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示而设计的新型Transformer架构，旨在优化不同输电断面的潮流调度调整。具体而言，我们提出的方法名为Powerformer，开发了一种专用的断面自适应注意力机制，该机制区别于传统Transformer中使用的自注意力机制。该机制有效整合了电力系统状态与输电断面信息，有助于构建鲁棒的状态表示。此外，通过考虑电力系统的图拓扑结构和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示能力：图神经网络传播和多因素注意力机制。我们在三个电力系统场景上进行了广泛评估，包括IEEE 118节点系统、中国实际300节点系统以及包含9241个节点的大型欧洲系统，结果表明Powerformer在性能上优于多种基线方法。