Fault Diagnosis of Oil-Immersed Power Transformer Based on Difference-Mutation Brain Storm Optimized Catboost Model


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To address the problem of low accuracy of power transformer fault diagnosis, this study proposed a transformer fault diagnosis method based on DBSO-CatBoost model. Based on data feature extraction, this method adopted DBSO (Difference-mutation Brain Storm Optimization) algorithm to optimize CatBoost model and diagnose faults. First, for data preprocessing, the ratio method was introduced to add features to the original data, the SHAP (Shapley Additive Explanations) method was applied for feature extraction, and the KPCA (Kernel Principal Component Analysis) algorithm was employed to reduce the dimension of data. Subsequently, the preprocessed data were inputted into the CatBoost model for training, and the DBSO algorithm was adopted to optimize the parameters of the CatBoost model to yield the optimal model. Lastly, the DBSO-CatBoost model was exploited to diagnose the transformer fault and output the fault type. As indicated from the example results, the accuracy of the transformer fault diagnosis based on DBSO-Catboost model could be 93.71%, 3.958% higher than that of CatBoost model and significantly exceeding that of some common models. Furthermore, compared with other preprocessing methods, the accuracy of fault diagnosis by employing the data preprocessing method proposed in this study was significantly improved.
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Key words
Fault diagnosis,Classification algorithms,Oil insulation,Power transformer insulation,Optimization,Training,Storms,Power transformer,fault diagnosis,catboost model,DBSO algorithm,feature extraction
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