Transformer partial discharge fault diagnosis based on improved adaptive local iterative filtering‐bidirectional long short‐term memory

Insulation deterioration, which is mainly caused by partial discharge (PD) occurring inside power transformers, is one of the prime reasons to cause transformer faults. Therefore, an effective diagnosis of PD is crucial to ensure the safe and stable operation of transformers. To extract more effective features that characterise transformers PD signals and enhance the recognition accuracy, a novel transformer PD fault diagnosis model based on improved adaptive local iterative filtering (ALIF) and bidirectional long short‐term memory (BILSTM) neural network is proposed. Addressing the issue of predetermined decomposition levels and accuracy in ALIF decomposition, the golden jackal optimisation (GJO) algorithm is introduced to optimise the parameters. The proposed fault diagnostic model extracts dominant PD features employing the improved ALIF and Refined Composite Multi‐Scale Dispersion Entropy and improves the diagnostic accuracy with the optimised BILSTM by introducing GJO. Experimental data evaluates the performance of support vector machine, long short‐term memory and BILSTM. The results verify the effectiveness and superiority of the proposed model. A novel transformer partial discharge fault diagnosis method based on improved adaptive local iterative filtering and bidirectional long short‐term memory is proposed.