Abstract: Abstract: In this paper， a method for identifying the compound disturbances of power quality based on the selection of time-frequency domain features and the improved Northern Goshawk Optimization （SCNGO）-Hybrid Kernel Extreme Learning Machine （HKELM） is proposed to address issues such as the singularity of feature indicators， complexity of classifier network structure， and difficulty in hyperparameter tuning during the process of disturbance recognition in the electric power system. Firstly， focusing on 9 typical compound disturbances of power quality， their mathematical signal characteristics models are constructed， analyzing the time-frequency domain characteristics of various types of disturbances. Based on this analysis， 19 time-frequency domain indicators for disturbance feature extraction are proposed. Subsequently， considering the impact of feature indicator redundancy on the accuracy of disturbance recognition， Kernel Principal Component Analysis （KPCA） algorithm is utilized for feature indicator selection to establish an optimal indicator set. Finally， a disturbance classifier based on SCNGO-HKELM is introduced. Through the SCNGO algorithm， adaptive adjustment of the kernel function hyperparameters and weight coefficients of HKELM is achieved， enhancing the classifier’s generalization ability while ensuring its learning capability and improving the accuracy and efficiency of disturbance recognition. Experimental results demonstrate that the proposed method achieves an identification accuracy of 97.64% for the 9 classes of typical compound power quality disturbances， with stable classification accuracy in different noise environments， validating the effectiveness and accuracy of the proposed method.

Key words: Key words: power quality, disturbance identification, kernel principal component analysis, improved northern goshawk optimization, hybrid kernel extreme learning machine