Accurately monitoring the system's operating point is central to the reliable and economic operation of an autonomous energy grid. Power system state estimation (PSSE) aims to obtain complete voltage magnitude and angle information at each bus given a number of system variables at selected buses and lines. Power flow analysis amounts to solving a set of noise-free power flow equations, and is cast as a special case of PSSE. Physical laws dictate quadratic relationships between available quantities and unknown voltages, rendering general instances of power flow and PSSE nonconvex and NP-hard. Past approaches are largely based on gradient-type iterative procedures or semidefinite relaxation (SDR). Due to nonconvexity, the solution obtained via gradient-type schemes depends on initialization, while SDR methods do not perform as desired in challenging scenarios. This paper puts forth novel feasible point pursuit (FPP)-based solvers for power flow analysis and PSSE, which iteratively seek feasible solutions for a nonconvex quadratically constrained quadratic programming reformulation of the weighted least-squares (WLS). Relative to the prior art, the developed solvers offer superior numerical performance at the cost of higher computational complexity. Furthermore, they converge to a stationary point of the WLS problem. As a baseline for comparing different estimators, the Cramér-Rao lower bound is derived for the fundamental PSSE problem in this paper. Judicious numerical tests on several IEEE benchmark systems showcase markedly improved performance of our FPP-based solvers for both power flow and PSSE tasks over popular WLS-based Gauss-Newton iterations and SDR approaches.
Bibliographical noteFunding Information:
Manuscript received May 11, 2017; revised September 18, 2017 and December 6, 2017; accepted December 25, 2017. Date of publication January 11, 2018; date of current version February 7, 2018. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Ami Wiesel. The work of G. Wang and G. B. Giannakis was supported in part by NSF grants 1423316, 1442686, 1508993, and 1509040. The work of A. S. Zamzam and N. D. Sidiropoulos was supported in part by NSF grants 1231504 and 1525194. This paper was presented in part at the 2016 IEEE Global Conference on Signal and Information Processing, Washington, DC, USA, December 7–9, 2016. (Corresponding author: Georgios B. Giannakis.) G. Wang is with the State Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing Institute of Technology, Beijing 100081, China, and also with the Digital Technology Center and the Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN 55455 USA (e-mail: email@example.com).
- Power flow analysis
- autonomous energy grid
- feasible point pursuit
- nonconvex quadratically constrained quadratic programming
- state estimation