论文标题
多保真动力流求解器
Multi-fidelity power flow solver
论文作者
论文摘要
我们提出了一个针对快速高维网格功率流量模拟和偶然性分析的多余神经网络（MFNN），并具有稀缺的高保真意义数据。所提出的模型包括两个网络 - 第一个网络是针对直流近似的第一个网络，作为低保真数据，并耦合到对低保真和高保真功率流量数据训练的高保真神经网。每个网络都有一个潜在模块，该模块通过用于概括的离散网格拓扑矢量来参数模型（例如，具有$ k $断开连接或突发事件（如果有）的$ n $功率线（如果有）），而有针对性的高效率输出是线性和非线性功能的加权总和。我们在14和118总线测试用例上测试了该模型，并根据不平衡的应急数据和高低获取样本比率，根据$ n-k $功率流预测的准确性评估了其性能。本文提供的结果证明了MFNN的潜力及其限制，其限制比DC近似更快，更准确的功率流解决方案。
We propose a multi-fidelity neural network (MFNN) tailored for rapid high-dimensional grid power flow simulations and contingency analysis with scarce high-fidelity contingency data. The proposed model comprises two networks -- the first one trained on DC approximation as low-fidelity data and coupled to a high-fidelity neural net trained on both low- and high-fidelity power flow data. Each network features a latent module which parametrizes the model by a discrete grid topology vector for generalization (e.g., $n$ power lines with $k$ disconnections or contingencies, if any), and the targeted high-fidelity output is a weighted sum of linear and nonlinear functions. We tested the model on 14- and 118-bus test cases and evaluated its performance based on the $n-k$ power flow prediction accuracy with respect to imbalanced contingency data and high-to-low-fidelity sample ratio. The results presented herein demonstrate MFNN's potential and its limits with up to two orders of magnitude faster and more accurate power flow solutions than DC approximation.
