  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.
