Index

A Resource-Efficient AC Power Flow Prediction Framework using Physics-Informed GNNs and RL-Based Model Compression

1. Motivation

Modern power grids require accurate, real-time AC power flow prediction to ensure secure and efficient operation. Graph Neural Networks (GNNs) are promising due to their ability to model the grid’s topological and nonlinear properties. However, standard GNNs are often too large for edge deployment, and naïve compression can lead to physically infeasible predictions. There is a pressing need for compression techniques that preserve physical accuracy.

2. Objective

This project aims to develop a two-phase framework:
1. Physics-Informed GNN: Predict voltage magnitudes and phase angles from power grid snapshots using AC power flow laws.
2. RL-Guided Compression: Learn to prune and quantize the model efficiently while preserving physical feasibility.

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