This paper investigates the anomaly-resistant decentralized state estimation(SE) problem for a class of wide-area power systems which are divided into several non-overlapping areas connected through transmission lines...This paper investigates the anomaly-resistant decentralized state estimation(SE) problem for a class of wide-area power systems which are divided into several non-overlapping areas connected through transmission lines. Two classes of measurements(i.e., local measurements and edge measurements) are obtained, respectively, from the individual area and the transmission lines. A decentralized state estimator, whose performance is resistant against measurement with anomalies, is designed based on the minimum error entropy with fiducial points(MEEF) criterion. Specifically, 1) An augmented model, which incorporates the local prediction and local measurement, is developed by resorting to the unscented transformation approach and the statistical linearization approach;2) Using the augmented model, an MEEF-based cost function is designed that reflects the local prediction errors of the state and the measurement;and 3) The local estimate is first obtained by minimizing the MEEF-based cost function through a fixed-point iteration and then updated by using the edge measuring information. Finally, simulation experiments with three scenarios are carried out on the IEEE 14-bus system to illustrate the validity of the proposed anomaly-resistant decentralized SE scheme.展开更多
Traditional cubature Kalman filter(CKF)is a preferable tool for the inertial navigation system(INS)/global positioning system(GPS)integration under Gaussian noises.The CKF,however,may provide a significantly biased es...Traditional cubature Kalman filter(CKF)is a preferable tool for the inertial navigation system(INS)/global positioning system(GPS)integration under Gaussian noises.The CKF,however,may provide a significantly biased estimate when the INS/GPS system suffers from complex non-Gaussian disturbances.To address this issue,a robust nonlinear Kalman filter referred to as cubature Kalman filter under minimum error entropy with fiducial points(MEEF-CKF)is proposed.The MEEF-CKF behaves a strong robustness against complex nonGaussian noises by operating several major steps,i.e.,regression model construction,robust state estimation and free parameters optimization.More concretely,a regression model is constructed with the consideration of residual error caused by linearizing a nonlinear function at the first step.The MEEF-CKF is then developed by solving an optimization problem based on minimum error entropy with fiducial points(MEEF)under the framework of the regression model.In the MEEF-CKF,a novel optimization approach is provided for the purpose of determining free parameters adaptively.In addition,the computational complexity and convergence analyses of the MEEF-CKF are conducted for demonstrating the calculational burden and convergence characteristic.The enhanced robustness of the MEEF-CKF is demonstrated by Monte Carlo simulations on the application of a target tracking with INS/GPS integration under complex nonGaussian noises.展开更多
Conventional adaptive filtering algorithms often exhibit performance degradation when processing multipath interference in raw echoes of spaceborne synthetic aperture radar(SAR)systems due to anomalous outliers,manife...Conventional adaptive filtering algorithms often exhibit performance degradation when processing multipath interference in raw echoes of spaceborne synthetic aperture radar(SAR)systems due to anomalous outliers,manifesting as insufficient convergence and low estimation accuracy.To address this issue,this study proposes a novel robust adaptive filtering algorithm,namely the M-estimation-based minimum error entropy with affine projection(APMMEE)algorithm.This algorithm inherits the joint multi-data-block update mechanism of the affine projection algorithm,enabling rapid adaptation to the dynamic characteristics of raw echoes and achieving fast convergence.Meanwhile,it incorporates the M-estimation-based minimum error entropy(MMEE)criterion,which weights error samples in raw echoes through M-estimation functions,effectively suppressing outlier interference during the algorithm update.Both the system identification simulations and practical multipath interference suppression experiments using raw echoes demonstrate that the proposed APMMEE algorithm exhibits superior filtering performance.展开更多
基金supported in part by the National Natural Science Foundation of China(61933007, U21A2019, 62273005, 62273088, 62303301)the Program of Shanghai Academic/Technology Research Leader of China (20XD1420100)+2 种基金the Hainan Province Science and Technology Special Fund of China(ZDYF2022SHFZ105)the Natural Science Foundation of Anhui Province of China (2108085MA07)the Alexander von Humboldt Foundation of Germany。
文摘This paper investigates the anomaly-resistant decentralized state estimation(SE) problem for a class of wide-area power systems which are divided into several non-overlapping areas connected through transmission lines. Two classes of measurements(i.e., local measurements and edge measurements) are obtained, respectively, from the individual area and the transmission lines. A decentralized state estimator, whose performance is resistant against measurement with anomalies, is designed based on the minimum error entropy with fiducial points(MEEF) criterion. Specifically, 1) An augmented model, which incorporates the local prediction and local measurement, is developed by resorting to the unscented transformation approach and the statistical linearization approach;2) Using the augmented model, an MEEF-based cost function is designed that reflects the local prediction errors of the state and the measurement;and 3) The local estimate is first obtained by minimizing the MEEF-based cost function through a fixed-point iteration and then updated by using the edge measuring information. Finally, simulation experiments with three scenarios are carried out on the IEEE 14-bus system to illustrate the validity of the proposed anomaly-resistant decentralized SE scheme.
基金supported by the Fundamental Research Funds for the Central Universities(xzy022020045)the National Natural Science Foundation of China(61976175)。
文摘Traditional cubature Kalman filter(CKF)is a preferable tool for the inertial navigation system(INS)/global positioning system(GPS)integration under Gaussian noises.The CKF,however,may provide a significantly biased estimate when the INS/GPS system suffers from complex non-Gaussian disturbances.To address this issue,a robust nonlinear Kalman filter referred to as cubature Kalman filter under minimum error entropy with fiducial points(MEEF-CKF)is proposed.The MEEF-CKF behaves a strong robustness against complex nonGaussian noises by operating several major steps,i.e.,regression model construction,robust state estimation and free parameters optimization.More concretely,a regression model is constructed with the consideration of residual error caused by linearizing a nonlinear function at the first step.The MEEF-CKF is then developed by solving an optimization problem based on minimum error entropy with fiducial points(MEEF)under the framework of the regression model.In the MEEF-CKF,a novel optimization approach is provided for the purpose of determining free parameters adaptively.In addition,the computational complexity and convergence analyses of the MEEF-CKF are conducted for demonstrating the calculational burden and convergence characteristic.The enhanced robustness of the MEEF-CKF is demonstrated by Monte Carlo simulations on the application of a target tracking with INS/GPS integration under complex nonGaussian noises.
基金supported by Shandong Provincial Natural Science Foundation(No.ZR2022MF314).
文摘Conventional adaptive filtering algorithms often exhibit performance degradation when processing multipath interference in raw echoes of spaceborne synthetic aperture radar(SAR)systems due to anomalous outliers,manifesting as insufficient convergence and low estimation accuracy.To address this issue,this study proposes a novel robust adaptive filtering algorithm,namely the M-estimation-based minimum error entropy with affine projection(APMMEE)algorithm.This algorithm inherits the joint multi-data-block update mechanism of the affine projection algorithm,enabling rapid adaptation to the dynamic characteristics of raw echoes and achieving fast convergence.Meanwhile,it incorporates the M-estimation-based minimum error entropy(MMEE)criterion,which weights error samples in raw echoes through M-estimation functions,effectively suppressing outlier interference during the algorithm update.Both the system identification simulations and practical multipath interference suppression experiments using raw echoes demonstrate that the proposed APMMEE algorithm exhibits superior filtering performance.
