# Powerformer: A Section-adaptive Transformer for Power Flow Adjustment

arXiv: https://arxiv.org/abs/2401.02771 · HTML: https://mlsanity.com/paper/2401.02771/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 58 references that are in the map.
- First posted on arXiv 5 Jan 2024.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Dueling Network Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1511.06581/) (2015)
- [Graph Transformer Networks](https://mlsanity.com/paper/1911.06455/) (2019)
- [GraphFormers: GNN-nested Transformers for Representation Learning on Textual Graph](https://mlsanity.com/paper/2105.02605/) (2021)
- [Transformer for Graphs: An Overview from Architecture Perspective](https://mlsanity.com/paper/2202.08455/) (2022)
- [Riemannian Local Mechanism for SPD Neural Networks](https://mlsanity.com/paper/2201.10145/) (2022)
- [Two-Timescale Voltage Control in Distribution Grids Using Deep Reinforcement Learning](https://mlsanity.com/paper/1904.09374/) (2019)
- [Distribution Knowledge Embedding for Graph Pooling](https://mlsanity.com/paper/2109.14333/) (2021)
- [On Structural Expressive Power of Graph Transformers](https://mlsanity.com/paper/2305.13987/) (2023)
- [Tele-Knowledge Pre-training for Fault Analysis](https://mlsanity.com/paper/2210.11298/) (2022)
- [MixupExplainer: Generalizing Explanations for Graph Neural Networks with Data Augmentation](https://mlsanity.com/paper/2307.07832/) (2023)
- [AC Power Flow Data in MATPOWER and QCQP Format: iTesla, RTE Snapshots, and PEGASE](https://mlsanity.com/paper/1603.01533/) (2016)
- [Improving Expressivity of GNNs with Subgraph-specific Factor Embedded Normalization](https://mlsanity.com/paper/2305.19903/) (2023)
- [Bootstrapping ViTs: Towards Liberating Vision Transformers from Pre-training](https://mlsanity.com/paper/2112.03552/) (2021)
- [Contrastive Knowledge Amalgamation for Unsupervised Image Classification](https://mlsanity.com/paper/2307.14781/) (2023)
- [Stabilizing Voltage in Power Distribution Networks via Multi-Agent Reinforcement Learning with Transformer](https://mlsanity.com/paper/2206.03721/) (2022)
- [Message-passing selection: Towards interpretable GNNs for graph classification](https://mlsanity.com/paper/2306.02081/) (2023)

## Built on by (most cited first, 5 of 5)

- [A2PO: Towards Effective Offline Reinforcement Learning from an Advantage-aware Perspective](https://mlsanity.com/paper/2403.07262/) (2024)
- [Unveiling Global Interactive Patterns across Graphs: Towards Interpretable Graph Neural Networks](https://mlsanity.com/paper/2407.01979/) (2024)
- [BiTrajDiff: Bidirectional Trajectory Generation with Diffusion Models for Offline Reinforcement Learning](https://mlsanity.com/paper/2506.05762/) (2025)
- [PowerAtlas: Towards Electricity-Computing Co-Scheduling for Power Systems](https://mlsanity.com/paper/2607.26710/) (2026)
- [Policy Plasticity Matters in Offline-to-Online Reinforcement Learning: Refitting Offline Policies for Online Adaptation](https://mlsanity.com/paper/2609.33127/) (2026)

## Abstract (arXiv)

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.