Enabling Money-Saving Preventive Maintenance for Wind Energy Turbine Blades
Company Name: Sentient Science Corporation
Program Office: Wind
Location: Buffalo, NY
Email: Elon Terrell, Computational Tribologist; firstname.lastname@example.org
Award Amount: $295,000
Project Term: 12 months
Project Status: Active
Participating Lab(s): Sandia National Laboratories/ National Renewable Energy Laboratory
Wind energy is America's fastest-growing domestic energy source, but its growth is hampered by relatively high operation and maintenance (O&M) costs. Repair and maintenance of wind turbine blades – and the downtime, personnel and equipment costs that go along with unplanned repair and replacement – represents a significant portion of these O&M costs.
Sentient, whose commercially-deployed software has been contracted to extend the life of gearbox gears and bearings of more than 18,000 wind turbines across the globe, has a solution: a preventive-health tool for wind turbine blades. It uses noninvasive video capture of wind turbine blades and incorporates it into a computer vision algorithm to determine how each blade is bending and deforming during operation. When combined with physics-based prognostics modeling, Sentient can assess internal and external stresses and predict a blade's fatigue life. This information will allow wind turbine operators to plan repair and replacement operations well in advance, with minimal downtime, saving time and money.
Wind turbine blade fatigue data provided by Sandia National Laboratories and the National Renewable Energy Laboratory in the course of this project will allow Sentient to develop experimentally validated blade-life predictive models. These models will be combined with streaming operational data from individual turbines to develop a subscription-based asset management tool for wind turbine blades. Using computational predictions of blade fatigue life, the asset management tool will provide online monitoring of blade health and recommendations for maintenance strategies to extend it.
PROJECT INNOVATION + ADVANTAGES
The blade asset management strategy most commonly used in the wind energy industry today involves yearly on-site visual inspections, combined with reactive maintenance or replacement of damaged blades. This method is subject to the long lead times and expenses that are incurred with manually inspecting each turbine, as well as the elevated costs of extensive, unexpected repair or replacement operations involving heavily damaged blades.
Sentient is developing the wind industry's first integrated, platform-independent solution for wind load sensing, life prediction, and asset management of wind turbine blades. This proactive, online wind turbine blade health management solution represents a dramatic improvement compared to current practice. Sentient's system will allow turbine operators to set blade maintenance and repair schedules, adjust their inventory of replacement blades, and derate turbines as needed, minimizing O&M costs.
Sentient's blade health tool has the potential to make a tremendous impact on the wind energy industry. There are approximately 148,000 blades in operation in the U.S.,with an observed failure rate of approximately 4,000 per year. Blade failure generally means two to eight weeks of turbine downtime and associated lost revenue, along with $300,000 to $700,000 in replacement costs. This costs the industry hundreds of millions of dollars annually.
The relatively low cost of Sentient's predictive blade health monitoring tool and the savings generated by proactive O&M would provide significant savings to wind operators. Reducing the cost of wind energy helps it proliferate as a domestically produced, clean energy source that is cost-competitive with traditional fossil-based energy sources.
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