A neural-fuzzy classifier for recognition of power quality disturbances

This paper presents a neural-fuzzy technology-based classifier for the recognition of power quality disturbances. The classifier adopts neural networks in the architecture of frequency sensitive competitive leaning and learning vector quantization (LVQ). With given size of codewords, the neural networks are trained to determine the optimal decision boundaries separating different categories of disturbances. To cope with the uncertainties in the involved pattern recognition, the neural network outputs, instead of being taken as the final classification, are used to activate the fuzzy-associative-memory (FAM) recalling for identifying the most possible type that the input waveform may belong to. Furthermore, the input waveforms are preprocessed by the wavelet transform for feature extraction so as to improve the classifier with respect to recognition accuracy and scheme simplicity. Each subband of the transform coefficients is then utilized to recognize the associated disturbances.