UAV based defect detection and fault diagnosis for static and rotating wind turbine blade: a review
Source abstract
Abstract
With the continuous development of wind energy resources, wind turbines have become a crucial element in the field of green energy. However, various malfunctions may occur during their operation, affecting performance and lifespan. To ensure the safety and efficiency of wind turbines, regular inspections and maintenance are essential. In recent years, rapid advancements in unmanned aerial vehicle (UAV) technology have opened new possibilities for the inspection and maintenance of wind turbines. The blades of wind turbines are the most critical components, and this review paper provides a comprehensive analysis of UAV applications in the detection, inspection, and diagnosis of wind turbine blades. It focuses on key UAV inspection technologies, highlighting their advantages and disadvantages in both static and rotating wind turbine blades. Additionally, the paper analyses control algorithms used by UAVs for blade inspection, verifying their accuracy and practicality in this field. Based on these findings, future technological trends are proposed, including UAV autonomous navigation, edge computing-based online monitoring, and intelligent fault diagnosis. It is anticipated that UAV inspection technology will increasingly play a significant role in the inspection and maintenance of wind turbine blades in the future.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Structural Health Monitoring Techniques · Machine Fault Diagnosis Techniques · Non-Destructive Testing Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Hunan University
West Pomeranian University of Technology
Shenyang University of Technology
Funding
National Natural Science Foundation of China · Leading Talents in Science and Technology Innovation in Hunan Province · GuangDong Basic and Applied Basic Research Foundation Offshore Wind Power Joint Fund Project · Natural Science Foundation for Distinguished Young Scholars of Hunan Province · Major Project of Natural Science Foundation of Hunan Province
References (100+)
Show source-provided references
1. 10.1016/j.apenergy.2014.12.04310.1016/j.apenergy.2014.12.043 ↗
2. 10.1016/j.rser.2016.02.02610.1016/j.rser.2016.02.026 ↗
3. 10.1016/j.ref.2022.08.00510.1016/j.ref.2022.08.005 ↗
4. 10.1016/j.apenergy.2015.02.05510.1016/j.apenergy.2015.02.055 ↗
5. 10.1016/j.apenergy.2015.03.01410.1016/j.apenergy.2015.03.014 ↗
6. 10.1016/j.apenergy.2013.05.03310.1016/j.apenergy.2013.05.033 ↗
7. 10.1016/j.renene.2012.03.00310.1016/j.renene.2012.03.003 ↗
8. 10.1016/j.apenergy.2014.03.03110.1016/j.apenergy.2014.03.031 ↗
9. 10.1016/j.renene.2021.12.05610.1016/j.renene.2021.12.056 ↗
10. 10.1109/TIE.2014.236454810.1109/TIE.2014.2364548 ↗
11. 10.1016/j.renene.2020.07.14510.1016/j.renene.2020.07.145 ↗
12. Juengert A, Grosse CUJPOTN. Inspection techniques for wind turbine blades using ultrasound and sound waves. In: Proceedings of the NDTCE Nantes, France; 2009, vol. 9.
13. 10.3390/robotics1001002610.3390/robotics10010026 ↗
14. 10.1179/1743289815Y.000000000310.1179/1743289815Y.0000000003 ↗
15. 10.1109/ICUAS48674.2020.921406610.1109/ICUAS48674.2020.9214066 ↗
16. Skaga SK. Feasibility study of unmanned aerial vehicles (UAV) application for ultrasonic non-destructive testing (NDT) of wind turbine rotor blades. Preliminary experiments of handheld and UAV utrasonic testing on glass fibre laminate. Norway: UiT The Arctic University of Norway; 2017.
17. 10.1016/j.compstruct.2019.11184010.1016/j.compstruct.2019.111840 ↗
18. 10.1080/15567036.2024.230295610.1080/15567036.2024.2302956 ↗
19. 10.1016/j.rser.2022.11232610.1016/j.rser.2022.112326 ↗
20. 10.3390/en509342510.3390/en5093425 ↗
21. 10.3390/en1418597410.3390/en14185974 ↗
22. 10.18178/JOCET.2016.4.6.32810.18178/JOCET.2016.4.6.328 ↗
23. 10.3390/ma1011128510.3390/ma10111285 ↗
24. 10.1016/j.renene.2018.08.05010.1016/j.renene.2018.08.050 ↗
25. 10.1016/j.renene.2015.06.05310.1016/j.renene.2015.06.053 ↗
26. 10.1088/0964-1726/24/3/03300110.1088/0964-1726/24/3/033001 ↗
27. 10.1016/j.engfailanal.2012.08.00210.1016/j.engfailanal.2012.08.002 ↗
28. 10.1016/j.ymssp.2019.10644510.1016/j.ymssp.2019.106445 ↗
29. Bent EJ Sørensen F Debel CP et al. “Improved design of large wind turbine blade of fibre composites based on studies of scale effects (phase 1). Summary report ” risø national laboratory. Denmark. Forskningscenter Risoe. Risoe-R No. 1390(EN). 2004.
30. 10.1002/we.164910.1002/we.1649 ↗
31. 10.1109/TPWRD.2014.237068210.1109/TPWRD.2014.2370682 ↗
32. 10.1109/TPWRD.2014.237068210.1109/TPWRD.2014.2370682 ↗
33. 10.1016/j.engstruct.2010.10.00210.1016/j.engstruct.2010.10.002 ↗
34. 10.1109/TIE.2015.242211210.1109/TIE.2015.2422112 ↗
35. 10.1007/978-3-319-39095-6_310.1007/978-3-319-39095-6_3 ↗
36. 10.1016/j.rser.2016.05.08310.1016/j.rser.2016.05.083 ↗
37. 10.1109/TIE.2015.242211210.1109/TIE.2015.2422112 ↗
38. 10.1016/j.neucom.2019.09.07110.1016/j.neucom.2019.09.071 ↗
39. 10.1016/j.measurement.2019.07.05110.1016/j.measurement.2019.07.051 ↗
40. 10.1016/j.measurement.2019.10714110.1016/j.measurement.2019.107141 ↗
41. 10.1016/j.ymssp.2023.11010110.1016/j.ymssp.2023.110101 ↗
42. 10.1016/j.measurement.2019.10700210.1016/j.measurement.2019.107002 ↗
43. 10.1080/02670836.2015.113302210.1080/02670836.2015.1133022 ↗
44. 10.3390/polym1415304110.3390/polym14153041 ↗
45. 10.1088/1742-6596/364/1/01204110.1088/1742-6596/364/1/012041 ↗
46. 10.1016/j.measurement.2020.10885510.1016/j.measurement.2020.108855 ↗
47. 10.1016/j.ijfatigue.2021.10666010.1016/j.ijfatigue.2021.106660 ↗
48. 10.1109/JSEN.2022.322483710.1109/JSEN.2022.3224837 ↗
49. 10.1016/j.mechatronics.2008.05.01110.1016/j.mechatronics.2008.05.011 ↗
50. 10.1109/MRA.2018.286675810.1109/MRA.2018.2866758 ↗
51. 10.3390/s2118622310.3390/s21186223 ↗
52. 10.3390/aerospace809025610.3390/aerospace8090256 ↗
53. 10.1088/1748-3182/7/2/02500310.1088/1748-3182/7/2/025003 ↗
54. 10.3390/s2403091110.3390/s24030911 ↗
55. 10.3390/s15071488710.3390/s150714887 ↗
56. 10.1109/MCS.2012.222593110.1109/MCS.2012.2225931 ↗
57. 10.1016/j.automatica.2011.09.02010.1016/j.automatica.2011.09.020 ↗
58. 10.1007/s10846-009-9383-110.1007/s10846-009-9383-1 ↗
59. 10.1007/s10846-011-9642-910.1007/s10846-011-9642-9 ↗
60. 10.1109/ACCESS.2020.300973810.1109/ACCESS.2020.3009738 ↗
61. 10.3390/pr908145210.3390/pr9081452 ↗
62. 10.1109/IETC57902.2023.1015213910.1109/IETC57902.2023.10152139 ↗
63. 10.1016/j.asej.2023.10248010.1016/j.asej.2023.102480 ↗
64. 10.1007/s10921-023-00967-510.1007/s10921-023-00967-5 ↗
65. 10.3390/infrastructures708010410.3390/infrastructures7080104 ↗
66. 10.3390/robotics1001002610.3390/robotics10010026 ↗
67. 10.1016/j.renene.2022.12.06310.1016/j.renene.2022.12.063 ↗
68. 10.1109/TNNLS.2021.308482710.1109/TNNLS.2021.3084827 ↗
69. 10.1109/TIE.2017.268203710.1109/TIE.2017.2682037 ↗
70. Zhang D, Burnham, K, Mcdonald, L, et al. Remote inspection of wind turbine blades using UAV with photogrammetry payload. In: 56th Annual British Conference of Non-Destructive Testing-NDT Telford, UK; 2017.
71. 10.1117/1.JEI.28.4.04302310.1117/1.JEI.28.4.043023 ↗
72. 10.3390/en1204067610.3390/en12040676 ↗
73. 10.1016/j.rser.2014.12.00510.1016/j.rser.2014.12.005 ↗
74. 10.1016/j.optlaseng.2021.10690110.1016/j.optlaseng.2021.106901 ↗
75. 10.1109/ACCESS.2022.322444610.1109/ACCESS.2022.3224446 ↗
76. 10.1007/s11760-022-02450-610.1007/s11760-022-02450-6 ↗
77. 10.1080/10589759.2023.223455410.1080/10589759.2023.2234554 ↗
78. 10.1016/j.renene.2024.12015210.1016/j.renene.2024.120152 ↗
79. 10.1016/j.engappai.2023.10783610.1016/j.engappai.2023.107836 ↗
80. 10.1109/JIOT.2023.333144210.1109/JIOT.2023.3331442 ↗
81. 10.1109/ICECIE47765.2019.897479510.1109/ICECIE47765.2019.8974795 ↗
82. 10.1109/IGARSS.2019.889982710.1109/IGARSS.2019.8899827 ↗
83. 10.1088/0957-0233/19/12/12200110.1088/0957-0233/19/12/122001 ↗
84. 10.1016/j.infrared.2016.04.01110.1016/j.infrared.2016.04.011 ↗
85. 10.1063/1.503154510.1063/1.5031545 ↗
86. 10.1109/JSEN.2023.325782410.1109/JSEN.2023.3257824 ↗
87. 10.1109/TIE.2023.333545510.1109/TIE.2023.3335455 ↗
88. 10.3390/en1703067310.3390/en17030673 ↗
89. 10.1016/j.rser.2022.11272310.1016/j.rser.2022.112723 ↗
90. 10.1007/978-981-15-9121-1_2910.1007/978-981-15-9121-1_29 ↗
91. 10.1088/1742-6596/1452/1/01205010.1088/1742-6596/1452/1/012050 ↗
92. 10.1177/147592171986038910.1177/1475921719860389 ↗
93. 10.1016/j.ymssp.2020.10675410.1016/j.ymssp.2020.106754 ↗
94. 10.1109/TPEL.2022.322590410.1109/TPEL.2022.3225904 ↗
95. 10.1080/10589759.2023.228351110.1080/10589759.2023.2283511 ↗
96. 10.1080/1058975080237897410.1080/10589750802378974 ↗
97. 10.1016/j.measurement.2017.07.05810.1016/j.measurement.2017.07.058 ↗
98. 10.1109/TIE.2015.242239410.1109/TIE.2015.2422394 ↗
99. 10.1016/j.rser.2022.11272310.1016/j.rser.2022.112723 ↗
100. 10.3390/app1202080810.3390/app12020808 ↗