Fault Diagnosis Method for Power Transformers Based on Improved Golden Jackal Optimization Algorithm and Random Configuration Network

Abstract

The problem of the low accuracy of Dissolved Gas Analysis (DGA) in diagnosing transformer faults is addressed by proposing an Improved Golden Jackal Optimization (IGJO) based Stochastic Configuration Network (SCN) method. The method of transformer fault diagnosis based on IGJO optimized SCN is proposed. Firstly, Kernel Principal Component Analysis (KPCA) is used to reduce the dimensionality of the gas data and extract the effective feature quantities. Secondly, the L2 parametric penalty term is introduced into the SCN to improve the generalisation ability of SCN in practical applications. The elite backward learning and golden sine algorithms are incorporated into the golden jackal algorithm, and the IGJO performance is tested using 13 typical test functions, demonstrating that the IGJO has greater stability and merit-seeking capability. The penalty term coefficient C of the SCN is optimised using the IGJO to develop a transformer fault diagnosis model with an Improved Golden Jackal algorithm optimised Random Configuration Network (IGJO-SCN). Finally, the feature quantities extracted by KPCA are used as the input set of the model and the different transformer fault diagnosis models are simulated and validated. The results show that the IGJO-SCN has higher transformer fault diagnosis accuracy compared to other models.