Most power systems' approaches are currently tending towards stochastic and probabilistic methods due to the high variability of renewable sources and the stochastic nature of loads. Conventional power flow (PF) approaches such as forward-backward sweep (FBS) and Newton-Raphson require a high number of iterations to solve non-linear PF equations making them computationally very intensive. PF is the most important study performed by utility, required in all stages of the power system, especially in operations and planning. This paper discusses the applications of deep learning (DL) to predict PF solutions for three-phase unbalanced power distribution grids. Three deep neural networks (DNNs); Radial Basis Function Network (RBFnet), Multi-Layer Perceptron (MLP), and Convolutional Neural Network (CNN), are proposed in this paper to predict PF solutions. The PF problem is formulated as a multi-output regression model where two or more output values are predicted based on the inputs. The training and testing data are generated through the OpenDSS-MATLAB COM interface. These methods are completely data-driven where the training relies on reducing the mismatch at each node without the need for the knowledge of the system. The novelty of the proposed methodology is that the models can accurately predict the PF solutions for the unbalanced distribution grids with mutual coupling and are robust to different R/X ratios, topology changes as well as generation and load variability introduced by the integration of distributed energy resources (DERs) and electric vehicles (EVs). To test the efficacy of the DNN models, they are applied to IEEE 4-node and 123-node test cases, and the American Electric Power (AEP) feeder model. The PF results for RBFnet, MLP, and CNN models are discussed in this paper demonstrating that all three DNN models provide highly accurate results in predicting PF solutions.

翻译：大多数电力系统方法目前正趋于采用随机和概率方法，这是由于可再生能源的高变异性和负荷的随机性。传统的潮流计算方法如前推回代法和牛顿-拉夫逊法需要通过大量迭代求解非线性潮流方程，导致其计算强度极大。潮流分析是电力公司开展的最重要研究，在电力系统的各个阶段，特别是运行与规划中均必不可少。本文探讨了深度学习在三相不平衡配电网潮流解预测中的应用。提出了三种深度神经网络模型：径向基函数网络、多层感知器和卷积神经网络，用于预测潮流解。潮流问题被构建为多输出回归模型，即基于输入预测两个或多个输出值。训练和测试数据通过OpenDSS-MATLAB COM接口生成。这些方法完全基于数据驱动，训练过程依赖减小各节点的不匹配量，无需系统知识。所提方法的创新性在于：模型能够准确预测存在互耦合的不平衡配电网潮流解，并对不同R/X比值、拓扑结构变化以及分布式能源和电动汽车接入带来的发电与负荷波动具有鲁棒性。为验证深度神经网络模型的有效性，将其应用于IEEE 4节点和123节点测试案例以及美国电力公司的馈线模型。本文讨论了径向基函数网络、多层感知器和卷积神经网络模型的潮流计算结果，表明三种深度神经网络模型在预测潮流解方面均能提供高精度结果。