Detection of Power Quality Disturbances Based on Residual Analysis Using Kalman Filter Based on Maximum Likelihood
Detection of Power Quality Disturbances Based on Residual Analysis Using Kalman Filter Based on Maximum Likelihood
复制标题
使用基于最大似然的卡尔曼滤波器进行残差分析的电能质量扰动检测
DOI：
10.1080/15325008.2019.1627607
复制
发表时间：
2019-06
影响因子：
1.5
通讯作者：
Zeng Xiangjun
中科院分区：
文献类型：
--
作者：
Xi Yanhui;Tang Xin;Li Zewen;Cui Yonglin;Zeng Xiangjun
摘要 提出了基于最大似然的卡尔曼滤波器（KF-ML）用于检测电能质量（PQ）扰动的估计残差，该滤波器利用ML方法自适应优化误差协方差矩阵，并以初始条件为参数。针对对噪声的敏感性，提出了KF-ML观测值和估计值之间的残差来检测干扰，并且估计残差在干扰的起点和终点处表现出突变。因此，残差的奇点可用于准确检测干扰。在存在噪声的情况下对各种干扰进行仿真，例如电压暂降、脉冲中断和暂降谐波。仿真结果验证了基于残差的检测方法能够准确判断扰动的起始和结束时间。此外，该方法对各种干扰高度敏感，而对随机噪声不太敏感。因此，该方法是干扰检测的更好选择，特别适合实际应用中未知测量噪声的估计。
Abstract This paper presents the estimated residuals for the detection of power quality (PQ) disturbances using Kalman filter (KF) based on the maximum likelihood (KF-ML), which uses the ML method to adaptively optimize the error covariance matrices and the initial conditions as the parameters. Aiming at the sensitiveness to noise, residuals between the observed values and the estimated values by the KF-ML are proposed to detect the disturbances, and the estimated residuals exhibit mutation at the starting point and the ending point of disturbances. Thus, the singularities of residuals can be used for exactly detecting disturbances. Simulations on a variety of disturbances, such as voltage sag, impulse interruption, and harmonics with sag, are performed in the presence of noise. Simulation results verify that the detection method based on residuals can exactly determine the starting and ending time of the disturbances. Also, this method is highly sensitive to a variety of disturbances and less sensitive to random noise. Therefore, the method is a better choice for disturbance detection and is especially appropriate for estimation of unknown measurement noise in real applications.
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DOI：
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发表时间：
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期刊：
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影响因子：
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