Power transformer fault detection using intelligent neural networks

This paper proposes intelligent neural networks (INNs) for fault detection of power transformers. The INNs automatically tune the network parameters, connection weights and bias terms of the neural networks, to achieve the best model based on the proposed evolutionary algorithm. The INNs 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 networks. The proposed INNs have been tested on the Taipower Company diagnostic records and compared with the artificial neural networks (ANNs). The test results confirm that the proposed INNs have remarkable diagnosis accuracy and require less learning time than the ANNs.