Dynamic Characteristics Monitoring of Large Wind Turbine Blades Based on Target‐Free DSST Vision Algorithm and UAV
Remote Sensing, ISSN: 2072-4292, Vol: 14, Issue: 13
2022
- 11Citations
- 6Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
The structural condition of blades is mainly evaluated using manual inspection methods. However, these methods are time‐consuming, labor‐intensive, and costly, and the detection results significantly depend on the experience of inspectors, often resulting in lower precision. Focusing on the dynamic characteristics (i.e., natural frequencies) of large wind turbine blades, this study proposes a monitoring method based on the target‐free DSST (Discriminative Scale Space Tracker) vision algorithm and UAV. First, the displacement drift of UAV during hovering is studied. Ac-cordingly, a displacement compensation method based on high‐pass filtering is proposed herein, and the scale factor is adaptive. Then, the machine learning is employed to map the position and scale filters of the DSST algorithm to highlight the features of the target image. Subsequently, a target‐free DSST vision algorithm is proposed, in which illumination changes and complex back-grounds are considered. Additionally, the algorithm is verified using traditional computer vision algorithms. Finally, the UAV and the target‐free DSST vision algorithm are used to extract the dynamic characteristic of the wind turbine blades under shutdown. Results show that the proposed method can accurately identify the dynamic characteristics of the wind turbine blade. This study can serve as a reference for assessment of the condition of wind turbine blades.
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