基于短窗自相关算法和数学形态学的电能质量扰动信号检测和定位新方法

提出了一种基于短窗自相关算法和数学形态学的电能质量扰动信号检测和定位新方法。该方法能够滤除电流、电压信号中的白噪声，易于在线实现。作者先利用最小二乘递推算法估计信号的基本成分，以提取信号中的扰动成分和白噪声的混合信息；再利用短窗自相关算法滤除混合信息中的随机噪声以提取扰动信息；最后利用数学形态学的基本腐蚀算子检测扰动并进行精确定位。仿真结果表明了该方法的有效性。

A NEW METHOD TO DETECT AND LOCATE POWER QUALITY DISTURBING SIGNALS BASED ON SELF-CORRELATION ALGORITHM OF SHORT DATA WINDOW AND MATHEMATICAL MORPHOLOGY

A novel approach to detect and locate the power quality disturbing signals is proposed and its on-line realization can be easily achieved. Using the proposed approach the white noise in voltage and current signals can be efficiently filtrated. In this approach firstly the fundamental component of signal is estimated to extract the mixed information constituted by disturbing component and white noise, then the random noise in the mixed information is filtrated by self-correlation algorithm of short data window to extract disturbing information, finally the disturbances are detected by erosion operator in mathematical morphology and located precisely. Simulation results show that the proposed approach is effective.