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.

翻译：本文提出了一种专为学习鲁棒电力系统状态表征而设计的新型Transformer架构，旨在通过优化电力调度实现不同输电断面的潮流调整。具体而言，我们提出的方法命名为Powerformer，开发了一种专用的截面自适应注意力机制，该机制有别于传统Transformer中的自注意力机制。该机制有效整合了电力系统状态与输电断面信息，有助于发展鲁棒的状态表征。此外，通过考虑电力系统的图拓扑结构与母线节点的电气属性，我们引入了两种定制化策略以进一步增强表征能力：图神经网络传播与多因子注意力机制。在三个电力系统场景（包括IEEE 118节点系统、中国某实际300节点系统以及包含9241个节点的大型欧洲系统）上进行了广泛评估，结果表明Powerformer在多个基线方法中展现出优越性能。