卡尔曼滤波器
估计理论
扩展卡尔曼滤波器
最小二乘函数近似
计算机科学
估计
递归最小平方滤波器
移动视界估计
不变扩展卡尔曼滤波器
快速卡尔曼滤波
控制理论(社会学)
统计
数学
算法
人工智能
自适应滤波器
工程类
系统工程
估计员
控制(管理)
标识
DOI:10.1109/pesgm.2015.7286332
摘要
In this paper, two types of generator model state and parameter estimation methods via Phasor Measurement Unit (PMU) data are described. The first type is the least square errors estimation (LSE) for parameter estimation and the second type is Kalman filter based estimation for both parameters and states. For LSE-based method, with parameters estimated, states can be estimated via event playback. LSE-based estimation employs a window of time-series data, while Kalman filtering method conducts estimation at every time step. LSE, extended Kalman filter (EKF) and unscented Kalman filter (UKF)-based estimation approaches will be demonstrated through case studies.
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