Abstract
AbstractReliability and cost are two important driving factors in the development of wind energy. Automation and digitalization of operation and maintenance (O&M) procedures help to increase turbine reliability and reduce the levelized cost of energy (LCOE). Here, we demonstrate a novel method, coined as AQUADA, which may change the current labor‐intensive and operation‐interfering blade inspection by using thermography and computer vision. We experimentally show that structural damages below the surfaces can be detected and quantified remotely when wind turbine blades are subject to fatigue loads. The data acquisition and analysis are automatically done in one single step, which may shift the current inspection paradigm through more automated O&M procedures. The cost analysis shows that the AQUADA method has a potential to at least half the total inspection cost and reduce the LCOE by 1%–2% when applied to a baseline land‐based wind farm consisting of twenty 2.45‐MW turbines. All data and source codes are published for researchers to reproduce our results and facilitate further development of AQUADA towards more mature industrial applications.
Highlights
Smart solutions such as automation and digitalization can contribute to levelized cost of energy (LCOE) reduction of wind energy, which remains the overall driver for the development of the wind energy.[1,2,3,4,5,6] Typically, the operation and maintenance (O&M) costs account for 20%–25% of the total LCOE for both onshore and offshore wind.[7]
The reduction in operational expenditures (OPEX) is considerable whereas the increase in annual energy production (AEP) is negligible, leading to a total LCOE reduction ranging from 0.5% to 2.6% comparing to three conventional methods when the new AQUADA method is used
Using thermography and computer-vison–based image processing, we experimentally demonstrate an automated, remote, and quantitative method, AQUADA, for smart wind turbine blade damage inspection
Summary
Smart solutions such as automation and digitalization can contribute to levelized cost of energy (LCOE) reduction of wind energy, which remains the overall driver for the development of the wind energy.[1,2,3,4,5,6] Typically, the operation and maintenance (O&M) costs account for 20%–25% of the total LCOE for both onshore and offshore wind.[7]. Current wind turbine blade inspection primarily uses rope and/or basket access techniques, ground-based cameras, and flying drones.[9,10,11,12,13,14] These methods are dominantly based on optical images at frequencies visual to the human eyes. The images of damages and features on the surface such as erosion, dirt, and surface cracks are acquired.[15] the critical damages often occur underneath the blade surface,[16,17] and they are hidden from the view especially from afar This is, at least partly, the reason why the more labor-intensive and expensive rope and/or basket access method is still widely used in the industry.[9] After the image acquisition, a significant amount of expert man-hours is still needed to analyze
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