基于布莱克曼窗S变换与数据库查询的电能质量扰动识别与分类新方法

Recognition and Classification New Method of Power Quality Disturbances Based on Blackman Window S Transform and Database Query

针对基于特征值方法电能质量扰动识别中存在庞大而复杂的特征值选取问题,提出以特征曲线为特征的布莱克曼窗S变换与数据库查询新方法。提出布莱克曼窗S变换采用布莱克曼窗宽函数并通过窗宽比控制窗宽,相较于多分辨率广义S变换具有更好的时频分辨率。通过布莱克曼窗S变换得到扰动信号的时频模矩阵,在模矩阵上提取时频特征曲线,然后通过波动能量密度与快速傅立叶变换进行特征曲线分割,排除噪声的干扰,降低特征曲线长度,最后建立树状结构的时频数据库,采用动态时间规整距离查询分类方法,根据隶属度的关系进行快速分类,提高识别的正确率。通过仿真数据分析表明高时频精度的布莱克曼窗S变换提高了算法的识别正确率并且特征曲线分割提高了算法的抗噪声干扰能力,现场数据验证了该算法的有效性。

Aiming at the huge and complex problem of eigenvalue selection in energy quality disturbance recognition based on the eigenvalue method, a new Blackman window S transform database query method with characteristic curve is proposed. In this paper, the Blackman window S transform uses the Blackman window width function and controls the window width through the window width ratio, which has better time-frequency resolution than Multiresolution Generalized S-Transform. The time-frequency characteristic curve of disturbance signal is extracted from the Blackman window S transform modulus matrix, and then it is segmented by the wave energy density transformation and the fast Fourier transform, which eliminates the interference of noise and reduces the length of characteristic curve. Finally, the tree-structured time-frequency database is established in which the dynamic time warping distance query classification method is used to classify the disturbance signal rapidly according to the membership degree in order to improve the recognition accuracy. The simulation data analysis shows that the Blackman window S transform with high time-frequency accuracy improves the recognition accuracy and that the feature curve segmentation improves the anti-noise ability of the algorithm. The validity of the algorithm is verified through the field data。