EnergyOMNI's Perspectives｜World's first: Danish startup uses drones to inspect blades of operating offshore wind turbines

Edited by EnergyOMNI

Danish startup Quali Drone, a firm that specializes in autonomous drone and AI technology, has made a massive breakthrough in January after completing the first-ever contact-free drone inspection of an operational offshore wind turbine blade, allowing damage checks to be carried out without shutting the turbines down.

Quali Drone teamed up with offshore wind developer RWE, along with several other partners, carrying out under the AQUADA-GO project. AQUADA-GO has received funding from the Energy Technology Development and Demonstration Programme (EUDP). These partners included Statkraft, TotalEnergies, DTU, and Energy Cluster Denmark. The project aims to develop an advanced solution that integrates state-of-the-art drone-based machine vision technology with AI algorithms to inspect surface damage and potential internal cracks in offshore wind turbine blades, while carrying out inspections with the turbines remaining fully operational and without the need for shutdowns.

The novel technology has successfully been tested several times onshore. It was now firstly demonstrated offshore at an offshore wind farm, Rødsand 2 Offshore Wind Farm off the coast of Denmark. The wind farm is operated by RWE since 2010 with a a total capacity of 207MW. Traditionally, wind turbine blade inspections must be carried out with the turbines shut down, which is time-consuming and results in lost power generation. Being able to inspect blades without interrupting turbine operation could significantly reduce downtime.

the solution greatly increases safety and reduces the cost and carbon dioxide (CO2) emissions associated with blade inspections.

Jesper Smit, Quali Drone CEO, stated that it is possible to autonomously inspect offshore wind turbines with a drone of a certain size equipped with a visual camera, while the turbine is in operation. RWE chief executive offshore wind Sven Utermöhlen said, "This could open up more cost-efficient, safer and more sustainable inspection options, reducing turbine downtime and associated CO2 emissions."

The drone combines advanced hardware with AI-powered image analysis. The AI model uses infrared imaging and deep learning to identify blade abnormalities. It improves with every new inspection by learning from fresh data. The drone was created at DTU Wind Energy's laboratory with the help of scientists from the Technical University of Denmark (DTU). It is fitted with a visual camera, thermography, and computer vision. During the test, the drone flew in close proximity to the rotating turbine blades. It scanned them in real time in order to spot potential surface damage along with any subsurface fractures.

Five Ways AI Enhances Offshore Wind Farm Maintenance

Investment in digital technologies for wind energy has grown by averaging 10% annually over the last five years. The AI in renewable energy market including wind is projected to reach $75.82 billion by 2030. Here are five ways AI can enhance offshore wind farm maintenance and operations:

1. Predictive Maintenance with AI-Driven Analytics

Traditional maintenance approaches often rely on scheduled intervals or reactive responses to equipment failures. AI-driven predictive maintenance continuously analyzes sensor data from turbines to identify patterns that indicate potential equipment failures before they occur. Predictive analytics can predict wind turbine failures up to 60 days in advance allowing for planned repairs. AI-driven predictive maintenance can reduce wind farm operation and maintenance costs by up to 20%.

Advanced algorithms process data from accelerometers, temperature sensors, vibration monitors and oil analysis systems to detect subtle changes in equipment behavior. These models can forecast issues with critical components like gearboxes, generators and turbine blades. Maintenance teams can plan interventions during optimal weather windows and minimize costly emergency repairs.

2. Optimized Maintenance Scheduling and Resource Allocation

Offshore wind farm maintenance requires coordination of multiple variables, including weather conditions, equipment, status, crew availability, vessel capacity and spare parts inventory. AI algorithms excel at processing these interconnected factors to create optimal maintenance schedules that minimize cost and maximize operational efficiency.

3. Automated Inspections Using Drones and Robotics

Manual inspections of offshore wind turbines traditionally require specially trained personnel to work at dangerous heights, making the process risky and time-consuming. AI-powered drones and robotics are modernizing maintenance by providing safer, faster and more thorough inspection capabilities.

4. Enhanced Worker Safety and Training

AI-powered digital twin technology creates highly realistic simulations that help maintenance workers practice complex procedures in a risk-free virtual setting. These simulations can replicate various scenarios and weather conditions. They provide thorough training that would be difficult or dangerous to conduct on actual turbines.

5. Data-Driven Performance Optimization

By analyzing vast amounts of operational data, AI systems can identify underperforming turbines and recommend corrective actions. AI algorithms continuously benchmark individual turbine performance against expected outputs based on wind conditions. Comparing results across the wind farm can help identify anomalies. This type of analysis can reveal issues — such as blade pitch misalignment. BitBloom, a UK-based technology company, identifies turbine underperformance via temperature and power anomalies and increases yield by 2–4%. The use of AI in wind turbine maintenance can extend the lifetime of assets by up to 20%.