Project description
Drones inspect wind turbines
Unmanned aerial vehicles (UAVs) are beginning to play a role in performing inspections of wind turbine blades. Compared to manual inspections, drone-based solutions can deliver a flexible, cost-effective inspection of wind turbines. The EU-funded Windrone Zenith project is developing a solution to provide 3-blade inspection in a single flight. Its technology is equipped with highly accurate inspection equipment hardware coupled with smart software. Machine learning algorithms will be used to continuously improve automated fault detection based on a growing database of captured images and their analysis. A special cloud reporting platform will make actionable reports available to customers.
Objective
"Over the lifetime of a wind turbine, operation and maintenance costs represent 25% of total levelised cost per kWh produced. The majority of these costs are attributed to the wind turbine’s blades, yet current methods of inspecting these blades are outdated and inefficient.
Blade inspection procedures still largely relies on qualified inspectors roping down each blade to manually inspect for any flaws or defects present on the blade. This is clearly a very hazardous, time-consuming (5 hours), and expensive method (€1500).
Other less used methods of blade inspection include capturing blade images from ground cameras and manual review by
experts. However, poor image quality and strong backlight leaves many blade flaws undetected.
Unmanned Aerial Vehicles (UAVs) are now being used to take pictures of the blades from much closer up. Current UAV's however require dedicated experts for both flight control as well as image processing, analysis, and fault detection.
Pro-Drone's integrated WindDrone Zenith’s solution is a breakthrough solution providing enabling 3-blade inspection in a single flight. Our technology solution is fully equipped with highly accurate inspection equipment hardware coupled with smart software. The software allows the UAV to be fly autonomously, avoid collisions, automatically detect any faults, and generate reports for the customer on each wind turbine inspected. Machine learning algorithms are used to continuously improve automated fault detection based on a growing database of captured images and their analysis. Our ""BladeInsight"" cloud reporting platform makes actionable reports available to our customers as part of this solution. Pro-Drone Zenith provides for a 50% direct cost saving, and decreases turbine inspection downtime by 6X, as compared to existing methods."
Fields of science
- natural sciencescomputer and information sciencessoftware
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringsensorsoptical sensors
- engineering and technologyenvironmental engineeringenergy and fuelsrenewable energywind power
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringroboticsautonomous robotsdrones
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
Programme(s)
Funding Scheme
SME-2 - SME instrument phase 2Coordinator
2740-122 PORTO SALVO
Portugal
The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.