Power System Robust Decentralized Dynamic State Estimation Based on Multiple Hypothesis Testing
Power System Robust Decentralized Dynamic State Estimation Based on Multiple Hypothesis Testing
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DOI：
10.1109/tpwrs.2017.2785344
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发表时间：
2018-07
影响因子：
6.6
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
提出了一种基于无迹卡尔曼滤波的电力系统分散动态状态估计器（DSE），用于电力系统在线监测和控制。所提出的鲁棒DSE能够检测、识别和抑制三种类型的异常值，即观测异常值、新息异常值和结构异常值。观测异常值是指由于严重错误或网络攻击而提供不可靠计量值的接收到的PMU测量值;创新异常值通常由脉冲系统过程噪声引起，而结构异常值由发电机或其相关控制器（如励磁机和调速器）的不正确参数引起。为了使快速估计的发电机状态的大规模电力系统的分散方式，两个模型解耦方法提出和比较。结果表明，本文提出的发电机解耦方法实现了更高的统计效率比在文献中提出的小和大的测量噪声的存在下。为了检测和区分三类异常值，提出了基于投影统计的多假设检验方法。具体而言，通过构建三个新息矩阵来假设与三种类型的离群值的发生相对应的三个假设;这些矩阵由时间相关的新息向量和/或预测状态和/或测量组成;然后将投影统计应用于每个新息矩阵，并通过统计检验来检查其计算的投影值以验证假设的假设。确定的离群值进一步抑制广义最大似然型估计。在IEEE 39节点系统上进行的数值计算结果表明了该方法的有效性和鲁棒性。
This paper proposes a fast and robust unscented Kalman filter based decentralized dynamic state estimator (DSE) for power system online monitoring and control. The proposed robust DSE is able to detect, identify, and suppress three types of outliers, namely the observation, innovation, and structural outliers. Observation outliers refer to the received PMU measurements providing unreliable metered values due to gross errors or cyber attacks; innovation outliers are typically caused by impulsive system process noise, whereas structural outliers are induced by incorrect parameters of the generators or its associated controllers, such as exciters and speed governors. To enable the fast estimation of generator states of large-scale power systems in a decentralized manner, two model decoupling approaches are presented and compared. It is shown that the generator decoupling approach presented in this paper achieves higher statistical efficiency than the ones proposed in the literature in the presence of both small and large measurement noise. To detect and distinguish three types of outliers, projection statistics based multiple hypothesis testing approach is proposed. Specifically, three hypotheses corresponding to the occurrence of three types of outliers are assumed by constructing three innovation matrices; these matrices are made up by time-correlated innovation vectors, and/or predicted states, and/or measurements; then projection statistics are applied to each of the innovation matrix and its calculated projection values are checked by a statistical test to validate the assumed hypothesis. The identified outliers are further suppressed by a generalized maximum-likelihood-type estimator. Numerical results carried out on the IEEE 39-bus system demonstrate the effectiveness and robustness of the proposed method.