Digital twin of wind turbines for real time continuous monitoring and inspection
With an estimated 216,000 wind turbines in operation globally there are approximately 3,8000 incidents annually of turbine failures. Research has shown that preventative maintenance costs 25% less than reactive maintenance and predictive maintenance costs 47% less. The solution is to build a digital platform for preventive and predictive maintenance using sensors, big data analytics and advanced visualisation and analysis tools to understand the behaviour and condition of the wind turbine in real-time.
The objective of this project is to develop a digital/virtual models or twins of a wind turbine which 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. These virtual models will allow wind farm operators to predict failure and plan maintenance thus reducing both maintenance costs and downtime.
WindTwin offers a digital platform for designing, maintaining and optimising real wind turbines, it will reduce maintenance cost by 30% for end user/operators. Early detection of defects will increase reliability by 99.5% and will reduce losses due to downtime by 70%. It will improve current state of the art by developing a platform capable of monitoring and controlling wind turbines digitally and remotely. WindTwin will enable users to maintain a real wind turbine asset by monitoring a digital dynamic virtual model remotely.