Sandia National Laboratories’ ARROW(e) system brings automated, high-tech wind blade inspections to the field
Wind turbine blades are the largest single-piece composite structures in the world, with some now exceeding the length of a football field. They undergo hundreds of millions of fatigue cycles during their life and are often located in remote areas, such as ridgelines or offshore platforms located many miles from the coast.
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Ensuring the reliability of these skyscraper-sized structures over their lifetime is a difficult challenge—these blades can’t be sent to a hangar in the same way airplanes and helicopters can when it’s time for maintenance. Inspections are performed either with telephoto cameras from the ground or using aerial drones. These methods are reasonably good at finding visible damage but currently lack the ability to detect early, hidden damage.
However, recent innovations in robotics may allow for a pathway to introduce low-cost, high-tech inspections to the market: inspections that can detect deep, subsurface damage.
The DOE Sandia National Laboratories-led Blade Reliability Initiative, funded by WETO, builds upon Sandia’s decades of aviation development experience. On the project, Sandia teamed up with International Climbing Machines (whose portable, remote-controlled devices can scale vertical or inverted surfaces) and Dolphitech (developers of advanced ultrasound cameras for two-dimensional and three-dimensional inspection of materials) to design, build, and validate a crawling robot to conduct automated, full-penetration inspections of wind turbine blades.
Controlled by an operator, the Assessment Robot for Resilient Optimized Wind energy, or ARROW(e), is deployed from the turbine nacelle and suctions itself to the vertical surface of a blade, crawling to where it is needed. Onboard cameras provide real-time, high-fidelity images to detect surface damage while phased-array ultrasonic imaging finds any nonvisible, subsurface damage.
“Autonomous inspection is going to be a huge area, in general, and it really makes sense in the wind industry given the size and location of the blades,” said Sandia project lead Josh Paquette. “I can envision each wind plant having a drone or a fleet of drones that take off every day, fly around the wind turbines, do all their inspections, and then come back and upload their data. Then, autonomous inspection systems look for differences in the blades based on previous inspections and note potential issues. An operator can then deploy a robotic crawler onto a blade with suspected damage to get a more detailed look and plan repairs.”
Dennis Roach, Sandia senior scientist and robotic crawler project lead, says that a phased-array ultrasonic inspection can detect damage at any layer inside the thick, composite blades.
“Impact or overstress from turbulence can create subsurface damage that is not visually evident,” Roach said. “The idea is to try to find damage before it grows to critical size and allow for less expensive repairs that decrease blade downtime. We also want to avoid any failures or the need to remove a blade.”
Future implementations of this technology could include the ARROW(e) inspection crawlers automatically moving to locations after results from visual inspections or service advisories from manufacturers indicate a need. The results of the inspections could also be viewed and analyzed by remote experts, much like a radiologist reviews patient X-rays or MRIs in today’s healthcare industry.
Roach said a large benefit of the ARROW(e) device is that it can automate phased-array ultrasonic inspections to detect surface and subsurface damage anywhere through the thickness of the blade. “In doing so,” he said, “it eases the reliance on expert inspectors to deploy the equipment.”
Sandia already demonstrated the technology at the Wind Turbine Blade Service & Maintenance conference in Dusseldorf, Germany, in late 2019. The next step is to deploy the robots in the field to complete the assessment of their viability to inspect all aspects of wind turbine blades. These tests will ensure their accuracy, repeatability, and ease of use. Once proven, the prototype system will be transferred to industry for widespread deployment.