With the extensive integration of distributed power sources and power electronic devices into distribution networks

new characteristics are manifesting in aspects of energy supply and load demand. A voltage sag source identification method combining complete ensemble 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 recognition accuracy. This method can accurately extract useful features from complex voltage signals and improve recognition accuracy by optimizing model parameters

providing an effective solution for voltage sag problems in power systems.