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CLC number: TP311

On-line Access: 2019-01-30

Received: 2018-09-23

Revision Accepted: 2018-11-27

Crosschecked: 2019-01-08

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Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Gang Wang

http://orcid.org/0000-0002-7266-2412

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Frontiers of Information Technology & Electronic Engineering  2019 Vol.20 No.1 P.4-17

http://doi.org/10.1631/FITEE.1800590


Distribution system state estimation: an overview of recent developments


Author(s):  Gang Wang, Georgios B. Giannakis, Jie Chen, Jian Sun

Affiliation(s):  Department of Electrical and Computer Engineering and Digital Technology Center, University of Minnesota, Minneapolis, MN 55455, USA; more

Corresponding email(s):   gangwang@umn.edu, georgios@umn.edu, chenjie@bit.edu.cn, sunjian@bit.edu.cn

Key Words:  State estimation, Cramér-Rao bound, Feasible point pursuit, Semidefinite relaxation, Proximal linear algorithm


Gang Wang, Georgios B. Giannakis, Jie Chen, Jian Sun. Distribution system state estimation: an overview of recent developments[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(1): 4-17.

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Abstract: 
In the envisioned {smart grid, high penetration of uncertain renewables, unpredictable participation of (industrial) customers, and purposeful manipulation of smart meter readings, all highlight the need for accurate, fast, and robust power system state estimation (PSSE). Nonetheless, most real-time data available in the current and upcoming transmission/distribution systems are nonlinear in power system states (i.e., nodal voltage phasors). Scalable approaches to dealing with PSSE tasks undergo a paradigm shift toward addressing the unique modeling and computational challenges associated with those nonlinear measurements. In this study, we provide a contemporary overview of PSSE and describe the current state of the art in the nonlinear weighted least-squares and least-absolute-value PSSE. To benchmark the performance of unbiased estimators, the Cramér-Rao lower bound is developed. Accounting for cyber attacks, new corruption models are introduced, and robust PSSE approaches are outlined as well. Finally, distribution system state estimation is discussed along with its current challenges. Simulation tests corroborate the effectiveness of the developed algorithms as well as the practical merits of the theory.

智能电网状态估计方法最新进展综述

摘要:随着大量不确定可再生能源注入、大规模工业和个体用户市场参与、恶意智能仪表数据篡改等,精确、快速、鲁棒的状态估计方法对未来智能电网系统变得尤为重要。然而,目前电力系统采用的数据采集与监视控制系统只能获取系统状态(即系统所有节点的电压相量)的非线性测量数据。最新智能电网状态估计研究正着力于解决非线性测量数据带给可扩展性状态估计方法建模和计算方面的挑战。为使读者更好理解该领域最新进展,本文综述了基于非线性最小二乘和最小绝对误差的智能电网状态估计方法。为更好比较不同状态估计方法性能,首先描述了智能电网状态估计问题的克拉美罗下界。针对网络攻击问题,引入新的电力系统测量数据攻击模型,并介绍相应鲁棒状态估计方法。最后,分析配电网系统状态估计最新研究进展和挑战。仿真实验验证了该状态估计方法和理论的有效性和优点。

关键词:状态估计;克拉美罗下界;可行解追逐;半正定松弛;近线性算法;复合优化;网络攻击;坏数据检测

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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