The growing interest in Power Quality Analysis (PQA) in recent years has brought a lotof tremendous changes to the supply of electrical energy. Many methods have been developedto analyze PQ to improve the quality for stable and efficiency. The S-Transform (ST) in thetime frequency distribution was developed in 1994 for analyzing geophysics data. The STransform is a generalization of the Short-time Fourier transform (STFT)，extending theContinuous wavelet transform and overcoming some of its disadvantages. This paper proposes Improved S-Transform (IST) to detect and classify power quality(PQ) disturbances with time-domain analysis. To enhance the analytic power of S-Transformin different non-stationary signal processing，IST is achieved by adding an adjustable factor tothe Gaussian window function of the normal S-Transform. The adjusted factor changes thevelocity in which the width of the window function varies inversely with the frequency. ISTpossesses an adjustable time-frequency resolution and higher practicability and adaptabilitythan ST in the actual application. IST analysis performed on the PQ disturbance signals canidentify the magnitude and duration of the disturbances. The comparison between theWavelet-transform-based method and the improved S-Transform-based method for powerquality disturbance recognition is also provided. For the classification of power quality disturbance，the Support Vector Machine (SVM)is applied. The disturbance qualities were categorized into six groups known as SVMl，SVM2,SVM3，SVM4，SVM5 and SVM6 depending on the amplitude of the disturbances. The simulation results show that the proposed method is effective and immune againstnoise. The proposed method is feasible and promising for real applications. Keywords: S-Transform;Wavelet;power quality;voltage disturbance;SVM;Classification tree