Power quality disturbance waveform recognition using wavelet-based neural classifier. II. Application

Abstract

For pt.I see ibid., vol.15, no.1, p.222-8 (2000). A wavelet-based neural classifier is constructed and thoroughly tested under various conditions, The classifier is able to provide a degree of belief for the identified waveform. The degree of belief gives an indication about the goodness of the decision made. It is also equipped with an acceptance threshold so that it can reject ambiguous disturbance waveforms. The classifier is able to achieve the accuracy rate of more than 90% by rejecting less than 10% of the waveforms as ambiguous.