Unscented Kalman Filter With Generalized Correntropy Loss for Robust Power System Forecasting-Aided State Estimation

Ma, Wentao; Qiu, Jinzhe; Liu, Xinghua; Xiao, Gaoxi; Duan, Jiandong; Chen, Badong

doi:10.1109/tii.2019.2917940

Cited by 71 publications

(35 citation statements)

References 29 publications

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“…According to (12), it is assumed that a weight w is assigned to the new measurement to be processed. Likewise, a weight t must be assigned to row u of the triangular matrix, as required in (13). The next rotation between h and u is aimed at zeroing out the ith entry of h. After applying such elementary rotation, we have…”

Section: Elementary Rotationsmentioning

confidence: 99%

“…The adoption of a judicious policy to promote the iterative adjustment of Parzen window widths enables the resulting estimator to effectively reject outliers [11]. In practice, the solution is obtained through an equation similar to the familiar normal equation of the WLS approach, with the relevant difference that individual measurement weights may vary significantly through the iterations [12][13][14]. The downside of this procedure is the possibility of working with widely distinct weighting factors, a well-known cause of numerical ill-conditioning problems when applying normal equation-like algorithms [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Orthogonal method for solving maximum correntropy‐based power system state estimation

Freitas¹,

Costa²,

Miranda

2020

IET Generation, Transmission & Distribution

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Section: Elementary Rotationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Orthogonal method for solving maximum correntropy‐based power system state estimation

Freitas¹,

Costa²,

Miranda

2020

IET Generation, Transmission & Distribution

View full text Add to dashboard Cite

“…The latest works on anomaly detection use robust FASEs. 16,17,[38][39][40][41][42] However, this method still cannot detect the sudden load change if the change of states is small in comparison to the change of NI of the measurements. In addition, both the innovation analysis method and the robust FASE have not considered the detection of the topology change after fault, and the identification of the anomalies.…”

Section: Introductionmentioning

confidence: 99%

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

Jin

Chakrabarti

et al. 2021

Int Trans Electr Energ Syst

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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.

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“…Nevertheless, PF has some inherent practical problems, such as complexity of calculation, selection strategy of importance function and so on. In the field of information theoretic learning, maximum correntropy (MC) criterion has been successfully utilized for the non-Gaussian signal processing problems [28][29][30][31][32][33][34][35][36][37][38][43][44][45][46]. Under the MC criterion, several effective filter design methods were also developed for the non-Gaussian systems.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Non-Gaussian Estimation Using Maximum Correntropy Square Root Cubature Information Filtering

Feng

Zhou

et al. 2020

IEEE Access

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This paper concerns the nonlinear filter designing methods in the information space of the nonlinear systems with non-Gaussian noises. Firstly, the prediction information vector is obtained by the traditional square root cubature information filtering algorithm. Then, under the maximum correntropy criterion, the prediction information vector is corrected with the contribution information vector obtained by the non-Gaussian measurement. The information filtering gain is obtained by utilizing the state information correntropy matrix and the measurement information correntropy matrix, in which, the state prediction is taken as the state value. In order to improve the advantage of the above nonlinear non-Gaussian information filter in filtering accuracy, with the help of fixed-point theory, an iterative computation method is further developed to update the estimation information vector and the state estimate. The effectiveness of the two proposed nonlinear non-Gaussian filtering methods is illustrated in final four simulation examples. INDEX TERMS Maximum correntropy criterion; information correntropy matrix; square root cubature information filter;

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Unscented Kalman Filter With Generalized Correntropy Loss for Robust Power System Forecasting-Aided State Estimation

Cited by 71 publications

References 29 publications

Orthogonal method for solving maximum correntropy‐based power system state estimation

Orthogonal method for solving maximum correntropy‐based power system state estimation

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

Nonlinear Non-Gaussian Estimation Using Maximum Correntropy Square Root Cubature Information Filtering

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