Evolving Feedforward Neural Network for Harmonics Signature Identification

This paper presents artificial neural network (ANN) evolved by genetic algorithm (GA) for signature identification of electrical devices based on the power harmonics generated. Initially, the GA was used to evolve the weights and biases of the single-hidden-layer perceptron ANN. To improve the ANN's performance, the GA's role was modified to evolve only the initial weights of the ANN while backpropagation training was employed for convergence towards the minima. Aetnal data obtained from several devices was used for evaluation and testing of this neural network for signature identification. Very promising results, with classification accuracy above 98% have been obtained with this hybrid approaches.

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