Protection of Power Transformer using Evolving Neural Nets

Z. Moravej (Iran)


Fault detection, power transformer, Evolving Neural Nets.


This paper presents evolving neural nets (ENNs) for protection of power transformer. Based on the proposed evolutionary algorithm, the ENNs automaticaly tune the network parameters(connection weights and bias terms) of the neural nets to achieve the best model. The ENNs can identify 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 evaluated using simulated data obtained through EMTP/ATP package. The results amply demonstrate the capabilities of scheme in terms of accuracy and speed with respect to detection of fault, classification and pattern recongintion of different event of power transformer.

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