针对传统静态状态估计方法的缺点,提出了一种改进的电力系统状态估计方法,即将部分节点相量测量单元(phasor measurement unit,PMU)量测数据与监控数据采集(supervisory control and data acquisition,SCADA)量测数据融合进行电力系统...针对传统静态状态估计方法的缺点,提出了一种改进的电力系统状态估计方法,即将部分节点相量测量单元(phasor measurement unit,PMU)量测数据与监控数据采集(supervisory control and data acquisition,SCADA)量测数据融合进行电力系统的全网状态估计。该方法简化了系统的雅可比矩阵,缩短了计算时间。文章研究了PMU和SCADA系统融合改进后的快速分解法,针对SCADA量测数据的缺点,通过历史数据库对潮流数据进行预测,并依据PMU量测量对系统进行分析,继而进行系统全网状态的动态监测。通过算例证明,与传统的估计方法相比,该方法改善了状态估计的精确性,减少了迭代次数,细致地描绘了电网状态的变化过程,为调度中心下一步的决策提供了依据。展开更多
Power flow(PF)is one of the most important calculations in power systems.The widely-used PF methods are the Newton-Raphson PF(NRPF)method and the fast-decoupled PF(FDPF)method.In smart grids,power generations and load...Power flow(PF)is one of the most important calculations in power systems.The widely-used PF methods are the Newton-Raphson PF(NRPF)method and the fast-decoupled PF(FDPF)method.In smart grids,power generations and loads become intermittent and much more uncertain,and the topology also changes more frequently,which may result in significant state shifts and further make NRPF or FDPF difficult to converge.To address this problem,we propose a data-driven PF(DDPF)method based on historical/simulated data that includes an offline learning stage and an online computing stage.In the offline learning stage,a learning model is constructed based on the proposed exact linear regression equations,and then the proposed learning model is solved by the ridge regression(RR)method to suppress the effect of data collinearity.In online computing stage,the nonlinear iterative calculation is not needed.Simulation results demonstrate that the proposed DDPF method has no convergence problem and has much higher calculation efficiency than NRPF or FDPF while ensuring similar calculation accuracy.展开更多
This paper introduces a robust sparse recovery model for compressing bad data and state estimation(SE),based on a revised multi-stage convex relaxation(R-Capped-L1)model.To improve the calculation efficiency,a fast de...This paper introduces a robust sparse recovery model for compressing bad data and state estimation(SE),based on a revised multi-stage convex relaxation(R-Capped-L1)model.To improve the calculation efficiency,a fast decoupled solution is adopted.The proposed method can be used for three-phase unbalanced distribution networks with both phasor measurement unit and remote terminal unit measurements.The robustness and the computational efficiency of the R-Capped-Ll model with fast decoupled solution are compared with some popular SE methods by numerical tests on several three-phase distribution networks.展开更多
文摘针对传统静态状态估计方法的缺点,提出了一种改进的电力系统状态估计方法,即将部分节点相量测量单元(phasor measurement unit,PMU)量测数据与监控数据采集(supervisory control and data acquisition,SCADA)量测数据融合进行电力系统的全网状态估计。该方法简化了系统的雅可比矩阵,缩短了计算时间。文章研究了PMU和SCADA系统融合改进后的快速分解法,针对SCADA量测数据的缺点,通过历史数据库对潮流数据进行预测,并依据PMU量测量对系统进行分析,继而进行系统全网状态的动态监测。通过算例证明,与传统的估计方法相比,该方法改善了状态估计的精确性,减少了迭代次数,细致地描绘了电网状态的变化过程,为调度中心下一步的决策提供了依据。
基金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.LAPS202118)。
文摘Power flow(PF)is one of the most important calculations in power systems.The widely-used PF methods are the Newton-Raphson PF(NRPF)method and the fast-decoupled PF(FDPF)method.In smart grids,power generations and loads become intermittent and much more uncertain,and the topology also changes more frequently,which may result in significant state shifts and further make NRPF or FDPF difficult to converge.To address this problem,we propose a data-driven PF(DDPF)method based on historical/simulated data that includes an offline learning stage and an online computing stage.In the offline learning stage,a learning model is constructed based on the proposed exact linear regression equations,and then the proposed learning model is solved by the ridge regression(RR)method to suppress the effect of data collinearity.In online computing stage,the nonlinear iterative calculation is not needed.Simulation results demonstrate that the proposed DDPF method has no convergence problem and has much higher calculation efficiency than NRPF or FDPF while ensuring similar calculation accuracy.
基金supported in part by the National Key Research and Development Plan of China(No.2018YFB0904200)in part by the National Natural Science Foundation of China(No.51725703).
文摘This paper introduces a robust sparse recovery model for compressing bad data and state estimation(SE),based on a revised multi-stage convex relaxation(R-Capped-L1)model.To improve the calculation efficiency,a fast decoupled solution is adopted.The proposed method can be used for three-phase unbalanced distribution networks with both phasor measurement unit and remote terminal unit measurements.The robustness and the computational efficiency of the R-Capped-Ll model with fast decoupled solution are compared with some popular SE methods by numerical tests on several three-phase distribution networks.
