基于改进S变换的电能质量扰动的研究
Research on Power Quality Disturbance Based on Improved S-Transform
The growing interest in Power Quality Analysis (PQA) in recent years has brought a lot of tremendous changes to the supply of electrical energy. Many methods have been developed to analyze PQ to improve the quality for stable and efficiency. The S-Transform (ST) in the time frequency distribution was developed in 1994 for analyzing geophysics data. The S Transform is a generalization of the Short-time Fourier transform (STFT), extending the Continuous 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-Transform in different non-stationary signal processing, IST is achieved by adding an adjustable factor to the Gaussian window function of the normal S-Transform. The adjusted factor changes the velocity in which the width of the window function varies inversely with the frequency. IST possesses an adjustable time-frequency resolution and higher practicability and adaptability than ST in the actual application. IST analysis performed on the PQ disturbance signals can identify the magnitude and duration of the disturbances. The comparison between the Wavelet- transform-based method and the improved S-Transform-based method for power quality 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 SVM1, 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 against noise. The proposed method is feasible and promising for real applications.
- 作者：
- 杰弗斯
- 学位授予单位：
- 信息科学与工程学院
- 专业名称：
- 电力系统及其自动化学科
- 授予学位：
- 硕士
- 学位年度：
- 2008年
- 导师姓名：
- 张石
- 关键词：
- 改进S变换 ；小波变换 电能质量 电压暂态扰动 扰动分类 暂动信号压缩S-Transform；Wavelet；power quality；voltage disturbance；SVM；Classification tree
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