Evolving neural nets for fault diagnosis of power transformers

This paper proposes evolving neural nets (ENNs) for fault diagnosis of power transformers. Based on the proposed evolutionary algorithm, the ENNs automatically tune the network parameters (connection weights and bias terms) of the neural nets to achieve the best model. The ENNs can identify complicated relationships among dissolved gas contents in transformer oil and corresponding fault types, using the global search capabilities of the evolutionary algorithm and the highly nonlinear mapping nature of the neural nets. The proposed ENNs have been tested on the Taipower Company diagnostic records and compared with the fuzzy diagnosis system, artificial neural networks, and the conventional method. The test results confirm that the proposed ENNs are much more diagnostically accurate and require less learning time than the existing approaches.