Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/12608
Title: A constrained optimization approach to dynamic state estimation for power systems including PMU and missing measurements
Authors: Hu, L
Wang, Z
Rahman, I
Liu, X
Keywords: Constrained optimization;Extended Kalman filter (EKF);Missing measurements;Particle swarm optimization (PSO);Power systems;State estimation (SE)
Issue Date: 2016
Publisher: IEEE
Citation: IEEE Transactions on Control Systems Technology, 24(2): pp. 703 - 710, (2016)
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.
URI: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7166299
http://bura.brunel.ac.uk/handle/2438/12608
DOI: http://dx.doi.org/10.1109/TCST.2015.2445852
ISSN: 1063-6536
Appears in Collections:Dept of Computer Science Research Papers

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