Graph Convolution Networks-Based Island Partition for Energy and Information Coupled Active Distribution Systems

When a fault occurs in the active distribution network, it's important to divide islands according to the realtime operating status of the grid to form multiple independent microgrid systems. However, existing methods of island partition ignore the actual communication requirements of the active distribution network, so it is difficult to adapt to the impact of fluctuations in the communication quality of grid nodes, which may cause the performance of the system to deteriorate. This paper proposes an active distribution network island partition method based on graph convolutional network combined with autoencoder, which considers grid communication delay constraints and multi-objective optimization. Detailed simulation and experimental results show that the method can divide the partitions reasonably and effectively which can meet the power grid's energy and information requirements and achieve the established multiple optimization goals.