基于混合特征选择和INGO-DHKELM的变压器故障诊断方法

Transformer Fault Diagnosis Method Based on Hybrid Feature Selection and INGO-DHKELM

针对变压器故障特征选择困难和诊断模型准确率较低的问题,提出一种混合式故障特征选择方法,并利用改进北方苍鹰优化算法(improved northern goshawk optimization algorithm,INGO)优化深度混合核极限学习机(deep hybrid kernel limit learning machine,DHKELM)实现变压器故障诊断。首先,基于相关比值法构建24维变压器故障特征集,从线性相关和非线性相关的角度出发,采用Pearson相关系数和互信息法,筛除相关性较低的特征。其次,引入Logistic混沌映射、随机反向学习和自适应t分布变异改进NGO算法,提升其寻优性能。然后,利用INGO算法对保留特征进行二次筛选,获得最优输入特征。最后,将极限学习机自动编码器引入混合核极限学习机中,建立DHKELM诊断模型,利用INGO对DHKELM模型初始参数进行优化,完成INGO-DHKELM变压器故障诊断模型的构建。实验表明,与常规特征选择方法相比,利用混合式故障特征选择方法所选择的输入特征进行故障诊断能够有效提升诊断准确率;相较于其他优化型诊断模型,INGO-DHKELM具有更高的准确率和更好的稳定性。

Aiming at the difficulty in selecting transformer fault features and the low accuracy of diagnosis model,a hybrid fault feature selection method is proposed,and the deep hybrid kernel limit learning machine(DHKELM)is optimized by the improved northern Goshawk optimization algorithm(INGO)to realize transformer fault diagnosis.Firstly,a 24-dimensional transformer fault feature set is constructed based on correlation ratio method.From the perspective of linear correlation and nonlinear correlation,Pearson correlation coefficient and mutual information method are used to filter out the features with low correlation.Secondly,Logistic chaotic mapping,stochastic reverse learning and adaptive t-distribution mutation are introduced to improve NGO algorithm,so as to improve its optimization performance.Then,INGO algorithm is used to filter the retained features for the second time to obtain the optimal input features.Finally,the automatic encoder of the extreme learning machine is introduced into the hybrid core extreme learning machine,and the DHKELM diagnosis model is established.The initial parameters of the DHKELM model are optimized by INGO,and the INGO-DHKELM transformer fault diagnosis model is completed.Experiments show that compared with the conventional feature selection method,the input features selected by the hybrid fault feature selection method can effectively improve the diagnosis accuracy.Compared with other optimized diagnosis models,INGO-DHKELM has higher accuracy and better stability.