An improved algorithm for cubature Kalman filter based forecasting‐aided state estimation and anomaly detection

Abstract

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.

Pmu-Based Real-Time Distribution System State Estimation Considering Anomaly Detection, Discrimination and Identification

“…By using the prediction results of EKF FASE, in this paper the following two methods are adopted for ADDI: the conventional innovation analysis method [14], and the improved innovation analysis method [3]. For the sake of clarity, the detection refers to the determination of the anomaly presence, the discrimination is the classification of the detected anomaly according to its type, whilst the identification is the procedure of finding out the origin of the anomaly in order to properly counter it and make the SE remain unbiased.…”

“…This property for NIs distribution can be used to discriminate between BD and SLC since the presence of BD may shift the NIs distribution from being symmetrical, whilst under SLC the distribution will remain symmetrical. The skewness is defined as [3,14,39]:…”

“…Apart from the skewness of NI distributions, it is reported in [3] that the skewness to the largest NI ratio (SIR), given as:…”

“…This calls for developing advanced Distribution System State Estimation (DSSE) approaches. At the same time, the practicability of the existing approaches against electrical distribution system nonlinearities: Extended Kalman Filter, Unscented Kalman Filter, Ensemble Kalman Filter, Cubature Kalman Filter, to name a few [3][4][5][6]. Each variant has its advantages and disadvantages, so the choice is usually influenced by the compromise between estimation accuracy, computational efficiency and robustness to different types of anomalies.…”

“…Consequently, ADDI methodology is selected to screen anomalies like SLC and BD. Two of the pre-estimation schemes from [3,14] have been tested for the detection and discrimination of SLC and BD. Next, they are tested to identify either the PMU placed at the bus with the disturbed load or the PMU corrupted with BD.…”