For wind turbine operators, the increasing demand for renewable energy and rising labor and equipment costs highlight the shortcomings of the traditional full-scope operation and maintenance (O&M) strategies. The statistics are alarming: 65% of O&M costs are unexpected, disrupting schedules and supply chains. Rotor blade maintenance alone consumes 22% of turbine costs, and blade and drivetrain failures account for 80% of these unplanned expenses. These figures underscore the failure of traditional full-scope O&M strategies to address the industry’s evolving needs.
However, innovative AI solutions for composite blade defect detection are emerging, leveraging cutting-edge technology to aid in the digital transformation of O&M practices.
SkyVisor, a French company established in 2018 and operating in 30 markets worldwide, offers an all-in-one software suite for renewable energy asset management, featuring an automatic drone flight app and an AI-powered asset management platform.
As the industry evolved, the company noticed a significant shift: operators, who previously focused on developing as many projects as possible, started to focus on turbine performance following the drop in energy guarantee prices, investing more in the operation and maintenance of their assets.
With six years of experience and 10 GW of inspected assets, SkyVisor has become a trusted industry leader, supporting and supplying operators with dedicated O&M software. The company is endorsed by clients such as EDF, RES, and RWE.
Today, the company is transforming inspections through a deep learning approach developed by its team of expert engineers. This innovative method enhances defect detection accuracy and efficiency, enabling operators to conduct inspections autonomously.
Gone are the days when original equipment manufacturers (OEMs) handled all maintenance. Today, operators are increasingly taking charge of asset management, using portable drones for in-house inspections and focusing on production beyond installation. Traditionally, blade health assessments were carried out through resource-intensive, contact-based inspections every 7 to 10 years.
However, these inspections failed to meet the real need: the ability to adjust inspection frequency based on the actual condition of the turbine. Now, autonomous drones equipped with SkyVisor’s software enable inspections to be performed whenever required, providing detailed, cost-effective insights for condition assessment and predictive maintenance.
By conducting frequent in-house drone inspections, potential issues can be addressed before they affect performance. For example, defects that require close monitoring can be inspected monthly with SkyVisor’s unlimited inspection model, while new turbines in low-erosion regions can be inspected at a reduced frequency.
With over 8,000 inspections completed, the company’s wind solution streamlines the inspection process. The automated drone flight software inspects turbine blades and towers in just 18 minutes per turbine, all while ensuring compliance with local regulations. This approach emphasizes high-quality, standardized data, flexible inspection scheduling, and significant time and cost savings.
Its blade inspection app guides 100% autonomous drones to gather precise, high quality data throughout the turbine’s lifecycle, enabling operators to monitor defects with the same accuracy as doctors tracking changes in a birthmark.
Reduce downtime and enhance tower maintenance
Reduce downtime and increase your blade awareness
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