基于图多任务学习的潮流分析模型

Power Flow Analysis Model Based on Graph Multi-Task Learning

基于深度学习的潮流计算模型可以直接拟合系统潮流初值到潮流结果之间的映射关系,在计算速度极快的同时不会产生病态潮流问题。然而现有深度学习潮流计算方法多基于回归模型,不具有潮流判敛功能,导致对输入的潮流不收敛样本仍映射出虚假系统潮流分布。针对此问题,提出一种基于图多任务学习的潮流分析方法,结合电力系统物理特性对输入案例进行潮流分析。最后,将所提模型在基于IEEE 14节点系统扩展的数据集上进行全面的仿真,生成了10000份包含不同网络拓扑的系统样本,通过仿真实验验证所提模型的计算耗时约为牛拉法的四分之一,在潮流判敛方面准确率达到98.81%,在潮流分布计算方面计算准确率达到98.58%,并通过对比实验消融实验验证所提模型在图卷积与图池化方面改进的有效性。

The power flow calculation model based on deep learning can directly fit the mapping relationship between the initial value of the system power flow and the result of the power flow,and the calculation speed is extremely fast and the ill-posed power flow problem is not generated.However,the existing deep learning power flow calculation methods are mostly based on regression models,which can’tidentify whether the power flow converges,resulting in false system power flow distribution still mapped to the input non-convergent power flow samples.To solve this problem,a power flow analysis method based on graph multi-task learning network is proposed,power flow analysis is performed on the input case combined with the physical characteristics of the power system.Finally,the proposed model is comprehensively simulated on the IEEE 14-node system,and 10000 system samples contain-ing different network topologies are generated.The simulation experiment verifies that the computational time of the proposed model is about a quarter of that of the Newton-Raphson method,and the accuracy of power flow convergence judgment reaches 98.81%,the calculation accuracy of power flow distribution calculation reaches 98.58%,and the effectiveness of the improvement in graph convolution and graph pooling is verified by comparative experimental ablation experiments.