基于多尺度形态学和Kalman滤波的基波分量提取  

Fundamental component extraction based on multi-scale morphology and Kalman filter

作　　者：吕思颖[1] 黎丹[1] 秦昕[1] 要航[1] 

机构地区：[1]广西大学电气工程学院,南宁530004

出　　处：《电测与仪表》2016年第5期16-21,共6页Electrical Measurement & Instrumentation

基　　金：广西研究生教育创新计划项目(YCSZ201404)

摘　　要：针对Kalman滤波在提取基波分量时存在暂态噪声抑制能力差和噪声统计特性不精确的缺点,提出了一种新的基波分量提取算法。采用改进的数学形态滤波器对采集信号进行多尺度分析得到平滑信号和细节信号,改进的滤波器使用不同形状的结构元素,有效地提高噪声抑制能力。利用平滑信号更新Kalman滤波器的观测值,减少故障信号暂态噪声的干扰,提高了滤波算法的收敛速度;利用细节信号实时在线计算测量噪声的方差,提高了滤波算法的收敛精度。在Matlab/Simulink环境下搭建仿真模型对算法进行验证与测试,仿真结果证实了算法的可行性和有效性。Aiming at the shortcomings of Kalman filter on poor ability of transient noises reduction and inaccurate statistical characteristics of noises in the extraction of the fundamental component,a new fundamental component extraction algorithm is presented. The sampling signals are decomposed to smooth signals and detail signals by the improved multi-scale mathematic morphology filter. The improved filter improves its ability of noise reduction by utilizing different shapes of structure elements. The smooth signals are adopted to update the observations value of the Kalman filter to reduce the interference of transient noise produced by fault signals,which can improve the convergence speed of the filter algorithm. Detailed signals are adopted to calculate the measurement noise variance in real-time to improve the convergence precision of the filter algorithm. The Matlab / Simulink simulation model is built to verify and test the algorithm,and the results show the feasibility and effectiveness of the algorithm.

关 键 词：数学形态学 多尺度分析 KALMAN滤波 基波分量 

分 类 号：TM933[电气工程—电力电子与电力传动]