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中文摘要：
针对当前应用于状态估计的广域量测系统（wide area measurement system，WAMS）和SCADA系统混合量测中相量测量单元（phasor measurement unit，PMU）最优配置点的选取问题，在分析WAMS/SCADA数据差异的基础上，提出一种基于数据兼容和改进模糊C均值（fuzzy C-means，FCM）聚类算法的 PMU 最优配置方案。采用大数据挖掘理念，通过改进FCM聚类算法对SCADA数据依据相关度分区，在分区内可观测度最大的节点配置PMU，各分区内采用该PMU节点的最优平滑系数进行Vondrak插值，得到满足兼容性的数据，应用于混合模型的状态估计。相对只考虑可观测度的 PMU配置方案，新方案不仅可以实现WAMS/SCADA数据有效兼容，提高估计精度，应用混合量测的状态估计还可有效控制系统负荷快速变化时的估计误差。通过在IEEE 39节点系统上模拟日负荷变化，验证了该PMU最优配置方案的有效性。
英文摘要：
On the basis of analyzing data differences between wide area measurement system （WAMS） and supervisory control and data acquisition （SCADA）, an optimal configuration scheme of PMU, which is based on data compatibility and improved fuzzy C-means （FCM） clustering algorithm, is proposed to solve the optimal configuration point for phasor measurement unit （PMU） during the hybrid measurement by WAMS and SCADA, which is currently widely applied in state estimation. Utilizing the idea of big data mining and using improved FCM clustering algorithm the SCADA data is partitioned according to the relevancy, and inside the partitions PMUs are configured at the node with maximum observability, within all partitions the same optimal smoothing factor of the PMU node is used for Vondrak interpolation to obtain the data satisfying the compatibility to applied in the state estimation of the hybrid model. Relative to the PMU configuration scheme only considering observability, using the proposed scheme not only the WAMS/SCADA data can be effectively compatible to improve the estimation accuracy, but also the state estimation using hybrid measurement can effectively control the estimation error during the rapid variation of system load. The effectiveness of the proposed optimal PMU configuration scheme is validated by simulation results of daily load variation in IEEE 39-bus system.
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