1.四川大学电气工程学院，四川省成都市 610065;2.国网四川省电力公司计量中心，四川省成都市 610045
1.College of Electrical Engineering, Sichuan University, Chengdu 610065, China;2.State Grid Sichuan Electric Power Corporation Metering Center, Chengdu 610045, China
Based on the μPMU (micro phasor measurement unit), this paper proposes a new method for identifying distribution network topology. The nonlinear relationship among distribution network topology, photovoltaic (PV), load and μPMU measured voltage is fitted by Bayesian network (BN). The interval of continuous nodes is measured by the grid division of maximal information coefficient (MIC) among variables. MIC solves the problem that traditional BN needs to specify the interval number artificially when processing continuous data, and is difficult to be applied to the case with lots of continuous variables. The photovoltaic-load data generated by the Latin hypercube sampling (LHS) can realize the uniform distribution of the scene in the sample space. The training process of BN and improves the generalization ability of identification is effectively simplified. The effectiveness of the method is verified by a simulation example. The simulation results show that the proposed method has the same identification accuracy and the higher timeliness as the real-time estimation matching method. The identification time does not increase linearly with the number of feasible topologies. Even in the case of partial failure of the μPMU or lack of key data, such as load and PV data, it can ensure a high identification effect and a strong robustness.
REN Pengzhe, LIU Youbo, LIU Tingjian, et al. Robust Identification Algorithm for Distribution Network Topology Based on Mutual Information-Bayesian Network[J/OL]. Automation of Electric Power Systems, http://doi. org/10.7500/AEPS20200818001.