Evolving Neural Nets for Protection and Condition Monitoring of Power Transformer

Abstract

This article presents evolving neural nets (ENNs) for protection and condition monitoring of power transformer. 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, classify and detect the fault and issue the trip signal in the case of internal fault only, using the global search capabilities of the evolutionary algorithm and the highly nonlinear mapping nature of the neural nets. The proposed protection scheme has been realized through two different structures using ENNs algorithm. This scheme has been evaluated using simulated data obtained through EMTP/ATP package. The results amply demonstrate the capabilities of fault detector (FD) and condition monitoring (CM) in terms of accuracy and speed with respect to detection of fault, classification and pattern recognition of different event of power transformer.