Detection And Classification Of Short Duration Power Quality Disturbances Based On Complex Wavelet And S-transform

Power quality (PQ) has been an important issue to the electric power utilities for the changes of loads composition in modern power systems and with the development of power market. The main problems in power quality are monitoring, control and evaluation, and power quality monitoring, which has been a significant part in power quality study, is the foundation for other research and work. In PQ monitoring analysis, short duration power quality disturbances (SDPQD) are the primary aspect, and they are known for their frequent and random occurrences, and harms to the sensitive loads. So with the increasing amount of measurement data from power quality monitoring devices, it is desirable that SDPQD detection and classification can be performed automatically and accurately.This paper reviews the method used in SDPQD analysis firstly, and analyzes the characteristics of different SDPQD in detail. Then the novel SDPQD detection and classification method based on complex wavelet and S-transform respectively is proposed, and the S-transform-based expert system for voltage dips (sags) classification is presented.Wavelet transform (WT) has been applied widely in SDPQD analysis, especially in SDPQD detection, because of its excellent ability in time-frequency resolution that makes WT suitable for analyzing non-stationary signals like SDPQD. However, WT is vulnerable by noise, and the current WT-based detection method has difficult in satisfying the noise robustness and real time at the same time in the practical application. Aiming at these problems, this paper study the feasibility using...