Proactive Turbine Blade Management Programs | Big Data Analytics, Simplified
Wind farm operators know degraded turbine blades are inefficient. In fact, blade damage may lead to 5-15% efficiency loss. This results in substantial revenue loss. Inspections can be costly and time consuming, but the big data analytics from inspections is critical to maintenance decisions and action plans to maintain maximum annual energy production (AEP), keep repair costs low and extend the life of the assets. Blade Management Programs based on UAV or drone inspection data are the future of the wind industry. UAV inspectors gather data ten times faster than manual inspections by human teams, are much less risky and provide a depth of information not previously available.
Whether your wind farm has 10 turbines or 1,000+, using data to keep track of damage and wear is essential to proactive maintenance and maximized profits.
The top UAV inspection teams at leading wind farm operators leverage the big data technologies of BladeEdge℠, the industry’s first software portal that transforms big data from aerial inspections into actionable intelligence that you can use to make decisions about your infrastructure. Our software was designed to help improve wind farm safety and efficiency, extend the lifespan of your infrastructure and dramatically reduce costs.
Instead of a set of overwhelming data, BladeEdge offers a single, high-resolution mosaic image for each wind turbine blade. Our software has been built upon AI that leverages deep-learning algorithms to identify and pinpoint the exact location of damage and wear and keep track over time with subsequent inspections. You’ll know exactly where your blades need attention, making maintenance planning simple, direct and effective.
BladeEdge Blade Management Programs include:
- A scalable inspection subscription
- Data storage capacity
- Annual BladeEdge Web Portal Access
- Blade condition assessments
- Flagged maintenance issues
- Full-blade inspection and results
- Tracked conditions over time