Online semidefinite programming for power system state estimation
Online semidefinite programming for power system state estimation
复制标题
电力系统状态估计的在线半定规划
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
G. Giannakis
中科院分区：
文献类型：
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作者：
Seung;G. Wang;G. Giannakis
电力系统状态估计是保证电网可靠运行的重要前提。精确PSSE的一个关键挑战是系统状态中SCADA测量的固有非线性。最近提出的静态PSSE解决这个问题，利用隐藏的凸性结构和解决半定规划（SDP）松弛。在这项工作中，提出了一种在线PSSE算法的基础上SDP松弛，它享有类似的凸性优势，同时利用过去的测量，以及提高性能。一个在线凸优化技术，推导出一个有效的算法，具有较强的性能保证。数值试验验证了该方法的有效性。
Power system state estimation (PSSE) constitutes a crucial prerequisite for reliable operation of the power grid. A key challenge for accurate PSSE is the inherent nonlinearity of SCADA measurements in the system states. Recent proposals for static PSSE tackle this issue by exploiting hidden convexity structure and solving a semidefinite programming (SDP) relaxation. In this work, an online PSSE algorithm based on SDP relaxation is proposed, which enjoys a similar convexity advantage, while capitalizing on past measurements as well for improved performance. An online convex optimization technique is adopted to derive an efficient algorithm with strong performance guarantees. Numerical tests verify the efficacy of the proposed approach.