Self-tuning triboelectric nanogenerator for omnidirectional broadband (1.2–13.8 m/s) wind energy collection and wind vector detection with deep learning

Xuemei Zhang, Qianxi Yang, Huake Yang, Dahu Ren, Qianying Li, Xiaochuan Li, Hanyuan Liu, Hongmei Yang and Yi Xi

Applied Energy, 2024, vol. 356, issue C, No S0306261923017397

Abstract: Wind speed and wind direction are not only important meteorological factors in environmental dynamic monitoring but also crucial parameters for harvesting considerable renewable wind energy. Although the multi-uniform unit arrangement of triboelectric nanogenerator (TENG) is a strategy for extending the responsive wind speed and direction, interunit crosstalk, low wind direction resolution, narrow working wind speed range, and unclear complex interaction between wake and wind capture component hinder further applications of wind-driven TENG. Here, a self-tuning triboelectric nanogenerator (S-TENG) is developed to effectively harvest broadband omnidirectional wind energy and simultaneously meet the high-resolution detection of wind vector. The gas-solid interaction model of this device is proposed as a two-step process. Meanwhile, owning to the self-tuning ability, the device greatly expands its working range from 2.4 to 6.4 m/s to 1.2–13.8 m/s, giving an excellent output power of 47.43 W/m3. Additionally, a real-time wind vector sensing system based on LabVIEW software exhibits wind direction resolution of up to 5° with only three channels. The recognition accuracy of wind direction relying on deep learning model reaches 98.98%. This work provides a guidance for designing effective high entropy wind energy harvesters and a deep understanding of gas-solid interaction processes.

Keywords: Triboelectric nanogenerator; Omnidirectional wind; Broadband wind; Wind vector sensing; Wind energy harvesting (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261923017397
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:356:y:2024:i:c:s0306261923017397

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2023.122375

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().