Wind turbines planted in the middle of the ocean were a maintenance challenge. Until the scanner drone appeared
Until very recently, performing a “health check” on an offshore wind turbine was a complex, slow and, above all, expensive logistical process. The industry standard dictated that to inspect the blades, the turbines had to come to a complete stop while specialized technicians traveled by boat to perform manual inspections. This practice represents a direct interruption in the generation of clean energy and loss of income for operators. However, this scenario has changed thanks to Danish startup Quali Drone, which has successfully completed the first contactless drone inspection of a fully operational offshore wind turbine. The landmark in the Baltic Sea. The setting for this advance has been the Rødsand 2 offshore wind farm, operated by RWE since 2010 off the coast of Denmark. There, the AQUADA-GO project team showed that it is possible for a large drone to fly autonomously at a short distance from the blades while they rotate at high speed. As detailed by RWEthe solution has gone from a laboratory experiment to an operational concept successfully demonstrated in real offshore conditions. “We have shown that it is possible to inspect offshore wind turbines with a drone equipped with a visual camera while the turbine is operational,” says Jesper Smit, CEO of Quali Drone. More in depth. To operate in the hostile conditions of the sea, no conventional equipment has been used. The drone is an advanced hardware platform designed for high-precision missions. State-of-the-art sensors: The drone is equipped with high-resolution cameras, infrared thermography and artificial vision systems. Autonomy and precision: It uses mission planning software and an online data infrastructure that allows the drone to track the movement of the blades autonomously. Digital Twins: The technology employs “Digital Twins” to document errors and ensure reports meet industry standards. Subsurface Inspection: Unlike traditional optical methods, this system can scan the internal layers to find damage that is not visible from the outside. Beyond the drone: what the human eye cannot see. The drone is not limited to taking photographs; It is an advanced diagnostic platform. As Xiao Chen explainsassociate professor at DTU (Technical University of Denmark), have developed artificial intelligence models that use algorithms deep learning to identify anomalies. This “digital brain” is capable of detecting everything from surface erosion to internal structural fractures through the use of thermography. Additionally, the AI model learns with every flight: each inspection feeds the system with new data, making it smarter and more accurate each time it is deployed at a wind farm. A paradigm shift. This breakthrough is not just a technical feat; It has profound economic and environmental implications. According to Energy Cluster Denmarkthe impact of the AQUADA-GO project is summarized in compelling figures: Cost reduction: Savings in inspections of at least 50% are estimated in the future. Energy efficiency: By not stopping the turbines, green electricity production is maximized and the levelized cost of energy (LCOE) is reduced by 2% to 3%. Safety and Climate: The risk for workers is reduced by avoiding the deployment of ships and technicians at height, also cutting CO₂ emissions associated with maintenance by between 30% and 50%. Economic driver: This technology is expected to generate between 33 and 55 new full-time jobs and increase the revenue of the companies involved by up to 230 million Danish crowns after commercialization. Towards a smart wind industry. What started as scientific research in Denmark is today a “market-ready commercial solution”, in the words of Jesper Smit. The ability to monitor blade health continuously and without interruption could be the missing piece to make offshore wind energy even more competitive and safer. Image | RWE Xataka | Northern Europe has launched itself into offshore wind. The problem is that there are countries that ‘thieve’ wind