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
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
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
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.
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.