Fuzzy classifiers for power quality events analysis

The present paper proposes the design of a tool to quantify power quality (PQ) parameters using wavelets and fuzzy sets theory. The tool merges the best characteristics of these two theories in establishing a method to analyze PQ events. The proposed method addresses two issues, such as selection of discriminative features and classifies event classes with minimum error. Wavelet features (WF) of PQ events are extracted using wavelet transform (WT) and fuzzy classifiers classify events using these features. Often the captured signals are corrupted by noise. Also the non-linear and non-stationary behavior of PQ events make the detection and classification tasks more cumbersome. WT has been proven an effective tool for detecting and classifying these. We exploited WT for noise removal to make the task of detection and/or localization of events simpler. In the proposed approach of event classification, fuzzy product aggregation reasoning rule based method has been used. Varieties of PQ events including voltage sag, swell, momentary interruption, notch, oscillatory transient and spikes are considered for performance analysis. Comparative simulation studies revealed the superiority of proposed method compared to WF based fuzzy explicit, fuzzy k-nearest neighbor and fuzzy maximum likelihood classifiers under noisy environment.