Computer Science > Machine Learning
arXiv:2401.02771 (cs)
[Submitted on 5 Jan 2024 (v1), last revised 29 Nov 2024 (this version, v5)]
Title:Powerformer: A Section-adaptive Transformer for Power Flow Adjustment
Authors:Kaixuan Chen,Wei Luo,Shunyu Liu,Yaoquan Wei,Yihe Zhou,Yunpeng Qing,Quan Zhang,Jie Song,Mingli Song
View a PDF of the paper titled Powerformer: A Section-adaptive Transformer for Power Flow Adjustment, by Kaixuan Chen and Wei Luo and Shunyu Liu and Yaoquan Wei and Yihe Zhou and Yunpeng Qing and Quan Zhang and Jie Song and Mingli Song
View PDFHTML (experimental)Abstract:In this paper, we present a novel transformer architecture tailored for learning robust power system state representations, which strives to optimize power dispatch for the power flow adjustment across different transmission sections. Specifically, our proposed approach, named Powerformer, develops a dedicated section-adaptive attention mechanism, separating itself from the self-attention used in conventional transformers. This mechanism effectively integrates power system states with transmission section information, which facilitates the development of robust state representations. Furthermore, by considering the graph topology of power system and the electrical attributes of bus nodes, we introduce two customized strategies to further enhance the expressiveness: graph neural network propagation and multi-factor attention mechanism. Extensive evaluations are conducted on three power system scenarios, including the IEEE 118-bus system, a realistic 300-bus system in China, and a large-scale European system with 9241 buses, where Powerformer demonstrates its superior performance over several baseline methods.
| Comments: | 8 figures |
| Subjects: | Machine Learning (cs.LG); Systems and Control (eess.SY) |
| Cite as: | arXiv:2401.02771 [cs.LG] |
| (orarXiv:2401.02771v5 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2401.02771 arXiv-issued DOI via DataCite |
Submission history
From: Kai-Xuan Chen [view email][v1] Fri, 5 Jan 2024 12:01:19 UTC (16,596 KB)
[v2] Wed, 10 Jan 2024 07:09:38 UTC (16,593 KB)
[v3] Tue, 30 Jan 2024 12:34:15 UTC (16,602 KB)
[v4] Tue, 26 Nov 2024 14:30:27 UTC (16,459 KB)
[v5] Fri, 29 Nov 2024 10:23:56 UTC (16,461 KB)
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View a PDF of the paper titled Powerformer: A Section-adaptive Transformer for Power Flow Adjustment, by Kaixuan Chen and Wei Luo and Shunyu Liu and Yaoquan Wei and Yihe Zhou and Yunpeng Qing and Quan Zhang and Jie Song and Mingli Song
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