标题： 基于高斯过程的多相配电网络潮流近似 显示英文标题

标题： Power Flow Approximations for Multiphase Distribution Networks using Gaussian Processes

摘要： 基于学习的方法越来越多地被用于管理和协调有源配电网中电网边缘资源的运行。 其中，基于模型的技术因其优于无模型方法的数据效率和鲁棒性而脱颖而出。然而，有效的模型学习需要基于学习的潮流模型近似器。本研究扩展了现有研究，引入了一种基于高斯过程 (GP) 的数据驱动潮流方法，通过将净负荷注入映射到节点电压来近似多相潮流模型。使用 IEEE 123 节点和 8500 节点配电测试馈线的仿真结果表明，经过训练的 GP 模型能够以最少的训练数据可靠地预测非线性潮流解。我们还对所提出的基于 GP 的潮流近似器与基于深度神经网络的近似器的训练效率和测试性能进行了比较分析，突出了我们数据高效方法的优势。 实际操作条件下的结果表明，尽管训练样本量减少了 85%（相当于训练时间缩短了 92.8%），但与深度神经网络的基线相比，高斯过程模型的平均绝对误差相对减少了 99.9%。 显示英文摘要

摘要： Learning-based approaches are increasingly leveraged to manage and coordinate the operation of grid-edge resources in active power distribution networks. Among these, model-based techniques stand out for their superior data efficiency and robustness compared to model-free methods. However, effective model learning requires a learning-based approximator for the underlying power flow model. This study extends existing work by introducing a data-driven power flow method based on Gaussian Processes (GPs) to approximate the multiphase power flow model, by mapping net load injections to nodal voltages. Simulation results using the IEEE 123-bus and 8500-node distribution test feeders demonstrate that the trained GP model can reliably predict the nonlinear power flow solutions with minimal training data. We also conduct a comparative analysis of the training efficiency and testing performance of the proposed GP-based power flow approximator against a deep neural network-based approximator, highlighting the advantages of our data-efficient approach. Results over realistic operating conditions show that despite an 85% reduction in the training sample size (corresponding to a 92.8% improvement in training time), GP models produce a 99.9% relative reduction in mean absolute error compared to the baselines of deep neural networks.