Application of fuzzy classification by evolutionary neural network in incipient fault detection of power transformer

Aiming at the incipient fault detection of power transformer, the paper proposes a novel fuzzy classification by evolutionary neural network. The method models the membership fuctions of all fuzzy sets by utilizing a three-layer feedforward neural network, and trains a group of neural networks by combining the modified Evolutionary Strategy with Levenberg-Marquardt optimization method in order to accelerate convergence and avoid falling into local minima. Thus each trained neural network denotes an "expert" model. The classification results obtained from all "expert" models are integrated according to the absolute-majority-voting rule. A lot of samples are tested, and the testing results demonstrate that the novel method is much better in neural network structure, classification accuracy, generalization capability, fault-tolerance ability and robustness that then other traditional methods.