基于改進S變換的電能質(zhì)量優(yōu)動的研究.pdf

1、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 sta

2、ble 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 theC

3、ontinuous 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 analyt

4、ic 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 whi

5、ch 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 perf

6、ormed 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 disturba

7、nce 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，S

8、VM5 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