Digital Twin-Based Monitoring and Predictive Maintenance of Renewable Energy Systems for Smart Power Networks: A Comprehensive Review

Abstract

The rapid integration of variable renewable energy (VRE) sources—predominantly solar photovoltaic (PV) and wind energy systems—into modern smart power networks has significantly increased operational complexity and dynamic instability. Traditional reactive and scheduled preventative maintenance strategies are increasingly inadequate for ensuring high asset reliability, minimizing unplanned outages, and optimizing life-cycle operational expenditure (OPEX). Digital Twin (DT) technology has emerged as a transformative paradigm, constructing dynamic, high-fidelity virtual representations of physical renewable assets synchronized in real-time via Internet of Things (IoT) sensor arrays, micro-PMUs, and advanced communication networks. This comprehensive review paper critically evaluates state-of-the-art developments in digital twin-based monitoring and predictive maintenance (PdM) frameworks tailored for smart power grids. We systematically synthesize architectural topologies, highlighting hybrid physics-informed and data-driven modeling paradigms that enable precise state estimation, fault diagnosis, and Remaining Useful Life (RUL) forecasting. Furthermore, this study maps the key enabling technologies—including edge-cloud computing, high-frequency telemetry, deep learning, and multi-physics simulation—while highlighting critical research gaps in real-time latency, cyber-physical security, and multi-scale grid integration. Detailed qualitative and performance comparisons across solar PV, wind turbine, and smart inverter digital twins are presented, supported by conceptual visual models. Finally, actionable future research directions are articulated to guide the deployment of resilient, self-healing, and AI-driven 

digital twin architectures in next-generation smart energy grids.

KEYWORDS: Digital Twin, Predictive Maintenance, Renewable Energy, Smart Power Networks, Physics-Informed Machine Learning, Remaining Useful Life (RUL), Photovoltaic Monitoring, Wind Turbine Diagnostics.