Proposed wavelet-neurofuzzy combined system for power quality violations detection and diagnosis

A system for the identification of power quality violations is proposed. It is a two-stage system that employs the potentials of the wavelet transform and the adaptive neurofuzzy networks. For the first stage, the wavelet mul tiresolution signal analysis is exploited to denoise and then decompose the monitored signals of the power quality events to extract its detailed info rmation. A new optimal feature-vector is suggested and adopted in learning the neurofuzzy classifier. Thus, the amount of needed training data is exte nsively reduced. A modified organisation map of the neurofuzzy classifier h as significantly improved the diagnosis efficiency. Simulation results conf irm the aptness and the capability of the proposed system in power quality violations detection and automatic diagnosis.