An adaptive Kalman filter for dynamic harmonic state estimation and harmonic injection tracking
An adaptive Kalman filter for dynamic harmonic state estimation and harmonic injection tracking
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DOI：
10.1109/tpwrd.2004.838643
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发表时间：
2005-04
影响因子：
4.4
通讯作者：
K. Yu;Neville R. Watson;J. Arrillaga
中科院分区：
文献类型：
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作者：
K. Yu;Neville R. Watson;J. Arrillaga
过程噪声协方差矩阵Q的知识对于卡尔曼滤波的应用是必不可少的。然而，它通常是一个困难的任务，以获得显式表达式的Q大时变系统。本文研究了一种用于动态谐波状态估计和谐波注入跟踪的自适应卡尔曼滤波方法。该方法将系统建模为线性频率无关状态模型，并且不需要噪声协方差矩阵Q的精确知识。作为一种替代方案，建议的自适应卡尔曼滤波器之间切换的两个基本的Q模型的稳态和瞬态估计。其自适应功能允许重新设置卡尔曼增益，以避免稳态下的卡尔曼滤波器发散问题，并允许在瞬态条件下快速跟踪系统变化。新西兰南岛南部220 kV电网的仿真结果验证了该方法的有效性。
Knowledge of the process noise covariance matrix Q is essential for the application of Kalman filtering. However, it is usually a difficult task to obtain an explicit expression of Q for large time varying systems. This paper looks at an adaptive Kalman filter method for dynamic harmonic state estimation and harmonic injection tracking. The method models the system as a linear frequency independent state model and does not require an exact knowledge of the noise covariance matrix Q. As an alternative, the proposed adaptive Kalman filter switches between the two basic Q models for steady-state and transient estimation. Its adaptive function allows for the resetting of the Kalman gain to avoid Kalman filter divergence problems under steady-state and allow fast tracking of system variations in transient conditions. Simulation results on the 220 kV network of the lower South Island of New Zealand are presented to validate this approach.