基于卷积神经网络的N-2线路开断潮流快速计算

Fast load flow calculation of N-2 contingency based on convolutional neural network

交流潮流(AC)算法需迭代求解,难以满足实际电力系统在线安全校核的需求。文中基于卷积神经网络,提出一种电力系统线路开断潮流的快速计算方法。离线训练阶段,从线路开断前后工况与拓扑的变化中提取特征作为输入信号(原始特征图),经大量算例训练后,卷积神经网络构建了原始特征图与线路开断后潮流结果的非线性映射关系。在线应用时,直接生成原始特征图,并基于离线训练的卷积神经网络计算测试集的潮流结果。经4个IEEE典型系统的N-2潮流仿真验证,文中方法具有良好的泛化能力。相比传统交流算法,文中方法将速度提高了接近80倍;相比传统人工神经网络模型,文中方法将精度提高近了1个数量级。

The AC algorithm solves the power flow equations by iterations,which is computationally infeasible to the online security analysis of power systems.A fast load flow calculation method is proposed based on a convolutional neural network(CNN).As to the offline training stage,the proposed method extracts inputs(initial feature maps)based on the changes in operating conditions and topologies.In abundant training samples,the CNN and maps the nonlinear relationship between the extracted feature maps and the targeted load flow results.When it comes to the online applications,the proposed method directly calculates the feature map and delivers the load flow results based on the CNN trained offline.As is indicated in the N-2 load flow simulations of four typical IEEE systems,the generalization capability is guaranteed.Compared with the AC algorithm,the proposed method accelerates the power flow computation by eighty times.The accuracy is enhanced by nearly one order of magnitude,compared with that of the traditional artificial neural network(ANN).