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GridFM - 电网基础模型.pdf

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1、PublicTowards Foundation Models for the Electric GridPRESENTED BY:FRANOIS MIRALLSRESEARCHERHYDRO-QUBEC RESEARCH CENTERCANADAOCTOBER 3RD2025 AT THE HYDRO-QUEBEC RESEARCH INSTITUTEPublicOpen-source foundation models for the electric gridSource:H.Hamann et al.Foundation models for the electric power gr

2、id,Joule,Cell Press,2024 https:/doi.org/10.1016/j.joule.2024.11.002Source:Optimal Power Grid Operations with Foundation Models,Alban Puech,Jonas Weiss,Thomas Brunschwiler,Hendrik F.Hamann 2024 https:/arxiv.org/abs/2409.021483H y d r o-Q u b e cPublicMany different time scales,phenomena and dataFract

3、ion of wave(EMT)Steady stateMinutes to hours(Quasi-static)A few seconds to minutes(electro-mechanical transients)Time scaleValue of AI accelerationDifficultyElectromagnetic steady state butElectromechanic and control dynamics+operating rulesElectromagnetic dynamicsElectromechanic and electromagnetic

4、 steady statePhenomenaData and pretraining taskLonger timeShorter timeSmall frequency variations hypothesis(but higher in case of HQ relative to other networks,up to 1Hz)Fixed frequency hypothesisLarge frequency variation hypothesisAI ModelsPretraining on node and edge imputation on a set of snapsho

5、tsPretraining on node and edge imputation on a time series of snapshotsPretraining on wave imputationStep:10sStep:4,16ms(1/4 cycle 60 Hz)4H y d r o-Q u b e cPublicModule 5:gridfm-federated Module 2:gridfm-datakitModule 3:gridfm-benchModule 6:gridfm-lifelong-learning.Module 4:gridfm-cybersecurityModu

6、le 1:gridfm-graphkitApache 2.0One foundation for allAll for one foundationOpen-source,open-weight,GridFM trained on open-data.can be tested more rapidly for a variety of applications including cybersecurity.GridFM finetuned behind utility firewall for further testing.GridFM finetuned behind associat

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根据《》标记中的内容,全文主要内容概括如下: 1. **GridFM项目**:一个开源的电力网格基础模型,旨在加速电力系统分析,包括网络安全。 2. **模块与功能**:包括数据生成(gridfm-datakit)、图库(gridfm-graphkit)、基准测试(gridfm-bench)等模块,支持多网格训练和物理信息损失模型。 3. **数据集**:生成具有多样性和现实性的数据集,包括随机扰动、实际负荷曲线、最优和非最优模式,适用于大型电网。 4. **模型性能**:GNN模型在计算效率上比AC求解器快两个数量级,Graph Transformer在准确性上比DC求解器高近两个数量级。 5. **模型设计空间**:研究模型参数与性能之间的关系,以优化模型部署。 6. **社区与未来**:GridFM社区致力于合作开发,并鼓励更多人加入。
"电网AI加速,揭秘GridFM!" 电网安全新利器!" "从数据到模型,GridFM如何革新电网?
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