Robust Unscented Kalman Filter for Power System Dynamic State Estimation With Unknown Noise Statistics
Robust Unscented Kalman Filter for Power System Dynamic State Estimation With Unknown Noise Statistics
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DOI：
10.1109/tsg.2017.2761452
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发表时间：
2019-03
影响因子：
9.6
通讯作者：
Junbo Zhao;L. Mili
中科院分区：
文献类型：
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作者：
Junbo Zhao;L. Mili
由于通信信道噪声、GPS同步过程、变化的环境温度和系统的不同操作条件，系统过程和测量噪声的统计可能是未知的，并且它们可能不遵循高斯分布。因此，传统的基于卡尔曼滤波器的动态状态估计器可能提供强偏差的状态估计。为了解决这些问题，本文提出了一种鲁棒的广义最大似然无迹卡尔曼滤波器（GM-UKF）。统计线性化的方法，推导出一个紧凑的批处理模式的回归形式，通过同时处理预测的状态向量和接收到的测量。这种回归形式增强了数据冗余度，使我们能够检测坏的相量测量单元测量和不正确的状态预测，并通过广义最大似然估计器过滤掉未知的高斯和非高斯噪声。后者最小化凸Huber函数，其权重通过投影统计（PS）计算。特别是，PS被应用到一个建议的2维矩阵，由时间相关的创新向量和预测状态。最后，总的影响函数被用来推导GM UKF状态估计的误差协方差矩阵，从而产生下一时刻的鲁棒状态预测。在IEEE 39节点测试系统上进行的大量仿真验证了该方法的有效性和鲁棒性。
Due to the communication channel noises, GPS synchronization process, changing environment temperature and different operating conditions of the system, the statistics of the system process and measurement noises may be unknown and they may not follow Gaussian distributions. As a result, the traditional Kalman filter-based dynamic state estimators may provide strongly biased state estimates. To address these issues, this paper develops a robust generalized maximum-likelihood unscented Kalman filter (GM-UKF). The statistical linearization approach is presented to derive a compact batch-mode regression form by processing the predicted state vector and the received measurements simultaneously. This regression form enhances the data redundancy and allows us to detect bad phasor measurement unit measurements and incorrect state predictions, and filter out unknown Gaussian and non-Gaussian noises through the generalized maximum likelihood-estimator. The latter minimizes a convex Huber function with weights calculated via the projection statistics (PS). Particularly, the PS is applied to a proposed 2-dimensional matrix that consists of temporally correlated innovation vectors and predicted states. Finally, the total influence function is used to derive the error covariance matrix of the GM-UKF state estimates, yielding the robust state prediction at the next time instant. Extensive simulations carried out on the IEEE 39-bus test system demonstrate the effectiveness and robustness of the proposed method.