Fast reconfiguration method of low-carbon distribution network based on convolutional neural network

Abstract

The existing meta-heuristic distribution network reconfiguration (DNR) algorithm has excellent optimization ability through iteration. However, it is difficult to realize the large-scale fast calculation and online real-time response of DNR solution. In order to improve the security and low-carbon economy of distribution network, this paper proposes a fast reconfiguration method of distribution network based on convolutional neural network (CNN). Taking IEEE 33 system and 185 node system as examples, the effectiveness of the proposed method is verified. The reasons why the proposed method can achieve better results are as follows: By mining the historical data of distribution network, the corresponding relationship between load mode (LM) and its optimal topology is established. For a load mode in actual operation, the reconfiguration scheme can be quickly obtained according to the established corresponding relationship. Thus, iterative calculation is avoided and computational efficiency is improved. A multi-branch CNN model is established based on the distribution network structure, and an inception module is introduced into CNN to improve the ability of CNN to extract data features. This model can reduce the dependence on the specific distribution network structure and is easy to expand.