Abstract：At present, real-time measurements implemented in distribution networks only have simple structures and limited quantities. The randomness from DGs (Distributed Generations) and loads are usually non-Gaussian, which exacerbate the uncertainties and errors of state estimations in hybrid AC-DC distribution network. These problems seriously inhibit the development of flexible management in AC-DC distribution networks. Therefore, it is necessary to develop more effective methods for AC-DC distribution network state estimations to support hybrid distribution network management. This paper proposes a two-stage probabilistic estimation for AC-DC distribution network. The proposed method is based on voltage source converter (VSC) loss model and Gaussian mixture model (GMM). It gives full consideration of the coupling uncertainties between AC and DC distribution subnetworks. Simulations conducted based on the modified IEEE-33 node AC-DC distribution network show that the proposed two-stage estimation method is suitable for situations with limited real-time measurements and low data redundancy. The method can be practically implemented in existing AC state estimation modules. It can also be extended to consider three-phase unbalanced AC subnetwork in the future.
李幸芝, 韩蓓, 李国杰, 汪可友, 江秀臣. 考虑非高斯耦合不确定性的交直流配电网两阶段概率状态估计[J]. 电工技术学报, 2020, 35(23): 4949-4960.
Li Xingzhi, Han Bei, Li Guojie, Wang Keyou, Jiang Xiuchen. Two-Stage Probabilistic State Estimation for AC/DC Distribution Network Considering Non-Gaussian Coupling Uncertainties. Transactions of China Electrotechnical Society, 2020, 35(23): 4949-4960.
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