Research on the Method of Identifying the Operating State of Converter Transformers Based on WOA-DBN

In recent years, the global interconnection of UHVDC and EHV systems has made the operating environment of transformers increasingly harsh. As the core equipment of the Electric power transmission, the safe and stable operation of converter transformers is crucial. At present, domestic and foreign scholars have conducted extensive research on the vibration characteristics and status monitoring of ordinary power transformers based on vibration methods, but the research on converter transformers is not perfect. Conduct research on the state recognition method of converter transformers, introduce deep confidence networks as the basic algorithm for converter transformer operation state recognition, and use the WOA algorithm to optimize the selection of DBN hyperparameters, forming a classification recognition algorithm based on WOA-DBN. State recognition was conducted to verify the feasibility of the algorithm, and the classification performance of the algorithm was verified with the help of classification accuracy and recognition efficiency. The experiment shows that the proposed method for identifying the status of converter transformers has good engineering practicality and can be further applied to fault identification and operational status monitoring of converter transformers.