AI 进展改进潮流计算和可行性评估 · 跟踪 2 个来源

报道来源 [4]

1. arXiv cs.LG TIER_1 English(EN) · Ayushi Jolotia, Parikshit Pareek · 2026-07-15 04:00

   通过上下文白化实现无梯度拓扑自适应以用于潮流代理模型

   arXiv:2607.12241v1 Announce Type: cross Abstract: Machine-learned surrogates for the AC power flow (ACPF) problem amortize the cost of repeated solves on a fixed network, but lose one to two orders of magnitude of accuracy when a line outage changes the topology. This degradation…

2. arXiv cs.LG TIER_1 English(EN) · Parikshit Pareek · 2026-07-14 00:57

   通过上下文白化实现无梯度拓扑自适应以用于潮流代理模型

   Machine-learned surrogates for the AC power flow (ACPF) problem amortize the cost of repeated solves on a fixed network, but lose one to two orders of magnitude of accuracy when a line outage changes the topology. This degradation is an operator shift. The altered admittance matr…

3. arXiv cs.LG TIER_1 English(EN) · Ferran Bohigas-Daranas, Hamid Latif-Martinez, Eduardo Prieto-Araujo, Pere Barlet-Ros, Oriol Gomis-Bellmunt · 2026-07-13 04:00

   使用变分图自编码器进行功率流可行性评估

   arXiv:2607.09122v1 Announce Type: new Abstract: Data-driven methods, including graph neural networks, have been studied for accelerating power flow calculations in recent years, but very little attention has been paid to the solution feasibility, which can be obtained by traditio…

4. arXiv cs.LG TIER_1 English(EN) · Oriol Gomis-Bellmunt · 2026-07-10 06:22

   使用变分图自编码器进行功率流可行性评估

   Data-driven methods, including graph neural networks, have been studied for accelerating power flow calculations in recent years, but very little attention has been paid to the solution feasibility, which can be obtained by traditional solvers. This paper presents a Variational G…