Power Quality Disturbance Detection and Classification Based on Chromatic Monitoring and Fuzzy Logic
The issue of PQ disturbance has now emerged as a serious problem in contemporary electrical power systems owing to the increasing use of non-linear electronic loads. Detecting and classifying PQ disturbances is crucial for maintaining the stability of electrical power systems; nevertheless, most traditional techniques for achieving this objective involve relatively complex computations and rely on extensive features and data sets. A new method of detecting and classifying PQ disturbances using chromatic monitoring along with fuzzy logic classification technique is suggested in this paper. The proposed technique works in the time domain through the constant monitoring of voltage signal and mapping them to the compact chromatic feature space through Hue-Lightness-Saturation (HLS) conversion technique. Parameters such as Hue and Lightness obtained from statistical measures of RGB filters are clear and unique in representing different PQ events. Parameters are then classified using the rules-based fuzzy logic controller without the requirement for large training data sets. Results from simulation show that the proposed technique is highly accurate in classifying PQ disturbances. It performs well with respect to disturbances and offers less computation than previous methods.