSSVM的矿用变压器故障诊断方法[J].机电工程技术,2024,53(11):240-244,305,6.基金项目
国家自然科学基金资助项目(51974151) （51974151）
辽宁省高等学校创新团队项目(LT2019007) （LT2019007）
