Research on transformer fault diagnosis method based on feature selection and random forest

Kai Zou; Xianwen Zeng; Guige Gao

doi:10.1117/12.3032947

5 July 2024 Research on transformer fault diagnosis method based on feature selection and random forest

Kai Zou, Xianwen Zeng, Guige Gao

Proceedings Volume 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024); 131840Y (2024) https://doi.org/10.1117/12.3032947
Event: 3rd International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 2024, Kuala Lumpur, Malaysia

Abstract

In order to solve the problem of low transformer fault recognition rate, this paper proposes a fault diagnosis method based on feature selection and improved bald eagle search algorithm (IBES) to optimize random forest (RF). Firstly, to solve the problem that the specific gravity of dissolved gas in transformer oil is difficult to diagnose efficiently, this paper proposes to use 7 ratio relations between five gases and the specific gravity of the original five gases as fault characteristics, and use RF for feature selection. Secondly, to solve the problem of low convergence accuracy of the Bald eagle search algorithm (BES), this paper uses Sine chaotic mapping, Levy flight and Cauchy Gaussian variation perturbation strategy to improve BES. Finally, in order to improve the diagnostic accuracy of RF model, IBES is used to optimize RF parameters and build IBES-RF model. The simulation results show that compared with BES-RF and PSO-RF models, IBES-RF has the best fault diagnosis effect, and the accuracy rate is 90.51%.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Kai Zou, Xianwen Zeng, and Guige Gao "Research on transformer fault diagnosis method based on feature selection and random forest", Proc. SPIE 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 131840Y (5 July 2024); https://doi.org/10.1117/12.3032947

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