一种计及全量测相关性的混合电力系统状态估计方法

A Hybrid Power System State Estimation Method Considering Measurement Correlations

针对电力系统状态估计中全量测存在相关性的实际情况,提出一种计及全量测相关性的混合电力系统状态估计方法。首先,利用无迹变换法计算来自数据采集与监视控制（supervisory control and data acquisition,SCADA）系统量测之间的统计相关性;其次采用量测缓冲器和向量自递归模型（vector autoregressive model,VAR）计及相量测量单元（phasor measurement units,PMU）量测的时空相关性,最终形成两部分相结合的混合线性状态估计方法。该方法在充分考虑PMU量测量之间时相关性的同时,能够确保PMU量测量与基于具有相关性的SCADA量测状态估计结果保持时标的一致性,进而有效地得到计及全量测相关性的电力系统状态估计结果。通过在IEEE-118标准节点系统上进行大量仿真算例分析,结果表明所提方法能够明显提高状态估计结果的精度。

In this paper, a new power system state estimator considering both correlations of SCADA（supervisory control and data acquisition） measurements and PMU（phasor measurement units） measurements is proposed. In particular, utilization of unscented transformation approach is proposed to estimate SCADA measurement correlations while leveraging vector autoregressive model to capture temporal and spatial correlations of PMU measurements. Measurement buffering strategy is presented to solve time skewness between SCADA and PMU measurements. Results of extensive simulation carried out on IEEE-118 bus system demonstrate effectiveness of the proposed approach.