Fault Diagnosis Method for On-load Tap Changer Based on Multiscale Feature Extraction and IAO-LSTM

To realize the accurate fault diagnosis of on-load tap changer (OLTC) under compound faults, a fault diagnosis method for transformer OLTC based on multi-scale feature extraction and IAO-LSTM was proposed. Firstly, features of the time domain scale, frequency domain scale and energy entropy scale were extracted from OLTC vibration signals to form feature vectors. By incorporating the mixing initialization strategy and elite solution retention strategy, the aquila optimizer (AO) was improved to enhance the convergence. The improved aquila optimizer (IAO) was used to optimize the number of hidden layer nodes and learning rate of LSTM, and thus an optimal LSTM model was obtained. Taking the fusion eigenvector of the single fault and compound fault as the input and the fault state as the output, the optimal model was trained. After that, the fault diagnosis was carried out. The results indicate that the method yields an average accuracy of 97.2% and is appropriate for OLTC fault diagnosis.