Completed Clean Energy Engineering

WindTwin - Digital Twin of Wind Turbines for real time continuous monitoring and inspection

Funder InnovateUK (part of UKRI)
Recipient organisation Esi UK Limited
Funding £1.4M
Period Jun 2017 — Dec 2019

In plain English

AI plain-English summary

A digital twin of a wind turbine—a virtual model that mirrors the machine’s real-time behaviour—will let operators predict failures before they happen. This matters because wind turbines, especially offshore ones, are expensive to maintain. A gearbox failure can shut down a turbine for days, costing thousands in lost power and repair bills. Currently, operators often run turbines until something breaks, then fix it. The WindTwin platform changes that by combining physics-based models with live sensor data from the turbine itself. Sensors on the gearbox, for example, feed data continuously into the digital twin, which shows the turbine’s actual performance in near-real time. Operators can then spot wear and tear early, plan maintenance when it’s cheapest, and avoid unplanned downtime. If this succeeds, wind farm operators could cut maintenance costs and keep turbines running longer. The platform also allows testing of different power settings and simulated wear before a turbine is even built. That means better design, fewer breakdowns, and more reliable renewable energy—a quiet but critical improvement to the energy grid that powers homes and businesses.

View original technical description
WindTwin project aims to revolutionise the monitoring and maintenance of wind turbines both onshore and offshore by developing an innovative digital platform that will virtualise with a digital twin the wind turbine behaviour and operation. These virtual models or twins will combine the mathematical models describing the physics of the turbine's operation, with sensor data collected and processed from real assets during real world operations. For example, condition monitoring on gearbox will be applied and sensors will be placed on the real wind turbine asset; the data being collected will be processed and transferred to the digital twin, continuously resulting in a close to real digital twin of the wind turbine showing real time performance. These virtual models will allow wind farm operators to predict failure and plan maintenance thus reducing both maintenance costs and downtime. The application of WindTwin platform will include (1) design using data and knowledge based tools and simulated testing of wind turbines before manufacturing, (2) continous predictive and preventive maintenance and condition monitoring of wind turbine asset (3) different power setting operation scenerios analysis, and associated wear and tear at different power outputs.

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Original classification

Collaborative R&D

Plain English summaries and category classifications on this site are generated by AI and may not perfectly reflect the original research.