Study on the Diagnosis of Transformer Failure Diagnosis Based on the BWODO-VMD-LSTM Model
Lifu Wang, Wei Cai · 2023
Aiming at the shortcomings of transformer fault diagnosis, a fusion algorithm (BWODO) based on Beluga algorithm (BWO) and Dandelion algorithm (DO) is proposed, which is combined with variational mode decomposition (VMD) and short and long time memory neural network (LSTM). Firstly, fault classification and feature extraction are carried out based on dissolved gas (DGA) data in oil. Then, the Dandelion algorithm is optimized through chaotic mapping opposition learning strategy, mixed reverse learning strategy, and Beluga whale predation strategy to improve the algorithm's optimization speed and solving accuracy. Then, the improved algorithm is used to optimize VMDLSTM model parameters to improve the model's accuracy of transformer fault identification. The test results show that the accuracy of BWODO-VMD-LSTM model is 97.4%, which is 5.9% and 4.2% higher than that of DO-VMD-LSTM and PSO-VMD-LSTM models, which proves that the proposed method can effectively improve the accuracy of transformer fault diagnosis.