A Robust State Estimation Framework Considering Measurement Correlations and Imperfect Synchronization
A Robust State Estimation Framework Considering Measurement Correlations and Imperfect Synchronization
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DOI：
10.1109/tpwrs.2018.2790390
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发表时间：
2018-01
影响因子：
6.6
通讯作者：
Junbo Zhao;Shaobu Wang;L. Mili;B. Amidan;Renke Huang;Zhenyu Huang
中科院分区：
文献类型：
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作者：
Junbo Zhao;Shaobu Wang;L. Mili;B. Amidan;Renke Huang;Zhenyu Huang
本文开发了一个鲁棒的电力系统状态估计框架，该框架考虑了测量的相关性和不完全时间同步。在该框架中，通过unscented变换和向量自回归（VAR）模型分别计算从监控和数据采集（SCADA）系统和相量测量单元（pmu）获得的测量值的相关性。具体来说，在两次连续SCADA测量扫描的等待期间，PMU测量值通过VAR模型进行缓冲，VAR模型的参数使用投影统计量进行稳健估计。后者考虑了它们的时间和空间相关性，并提供了必要的测量冗余来抑制不良数据和减轻不完美的时间同步。在SCADA和PMU测量没有同时到达控制中心的情况下，会产生不完美的测量时间同步，可以利用预测的PMU测量或来自最新状态估计运行的先前SCADA测量来恢复系统的可观察性。最后，扩展了一种鲁棒广义极大似然估计器，用于集成测量误差相关性和处理异常值（也称为坏数据）。仿真结果源于在各种条件下与其他替代方案的综合比较，证明了所提出框架的优点。
This paper develops a robust power system state estimation framework that accounts for correlations and imperfect time synchronization of the measurements. In this framework, correlations of the measurements obtained from the supervisory control and data acquisition (SCADA) system and the phasor measurement units (PMUs) are separately calculated through the unscented transformation and a vector auto-regression (VAR) model. Specifically, the PMU measurements during the waiting period of two successive SCADA measurement scans are buffered via a VAR model whose parameters are robustly estimated using the projection statistics. The latter take into account their temporal and spatial correlations and provide the needed measurement redundancy to suppress bad data and mitigate imperfect time synchronization. In the case where the SCADA and the PMU measurements do not arrive simultaneously at the control center, yielding imperfect measurement time synchronization, either the forecasted PMU measurements or the prior SCADA measurements from the latest state estimation run are leveraged to restore system observability. Finally, a robust generalized maximum-likelihood (GM)-estimator is extended to integrate the measurement error correlations and to handle the outliers, also known as bad data. Simulation results that stem from a comprehensive comparison with other alternatives under various conditions demonstrate the benefits of the proposed framework.