基于混合采样与IHBA-SVM的变压器故障辨识方法

Transformer fault identification method based on hybrid sampling and IHBA-SVM

针对变压器故障数据的不平衡性弱化故障分类能力的问题,提出混合采样与改进蜜獾算法(IHBA)优化支持向量机(SVM)的变压器故障诊断方法。首先采用K近邻去噪、K均值聚类(K-means)与合成少数类过采样(SMOTE)对数据进行混合采样处理,以缓解诊断结果向多数类的偏移;然后使用Tent映射、轮盘赌随机搜索机制和最优个体扰动策略对传统蜜獾算法(HBA)进行改进,并使用IHBA优化SVM参数,以进一步提升变压器故障辨识能力;最后对所提方法进行算例仿真,结果显示,相较于传统的变压器故障辨识方法,采用K近邻去噪、K-means、SMOTE混合采样与IHBA-SVM相结合的故障诊断模型获得了最高的宏F1和微F1值,分别达到0.877和0.886,表明提出模型不仅具有更高的整体分类能力,且更能兼顾对少数类故障的辨识。

Aiming at the problem that the unbalance of transformer fault data weakens the ability of fault classification,a transformer fault diagnosis method based on hybrid sampling and improved honey badger algorithm(IHBA)and optimized support vector machine(SVM)is proposed.Firstly,K-nearest neighbor denoising,K-means and SMOTE are used for hybrid sampling of data to alleviate the shift of diagnosis results to the majority class.Then,the traditional honey badger algorithm(HBA)is improved by using tent mapping,roulette random search mechanism and optimal individual perturbation strategy,and the SVM parameters are optimized by IHBA to further improve the transformer fault identification ability.Finally,the simulation results of the proposed method show that,compared with the traditional transformer fault identification method,the fault diagnosis model combining K-Nearest Neighbor denoising,K-means,SMOTE hybrid sampling and IHBA-SVM obtains the highest macro F1 and micro F1 values,reaching 0.877 and 0.886 respectively,which indicates that the proposed model not only has higher overall classification ability,but also can better identify minority faults.