基于改进SSA优化MDS-SVM的变压器故障诊断方法

为了提高变压器故障诊断精度,提出一种基于改进SSA优化MDS-SVM的变压器故障诊断方法.首先,利用多维尺度缩放法(multiple dimensional scaling,MDS)对20维变压器故障特征数据进行特征提取,降低高维数据存在的稀疏性和多重共线性;其次,引入樽海鞘群算法(salp swarm algorithm,SSA),并对该算法进行改进,增置信赖机制和突变,以提高算法的收敛速度和收敛能力;然后,通过与原始SSA、PSO、GWO和$ beta $-GWO算法进行寻优测试对比来验证改进SSA算法的优越性;最后,使用改进SSA算法对MDS降低维数和支持向量机(support vector machine,SVM)的参数联合寻优,构建新的故障诊断模型.分析并比较其与常用算法优化的SVM故障诊断模型、BP神经网络(back propagation neural network,BPNN)、K最近邻(K-nearest neighbor,KNN)以及随机森林(random forest,RF)故障诊断模型的故障诊断精确度,结果表明,基于改进SSA的MDS-SVM变压器故障诊断模型的精确度高于其他算法模型,且泛化能力较强.

Transformer fault diagnosis method based on improved SSA optimized MDS-SVM

In order to improve the accuracy of transformer fault diagnosis, a transformer fault diagnosis method based on an improved SSA optimized MDS-SVM is proposed. Firstly, a multi-dimensional scaling(MDS) method is used to extract features from 20 dimensional transformer fault feature data to reduce the sparsity and multicollinearity of high-dimensional data. Then, the paper introduces a salp swarm algorithm(SSA) and improves the algorithm by adding trust mechanism and mutation to improve the convergence speed and ability of the algorithm. By comparing with the original SSA, PSO, GWO, and beta-GWO, the improved SSA algorithm is tested to verify its superiority. Finally, the improved algorithm is used to reduce the dimension of the MDS and optimize the parameters of a support vector machine(SVM) to build a new fault diagnosis model. The fault diagnosis accuracy is analyzed and compared with that of the SVM fault diagnosis model optimized by common algorithms, the BP neural network(BPNN), the $ K $-nearest neighbor(KNN) and random forest(RF) fault diagnosis models. The results show that the accuracy of the MDS-SVM transformer fault diagnosis model based on the improved SSA is higher than that of other algorithm models, and the generalization ability is stronger.