用高斯过程逼近多相配电网潮流，数据少也能准
Power Flow Approximations for Multiphase Distribution Networks using Gaussian Processes
- 用高斯过程建模负荷与电压关系，实现高效潮流逼近
- 训练样本减少85%，误差比深度网络低99.9%
- 适合电力系统仿真与边缘资源调度场景
基于学习的方法在主动配电网中日益用于管理与协调边缘资源。其中，模型驱动方法相比无模型方法具有更高的数据效率和鲁棒性。然而，有效建模需依赖对底层潮流模型的近似。本文提出一种基于高斯过程（GPs）的数据驱动潮流近似方法，将净负荷注入映射到节点电压。在IEEE 123-母线和8500节点配电测试馈线上的仿真结果表明，训练后的GP模型仅需少量数据即可可靠预测非线性潮流解。与基于深度神经网络的近似器对比显示，本方法在训练样本减少85%（训练时间提升92.8%）的前提下，均方绝对误差降低99.9%。
原文摘要 · Abstract (English)
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.
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