面向电力系统多重扰动的鲁棒动态状态估计方法

A Robust Dynamic State Estimation Against Multiple Disturbances in Power Systems

针对电力系统面临的系统异常和网络攻击等多重扰动威胁,提出一种基于稳健minimization maximization estimation(MM估计)的无迹卡尔曼滤波动态状态估计方法。首先,分析了卡尔曼滤波及其拓展滤波器和鲁棒回归方法的优缺点;其次,采用统计线性化方法将系统模型线性化并推导状态向量与测量向量的批处理回归形式,设计无迹卡尔曼滤波(unscented Kalman filter,UKF)与MM估计的融合方法;再次,为MM-UKF鲁棒状态估计选择合适的权重函数以达到最有效地去除多重扰动的影响;最后,实验结果表明,相较于已有的动态状态估计方法,所提方法在应对系统异常、网络攻击时表现出更加的鲁棒性。

Considering the disturbances such as the system anomalies and cyberattacks in power systems,a dynamic state estimation method by combining the unscented Kalman filter with the robust minimization maximization estimation(MM estimation) is proposed.Firstly,the Kalman filter and robust estimator approaches are revisited and analyzed their advantages and disadvantages;Secondly,the statistical linearization method is used to linearize the system model and derive the batch regression form for the system states and sensor measurement;Thirdly,we choose an appropriate weight function for the MM-UKF is chosen to achieve the maximum effect of removing the influence of power disturbances;Finally,the simulation results show that the proposed MM-UKF performs more robust than the other dynamic estimation method in dealing with system anomalies and cyberattacks.