This paper proposes a distributed real-time state estimation(RTSE)method for the combined heat and power systems(CHPSs).First,a difference-based model for the heat system is established considering the dynamics of hea...This paper proposes a distributed real-time state estimation(RTSE)method for the combined heat and power systems(CHPSs).First,a difference-based model for the heat system is established considering the dynamics of heat systems.This heat system model is further used along with the power system steady-state model for holistic CHPS state estimation.A cubature Kalman filter(CKF)-based RTSE is developed to deal with the system nonlinearity while integrating both the historical and present measurement information.Finally,a multi-timescale asynchronous distributed computation scheme is designed to enhance the scalability of the proposed method for largescale systems.This distributed implementation requires only a small amount of information exchange and thus protects the privacy of different energy systems.Simulations carried out on two CHPSs show that the proposed method can significantly improve the estimation efficiency of CHPS without loss of accuracy compared with other existing models and methods.展开更多
针对中压配电网缺少实时量测、伪量测精度较低以及现有的动态状态估计(dynamic state estimation,DSE)方法均采用恒定系统处理状态过程噪声的问题,提出了一种基于改进自适应无迹卡尔曼滤波(unscented kalman filter,UKF)算法的中压配电...针对中压配电网缺少实时量测、伪量测精度较低以及现有的动态状态估计(dynamic state estimation,DSE)方法均采用恒定系统处理状态过程噪声的问题,提出了一种基于改进自适应无迹卡尔曼滤波(unscented kalman filter,UKF)算法的中压配电网鲁棒DSE方法。首先,利用中压配电网变压器低压侧的智能电表量测和变压器模型,推导出等效中压量测以增强中压配网量测冗余度;然后,借鉴信号处理技术对系统状态过程噪声的协方差矩阵实时更新并融入UKF算法,以减轻状态预测和量测滤波的不确定性;最后,基于15节点中压配电网进行仿真。仿真结果表明:所提方法能够有效地进行中压配电网的动态状态估计,获取更为精确的态势感知信息。展开更多
基金supported by the Science and Technology Project of State Grid Corporation of China(No.52060019001H)。
文摘This paper proposes a distributed real-time state estimation(RTSE)method for the combined heat and power systems(CHPSs).First,a difference-based model for the heat system is established considering the dynamics of heat systems.This heat system model is further used along with the power system steady-state model for holistic CHPS state estimation.A cubature Kalman filter(CKF)-based RTSE is developed to deal with the system nonlinearity while integrating both the historical and present measurement information.Finally,a multi-timescale asynchronous distributed computation scheme is designed to enhance the scalability of the proposed method for largescale systems.This distributed implementation requires only a small amount of information exchange and thus protects the privacy of different energy systems.Simulations carried out on two CHPSs show that the proposed method can significantly improve the estimation efficiency of CHPS without loss of accuracy compared with other existing models and methods.
文摘针对中压配电网缺少实时量测、伪量测精度较低以及现有的动态状态估计(dynamic state estimation,DSE)方法均采用恒定系统处理状态过程噪声的问题,提出了一种基于改进自适应无迹卡尔曼滤波(unscented kalman filter,UKF)算法的中压配电网鲁棒DSE方法。首先,利用中压配电网变压器低压侧的智能电表量测和变压器模型,推导出等效中压量测以增强中压配网量测冗余度;然后,借鉴信号处理技术对系统状态过程噪声的协方差矩阵实时更新并融入UKF算法,以减轻状态预测和量测滤波的不确定性;最后,基于15节点中压配电网进行仿真。仿真结果表明:所提方法能够有效地进行中压配电网的动态状态估计,获取更为精确的态势感知信息。
