摘要
为解决变压器故障诊断精度较低的问题,提出了一种多策略改进黏菌算法(ISMA)阶段优化混合核支持向量机(HSVM)的变压器故障诊断新方法。首先,利用主成分分析(PCA)来消除变量之间的信息冗余并降低数据集维度。其次,引入黏菌算法(SMA),并结合Logistic混沌映射、二次插值、自适应权重多策略改进SMA,以提高SMA算法收敛速度和局部搜索能力;然后,与原始SMA、WHO和GWO算法进行寻优测试,对比验证改进后SMA算法的优越性;最后,使用改进SMA算法分阶段对混合核支持向量机参数寻优,构建ISMA-HSVM变压器故障诊断模型。将降维后的特征数据输入HSVM模型与BPPN、ELM和SVM进行比较,HSVM模型的诊断准确性分别提高了5.55%、8.89%、5.55%。使用ISMA优化HSVM模型参数,与WHO、GWO、SMA算法优化效果比较,结果准确性提高了13.33%、12.22%、5.55%。其中,ISMA-HSVM模型的诊断精度为93.33%。实验结果表明,所提模型有效提升故障诊断分类性能,且具有较高的故障诊断精度。
A new method for transformer fault diagnosis has been proposed to address the issue of low diagnostic accuracy.This approach involves the use of a multi-strategy improved slime mould algorithm(ISMA)for phase optimization in conjunction with a hybrid kernel support vector machine(HSVM).Firstly,principal component analysis(PCA)is employed to eliminate information redundancy among variables and reduce the dimensionality of the dataset.Secondly,the slime mould algorithm(SMA)is introduced,and a Logistic chaotic mapping,quadratic interpolation,and adaptive weight multi-strategy improved SMA are proposed to enhance the convergence speed and local search capability of the SMA algorithm.Subsequently,optimization tests are conducted by comparing the improved SMA algorithm with the original SMA,WHO,and GWO algorithms to validate its superiority.Finally,the improved SMA algorithm is utilized in a phased manner for parameter optimization of HSVM,leading to the construction of the ISMA-HSVM transformer fault diagnosis model.After inputting the dimension-reduced feature data into the HSVM model and comparing it with BPPN,ELM,and SVM,the diagnostic accuracy of the HSVM model improved by 5.55%,8.89%,and 5.55%,respectively.By optimizing the HSVM model using ISMA and comparing it with WHO,GWO,and SMA algorithm optimizations,the accuracy increased by 13.33%,12.22%,and 5.55%,respectively.Specifically,the diagnostic accuracy of the ISMA-HSVM model reached 93.33%.The experimental results indicate that the proposed model effectively enhances fault diagnosis classification performance and demonstrates a high level of diagnostic accuracy.
作者
谢国民
林忠宝
Xie Guomin;Lin Zhongbao(Faculty of Electrical and Control Engineering,Liaoning Technical University,Huludao 125105,China)
出处
《电子测量与仪器学报》
CSCD
北大核心
2024年第3期67-76,共10页
Journal of Electronic Measurement and Instrumentation
基金
国家自然科学基金(51974151)
辽宁省教育厅重点实验室基金(LJZS003)项目资助。
关键词
故障诊断
主成分分析
黏菌算法
混合核支持向量机
fault diagnosis
principal component analysis
slime mold algorithm
hybrid kernel support vector machine
