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# GridFormer & InformaNet: Spatio-Informational Intelligence for Power Grid Informatization This repository provides a reference implementation of **GridFormer** and the **InformaNet Policy Layer**, a unified modeling and decision framework for modern power grid informatization. The goal of this project is to bridge **physical grid dynamics**, **information flow**, and **adaptive control** into a single, coherent learning system. The implementation is inspired by the paper *"Big Data Platform Architecture and Information Flow Mechanisms in Power Grid Informatization"* and its proposed graph-based transformer architecture for spatio-temporal reasoning in cyber-physical power systems. --- ## 1. Motivation Modern power grids are evolving into highly digitized, cyber-physical infrastructures. They are: - Geographically distributed - Topologically complex - Continuously monitored by heterogeneous sensors and control devices - Subject to uncertainty from renewables, changing demand, and communication delays Traditional centralized or purely data-driven models: - Struggle to scale to large, dynamic networks - Often ignore the explicit grid topology - Provide limited interpretability for operators - Are not tightly integrated with control and operational constraints This repository focuses on an architecture that explicitly encodes: - The **physical network graph** of the grid - The **information and communication graph** - The **temporal evolution** of system states - A **policy layer** that respects operational feasibility and domain constraints --- ## 2. Core Ideas The project is organized around two major components. ### 2.1 GridFormer GridFormer is a spatio-informational modeling module that: - Represents the grid as a graph of buses and transmission lines - Integrates multimodal node features (physical states, measurements, control inputs) - Uses dual-graph propagation over: - The physical grid topology - The communication / information topology - Employs a temporal transformer to capture long-range time dependencies - Outputs: - Next-step state estimates - Candidate control actions Key design aspects: - Multimodal encoder for fusing physical, informational, and control features - Graph attention layers that propagate messages across the physical and communication graphs - Transformer-based temporal reasoning over a rolling time window - Decoders for state prediction and control proposals ### 2.2 InformaNet Policy Layer The InformaNet policy layer sits on top of GridFormer and transforms raw control proposals into **feasible**, **risk-aware**, and **structurally consistent** actions. It provides: - A **domain-constrained decision manifold**, approximating operational constraints such as voltage bounds and line flow limits - **Spatio-temporal action refinement**, which adjusts actions based on recent trajectories and residual errors - **Graph-based priors**, enforcing smoothness and structural coherence across electrically or topologically close nodes - **Risk-aware diversification**, which uses uncertainty estimates to inject controlled stochasticity for robustness --- ## 3. Conceptual Workflow The typical end-to-end workflow for this project looks like this: 1. **Data ingestion** - Load time-series data of grid states and measurements. - Load or construct graph descriptions of: - Physical grid topology - Communication / information topology 2. **Feature construction** - Build multimodal node features, including: - Physical states (voltages, angles, flows, etc.) - Measurements and sensor readings - Local or regional control-related signals 3. **Spatio-temporal modeling with GridFormer** - Encode node features with the multimodal encoder. - Propagate information across both graphs using dual-graph attention. - Stack a temporal transformer over sliding windows of historical states. - Decode: - Next-step state predictions - Candidate control action proposals 4. **Policy refinement with InformaNet** - Project actions into an approximate feasible set using simple domain-inspired operations. - Refine actions using recent state and prediction history. - Adjust actions with graph-based regularization. - Optionally diversify actions using uncertainty-aware perturbations. 5. **Deployment or simulation** - Use the final actions in: - Power system simulators - Digital twins - Decision-support tools - Log performance metrics such as forecast error, constraint violations, and stability indicators. --- ## 4. Key Features - Explicit handling of **two coupled graphs**: - The physical power network - The information / communication network - Support for **multimodal inputs**, including: - Physical measurements - Control and scheduling signals - High-level system descriptors - **Temporal reasoning** via transformer-style mechanisms for: - Capturing long-term dependencies - Handling variable-length histories - **Policy layer** designed around: - Operational constraints - Structural priors from grid topology - Risk-aware decision diversification - Modular design: - Components can be replaced or extended (for example, using different GNNs, temporal models, or policy mechanisms) --- ## 5. Getting Started High-level steps to use this codebase: 1. Install Python and standard machine learning dependencies (for example PyTorch). 2. Prepare datasets that contain: - Time sequences of node-level features - Physical adjacency matrices for the grid - Communication adjacency matrices (or approximations) 3. Configure model dimensions and hyperparameters. 4. Instantiate the model, construct data loaders, and implement a training loop. 5. Evaluate model performance using application-specific metrics such as: - Prediction error metrics - Rate of constraint violations - Stability or resilience indicators --- ## 6. Potential Use Cases - Short-term state forecasting in power grids - Proactive control and decision support for grid operators - Analysis of information flow and its impact on operational robustness - Research on cyber-physical system integration in smart grids - Educational demos on graph-based and transformer-based models for infrastructure systems --- ## 7. Disclaimer This repository provides a **research-oriented reference implementation**. It is not certified for real-time operation in critical infrastructure. Any deployment in real-world grids must include additional validation, safety layers, and compliance with local regulations and operational standards.

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