Research On Power Quality Disturbance Analysis Based On Improved Incompiete S-transform And Multi-feature Extraction

With the widespread use of power electronic equipment and other non-linear loads in power systems,electrical energy has been more fully utilized.However,the problem of power quality disturbance caused by it is becoming increasingly prominent,which seriously threatens the safe and stable operation of the power system.How to reduce the harm caused by power quality disturbances is a major technical problem to be solved urgently for power systems.The identification,classification and detection of power quality disturbances are the basis and premise for solving power quality disturbances.Therefore,it is of great theoretical and practical significance to study effective methods for detecting and classifying power quality disturbances.This thesis has conducted in-depth and systematic research on the detection and classification of power quality disturbances.The main research work and results achieved are summarized as follows:1.The characterization and modeling methods of power quality disturbance signals are thoroughly studied,the causes of various power quality disturbances are analyzed,and a signal model suitable for power quality disturbance analysis is constructed.2.The basic principles of general S-transformation and power quality disturbance detection based on S-transformation are thoroughly studied.An improved Bi-Gaussian window and an incomplete S-transform power quality disturbance detection method based on this improved window are proposed.This method first uses the maximum power spectrum dynamics to detect the main frequency points of power quality disturbance,and then performs an improved Bi-Gaussian window S transformation on these main frequency points.The improved Bi-Gaussian window is based on the Bi-Gaussian window byintroducing two new parameters {p,r} designed to flexibly change the window shape to improve the detection speed.3.In-depth study of an improved empirical mode decomposition(MEEMD)method and the basic principles of MEEMD-based power quality disturbance classification,an improved time-frequency domain multi-feature power quality disturbance classification recognition method is proposed.This method first extracts six power quality disturbances by improving incomplete S-transform and MEEMD,and then constructs a modular automatic classification and recognition system which can recognize not only single power quality disturbance,but also multiple power quality disturbances.4.A lot of simulation experiments are carried out on the improved incomplete S-transform power quality disturbance detection method and the improved time-frequency multi-feature power quality disturbance classification and recognition method.The results show that the power quality disturbance detection method proposed in this thesis has the characteristics of high accuracy,fast operation speed and strong anti-interference ability;the proposed classification and recognition method has high classification and recognition accuracy for single power quality disturbance and multiple power quality disturbances.