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 transformer fault diagnostic methods based on dissolved gas analysis are limited due to the low accuracy of fault identification. In this study, an effective transformer fault diagnosis system is proposed to improve identification accuracy. The proposed approach combines an improved genetic algorithm (IGA) with the XGBoost to form a hybrid diagnosis network. The combination of the improved genetic algorithm and the XGBoost (IGA-XGBoost) forms the basic unit of the proposed method, which decomposes and reconstructs the transformer fault recognition problem into several minor problems IGA-XGBoosts can solve. The results of simulation experiments show that the IGA performs excellently in the combined optimization of input feature selection and the XGBoost parameter, and the proposed method can accurately identify the transformer fault types with an average accuracy of 99.2%. Compared to IEC ratios, dual triangle, support vector machine and common vector approach 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.