基于KPCA-CGSSA-KELM的变压器故障识别方法

Transformer fault recognition method based on KPCA-CGSSA-KELM

针对冗余特征对变压器故障识别影响和传统方法识别准确率低的问题,提出一种基于核主成分分析(kernal principal component analysis, KPCA)与混沌麻雀搜索算法(chaos gauss sparrow search algorithm, CGSSA)优化核极限学习机(kernelized extreme learning machine, KELM)的变压器故障识别方法。首先,通过KPCA对变压器故障数据进行预处理,降低特征间相关性。其次,通过引入改进Tent映射和高斯变异策略优化麻雀搜索算法提高其搜索精度和收敛速度,并将CGSSA与麻雀搜索算法(SSA)、灰狼优化算法(GWO)及鲸鱼优化算法(WOA)效果进行对比。最后,利用经KPCA处理后的特征数据作为模型输入,并通过CGSSA准确选择KELM的核函数参数和正则化系数,建立KPCA-CGSSA-KELM变压器故障识别模型。实验结果表明,在相同输入数据的情况下,CGSSA在收敛速度和寻优精度方面均有提升,且所提方法识别准确率为95.7%,较WOA-KELM、GWO-KELM、SSA-KELM分别提高18.6%、10%、15.7%。结果表明所提方法能有效处理冗余特征,提高故障识别准确率,证明了使用所提方法在在冗余特征影响的情况下进行变压器故障识别的有效性与可行性。

To address the problems posed by redundant features in transformer fault recognition and the low accuracy of traditional methods,a transformer fault recognition method leveraging kernel principal component analysis(KPCA)in conjunction with chaotic sparrow search algorithm(CGSSA)is introduced.Initially,KPCA is employed to preprocess the transformer fault data,aiming to mitigate the correlations among features.Subsequently,CGSSA is improved by incorporating the improved Tent map and Gaussian mutation to increase the search accuracy and convergence speed of the algorithm.Comparing the results involving CGSSA,SSA,GWO and WOA.Utilizing the data extracted through the KPCA as the model input,CGSSA is then used to select the kernel function parameters and regularization coefficient of KELM,thereby establishing the KPCA-CGSSA-KELM transformer fault recognition model.The experimental results demonstrate that,with the identical input data,CGSSA has the best results in terms of convergence speed and optimization accuracy.In addition,the proposed method shows the fault recognition accuracy of 95.7%,which is 18.6%,10%,and 15.7%higher than WOA-KELM,GWO-KELM,and SSA-KELM,respectively.These findings suggest that the proposed method effectively manages the impact of redundant features and enhances the precision of transformer fault recognition,thus verifying the validity and feasibility of the proposed method for transformer fault recognition under the feature redundancy.