Physics-Informed Large Language Models for Predictive Maintenance and Fault Diagnosis of Renewable Energy Systems

Abstract

The accelerating global transition toward renewable energy systems introduces unprecedented operational complexity in asset management, where predictive maintenance and fault diagnosis must reconcile high-dimensional sensor streams, physical degradation laws, and stochastic environmental forcing. Conventional data-driven machine learning models often fail under distributional shift, limited fault samples, and the absence of physically grounded reasoning, while purely physics-based approaches struggle to scale across heterogeneous fleets. This paper presents a system-level investigation of physics-informed large language models (LLMs) as a unifying architectural paradigm for maintenance intelligence in wind, solar, and hybrid renewable installations. We examine how the generative and reasoning capabilities of LLMs can be structurally coupled with physics-informed priors—through digital twin surrogates, embedded domain knowledge, and differentiable physical constraints—to enable interpretable anomaly narration, causal fault localization, and context-aware prescriptive guidance. Departing from component-level algorithm design, the analysis emphasizes cross-system trade-offs among model fidelity, computational latency, data governance, and deployment topology. We discuss infrastructure requirements for federated fine-tuning across geographically distributed assets, edge-cloud orchestration, and the integration of real-time supervisory control and data acquisition streams with latent physical representations. Critical attention is devoted to governance challenges, including auditability of LLM-generated maintenance recommendations, fairness in resource allocation across interconnected energy nodes, and the policy implications of delegating diagnostic authority to hybrid neuro-symbolic systems. Robustness under adversarial sensor degradation and concept drift is analyzed alongside sustainability considerations related to model update frequency and carbon-aware inference scheduling. By synthesizing insights from industrial AI, prognostics and health management, and socio-technical systems theory, the paper articulates a forward-looking research agenda for physics-informed LLMs as resilient cognitive infrastructure in next-generation renewable energy operations.