Robust and Scalable Power System State Estimation via Composite Optimization
Robust and Scalable Power System State Estimation via Composite Optimization
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DOI：
10.1109/tsg.2019.2897100
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发表时间：
2017-08
影响因子：
9.6
通讯作者：
G. Wang;G. Giannakis;Jie Chen
中科院分区：
文献类型：
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作者：
G. Wang;G. Giannakis;Jie Chen
在当今的网络智能电网中，不确定的可再生能源的高渗透率，有目的地操纵仪表读数，以及对广域态势感知的需求，要求快速，准确和鲁棒的电力系统状态估计。相对于加权最小二乘估计，最小绝对值（LAV）估计具有较强的鲁棒性。然而，由于非凸性和非光滑性，现有的LAV求解器基于线性规划通常是缓慢的，因此，不足以实时系统监控。本文，开发了两种新的算法，有效的LAV估计，其中借鉴了最新进展的复合优化。第一个是一个确定性的线性邻近计划，处理一系列（一般5 ~ 10）凸二次问题，每个有效地解决通过现成的工具箱或通过交替方向的方法乘数。利用电力网络固有的稀疏连接性，第二种方案是随机的，并且每次迭代仅更新复电压状态向量的几个条目。特别地，当仅使用电压幅值和（无）有功功率流测量时，无论网络中的总线数量如何，该数量都减少到一个或两个。这种计算复杂性显然可以很好地扩展到大型电力系统。此外，通过仔细的最小化电压和潮流测量，加速随机迭代的实现成为可能。所开发的算法进行了数值评估，使用各种基准电力网络。模拟测试证实，改进的鲁棒性可以达到相当或显着减少计算时间的中型或大型网络相对于现有的替代品。
In today’s cyber-enabled smart grids, high penetration of uncertain renewables, purposeful manipulation of meter readings, and the need for wide-area situational awareness, call for fast, accurate, and robust power system state estimation. The least-absolute-value (LAV) estimator is known for its robustness relative to the weighted least-squares one. However, due to nonconvexity and nonsmoothness, existing LAV solvers based on linear programming are typically slow and, hence, inadequate for real-time system monitoring. This paper, develops two novel algorithms for efficient LAV estimation, which draw from recent advances in composite optimization. The first is a deterministic linear proximal scheme that handles a sequence of (5 ~ 10 in general) convex quadratic problems, each efficiently solvable either via off-the-shelf toolboxes or through the alternating direction method of multipliers. Leveraging the sparse connectivity inherent to power networks, the second scheme is stochastic and updates only a few entries of the complex voltage state vector per iteration. In particular, when voltage magnitude and (re)active power flow measurements are used only, this number reduces to one or two regardless of the number of buses in the network. This computational complexity evidently scales well to large-size power systems. Furthermore, by carefully mini-batching the voltage and power flow measurements, accelerated implementation of the stochastic iterations becomes possible. The developed algorithms are numerically evaluated using a variety of benchmark power networks. Simulated tests corroborate that improved robustness can be attained at comparable or markedly reduced computation times for medium- or large-size networks relative to existing alternatives.