This article proposes a new algorithm for forecasting‐aided state estimation based on the cubature Kalman filter (CKF) and new methods for detecting and identifying data anomalies. In this article, through extensive simulations, the CKF was compared to four different types of forecasting‐aided state estimators (FASEs) including extended Kalman filter (EKF), iterated EKF, second‐order Kalman filter and unscented Kalman filter under normal operation and bad data conditions. Identifying the challenge that the estimation accuracy of the existing CKF‐based estimator is significantly lower than that of the other FASEs in the cases of sudden load change, and sudden topology change caused by faults an attempt to improve the CKF accuracy has been undertaken. The existing detection methods cannot accurately detect and distinguish those anomalies, and they cannot identify the anomaly location. This article proposes an improved algorithm for CKF‐based FASE that overcomes the drawbacks of the existing CKF‐based FASEs using a novel anomaly detection algorithm. The simulation results show that the new anomaly detection method is superior to the two existing anomaly detection algorithms. The simulations are performed in the above‐mentioned four cases in IEEE 14 and 118 bus test systems in MATLAB.

中文翻译：

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一种基于改进卡尔曼滤波的预测状态估计和异常检测算法

本文提出了一种基于库尔曼卡尔曼滤波器（CKF）的预测状态估计的新算法，以及检测和识别数据异常的新方法。在本文中，通过广泛的仿真，将CKF与四种不同类型的预测状态估计器（FASE）进行了比较，包括扩展卡尔曼滤波器（EKF），迭代EKF，二阶卡尔曼滤波器和无味卡尔曼滤波器。数据条件。确定了以下挑战：在负载突然变化，由故障导致的拓扑突然变化的情况下，现有基于CKF的估算器的估算精度明显低于其他FASE的估算精度，因此人们试图提高CKF的精度。现有的检测方法无法准确地检测和区分这些异常，并且他们无法识别异常位置。本文提出了一种针对基于CKF的FASE的改进算法，该算法使用一种新颖的异常检测算法克服了现有基于CKF的FASE的缺点。仿真结果表明，新的异常检测方法优于现有的两种异常检测算法。在上述四种情况下，在IEEE 14和MATLAB的118总线测试系统中进行了仿真。