基于CNN的配电网快速重构方法

Fast Reconfiguration Method of Distribution Network of Based on Convolutional Neural Network

针对现有配电网重构算法求解速度慢的问题,提出基于卷积神经网络(CNN)的配电网快速重构方法。首先,搭建基于配电网环路结构的多分支CNN模型,减少建模过程对配电网具体结构的依赖;其次,利用混合训练方法训练CNN模型,使模型具备对不同负载模式的配网进行快速重构的能力;最后,以IEEE33节点测试系统为例,验证所提方法的有效性。

Aiming at the problem of slow solving speed of existing distribution network(DN)reconstruction algorithm,a fast distribution network reconfiguration method based on convolutional neural network(CNN)is proposed.Firstly,a multi-branch CNN model based on the loop structure of DN is established,which can reduce the dependence on the concrete structure of DN in the process of modeling.Secondly,the CNN model is trained based on the hybrid training method,so that the model has the ability to quickly reconfigure the DN with different load modes.Finally,the performance of the model is analyzed in IEEE 33-bus system to verify the effectiveness of the proposed method.