Abstract：A novel distribution management system (DMS) architecture combined with the data mining technology and the relevant state estimation for smart distribution networks is introduced due to the fact that the measurement in the root node is highly accurate while the measurement redundancies of the rest nodes are low,some of which even have no measurements.The method proposed in this paper takes use of the quasi real-time measurements to form the objective function.The historical load curves are adopted as the inequality constraints.Unlike the traditional distribution state estimation,the proposed method is suitable for the distribution network with few on-line quasi real-time measurements.This method is a kind of state estimation whose structure is similar to the optimal power flow.So it can be solved with the interior point method.The case study is carried out with a real 9-node distribution network and the results are discussed in detail.At the same time,a widely used 33-node distribution network is also used for further validation.Simulation results show that the calculating speed and the convergence of the proposed method can realize the online state estimation in the smart distribution network.The proposed method produces satisfactory estimations in the distribution networks with a few on-line quasi real-time measurements.Especially,the current estimation meets the requirement for advanced smart distribution power applications.
李滨,杜孟远,祝云,韦化. 基于准实时数据的智能配电网状态估计[J]. 电工技术学报, 2016, 31(1): 34-44.
Li Bin,Du Mengyuan,Zhu Yun,Wei Hua. A State Estimator for Smart Distribution Networks with Quasi-real Time Data. Transactions of China Electrotechnical Society, 2016, 31(1): 34-44.
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