期刊论文

Application of Adaptive EKF-ML Filter in Voltage Sag Detection

中文

电力系统及其自动化学报

1003-8930

2019

31

5

42-48,63

10.3969/j.issn.1003-8930.2019.05.007

国家自然科学基金资助项目(51507015,51877012,61540037,71271215,61233008,51425701,70921001,51577014) 湖南省自然科学基金资助面上项目(2018JJ2439).

本校为第一机构

电压暂降是电能质量问题中的一个关键难题。为了更准确地检测电压暂降发生时刻,提出了基于最大似然的自适应扩展卡尔曼滤波EKF-ML(extended Kalman filter based on maximum likelihood)算法的检测方法。首先选取不同的状态向量,在电网信号中建立2种卡尔曼滤波系统模型;其次,利用最大似然自适应优化误差协方差矩阵R和Q以及初始条件参数;最后,引入不同电能质量扰动对电压暂降进行检测证明该方法的有效性。仿真结果表明:在谐波干扰、脉冲干扰以及不同信噪比干扰情况下,EKF-ML算法能实时准确地检测电压暂降起止时间。与已有的传统方法比较,该方法适合于在未知测量噪声的条件下对电压暂降进行检测。...

Voltage sag is a key issue in power quality problems. To more accurately detect the time point of voltage sag, an adaptive extended Kalman filter based on maximum likelihood(EKF-ML)is proposed in this paper. First,two kinds of Kalman filter system models are established by selecting different state vectors. Then,the error covariance matrixes (e.g.,R and Q)and the initial condition parameters are optimized using the maximum likelihood adaptive method. To prove the effectiveness of the proposed method,different disturbances for power quality are introduced to detect voltage sag. Simulation resul...