TY - GEN
T1 - Power system state estimation via feasible point pursuit
AU - Wang, Gang
AU - Zamzam, Ahmed S.
AU - Giannakis, Georgios B.
AU - Sidiropoulos, Nicholas D.
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/4/19
Y1 - 2017/4/19
N2 - Power system state estimation (PSSE) is a critical task for grid operation efficiency and system stability. Physical laws dictate quadratic relationships between observable quantities and voltage state variables, hence rendering the PSSE problem nonconvex and NP-hard. Existing SE solvers largely rely on iterative optimization methods or semidefinite relaxation (SDR) techniques. Even when based on noiseless measurements, convergence of the former is sensitive to the initialization, while the latter is challenged by small-size measurements especially when voltage magnitudes are not available at all buses. At the price of running time, this paper proposes a novel feasible point pursuit (FPP)-based SE solver, which iteratively seeks feasible solutions for a nonconvex quadratically constrained quadratic programming reformulation of the weighted least-squares (WLS) SE problem. Numerical tests corroborate that the novel FPP-based SE markedly improves upon the Gauss-Newton based WLS and SDR-based SE alternatives, also when noisy measurements are available.
AB - Power system state estimation (PSSE) is a critical task for grid operation efficiency and system stability. Physical laws dictate quadratic relationships between observable quantities and voltage state variables, hence rendering the PSSE problem nonconvex and NP-hard. Existing SE solvers largely rely on iterative optimization methods or semidefinite relaxation (SDR) techniques. Even when based on noiseless measurements, convergence of the former is sensitive to the initialization, while the latter is challenged by small-size measurements especially when voltage magnitudes are not available at all buses. At the price of running time, this paper proposes a novel feasible point pursuit (FPP)-based SE solver, which iteratively seeks feasible solutions for a nonconvex quadratically constrained quadratic programming reformulation of the weighted least-squares (WLS) SE problem. Numerical tests corroborate that the novel FPP-based SE markedly improves upon the Gauss-Newton based WLS and SDR-based SE alternatives, also when noisy measurements are available.
KW - Feasible point pursuit
KW - Nonconvex QCQP
KW - Power system state estimation
UR - https://www.scopus.com/pages/publications/85019175011
U2 - 10.1109/GlobalSIP.2016.7905947
DO - 10.1109/GlobalSIP.2016.7905947
M3 - Conference contribution
AN - SCOPUS:85019175011
T3 - 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings
SP - 773
EP - 777
BT - 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016
Y2 - 7 December 2016 through 9 December 2016
ER -