Research on transformer fault diagnosis based on ISOMAP and IChOA‐LSSVM

Abstract

Abstract Oil‐immersed transformers play an important role in the stable operation of power systems. In order to improve the accuracy of transformer fault diagnosis, a transformer fault diagnosis method based on Isomap and IChOA‐LSSVM is proposed. Firstly, Isomap is used to reduce the dimensionality of the 14‐dimensional transformer fault characteristics data to eliminate redundant data between variables. In addition, IChOA is proposed to optimise the Least Squares Support Vector Machine (LSSVM) parameters and establish an optimal diagnosis model based on LSSVM. For ChOA, three improvement methods are proposed. Circle mapping is used instead of the original population initialisation to improve population diversity. In addition, the location weighting strategy with proportional weights and the Gaussian Corsi variation strategy is proposed to improve the optimization accuracy and efficiency of ChOA. The improved ChOA is compared with the original ChOA, PSO and GWO algorithms by five benchmark test functions. Finally, the improved Chimpanzee Optimisation algorithm was used to find the parameters of the LSSVM to obtain the fault diagnosis model combining Isomap and IChOA‐LSSVM. The model is compared with PSO‐LSSVM, ChOA‐LSSVM and GWO‐LSSVM. The diagnostic accuracy is 90.83%, 81.67%, 83.33% and 80%, respectively. The results demonstrate that the proposed method can effectively improve the performance of transformer fault diagnosis.

Keywords

• power transformers
• reliability
• support vector machines
• transformers