With the development of the smart grid,the distribution system operation conditions become more complex and changeable.Furthermore,due to the influence of observation outliers and uncertain noise statistics,it is more...With the development of the smart grid,the distribution system operation conditions become more complex and changeable.Furthermore,due to the influence of observation outliers and uncertain noise statistics,it is more difficult to grasp the dynamic operation characteristics of distribution system.In order to address these problems,by using projection statistics and the noise covariance updating technology based on the Sage-Husa noise estimator,for distribution power system with outliers and uncertain noise statistics,a robust adaptive cubature Kalman filter forecasting-aided state estimation method is proposed based on generalized-maximum likelihood type estimator.Furthermore,an adaptive strategy,which can enhance the filtering accuracy under normal conditions,is presented.In the simulation part,the branch parameters and node load parameters of the test system are appropriately modified to simulate the asymmetry of the three-phase branch parameters and the asymmetry of the three-phase loads.Finally,through simulation experiments on the improved test system,it is verified that the robust forecasting-aided state estimation method,presented in this paper,can effectively perceive the actual operating state of the distribution network in different simulation scenarios.展开更多
This study investigated the problems of non-cooperative target recognition and relative motion estimation during spacecraft rendezvous maneuvers.A structure integrating an Inertial Measurement Unit(IMU)and a visual ca...This study investigated the problems of non-cooperative target recognition and relative motion estimation during spacecraft rendezvous maneuvers.A structure integrating an Inertial Measurement Unit(IMU)and a visual camera was presented.The angular velocity output of the IMU was used to calculate the motion trajectories of star points in multiple image frames,which can highlight the motion of non-cooperative targets with respect to the image background to improve the probability of target recognition.To solve the problem of target misidentification caused by new star points entering the field of view,a target-tracking link based on IMU prediction was introduced to track the position of the target in the image.Furthermore,a measurement model was constructed using the line-of-sight vector generated from target recognition,and the relative motion state was estimated using a Huber-based non-linear filter.Semi-physical and numerical simulations were performed to evaluate the effectiveness and efficiency of the proposed method.展开更多
To achieve more precise monitoring of state fluctuations in the power network close to renewable energy sources, it is necessary to utilize phasor measurements and shorten the time interval between state estimations. ...To achieve more precise monitoring of state fluctuations in the power network close to renewable energy sources, it is necessary to utilize phasor measurements and shorten the time interval between state estimations. For large-scale power systems, however, estimating all of their states with shorter time intervals means a drastic increase in computational burden. As a tradeoff between accuracy and computational efficiency, a multi-time interval forecasting-aided state estimation approach is proposed in this paper, where states with various degrees of fluctuations are estimated asynchronously with different time intervals. Based on the newest state estimate, forecasting-aided state estimators are employed to predict states at time moments prior to the next round of measurement update and state estimation. Extensive numerical tests have demonstrated the effectiveness of the proposed approach.展开更多
Sideslip angle,yaw rate,and vehicle velocity are essential for intelligent vehicle control.Since these vehicle states are not measured directly,some Kalman-based approaches have been developed to estimate these states...Sideslip angle,yaw rate,and vehicle velocity are essential for intelligent vehicle control.Since these vehicle states are not measured directly,some Kalman-based approaches have been developed to estimate these states using in-vehicle sensors.However,the existing studies seldom account for the influence of sensor data loss on estimation accuracy.In addition,the process and measurement noise change during the estimation process because of the various driving conditions.To address these problems,an expectation-maximization robust extended Kalman filter(EMREKF)is proposed.Firstly,a robust extended Kalman filter(REKF)is developed to deal with the impact of missing measurements.Then,an improved expectation maximization(EM)algorithm that considers data loss is presented to update the noise parameter of the REKF dynamically.Finally,the improved EM is fused with the REKF to form the EMREKF to estimate vehicle state.The experimental results demonstrate that the EMREKF outperforms EKF,REKF,and maximum correntropy criterion EKF for various degrees of data loss and the proposed algorithm has a strong adaptive ability to different driving conditions.展开更多
Networked microgrids(NMGs)are critical in theaccommodation of distributed renewable energy.However,theexisting centralized state estimation(SE)cannot meet the demandsof NMGs in distributed energy management.The curren...Networked microgrids(NMGs)are critical in theaccommodation of distributed renewable energy.However,theexisting centralized state estimation(SE)cannot meet the demandsof NMGs in distributed energy management.The currentestimator is also not robust against bad data.This study introducesthe concepts of relative error to construct an improvedrobust SE(IRSE)optimization model with mixed-integer nonlinearprogramming(MINLP)that overcomes the disadvantage ofinaccurate results derived from different measurements whenthe same tolerance range is considered in the robust SE(RSE).To improve the computation efficiency of the IRSE optimizationmodel,the number of binary variables is reduced based on theprojection statistics and normalized residual methods,which effectivelyavoid the problem of slow convergence or divergenceof the algorithm caused by too many integer variables.Finally,an embedded consensus alternating direction of multiplier method(ADMM)distribution algorithm based on outer approximation(OA)is proposed to solve the IRSE optimization model.This algorithm can accurately detect bad data and obtain SE resultsthat communicate only the boundary coupling informationwith neighbors.Numerical tests show that the proposed algorithmeffectively detects bad data,obtains more accurate SE results,and ensures the protection of private information in all microgrids.展开更多
For a 5G wireless communication system,a convolutional deep neural network(CNN)is employed to synthesize a robust channel state estimator(CSE).The proposed CSE extracts channel information from transmit-and-receive pa...For a 5G wireless communication system,a convolutional deep neural network(CNN)is employed to synthesize a robust channel state estimator(CSE).The proposed CSE extracts channel information from transmit-and-receive pairs through offline training to estimate the channel state information.Also,it utilizes pilots to offer more helpful information about the communication channel.The proposedCNN-CSE performance is compared with previously published results for Bidirectional/long short-term memory(BiLSTM/LSTM)NNs-based CSEs.The CNN-CSE achieves outstanding performance using sufficient pilots only and loses its functionality at limited pilots compared with BiLSTM and LSTM-based estimators.Using three different loss function-based classification layers and the Adam optimization algorithm,a comparative study was conducted to assess the performance of the presented DNNs-based CSEs.The BiLSTM-CSE outperforms LSTM,CNN,conventional least squares(LS),and minimum mean square error(MMSE)CSEs.In addition,the computational and learning time complexities for DNN-CSEs are provided.These estimators are promising for 5G and future communication systems because they can analyze large amounts of data,discover statistical dependencies,learn correlations between features,and generalize the gotten knowledge.展开更多
为了解决高比例分布式电源(distributed generation,DG)大规模并网后实时量测数目缺失、传统预测辅助状态估计方法(forecasting-aided state estimation,FASE)估计精度有限等问题,提出了基于改进Crossformer伪量测构建的主动配电网FASE...为了解决高比例分布式电源(distributed generation,DG)大规模并网后实时量测数目缺失、传统预测辅助状态估计方法(forecasting-aided state estimation,FASE)估计精度有限等问题,提出了基于改进Crossformer伪量测构建的主动配电网FASE方法。首先,基于最大信息系数法(maximal information coefficient,MIC)筛选出高相关性的输入特征,提高预测模型的精度;然后,通过全变差正则化技术(total variation regularized,TV)优化鲁棒主成分分析法(robust principal component analysis,RPCA),构建TRPCA层,并将其嵌入到Crossformer中,以填补Crossformer无法有效处理非高斯噪声的空白;最后,利用改进的预测模型进行超短期负荷预测,经潮流计算得到节点伪量测,在量测不足情况下补全缺失数据,并结合扩展卡尔曼滤波器(extended Kalman filter,EKF)进行状态估计。在IEEE 33节点和IEEE 118节点标准配电网上进行仿真测试,结果表明所提方法在估计精度和鲁棒性等方面具有一定优势,可为主动配电网FASE提供参考。展开更多
Recently,we demonstrated the success of a time-synchronized state estimator using deep neural networks(DNNs)for real-time unobservable distribution systems.In this paper,we provide analytical bounds on the performance...Recently,we demonstrated the success of a time-synchronized state estimator using deep neural networks(DNNs)for real-time unobservable distribution systems.In this paper,we provide analytical bounds on the performance of the state estimator as a function of perturbations in the input measurements.It has already been shown that evaluating performance based only on the test dataset might not effectively indicate the ability of a trained DNN to handle input perturbations.As such,we analytically verify the robustness and trustworthiness of DNNs to input perturbations by treating them as mixed-integer linear programming(MILP)problems.The ability of batch normalization in addressing the scalability limitations of the MILP formulation is also highlighted.The framework is validated by performing time-synchronized distribution system state estimation for a modified IEEE 34-node system and a real-world large distribution system,both of which are incompletely observed by micro-phasor measurement units.展开更多
To overcome the shortcomings of model-driven state estimation methods, this paper proposes a data-driven robust state estimation (DDSE) method through off-line learning and on-line matching. At the off-line learning s...To overcome the shortcomings of model-driven state estimation methods, this paper proposes a data-driven robust state estimation (DDSE) method through off-line learning and on-line matching. At the off-line learning stage, a linear regression equation is presented by clustering historical data from supervisory control and data acquisition (SCADA), which provides a guarantee for solving the over-learning problem of the existing DDSE methods;then a novel robust state estimation method that can be transformed into quadratic programming (QP) models is proposed to obtain the mapping relationship between the measurements and the state variables (MRBMS). The proposed QP models can well solve the problem of collinearity in historical data. Furthermore, the off-line learning stage is greatly accelerated from three aspects including reducing historical categories, constructing tree retrieval structure for known topologies, and using sensitivity analysis when solving QP models. At the on-line matching stage, by quickly matching the current snapshot with the historical ones, the corresponding MRBMS can be obtained, and then the estimation values of the state variables can be obtained. Simulations demonstrate that the proposed DDSE method has obvious advantages in terms of suppressing over-learning problems, dealing with collinearity problems, robustness, and computation efficiency.展开更多
Load simulator is a key test equipment for aircraft actuation systems in hardware-in-the-loop-simulation. Static loading is an essential function of the load simulator and widely used in the static/dynamic stiffness t...Load simulator is a key test equipment for aircraft actuation systems in hardware-in-the-loop-simulation. Static loading is an essential function of the load simulator and widely used in the static/dynamic stiffness test of aircraft actuation systems. The tracking performance of the static loading is studied in this paper. Firstly, the nonlinear mathematical models of the hydraulic load simulator are derived, and the feedback linearization method is employed to construct a feed-forward controller to improve the force tracking performance. Considering the effect of the friction, a LuGre model based friction compensation is synthesized, in which the unmeasurable state is estimated by a dual state observer via a controlled learning mechanism to guarantee that the estimation is bounded. The modeling errors are attenuated by a well-designed robust controller with a control accuracy measured by a design parameter. Employing the dual state observer is to capture the different effects of the unmeasured state and hence can improve the friction compensation accuracy. The tracking performance is summarized by a derived theorem. Experimental results are also obtained to verify the high performance nature of the proposed control strategy.展开更多
In this paper,a mixed integer linear programming(MILP)formulation for robust state estimation(RSE)is proposed.By using the exactly linearized measurement equations instead of the original nonlinear ones,the existingmi...In this paper,a mixed integer linear programming(MILP)formulation for robust state estimation(RSE)is proposed.By using the exactly linearized measurement equations instead of the original nonlinear ones,the existingmixed integer nonlinear programming formulation for RSE is converted to a MILP problem.The proposed approach not only guarantees to find the global optimum,but also does not have convergence problems.Simulation results on a rudimentary 3-bus system and several IEEE standard test systems fully illustrate that the proposed methodology is effective with high efficiency.展开更多
Robust state estimation problem for wireless sensor networks consisting of multiple remote units and a fusion unit is investigated subject to a limitation on the communication rate.An analytical robust fusion estimato...Robust state estimation problem for wireless sensor networks consisting of multiple remote units and a fusion unit is investigated subject to a limitation on the communication rate.An analytical robust fusion estimator based on an event-triggered transmission approach is derived to reduce the network traffic congestion and save the energy consumption of the sensor units.Some conditions guaranteeing the uniformly bounded estimation errors of the robust fusion estimator are investigated.Numerical simulations are provided to show the effectiveness of the proposed approach.展开更多
In this paper,a robust adaptive unscented Kalman filter(RAUKF)is developed to mitigate the unfavorable effects derived from uncertainties in noise and in the model.To address these issues,a robust M-estimator is first...In this paper,a robust adaptive unscented Kalman filter(RAUKF)is developed to mitigate the unfavorable effects derived from uncertainties in noise and in the model.To address these issues,a robust M-estimator is first utilized to update the measurement noise covariance.Next,to deal with the effects of model parameter errors while considering the computational complexity and real-time requirements of dynamic state estimation,an adaptive update method is produced.The proposed method is integrated with spherical simplex unscented transformation technology,and then a novel derivative-free filter is proposed to dynamically track the states of the power system against uncertainties.Finally,the effectiveness and robustness of the proposed method are demonstrated through extensive simulation experiments on an IEEE 39-bus test system.Compared with other methods,the proposed method can capture the dynamic characteristics of a synchronous generator more reliably.展开更多
The volatile and intermittent nature of distributed generators(DGs) in active distribution networks(ADNs) increases the uncertainty of operating states. The introduction of distribution phasor measurement units(D-PMUs...The volatile and intermittent nature of distributed generators(DGs) in active distribution networks(ADNs) increases the uncertainty of operating states. The introduction of distribution phasor measurement units(D-PMUs) enhances the monitoring level. The trade-offs of computational performance and robustness of state estimation in monitoring the network states are of great significance for ADNs with D-PMUs and DGs. This paper proposes a second-order cone programming(SOCP) based robust state estimation(RSE) method considering multi-source measurements. Firstly, a linearized state estimation model related to the SOCP state variables is formulated. The phase angle measurements of D-PMUs are converted to equivalent power measurements. Then, a revised SOCP-based RSE method with the weighted least absolute value estimator is proposed to enhance the convergence and bad data identification. Multi-time slots of D-PMU measurements are utilized to improve the estimation accuracy of RSE. Finally, the effectiveness of the proposed method is illustrated in the modified IEEE 33-node and IEEE 123-node systems.展开更多
为降低实际应用中由强未知干扰和仪器故障对观测造成的影响,减轻随机和未建模干扰对系统的侵蚀,从而提升系统在非高斯噪声环境下的状态估计精度,提高滤波器的鲁棒性能,提出了一种基于高斯-重尾切换分布的鲁棒卡尔曼滤波器(Gaussian-heav...为降低实际应用中由强未知干扰和仪器故障对观测造成的影响,减轻随机和未建模干扰对系统的侵蚀,从而提升系统在非高斯噪声环境下的状态估计精度,提高滤波器的鲁棒性能,提出了一种基于高斯-重尾切换分布的鲁棒卡尔曼滤波器(Gaussian-heavy-tailed switching distribution based robust Kalman filter,GHTSRKF)。首先,通过自适应学习高斯分布和一种重尾分布之间的切换概率将噪声建模为GHTS(Gaussian-heavy-tailed switching)分布,所设计的GHTS分布可以通过在线调整高斯分布和新的重尾分布之间的切换概率来对非平稳重尾噪声进行建模,具有虚拟协方差的高斯分布用于处理协方差矩阵不准确的高斯噪声。其次,引入两个分别服从Categorical分布与伯努利分布的辅助参数将GHTS分布表示为一个分层高斯形式,进一步利用变分贝叶斯方法推导了GHTSRKF。最后,利用一个仿真场景对几种不同的RKFs(robust Kalman filters)进行了对比验证。结果表明,所提出的GHTSRKF算法的估计精度对初始状态的选取不敏感,精度优于其他RKFs,它的RMSEs最接近噪声信息准确的KFTNC(KF with true noise covariances)的RMSEs(root mean square errors),且当系统与量测噪声是未知时变高斯噪声时,相比于现有的滤波器,GHTSRKF具有更好的估计性能,从而验证了GHTSRKF的有效性。展开更多
基金partially supported by the National Natural Science Foundation of China under Grant 62073121partially supported by National Natural Science Foundation of China-State Grid Joint Fund for Smart Grid under Grant U1966202partially supported by Six Talent Peaks High Level Project of Jiangsu Province under Grant 2017-XNY-004.
文摘With the development of the smart grid,the distribution system operation conditions become more complex and changeable.Furthermore,due to the influence of observation outliers and uncertain noise statistics,it is more difficult to grasp the dynamic operation characteristics of distribution system.In order to address these problems,by using projection statistics and the noise covariance updating technology based on the Sage-Husa noise estimator,for distribution power system with outliers and uncertain noise statistics,a robust adaptive cubature Kalman filter forecasting-aided state estimation method is proposed based on generalized-maximum likelihood type estimator.Furthermore,an adaptive strategy,which can enhance the filtering accuracy under normal conditions,is presented.In the simulation part,the branch parameters and node load parameters of the test system are appropriately modified to simulate the asymmetry of the three-phase branch parameters and the asymmetry of the three-phase loads.Finally,through simulation experiments on the improved test system,it is verified that the robust forecasting-aided state estimation method,presented in this paper,can effectively perceive the actual operating state of the distribution network in different simulation scenarios.
基金funded by the China Postdoctoral Science Foundation(No.2023M730337)。
文摘This study investigated the problems of non-cooperative target recognition and relative motion estimation during spacecraft rendezvous maneuvers.A structure integrating an Inertial Measurement Unit(IMU)and a visual camera was presented.The angular velocity output of the IMU was used to calculate the motion trajectories of star points in multiple image frames,which can highlight the motion of non-cooperative targets with respect to the image background to improve the probability of target recognition.To solve the problem of target misidentification caused by new star points entering the field of view,a target-tracking link based on IMU prediction was introduced to track the position of the target in the image.Furthermore,a measurement model was constructed using the line-of-sight vector generated from target recognition,and the relative motion state was estimated using a Huber-based non-linear filter.Semi-physical and numerical simulations were performed to evaluate the effectiveness and efficiency of the proposed method.
基金supported in part by the National Natural Science Foundation of China(No.51977115).
文摘To achieve more precise monitoring of state fluctuations in the power network close to renewable energy sources, it is necessary to utilize phasor measurements and shorten the time interval between state estimations. For large-scale power systems, however, estimating all of their states with shorter time intervals means a drastic increase in computational burden. As a tradeoff between accuracy and computational efficiency, a multi-time interval forecasting-aided state estimation approach is proposed in this paper, where states with various degrees of fluctuations are estimated asynchronously with different time intervals. Based on the newest state estimate, forecasting-aided state estimators are employed to predict states at time moments prior to the next round of measurement update and state estimation. Extensive numerical tests have demonstrated the effectiveness of the proposed approach.
基金supported by the National Natural Science Foundation of China(Grant No.52402482).
文摘Sideslip angle,yaw rate,and vehicle velocity are essential for intelligent vehicle control.Since these vehicle states are not measured directly,some Kalman-based approaches have been developed to estimate these states using in-vehicle sensors.However,the existing studies seldom account for the influence of sensor data loss on estimation accuracy.In addition,the process and measurement noise change during the estimation process because of the various driving conditions.To address these problems,an expectation-maximization robust extended Kalman filter(EMREKF)is proposed.Firstly,a robust extended Kalman filter(REKF)is developed to deal with the impact of missing measurements.Then,an improved expectation maximization(EM)algorithm that considers data loss is presented to update the noise parameter of the REKF dynamically.Finally,the improved EM is fused with the REKF to form the EMREKF to estimate vehicle state.The experimental results demonstrate that the EMREKF outperforms EKF,REKF,and maximum correntropy criterion EKF for various degrees of data loss and the proposed algorithm has a strong adaptive ability to different driving conditions.
基金supported by the National Natural Science Foundation of China(No.5217070269).
文摘Networked microgrids(NMGs)are critical in theaccommodation of distributed renewable energy.However,theexisting centralized state estimation(SE)cannot meet the demandsof NMGs in distributed energy management.The currentestimator is also not robust against bad data.This study introducesthe concepts of relative error to construct an improvedrobust SE(IRSE)optimization model with mixed-integer nonlinearprogramming(MINLP)that overcomes the disadvantage ofinaccurate results derived from different measurements whenthe same tolerance range is considered in the robust SE(RSE).To improve the computation efficiency of the IRSE optimizationmodel,the number of binary variables is reduced based on theprojection statistics and normalized residual methods,which effectivelyavoid the problem of slow convergence or divergenceof the algorithm caused by too many integer variables.Finally,an embedded consensus alternating direction of multiplier method(ADMM)distribution algorithm based on outer approximation(OA)is proposed to solve the IRSE optimization model.This algorithm can accurately detect bad data and obtain SE resultsthat communicate only the boundary coupling informationwith neighbors.Numerical tests show that the proposed algorithmeffectively detects bad data,obtains more accurate SE results,and ensures the protection of private information in all microgrids.
基金funded by Taif University Researchers Supporting Project No.(TURSP-2020/214),Taif University,Taif,Saudi Arabia。
文摘For a 5G wireless communication system,a convolutional deep neural network(CNN)is employed to synthesize a robust channel state estimator(CSE).The proposed CSE extracts channel information from transmit-and-receive pairs through offline training to estimate the channel state information.Also,it utilizes pilots to offer more helpful information about the communication channel.The proposedCNN-CSE performance is compared with previously published results for Bidirectional/long short-term memory(BiLSTM/LSTM)NNs-based CSEs.The CNN-CSE achieves outstanding performance using sufficient pilots only and loses its functionality at limited pilots compared with BiLSTM and LSTM-based estimators.Using three different loss function-based classification layers and the Adam optimization algorithm,a comparative study was conducted to assess the performance of the presented DNNs-based CSEs.The BiLSTM-CSE outperforms LSTM,CNN,conventional least squares(LS),and minimum mean square error(MMSE)CSEs.In addition,the computational and learning time complexities for DNN-CSEs are provided.These estimators are promising for 5G and future communication systems because they can analyze large amounts of data,discover statistical dependencies,learn correlations between features,and generalize the gotten knowledge.
文摘为了解决高比例分布式电源(distributed generation,DG)大规模并网后实时量测数目缺失、传统预测辅助状态估计方法(forecasting-aided state estimation,FASE)估计精度有限等问题,提出了基于改进Crossformer伪量测构建的主动配电网FASE方法。首先,基于最大信息系数法(maximal information coefficient,MIC)筛选出高相关性的输入特征,提高预测模型的精度;然后,通过全变差正则化技术(total variation regularized,TV)优化鲁棒主成分分析法(robust principal component analysis,RPCA),构建TRPCA层,并将其嵌入到Crossformer中,以填补Crossformer无法有效处理非高斯噪声的空白;最后,利用改进的预测模型进行超短期负荷预测,经潮流计算得到节点伪量测,在量测不足情况下补全缺失数据,并结合扩展卡尔曼滤波器(extended Kalman filter,EKF)进行状态估计。在IEEE 33节点和IEEE 118节点标准配电网上进行仿真测试,结果表明所提方法在估计精度和鲁棒性等方面具有一定优势,可为主动配电网FASE提供参考。
基金supported in part by the Department of Energy(No.DE-AR-0001001,No.DE-EE0009355)the National Science Foundation(NSF)(No.ECCS-2145063)。
文摘Recently,we demonstrated the success of a time-synchronized state estimator using deep neural networks(DNNs)for real-time unobservable distribution systems.In this paper,we provide analytical bounds on the performance of the state estimator as a function of perturbations in the input measurements.It has already been shown that evaluating performance based only on the test dataset might not effectively indicate the ability of a trained DNN to handle input perturbations.As such,we analytically verify the robustness and trustworthiness of DNNs to input perturbations by treating them as mixed-integer linear programming(MILP)problems.The ability of batch normalization in addressing the scalability limitations of the MILP formulation is also highlighted.The framework is validated by performing time-synchronized distribution system state estimation for a modified IEEE 34-node system and a real-world large distribution system,both of which are incompletely observed by micro-phasor measurement units.
基金This work was supported in part by National Natural Science Foundation of China(No.52077076)in part by the State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources(No.LAPS2021-18).
文摘To overcome the shortcomings of model-driven state estimation methods, this paper proposes a data-driven robust state estimation (DDSE) method through off-line learning and on-line matching. At the off-line learning stage, a linear regression equation is presented by clustering historical data from supervisory control and data acquisition (SCADA), which provides a guarantee for solving the over-learning problem of the existing DDSE methods;then a novel robust state estimation method that can be transformed into quadratic programming (QP) models is proposed to obtain the mapping relationship between the measurements and the state variables (MRBMS). The proposed QP models can well solve the problem of collinearity in historical data. Furthermore, the off-line learning stage is greatly accelerated from three aspects including reducing historical categories, constructing tree retrieval structure for known topologies, and using sensitivity analysis when solving QP models. At the on-line matching stage, by quickly matching the current snapshot with the historical ones, the corresponding MRBMS can be obtained, and then the estimation values of the state variables can be obtained. Simulations demonstrate that the proposed DDSE method has obvious advantages in terms of suppressing over-learning problems, dealing with collinearity problems, robustness, and computation efficiency.
基金National Science Fund for Distinguished Young Scholars (50825502)
文摘Load simulator is a key test equipment for aircraft actuation systems in hardware-in-the-loop-simulation. Static loading is an essential function of the load simulator and widely used in the static/dynamic stiffness test of aircraft actuation systems. The tracking performance of the static loading is studied in this paper. Firstly, the nonlinear mathematical models of the hydraulic load simulator are derived, and the feedback linearization method is employed to construct a feed-forward controller to improve the force tracking performance. Considering the effect of the friction, a LuGre model based friction compensation is synthesized, in which the unmeasurable state is estimated by a dual state observer via a controlled learning mechanism to guarantee that the estimation is bounded. The modeling errors are attenuated by a well-designed robust controller with a control accuracy measured by a design parameter. Employing the dual state observer is to capture the different effects of the unmeasured state and hence can improve the friction compensation accuracy. The tracking performance is summarized by a derived theorem. Experimental results are also obtained to verify the high performance nature of the proposed control strategy.
基金This work was supported in part by the National High Technology Research and Development Program(2012AA 050208)in part by the National Natural Science Foundation of China(51407069)in part by the Fundamental Research Funds for the Central Universities(2014QN02).
文摘In this paper,a mixed integer linear programming(MILP)formulation for robust state estimation(RSE)is proposed.By using the exactly linearized measurement equations instead of the original nonlinear ones,the existingmixed integer nonlinear programming formulation for RSE is converted to a MILP problem.The proposed approach not only guarantees to find the global optimum,but also does not have convergence problems.Simulation results on a rudimentary 3-bus system and several IEEE standard test systems fully illustrate that the proposed methodology is effective with high efficiency.
基金the National Natural Science Foundation of China[grant number 61573203][grant number 61573204]China Postdoctoral Science Foundation[grant number 2017M612190]。
文摘Robust state estimation problem for wireless sensor networks consisting of multiple remote units and a fusion unit is investigated subject to a limitation on the communication rate.An analytical robust fusion estimator based on an event-triggered transmission approach is derived to reduce the network traffic congestion and save the energy consumption of the sensor units.Some conditions guaranteeing the uniformly bounded estimation errors of the robust fusion estimator are investigated.Numerical simulations are provided to show the effectiveness of the proposed approach.
基金supported by the National Natural Science Foundation of China(No.62073121)the National Natural Science Foundation of China-State Grid Joint Fund for Smart Grid(No.U1966202)the Six Talent Peaks High Level Project of Jiangsu Province(No.2017-XNY-004)。
文摘In this paper,a robust adaptive unscented Kalman filter(RAUKF)is developed to mitigate the unfavorable effects derived from uncertainties in noise and in the model.To address these issues,a robust M-estimator is first utilized to update the measurement noise covariance.Next,to deal with the effects of model parameter errors while considering the computational complexity and real-time requirements of dynamic state estimation,an adaptive update method is produced.The proposed method is integrated with spherical simplex unscented transformation technology,and then a novel derivative-free filter is proposed to dynamically track the states of the power system against uncertainties.Finally,the effectiveness and robustness of the proposed method are demonstrated through extensive simulation experiments on an IEEE 39-bus test system.Compared with other methods,the proposed method can capture the dynamic characteristics of a synchronous generator more reliably.
基金supported by the National Key R&D Program of China (No. 2020YFB0906000 and 2020YFB0906001)。
文摘The volatile and intermittent nature of distributed generators(DGs) in active distribution networks(ADNs) increases the uncertainty of operating states. The introduction of distribution phasor measurement units(D-PMUs) enhances the monitoring level. The trade-offs of computational performance and robustness of state estimation in monitoring the network states are of great significance for ADNs with D-PMUs and DGs. This paper proposes a second-order cone programming(SOCP) based robust state estimation(RSE) method considering multi-source measurements. Firstly, a linearized state estimation model related to the SOCP state variables is formulated. The phase angle measurements of D-PMUs are converted to equivalent power measurements. Then, a revised SOCP-based RSE method with the weighted least absolute value estimator is proposed to enhance the convergence and bad data identification. Multi-time slots of D-PMU measurements are utilized to improve the estimation accuracy of RSE. Finally, the effectiveness of the proposed method is illustrated in the modified IEEE 33-node and IEEE 123-node systems.
文摘为降低实际应用中由强未知干扰和仪器故障对观测造成的影响,减轻随机和未建模干扰对系统的侵蚀,从而提升系统在非高斯噪声环境下的状态估计精度,提高滤波器的鲁棒性能,提出了一种基于高斯-重尾切换分布的鲁棒卡尔曼滤波器(Gaussian-heavy-tailed switching distribution based robust Kalman filter,GHTSRKF)。首先,通过自适应学习高斯分布和一种重尾分布之间的切换概率将噪声建模为GHTS(Gaussian-heavy-tailed switching)分布,所设计的GHTS分布可以通过在线调整高斯分布和新的重尾分布之间的切换概率来对非平稳重尾噪声进行建模,具有虚拟协方差的高斯分布用于处理协方差矩阵不准确的高斯噪声。其次,引入两个分别服从Categorical分布与伯努利分布的辅助参数将GHTS分布表示为一个分层高斯形式,进一步利用变分贝叶斯方法推导了GHTSRKF。最后,利用一个仿真场景对几种不同的RKFs(robust Kalman filters)进行了对比验证。结果表明,所提出的GHTSRKF算法的估计精度对初始状态的选取不敏感,精度优于其他RKFs,它的RMSEs最接近噪声信息准确的KFTNC(KF with true noise covariances)的RMSEs(root mean square errors),且当系统与量测噪声是未知时变高斯噪声时,相比于现有的滤波器,GHTSRKF具有更好的估计性能,从而验证了GHTSRKF的有效性。
