A constrained optimization approach to dynamic state estimation for power systems including PMU and missing measurements

Date

2015-07-24

Advisors

Journal Title

Journal ISSN

ISSN

1063-6536

Volume Title

Publisher

IEEE

Type

Article

Peer reviewed

Yes

Abstract

In this brief, a hybrid filter algorithm is developed to deal with the state estimation (SE) problem for power systems by taking into account the impact from the phasor measurement units (PMUs). Our aim is to include PMU measurements when designing the dynamic state estimators for power systems with traditional measurements. Also, as data dropouts inevitably occur in the transmission channels of traditional measurements from the meters to the control center, the missing measurement phenomenon is also tackled in the state estimator design. In the framework of extended Kalman filter (EKF) algorithm, the PMU measurements are treated as inequality constraints on the states with the aid of the statistical criterion, and then the addressed SE problem becomes a constrained optimization one based on the probability-maximization method. The resulting constrained optimization problem is then solved using the particle swarm optimization algorithm together with the penalty function approach. The proposed algorithm is applied to estimate the states of the power systems with both traditional and PMU measurements in the presence of probabilistic data missing phenomenon. Extensive simulations are carried out on the IEEE 14-bus test system and it is shown that the proposed algorithm gives much improved estimation performances over the traditional EKF method.

Description

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link

Keywords

Constrained optimization, extended Kalman filter (EKF), missing measurements, particle swarm optimization (PSO), state estimation

Citation

Hu, L., Wang, Z., Rahman, I. and Liu, X. (2016) A constrained optimization approach to dynamic state estimation for power systems including PMU and missing measurements. IEEE Transactions on Control Systems Technology, 24(2), pp. 703--710.

Rights

Research Institute

Institute of Artificial Intelligence (IAI)