Intelligent Wind Vector Monitoring System Based on Wind Energy Harvesting
Source abstract
Abstract
Wind conditions are crucial in agricultural production, and wind vectors play a significant role in agricultural planting plans. However, traditional anemometers rely on external power sources such as lithium batteries, while wind energy in farmlands is usually neglected. This paper proposes an intelligent wind vector monitoring system based on a dual-module triboelectric nanogenerator (DM-TENG), which consists of a fan-blade type soft-contact triboelectric nanogenerator (FBTSC-TENG) and a disc-shaped triboelectric nanogenerator (DS-TENG). FBTSC-TENG collects wind energy in the environment to power the temperature and humidity sensors, while determining wind speed through the frequency of voltage pulses. DS-TENG can monitor wind direction, identifying 8 wind directions through output pulse signals and deep learning algorithms. Therefore, the DM-TENG proposed in this study is expected to play a significant role in the field of smart agriculture in the future.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Innovative Energy Harvesting Technologies · Tactile and Sensory Interactions · Wireless Power Transfer Systems
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, PR China
School of Advanced Manufacturing, Fuzhou University, Quanzhou 362251, China
Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou 350108, PR China
Funding
National Key Research and Development Program of China · Fujian Science and Technology Innovation Laboratory for Optoelectronic Information of China
References (39)
Show source-provided references
1. 10.1016/j.agsy.2017.01.02310.1016/j.agsy.2017.01.023 ↗
2. 10.1002/adfm.20231203410.1002/adfm.202312034 ↗
3. 10.1002/adfm.20241698410.1002/adfm.202416984 ↗
4. 10.1002/adma.20240732910.1002/adma.202407329 ↗
5. Zhu H. · Chin. Agricult. Sci. Bull. · 2011 · 27 · 310
6. El-Fotouh A. M. A. · J. Res. Sci. Eng. · 2024 · 6 · 54
7. 10.1023/A:100606772103910.1023/A:1006067721039 ↗
8. 10.17775/CSEEJPES.2017.0129010.17775/CSEEJPES.2017.01290 ↗
9. 10.1039/D1EE00691F10.1039/D1EE00691F ↗
10. Long Z. · World Sci-Tech R & D · 2003 · 25 · 26
11. 10.3390/s2021597710.3390/s20215977 ↗
12. 10.1016/j.nanoen.2012.01.00410.1016/j.nanoen.2012.01.004 ↗
13. 10.1007/s40544-018-0217-710.1007/s40544-018-0217-7 ↗
14. 10.1039/C5EE01532D10.1039/C5EE01532D ↗
15. 10.1016/j.mattod.2016.12.00110.1016/j.mattod.2016.12.001 ↗
16. 10.1016/j.mattod.2019.05.01610.1016/j.mattod.2019.05.016 ↗
17. 10.1016/j.nanoen.2019.10427210.1016/j.nanoen.2019.104272 ↗
18. 10.3390/en1421694910.3390/en14216949 ↗
19. 10.1039/C4FD00159A10.1039/C4FD00159A ↗
20. 10.1039/D1EE03961J10.1039/D1EE03961J ↗
21. 10.1007/s11664-023-10231-110.1007/s11664-023-10231-1 ↗
22. 10.1038/s41467-021-25046-z10.1038/s41467-021-25046-z ↗
23. 10.1016/j.nanoen.2021.10649210.1016/j.nanoen.2021.106492 ↗
24. 10.1016/j.nanoen.2022.10808410.1016/j.nanoen.2022.108084 ↗
25. 10.1016/j.nanoen.2022.10815710.1016/j.nanoen.2022.108157 ↗
26. 10.1002/smll.20230084710.1002/smll.202300847 ↗
27. 10.1016/j.eml.2021.10128510.1016/j.eml.2021.101285 ↗
28. 10.1016/j.nanoen.2018.11.03710.1016/j.nanoen.2018.11.037 ↗
29. 10.1016/j.apenergy.2020.11579910.1016/j.apenergy.2020.115799 ↗
30. 10.1016/j.nanoen.2017.10.02910.1016/j.nanoen.2017.10.029 ↗
31. 10.1021/acsnano.6b0156910.1021/acsnano.6b01569 ↗
32. 10.1002/aenm.20200006410.1002/aenm.202000064 ↗
33. 10.1021/acsenergylett.1c0070410.1021/acsenergylett.1c00704 ↗
34. 10.1016/j.nanoen.2019.03.09410.1016/j.nanoen.2019.03.094 ↗
35. 10.1021/acsnano.5b0715710.1021/acsnano.5b07157 ↗
36. 10.1021/nn404315710.1021/nn4043157 ↗
37. 10.1016/j.nanoen.2020.10452410.1016/j.nanoen.2020.104524 ↗
38. 10.1002/admi.20220029310.1002/admi.202200293 ↗
39. 10.1021/nn402477h10.1021/nn402477h ↗