| Power system state estimation is the foundation and core component of the energy management system,which plays an important role in the economic dispatch,operation and real-time control of the power system.With the expansion of power system scale,traditional centralized state estimation is difficult to meet the real-time and accuracy requirements of large-scale power system state estimation.In order to make the dispatcher able to predict the future trend of the system quickly and accurately,this paper improves the traditional unscented Kalman filter state estimation algorithm,and proposes an improved robust unscented Kalman filter state estimation algorithm,and applies it to the state estimation of the power system.In order to solve the traditional unscented Kalman filter proportional correction factor,it takes the problem of poor performance in fixed value estimation,and modifies the correction factor in every estimation to improve the filtering performance.Aiming at the gross error existing in the system,a robust and unscented Kalman filter algorithm is proposed.By introducing augmented factor,we can reduce the influence of gross error and reduce the estimation error.In order to solve the problem of large calculation and high dimension of centralized state estimation and improve the real-time performance of state estimation,the power grid is partitioned by BCC optimized spectral clustering algorithm.Finally,on the basis of improved robust unscented Kalman filter algorithm,the partition state of the power system is estimated,and the boundary nodes are fused to improve the estimation accuracy.The IEEE30 and IEEE118 node test system validates the state estimation algorithm of this paper,and compares it with multiple algorithms.Through simulation analysis,it is proved that the algorithm has good estimation effect,can adapt to large-scale power system and provide more accurate and real-time estimate information for the dispatcher... |
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