Voltage sag source identification using IHPO-CSSVM with consideration of three-phase voltage characteristics on the distribution network
With the extensive integration of distributed power sources and power electronic devices into distribution networks,new charac-teristics are manifesting in aspects of energy supply and load demand.A voltage sag source identification method combining complete en-semble empirical mode decomposition with adaptive noise(CEEMDAN)and improved hunter-prey optimizer cost sensitive support vector machine(IHPO-CSSVM)is proposed to address the difficulties in selecting hyperparameters for support vector machine(SVM)and the imbalance of voltage sag source signal data categories.By simulating circuits on the Matlab/Simulink simulation platform,different types of voltage sag sources are obtained.The CEEMDAN is used to extract the feature vectors of the three-phase voltage of the voltage sag source signal,and its approximate entropy is calculated.A new feature vector is constructed and input into the IHPO-CSSVM classifier for training.Compared with SVM,CSSVMand extreme learning machine,simulation results show that IHPO-CSSVM has the highest recogni-tion accuracy.This method can accurately extract useful features from complex voltage signals and improve recognition accuracy by opti-mizing model parameters,providing an effective solution for voltage sag problems in power systems.
complete ensemble empirical mode decomposition with adaptive noiseimproved hunter-prey optimizeralgorithm-cost-sensi-tive support vector machinedistribution network side voltage sag sources
万方数据
维普
