Real-Time Detection and Classification of Power Quality Problems Based on Wavelet Transform

Abstract

A new technique for real-time power quality (PQ) disturbances detection and classification based on wavelet multi-resolution analysis (MRA) is presented in this paper. The detection of start time, end time and duration of PQ event is based on the finest detail level of MRA while the classification of the event is based on the coarsest approximation level of MRA. LabVIEW platform has been used to implement the proposed technique in a laboratory setup. Several voltage events: interruption, swell and sag have been generated to test the performance of the proposed technique. The experimental results demonstrate the superiority, accuracy, and robustness of the proposed method in detecting the details of the voltage events as well as the event type classification. The effectiveness, accuracy and robustness of the proposed technique in the detection and classification of the PQ events have been demonstrated by experimental results. Moreover, the proposed technique shows a significant reduction in execution time with less complexity compared to conventional methods, for that the proposed technique is more suitable for online detection and classification applications.