With more data-driven applications introduced in wide-area monitoring systems(WAMS),data quality of phasor measurement units(PMUs)becomes one of the fundamental requirements for ensuring reliable WAMS applications.Thi...With more data-driven applications introduced in wide-area monitoring systems(WAMS),data quality of phasor measurement units(PMUs)becomes one of the fundamental requirements for ensuring reliable WAMS applications.This paper proposes a doubly-fed deep learning method for bad data identification in linear state estimation,which can:(1)identify bad data under both steady states and contingencies;(2)achieve higher accuracy than conventional pre-filtering approaches;(3)reduce iteration burden for linear state estimation;(4)efficiently identify bad data in a parallelizable scheme.The proposed method consists of four key steps:(1)preprocessing filter;(2)online training of short-term deep neural network;(3)offline training of long-term deep neural network;(4)a decision merger.Through delicate design and comprehensive training,the proposed method can effectively differentiate the bad data from event data without relying on real-time topology information.An IEEE 39-bus system simulated by DSATools TSAT and a provincial electric power system with real PMU data collected are used to verify the proposed method.Multiple test scenarios are applied,which include steady states,three-phase-to-ground faults with(un)successful auto-reclosing,low-frequency oscillation,and low-frequency oscillation with simultaneous threephase-to-ground faults.The proposed method demonstrates satisfactory performance during both the training session and the testing session.展开更多
With integration of a larger amount of clean power sources and power electronic equipment,operation and dynamic characteristics of the power grid are becoming more and more complicated and stochastic.Therefore,it is n...With integration of a larger amount of clean power sources and power electronic equipment,operation and dynamic characteristics of the power grid are becoming more and more complicated and stochastic.Therefore,it is necessary and urgent to obtain accurate real-time states,which is difficult from traditional state estimation.This paper systematically develops a phasor measurement unit(PMU)based real-time state estimator for a realistic large-scale power grid for the first time.The estimator mainly relies on three refined algorithms,i.e.,an improved linear state estimation algorithm,a practical bad data identification method and a distributed topology check technique.Furthermore,a novel system architecture is designed and implemented for the China Southern Power Grid.Numerical simulations and extensive field operation results of the state estimator recorded under both normal and abnormal situations are presented.All the tests and field results demonstrate the advantages of the proposed algorithms in terms of online system monitoring and feasibility of refreshing the states of the whole system at intervals of tens of milliseconds.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
State estimation(SE)is essential for combined heat and electric networks(CHENs)to provide a global and selfconsistent solution for multi-energy flow analysis.This paper proposes an SE approach for CHEN based on steady...State estimation(SE)is essential for combined heat and electric networks(CHENs)to provide a global and selfconsistent solution for multi-energy flow analysis.This paper proposes an SE approach for CHEN based on steady models of electric networks(ENs)and district heating networks(DHNs).A range of coupling components are considered.The performance of the proposed estimator is evaluated using Monte Carlo simulations and case studies.Results show that a relationship between the measurements from ENs and DHNs can improve the estimation accuracy for the entire network by using the combined SE model,especially when ENs and DHNs are strongly coupled.The coupling constraints could also provide extra redundancy to detect bad data in the boundary injection measurements of both networks.An analysis of computation time shows that the proposed method is suitable for online applications.展开更多
基金supported by the Science and Technology Program of State Grid Corporation of China under project“AI based oscillation detection and control”(No.SGJS0000DKJS1801231)
文摘With more data-driven applications introduced in wide-area monitoring systems(WAMS),data quality of phasor measurement units(PMUs)becomes one of the fundamental requirements for ensuring reliable WAMS applications.This paper proposes a doubly-fed deep learning method for bad data identification in linear state estimation,which can:(1)identify bad data under both steady states and contingencies;(2)achieve higher accuracy than conventional pre-filtering approaches;(3)reduce iteration burden for linear state estimation;(4)efficiently identify bad data in a parallelizable scheme.The proposed method consists of four key steps:(1)preprocessing filter;(2)online training of short-term deep neural network;(3)offline training of long-term deep neural network;(4)a decision merger.Through delicate design and comprehensive training,the proposed method can effectively differentiate the bad data from event data without relying on real-time topology information.An IEEE 39-bus system simulated by DSATools TSAT and a provincial electric power system with real PMU data collected are used to verify the proposed method.Multiple test scenarios are applied,which include steady states,three-phase-to-ground faults with(un)successful auto-reclosing,low-frequency oscillation,and low-frequency oscillation with simultaneous threephase-to-ground faults.The proposed method demonstrates satisfactory performance during both the training session and the testing session.
基金supported by the National Natural Science Foundation of China(U1766214,U2066601).
文摘With integration of a larger amount of clean power sources and power electronic equipment,operation and dynamic characteristics of the power grid are becoming more and more complicated and stochastic.Therefore,it is necessary and urgent to obtain accurate real-time states,which is difficult from traditional state estimation.This paper systematically develops a phasor measurement unit(PMU)based real-time state estimator for a realistic large-scale power grid for the first time.The estimator mainly relies on three refined algorithms,i.e.,an improved linear state estimation algorithm,a practical bad data identification method and a distributed topology check technique.Furthermore,a novel system architecture is designed and implemented for the China Southern Power Grid.Numerical simulations and extensive field operation results of the state estimator recorded under both normal and abnormal situations are presented.All the tests and field results demonstrate the advantages of the proposed algorithms in terms of online system monitoring and feasibility of refreshing the states of the whole system at intervals of tens of milliseconds.
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 the National Natural Science Foundation of China(61733010)the China Postdoctoral Science Foundation(2019M650675).
文摘State estimation(SE)is essential for combined heat and electric networks(CHENs)to provide a global and selfconsistent solution for multi-energy flow analysis.This paper proposes an SE approach for CHEN based on steady models of electric networks(ENs)and district heating networks(DHNs).A range of coupling components are considered.The performance of the proposed estimator is evaluated using Monte Carlo simulations and case studies.Results show that a relationship between the measurements from ENs and DHNs can improve the estimation accuracy for the entire network by using the combined SE model,especially when ENs and DHNs are strongly coupled.The coupling constraints could also provide extra redundancy to detect bad data in the boundary injection measurements of both networks.An analysis of computation time shows that the proposed method is suitable for online applications.