Blade signal extraction based on laser clearance LiDAR data
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摘要: 为了降低雾天风力发电机组叶片信号的虚警率，以激光净空雷达获取的原始信号数据为基础数据源，在信号预处理的基础上，采用时空约束的方法，实现了叶片信号的准确提取；并将集成了该叶片信号提取方法的激光净空雷达在常年有雾的地区进行了挂机试验。结果表明，所提出的叶片信号提取方法取得了较好的效果，叶片虚警率低于2%，提升了激光净空雷达测距数据的可靠性和环境适应性。该研究为激光净空雷达用于风机净空监测提供了参考。Abstract: To reduce false alarm rate in foggy conditions for wind turbine blade signals, a method was adopted to accurately extract blades signals by using the raw signal data obtained from laser clearance light detection and ranging(LiDAR)as the primary data source, and incorporating spatiotemporal constraints in signal preprocessing. The laser clearance LiDAR integrated with blade signal extraction method was tested in areas with year-round fog. The experimental results show that blade signal extraction method proposed in this paper has achieved good results, with a false alarm rate of less than 2%, which improves the reliability and environmental adaptability of laser clearance LiDAR ranging data. This study provides a reference for the application of laser clearance LiDAR in wind turbine clearance monitoring.
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表 1 叶片信号提取结果
Table 1 Blade signal extraction results
number time weather R/(r·min-1) v/(m·s-1) N S/% 1 2023-04-13T05:00—05:10 high 11.00 10.06 330 1.82 2 2023-04-13T06:10—06:20 high 11.00 9.62 330 0 3 2023-04-13T08:20—08:30 high 10.83 6.02 325 1.91 4 2023-04-13T08:50—09:00 middle 10.20 5.81 306 0 5 2023-04-13T10:00—10:10 high 10.78 6.50 323 1.38 6 2023-04-13T10:10—10:20 high 10.26 6.41 308 0.88 7 2023-04-13T16:10—16:20 low 10.84 6.88 325 0 8 2023-04-13T18:40—18:50 high 9.63 5.53 289 0 9 2023-04-13T 21:10—21:20 low 11.00 9.52 330 0 10 2023-04-13T 21:20—21:30 low 11.00 10.31 330 0 11 2023-04-14T07:10—07:20 high 11.00 7.69 330 1.85 12 2023-04-14T07:20—07:30 middle 11.00 8.15 330 1.20 13 2023-04-14T20:10—20:20 high 11.00 7.02 330 0 14 2023-04-14T20:20—20:30 middle 11.00 6.35 330 0 15 2023-04-14T22:30—22:40 low 11.00 6.37 330 0 16 2023-04-14T22:40—22:50 low 11.00 6.51 330 0 17 2023-04-15T00:10—00:20 low 11.00 6.09 330 0 18 2023-04-15T00:20—00:30 middle 11.00 6.46 330 1.33 19 2023-04-15T01:00—01:10 high 11.00 8.02 330 0 20 2023-04-16T16:20—16:30 high 11.00 6.77 330 1.94 21 2023-04-16T20:10—20:20 middle 8.90 4.18 267 0 22 2023-04-16T21:10—21:20 middle 8.93 4.44 268 0 23 2023-04-16T21:30—21:40 high 9.90 6.17 297 0 24 2023-04-16T22:00—22:10 middle 9.44 5.84 283 0 25 2023-04-16T22:30—22:40 high 7.00 3.84 210 0.01 
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