Improved Genetic Algorithm and XGBoost Classifier for Power Transformer Fault Diagnosis - Wu, Zhanhong; Zhou, Mingbiao; Lin, Zhenheng; Chen, Xuejun; Huang, Yonghua (Frontiers in Energy Research, 2021)
Power transformer is an essential component for the stable and reliable operation of electrical power grid. The traditional diagnostic methods based on dissolved gas analysis (DGA) have been used to identify the power transformer faults. However, the application of these methods is limited due to the low accuracy of fault identification. In this paper, a transformer fault diagnosis system is developed based on the combination of an improved genetic algorithm (IGA) and the XGBoost. In the transformer fault diagnosis system, the improved genetic algorithm is employed to pre-select the input features from the DGA data and optimize the XGBoost classifier. Performance measures such as average unfitness value, likelihood of evolution leap, and likelihood of optimality are used to validate the efficacy of the proposed improved genetic algorithm. The results of simulation experiments show that the improved genetic algorithm can get the optimal solution stably and reliably, and the proposed method improves the average accuracy of transformer fault diagnosis to 99.2%. Compared to IEC ratios, dual triangle, support vector machine (SVM), and common vector approach (CVA), the diagnostic accuracy of the proposed method is improved by 30.2%, 47.2%, 11.2%, and 3.6%, respectively. The proposed method can be a potential solution to identify the transformer fault types.