Abstract：In order to analyze the power quality problem of actual power network under the influence of

uncertain interference factors，a power quality detection and recognition method combining empirical wavelet

transform（EWT）and improved S-transform was proposed. On the one hand，the frequency，amplitude and time

parameters of the AM-FM component were accurately extracted by using the EWT joint normalization direct

orthogona（l NDQ）algorithm and singular value decomposition（SVD）algorithm. On the other hand，considering the instantaneous amplitude fluctuation of the EWT algorithm in the high noise environment，the improved S-transform was introduced to extract the time-frequency information of power quality disturbances under the high noise interference. Finally，based on the disturbance feature vectors extracted by EWT and improved S transform，the power quality disturbance recognition classifier optimized by the support vector machine（SVM）based on improved particle swarm optimization（IPSO）algorithm was used to accurately identify the disturbance types. Simulation and experiments show that the average recognition accuracy of the proposed method is 93.23% in the case of composite disturbance recognition and classification，and it can accurately identify four kinds of measured disturbance signals.

      Key words：power quality；disturbance detection and identification；empirical wavelet transform（EWT）；fast

multi-resolution S-transform（FMST）；improved particle swarm optimization（IPSO）；support vector machines

（SVM）