基于高斯-牛顿局部信念传播的输配电网异步状态估计OA

With the dramatic evolution of power system operation modes and the growing coupling between transmission and distribution networks,independent state estimation methods for transmission and distribution networks can no longer meet the requirements of unified dispatching and refined management of transmission and distribution networks,giving rise to coordinated transmission-distribution state estimation.Considering the drawbacks of centralized state estimation,such as heavy computational burden and asynchronous measurement updates between transmission and distribution networks,this paper proposes an asynchronous state estimation method for transmission and distribution networks based on Gauss-Newton local belief propagation.Firstly,a dynamic partitioned factor graph model is constructed according to the received boundary information of transmission and distribution networks.On this basis,a Gauss-Newton local belief propagation algorithm is developed to realize dynamic local updating of the factor graph model and fully distributed state estimation for the transmission network,alleviating the computational burden of the transmission network control center.Secondly,the asynchronous state estimation method based on Gauss-Newton local belief propagation is formulated to achieve real-time asynchronous information updating of each boundary node in the transmission network,which fully exploits the distributed and local computing advantages of the algorithm.Under both normal and bad measurement scenarios,the proposed method can effectively reduce the number of message transmitted of the factor graph model while guaranteeing estimation accuracy,thus saving computational resources.