Guan Shan, Wu Tong-Yu, Yang Hai-Qi
School of Mechanic Engineering, Northeast Electric Power University, Jilin, 132012, China.
Sci Rep. 2024 Jul 30;14(1):17676. doi: 10.1038/s41598-024-68141-z.
This paper proposes a transformer fault diagnosis method based on ACGAN and CGWO-LSSVM to address the problem of misjudgment and low diagnostic accuracy caused by the small number and uneven distribution of some fault samples in transformer fault diagnosis. Firstly, generate adversarial networks through auxiliary classification conditions, The ACGAN method expands a small and imbalanced number of samples to obtain balanced and expanded data; Secondly, the non coding ratio method is used to construct the characteristics of dissolved gases in oil, and kernel principal component analysis is used, KPCA method for feature fusion; Finally, using the improved cubic gray wolf optimization algorithm, CGWO for least square support vector machines, optimize the parameters of the LSSVM model and construct a transformer fault diagnosis model. The results show that the proposed method has a low false alarm rate and a diagnostic accuracy of 97.66%, compared to IGOA-LSSVM the IChOA-LSSVM and PSO-LSSVM methods improved accuracy by 0.12, 1.76, and 2.58%, respectively. This method has been proven to solve the problems of misjudgment and low diagnostic accuracy caused by small sample sizes and uneven distribution. It is suitable for multi classification fault diagnosis of transformer imbalanced datasets and is superior to other methods.
针对变压器故障诊断中部分故障样本数量少且分布不均衡导致误判和诊断准确率低的问题，本文提出一种基于ACGAN和CGWO-LSSVM的变压器故障诊断方法。首先，通过辅助分类条件生成对抗网络，利用ACGAN方法对少量且不均衡的样本进行扩充，得到平衡且扩充后的数据；其次，采用非编码比例法构建油中溶解气体特征，并利用核主成分分析法，即KPCA方法进行特征融合；最后，利用改进的立方灰狼优化算法，即CGWO对最小二乘支持向量机进行优化，优化LSSVM模型参数，构建变压器故障诊断模型。结果表明，所提方法误报率低，诊断准确率达97.66%，与IGOA-LSSVM、IChOA-LSSVM和PSO-LSSVM方法相比，准确率分别提高了0.12%、1.76%和2.58%。该方法已被证明能够解决小样本规模和分布不均衡导致的误判和诊断准确率低的问题。它适用于变压器不平衡数据集的多分类故障诊断，且优于其他方法。
