Accurate identification of the topology in a distribution network is crucial for its operation and control.Ad-dressing the dynamic changes in the actual topology of distribution networks,an intelligent deep learning model ca-pable of recognizing distribution network topologies was developed.Firstly,measurement data for distribution net-works under different topologies were generated,followed by data preprocessing.Subsequently,an intelligent topol-ogy identification model was constructed,integrating convolutional neural network(CNN),long short-term memory network(LSTM),and Attention mechanism.The model was trained and tested using historical measurement data.Finally,in simulation scenarios using the IEEE 33-node and PG&E69-node distribution systems,the superiority of this CNN-LSTM-Attention-based topology identification method over traditional approaches in terms of identification accuracy was validated,and online application of the model was achieved.
distribution networkstopology identificationconvolutional neural networklong short-term memory networkAttention mechanism
维普
万方数据
