A Constrained Optimization Approach to Dynamic State Estimation for Power Systems Including PMU and Missing Measurements

Abstract

Abstract-In this paper, a hybrid filter algorithm is developed to deal with the state estimation problem for power systems by taking into account the impact from the phasor measurement units (PMU). 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 centre, 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 state estimation problem becomes a constrained optimization one based on the probability-maximization method. The resulting constrained optimization problem is then solved by using the particle swarm optimization (PSO) 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.

Robust Cubature Kalman Filter for Dynamic State Estimation of Synchronous Machines Under Unknown Measurement Noise Statistics

“…In recent years, the studies on state estimators began to focus on a synchronous generator and its electromechanical transient model [8][9][10].In essence,this is a typical nonlinear filter problem. Up to now, there has been a significantly amount of studies on DSE of synchronous machines by using particle filters (PF) [11,12] and variousKalman-type filtering algorithms, such asextended Kalman filter (EKF) [13][14][15][16][17], unscented Kalman filter (UKF) [18][19][20][21][22][23][24], and Cubature Kalman Filter (CKF) [3,25,26].The EKF is a classical nonlinear Kalman filter; the unscented transform-basedUKFprovidesreasonable filtering performance, but its convergence is dependent on the sampling methods of Sigma points [18,19]; the CKF based on the spherical-radial cubature rule is an emerging nonlinear filter, which can give a systematic solution for highdimensional nonlinear filtering issues.Extensive comparisons of all these Kalman-type estimators have been made from different perspectives, such as convergence, numerical stability, and computational complexity in [3,16].…”

FLiER: Practical Topology Update Detection Using Sparse PMUs

“…According to the IEEE specification, data from a correctly functioning PMU also satisfy tight phase and magnitude error tolerances [20]. Hence, many methods combine PMU data, SCADA data, and model information for state estimation [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33] and for detection and localization of faults [34], [35], [36], [37], [38], [14], [39], [15], [40], [41], [42], [43].…”

Recursive filtering for communication-based train control systems with packet dropouts

“…For instance, in [17], the Kalman filtering problem with intermittent observations has been illustrated where the packet dropouts have been modeled by the general finite state Markov channel (FSMC). A novel hybrid filtering algorithm has been developed in [6] to deal with the state estimation problem for power systems where the signal is obtained from the phasor measurement units. In [25], the robust H 2 and H ∞ filtering problems for linear discrete-time systems with polytopic parameter uncertainty has been studied based on a parameter-dependent Lyapunov function approach.…”