Physics-Informed Fractal-AI Framework for Resilience Prediction in Renewable Energy Systems

The rapid integration of renewable generation, power electronic converters, and distributed energy resources has fundamentally altered the dynamical behavior of modern energy systems, reducing inertia, increasing stochastic variability, and introducing new instability mechanisms. Traditional stability assessment methods rely primarily on physics-based models or data-driven prediction, each with inherent limitations. Physics-based models provide interpretability but may not capture evolving stochastic structure, while purely data-driven approaches often lack physical grounding and robustness. To address this gap, this paper proposes a methodologically novel physics-informed fractal-AI framework that unifies deterministic stability analysis, multiscale fractal diagnostics, and AI-based probabilistic prediction within a single predictive resilience monitoring architecture. Fractal analysis quantifies scaling behavior, long-range dependence, and multiscale correlation structure that may reveal early signs of resilience erosion. The proposed framework integrates physical system models, real-time fractal scaling diagnostics, and artificial intelligence to enable early detection and probabilistic prediction of instability. Physics-based indicators characterize deterministic stability margins, fractal metrics capture multiscale dynamical evolution, and AI models learn nonlinear relationships between scaling indicators and failure risk. The framework also supports integration with digital twin platforms for real-time resilience monitoring and anticipatory control. By explicitly linking physical principles, scaling theory, and machine learning, the proposed approach provides a robust and predictive foundation for proactive stability management in next-generation renewable-integrated energy systems.