Abstract: A method and a system of hybrid data-and-model-driven hierarchical network reconfiguration are provided. Taking into account that the optimal operation structure of the power grid may change after a new energy is connected into the power grid in the distribution manner, this method combines the mathematical model of network reconfiguration and clustering with the deep learning model, and uses data recorded by the smart meter, to reconfigure the network in a hybrid data-and-model driving mode. This method proposes a network compression method and a hierarchical decompression method based on deep learning, and improves the efficiency of network reconfiguration by the way of distributed calculation and combination of on-line and off-line calculation.

Abstract: The present disclosure relates to a method and a system of hybrid data-and-model-driven hierarchical network reconfiguration. Taking into account that the optimal operation structure of the power grid may change after a new energy is connected into the power grid in the distribution manner, this method combines the mathematical model of network reconfiguration and clustering with the deep learning model, and uses data recorded by the smart meter, to reconfigure the network in a hybrid data-and-model driving mode. This method proposes a network compression method and a hierarchical decompression method based on deep learning, and improves the efficiency of network reconfiguration by the way of distributed calculation and combination of on-line and off-line calculation. The present disclosure may reduce the solution space, lessen the calculation complexity, and improve the calculation speed to a certain extent.