Abstract
This paper develops an adaptive robust cubature Kalman filter (ARCKF) that is able to mitigate the adverse effects of the innovation and observation outliers while filtering out the system and measurement noises. To develop the ARCKF dynamic state estimator, a batch-mode regression form in the framework of cubature Kalman filter is first established by processing the predicted state and measurement data information simultaneously. Subsequently, based on the regression form, the outliers can be detected and downweighted by the robust projection statistics approach. Then, the adverse effects of innovation and observation outliers can be effectively suppressed by the generalized maximum likelihood (GM)-type estimator utilizing the iteratively reweighted least squares approach. Finally, an adaptive strategy is developed to adjust the state estimation error covariance matrix under different conditions. Extensive simulation results obtained from the IEEE New England 10-machine 39-bus test system under various operating conditions demonstrate the effectiveness and robustness of the proposed method, which is able to track the transients of power system in a more reliable way than the conventional cubature Kalman filter (CKF) and the unscented Kalman filter (UKF).
Highlights
Accurate and reliable dynamic state estimator (DSE) is gradually becoming more and more important for the secure and stable operation of power systems, since it can provide the essential information for power system real-time monitoring and control [1]–[5]
NUMERICAL RESULTS In this part, extensive simulations have been conducted on the IEEE 10-machine 39-bus system to evaluate the efficiency of the developed adaptive robust cubature Kalman filter (ARCKF) method
The simulation consists of the following steps: a three-phase fault is applied to bus 16 at t = 0.5s to simulate a large system disturbance, where the fault impedance is 0.001pu and the fault is cleared at t = 0.7s; the simulated values of the measurement variables in (4) are corrupted by additive noises to simulate the realistic phasor measurement units (PMUs) measurements; note that the sampling frequency of measurements is 50 frames per second
Summary
Accurate and reliable dynamic state estimator (DSE) is gradually becoming more and more important for the secure and stable operation of power systems, since it can provide the essential information for power system real-time monitoring and control [1]–[5]. Y. Wang et al.: ARCKF for Power System Dynamic State Estimation Against Outliers measurement information. As for the innovation outliers, they are often introduced by the undesirable system process impulsive noise or inaccurate approximations in the state prediction model, which might corrupt the predicted state estimates To address these issues, some robust dynamic state estimation methods were proposed. In order to mitigate the adverse effects of observation and innovation outliers, a robust iterated extended Kalman filter was developed in [24], but it may suffer from the divergence problem while the system model exhibits strong nonlinearity. To deal with the aforementioned challenges, by resorting to robust statistics, this paper develops an adaptive robust cubature Kalman filter that is able to suppress the outliers and achieve a high estimation accuracy.
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