Robust continuous-discrete extended Kalman filter for estimating machine states with model uncertainties
Robust continuous-discrete extended Kalman filter for estimating machine states with model uncertainties
复制标题
用于估计具有模型不确定性的机器状态的鲁棒连续离散扩展卡尔曼滤波器
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
A. Abur
中科院分区：
文献类型：
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作者：
Pengxiang Ren;H. Lev;A. Abur
同步发电机的动态估计由于其对大规模电网的广域控制和稳定性的影响而迅速变得重要。然而，发电机动态模型中潜在的不确定性可能会影响估计结果。在本文中，开发了一种鲁棒的扩展卡尔曼滤波器，用于在存在模型不确定性的情况下估计机器状态。所提出的滤波器基于最小化最坏可能情况下的平方残差范数，这表明模型中的不确定性应该受到限制。所提出的算法分两步推导。首先，对机器模型的非线性动态方程以及结构不确定性进行离散化和线性化。其次，通过构造最小-最大优化问题并用相当多的代数求解它，可以用修改的参数重新表述卡尔曼滤波器的时间和测量更新表达式。基于典型机器模型对所提出的滤波器进行了数值测试并给出了结果。
Dynamic state estimation for synchronous generators is rapidly gaining importance due to its impact on wide-area control and stability of large scale power grids. However, the underlying uncertainties in the dynamic models of the generators may influence the estimation results. In this paper, a robust extended Kalman filter is developed for estimating machine states in the presence of model uncertainties. The proposed filter is based on minimizing the squared residual norm under the worst possible case, which indicates the uncertainties in the model should be bounded. The proposed algorithm is derived in two steps. First, the nonlinear dynamic equations of the machine model as well as the structured uncertainties are discretized and linearized. Second, by constructing the min-max optimization problem and solving it with considerable algebra, the time- and measurement-update expressions of the Kalman filter can be reformulated with modified parameters. The proposed filter is tested numerically based on a typical machine model and the results are presented.