By Robert Boschetti & Dr Loau Al-Bahrani 

Abstract

Digital Twin (DT) technology is revolutionising predictive maintenance in renewable energy systems by enabling real-time monitoring, fault detection, and performance optimisation (Modelon, 2022). This article explores the integration of DTs in Solar Photovoltaic (PV) and Wind Energy Sectors, highlighting case studies that demonstrate their efficacy in enhancing operational efficiency and reducing maintenance costs.

Introduction

The transition to renewable energy sources necessitates advanced maintenance strategies to ensure system reliability and efficiency. Predictive maintenance, facilitated by Digital Twins, offers a proactive approach by simulating and analysing the performance of energy systems in real-time. DTs serve as virtual replicas of physical assets, enabling continuous monitoring and predictive analytics to pre-emptively address potential failures.

• Digital Twins in Solar Photovoltaic Systems
• Case Study: ENGIE’s El Aguila Solar Plant, Chile

ENGIE, in collaboration with Modelon, implemented a Digital Twin at the El Aguila solar plant in Chile, comprising 7,600 solar panels. The DT facilitated the identification of performance degradation due to broken fuses, enabling timely maintenance and minimising downtime. This proactive approach underscored the DT's role in enhancing asset reliability and operational efficiency (Modelon, 2022).

• Case Study: Solar Spy’s PV Site Management

Solar Spy integrated DT technology to enhance the management of Solar PV sites. The DT provided insights into true site performance and operational efficiency, allowing for informed decision-making and optimised maintenance schedules (Pragmile, n.d).

Digital Twins in Wind Energy Systems

• Case Study: Offshore Wind Farms

A study on offshore Wind Farms demonstrated the application of predictive Digital Twins to optimise performance and maintenance strategies. The DTs enabled real-time monitoring and analysis, leading to improved safety, reduced operational costs, and enhanced turbine performance (SpringerOpen, 2024).

• Case Study: Distributed Digital Twin Framework
• Research on a distributed DT framework for Wind Turbines highlighted the integration of fog computing and Industrial Internet of Things (IoT) technologies. This architecture facilitated real-time condition monitoring and predictive analytics, improving asset utilisation and maintenance planning (MDPI, 2024).

Comparative Analysis

The application of Digital Twins in both Solar and Wind Energy systems has demonstrated significant benefits in predictive maintenance. In Solar PV systems, DTs have enabled precise fault detection and performance optimisation. In Wind Energy, DTs have facilitated real-time monitoring and predictive analytics, enhancing turbine reliability and reducing maintenance costs.

Figure 1 shows the comparative analysis of predictive maintenance outcomes with and without DT integration across renewable energy systems. Here are simulated plots illustrating the benefits of DT networks in predictive maintenance for renewable energy systems, based on the four case studies:

Case 3.1: ENGIE Solar Plant – DT helps slow performance degradation.

Case 3.2: Solar Spy PV Monitoring – DT reduces the frequency of maintenance interventions.

Case 4.1: Offshore Wind Farms – DT improves turbine availability.

Case 4.2: Distributed Wind DT System – DT increases maintenance success rates over time.

Conclusion

Digital Twin technology is a transformative tool in the predictive maintenance of renewable energy systems. By providing real-time insights and enabling proactive maintenance strategies, DTs enhance the reliability, efficiency, and sustainability of energy assets. The case studies presented underscore the practical benefits and potential of DT integration in renewable energy operations.

References 

Modelon. (2022). ENGIE Collaborates with Modelon to Build Solar PV Plant Digital Twin for Predictive Maintenance. ENGIE & Modelon Work to Build Solar PV Plant Digital Twin.

Modelon. (2022). Digital Twin Enables Predictive Maintenance for Solar Power Plant.   Modelon_Case_Engie_FINAL.pdf.

Pragmile. (n.d.). Case Study: Enhancing Solar PV Site Management with Digital Twin Technology.

Case Study: Enhancing Solar PV Site Management with Digital Twin Technology – Pragmile

SpringerOpen. (2024). Industrial digital twins in offshore wind farms. Retrieved from

Industrial digital twins in offshore wind farms | Energy Informatics | Full Text

MDPI. (2024). Towards a Distributed Digital Twin Framework for Predictive Maintenance in Wind Turbines. Retrieved from https://www.mdpi.com/1424-8220/24/8/2663.