用于概率潮流分析的物理引导残差学习
Physics-guided Residual Learning for Probabilistic Power Flow Analysis
AI总结：
针对概率潮流分析中经典数值方法计算负担重的问题，提出融合AC-PF物理特性的残差学习神经网络框架，通过三种权重初始化方案提升训练收敛性与电压相量估计精度，可作为快速潮流求解器。
摘要：
概率潮流（Probabilistic power flow, PPF）分析对电力系统的运行与规划至关重要。PPF的目标是获取具有随机功率注入（例如可再生能源发电与负荷需求）的系统状态的概率描述。给定功率注入样本，数值方法会反复运行经典潮流（power flow, PF）求解器以计算电压相量。然而，由于需要进行大量PF仿真，计算负担十分沉重。近年来，随着充足测量数据的可得，许多数据驱动的PF求解器被相继提出。本文提出了一种新型神经网络（neural network, NN）框架，能够精确近似非线性AC-PF方程。训练后的NN可作为快速PF求解器使用，显著减轻了经典PPF分析中的沉重计算负担。受残差学习启发，我们在多层感知机（multilayer perceptron, MLP）的输入与输出之间构建了一个全连接线性层。为提升NN的训练收敛性，我们基于AC-PF方程的物理特性，提出了三种初始化捷径连接层NN权重的方案。具体而言，两种基于模型的方法需要知晓系统拓扑与线路参数，而纯数据驱动的方法无需电网参数即可运行。在五个基准系统上的数值测试表明，我们提出的方法在电压相量估计方面比现有方法具备更高的精度。此外，三种精心设计的初始化方案能够帮助NN训练过程更快收敛，这在训练时间有限的场景下颇具吸引力。
英文摘要：
Probabilistic power flow (PPF) analysis is critical to power system operation and planning. PPF aims at obtaining probabilistic descriptions of the state of the system with stochastic power injections (e.g., renewable power generation and load demands). Given power injection samples, numerical methods repeatedly run classic power flow (PF) solvers to find the voltage phasors. However, the computational burden is heavy due to many PF simulations. Recently, many data-driven based PF solvers have been proposed due to the availability of sufficient measurements. This paper proposes a novel neural network (NN) framework which can accurately approximate the non-linear AC-PF equations. The trained NN works as a rapid PF solver, significantly reducing the heavy computational burden in classic PPF analysis. Inspired by residual learning, we develop a fully connected linear layer between the input and output in the multilayer perceptron (MLP). To improve the NN training convergence, we propose three schemes to initialize the NN weights of the shortcut connection layer based on the physical characteristics of AC-PF equations. Specifically, two model-based methods require the knowledge of system topology and line parameters, while the purely data-driven method can work without power grid parameters. Numerical tests on five benchmark systems show that our proposed approaches achieve higher accuracy in estimating voltage phasors than existing methods. In addition, three meticulously designed initialization schemes help the NN training process converge faster, which is appealing under limited training time.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
