具有物理意识的神经网络用于配电系统状态估计
Physics-Aware Neural Networks for Distribution System State Estimation
AI总结：
本文提出了一种利用配电电网结构的新型学习模型，通过利用状态估计问题的分离性来减少参数化映射所需的系数数量，从而防止过拟合并降低训练复杂度，同时提出了一种贪心算法用于相量测量单元的放置，以最小化实现状态估计映射所需的神经网络复杂度。
摘要：
配电系统状态估计问题旨在从可用测量中确定网络状态。广泛使用的高斯-牛顿方法对初始化非常敏感，通常不适用于实时估计。学习方法在实时估计中很有前景，因为它们将计算负担转移到了离线训练阶段。以往用于电力系统状态估计的机器学习方法都是电气模型无关的，即没有利用电力网络和物理定律来设计学习模型的架构。在本文中，我们提出了一种新的学习模型，利用电力电网的结构。所提出的神经网络架构通过利用状态估计问题的分离性，减少了参数化从测量到网络状态映射所需的系数数量。这防止了过拟合并降低了训练阶段的复杂性。我们还提出了一种贪心算法用于相量测量单元的放置，旨在最小化实现状态估计映射所需的神经网络复杂度。仿真结果表明，所提出的方法在Gauss-Newton方法上表现更优。
英文摘要：
The distribution system state estimation problem seeks to determine the network state from available measurements. Widely used Gauss-Newton approaches are very sensitive to the initialization and often not suitable for real-time estimation. Learning approaches are very promising for real-time estimation, as they shift the computational burden to an offline training stage. Prior machine learning approaches to power system state estimation have been electrical model-agnostic, in that they did not exploit the topology and physical laws governing the power grid to design the architecture of the learning model. In this paper, we propose a novel learning model that utilizes the structure of the power grid. The proposed neural network architecture reduces the number of coefficients needed to parameterize the mapping from the measurements to the network state by exploiting the separability of the estimation problem. This prevents overfitting and reduces the complexity of the training stage. We also propose a greedy algorithm for phasor measuring units placement that aims at minimizing the complexity of the neural network required for realizing the state estimation mapping. Simulation results show superior performance of the proposed method over the Gauss-Newton approach.
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。
