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摘要
Along with the rapid development of new-type power systems, the proportion of access to various types of distributed power sources, energy storage, electric vehicles, and flexible loads in the distribution network is increasing. The operation mode is becoming more complex and changeable, and the accurate identification of their topology is more difficult. There is a problem in that the data collection cycle of distribution network measurement is long and the identification method is sensitive to data imbalance, resulting in low identification accuracy. Thus this paper proposes a two-stage topology identification method of a new-type distribution network. First, a two-layer stacked graph convolutional network is used to generate a series of label classifiers, and then the convolutional neural network is used to extract the features of the measured time series, and the preliminary identification of the first stage is realized by combining multi-label classification learning. Second, the branches with a state of negative (breaking) in the initial topology obtained by preliminary identification are searched for the whole state space. The state samples with the smallest dissipation value are screened out through a power flow matching model to achieve a secondary identification of false negative . Finally, simulation verification is carried out on the modified IEEE33 node power distribution network with new energy. The results show that the proposed model and method can effectively solve the data imbalance problem and have higher identification accuracy. ? 2023 Power System Protection and Control Press.
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