A Dynamic Recurrent Adjacency Memory Network for Mixed-Generation Power System Stability Forecasting

TL;DR

This paper introduces DRAMN, a deep learning model that combines physics-informed analysis with graph and recurrent neural networks for real-time power system stability forecasting, demonstrating high accuracy and interpretability.

Contribution

The paper proposes a novel dynamic recurrent adjacency memory network that integrates physics-based dynamic mode decomposition with graph convolutional recurrent mechanisms for improved stability prediction.

Findings

Achieves over 99.8% accuracy on benchmark systems.

Reduces feature dimensionality by 82% without loss of performance.

Validates generalizability across different stability phenomena.

Abstract

Modern power systems with high penetration of inverter-based resources exhibit complex dynamic behaviors that challenge the scalability and generalizability of traditional stability assessment methods. This paper presents a dynamic recurrent adjacency memory network (DRAMN) that combines physics-informed analysis with deep learning for real-time power system stability forecasting. The framework employs sliding-window dynamic mode decomposition to construct time-varying, multi-layer adjacency matrices from phasor measurement unit and sensor data to capture system dynamics such as modal participation factors, coupling strengths, phase relationships, and spectral energy distributions. As opposed to processing spatial and temporal dependencies separately, DRAMN integrates graph convolution operations directly within recurrent gating mechanisms, enabling simultaneous modeling of evolving dynamics and temporal dependencies. Extensive validations on modified IEEE 9-bus, 39-bus, and a multi-terminal HVDC network demonstrate high performance, achieving 99.85%, 99.90%, and 99.69% average accuracies, respectively, surpassing all tested benchmarks, including classical machine learning algorithms and recent graph-based models. The framework identifies optimal combinations of measurements that reduce feature dimensionality by 82% without performance degradation. Correlation analysis between dominant measurements for small-signal and transient stability events validates generalizability across different stability phenomena. DRAMN achieves state-of-the-art accuracy while providing enhanced interpretability for power system operators, making it suitable for real-time deployment in modern control centers.