摘要
针对变压器故障数据分布不平衡而导致的故障诊断模型分类性能下降和模型诊断结果不可解释的问题,提出一种基于Borderline-SMOTE-CatBoost-SHAP的变压器故障诊断方法。首先,利用Borderline-SMOTE算法在保留少数类样本分布特征的同时对数据进行均衡化处理,解决故障数据分布不平衡造成的偏倚问题;然后,构建CatBoost变压器故障诊断模型,利用变压器的实际故障数据进行仿真实验,与不同数据均衡化方法和其他变压器故障诊断模型进行对比;最后,引入SHAP模型对CatBoost故障诊断模型进行可解释性分析,解决“黑箱模型”可解释性差的问题。实验表明,基于Borderline-SMOTE-CatBoost-SHAP的变压器故障诊断模型总体诊断准确率为92.99%,F1分数为0.91,Kappa系数为0.91,同时可视化输入特征对决策的影响程度与过程,验证了所提方法的有效性。
A transformer fault diagnostic method based on Borderline-SMOTE-CatBoost-SHAP is proposed for the problem of degradation of classification performance of fault diagnostic model and uninterpretable model diagnostic results caused by unbalanced distribution of transformer fault data.Firstly,the Borderline-SMOTE algorithm is used to equalize the data while retaining the distribution characteristics of a few classes of samples,solving the bias problem caused by the imbalance of fault data distribution.And then,the CatBoost transformer fault diagnosis model is constructed,and simulation experiments are carried out by using the actual fault data of the transformer to compare it with different data equalization methods and other transformer fault diagnosis models.Finally,the SHAP model is introduced to provide a more accurate and reliable classification model for transformer fault diagnosis.The SHAP model is introduced to analyze the interpretability of the CatBoost fault diagnosis model to solve the problem of poor interpretability of the“black box model”.The result shows that the overall diagnostic accuracy of the transformer fault diagnosis model based on Borderline-SMOTE-CatBoost-SHAP is 92.99%,the F1 value is 0.91,and the Kappa coefficient is 0.91.Meanwhile,the process of input features for decision-making is visualized to verify the validity of the proposed method.
作者
梁法政
张莲
杨家豪
杨玉洁
李蘅
肖远强
LIANG Fazheng;ZHANG Lian;YANG Jiahao;YANG Yujie;LI Heng;XIAO Yuanqiang(School of Electrical and Electronic Engineering,Chongqing University of Technology,Chongqing 400054,China)
出处
《湖南电力》
2024年第5期131-138,共8页
Hunan Electric Power
基金
重庆市教委科技项目(KJQN202001150)。