Fault Diagnosis of Oil-immersed Transformer Based on Belief Rule Base

Aiming at the fault diagnosis of oil-immersed transformers, a method of establishing an effective diagnostic model was proposed based on belief rule base (BRB). The efficiency and accuracy of BRB model was improved by simplifying the model structure and optimizing the model parameters. First, the structure of BRB model was simplified by reasonably reducing the types of faulty gas and reducing the parameters of the training model. Second, a seeker optimization algorithm with adaptive update strategy (AUS-SOA) was proposed to optimize the parameters of the simplified BRB model. Third, an AUS-SOA-BRB diagnostic model was established based on the simplified model and the optimized parameters. Results show that the AUS-SOA-BRB model has higher diagnostic accuracy, and verifies the effectiveness of the proposed modeling method.