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Fire warning and monitoring are very important for public safety and environmental protection. However, most of the proposed wind energy conversion devices based on triboelectric nanogenerator (TENG) only work for unidirectional and high‐speed wind and face the challenge of fatigue damage and even failure caused by cyclic stress. Moreover, TENG guided by the theory of fluid dynamics needs further exploration. Herein, a flow‐induced vibration effect based TENG (F‐TENG) for continuously capturing and monitoring multidirectional breeze (1.8–4.3 m s−1) is developed to build a self‐powered intelligent fire detection system (SIFDS). A dynamic model is proposed to study the intrinsic interaction between the electrical properties of F‐TENG and wind. Since the model optimized F‐TENG is more adaptable to wind characteristics, it delivers better performance and higher durability compared with previous studies. Relying on the dynamic model and combining the relationship between F‐TENG's electrical output and wind characteristics, a self‐powered visual wind sensing system is obtained. F‐TENG successfully drives some electronic devices to monitor environmental information, which is expected to provide data for SIFDS to reduce fire hazards. This study can provide an in‐depth understanding of the electromechanical conversion mechanism and large‐scale capture and utilization of breeze energy. [ABSTRACT FROM AUTHOR]

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. A self-tuning triboelectric nanogenerator (S-TENG) is proposed and optimized by the two-step gas-solid interaction model. The S-TENG efficiently converts omnidirectional broadband wind energy (1.2–13.8 m/s) into stable electric energy while also serving as wind vector sensing system. The device achieves power density of 47.43 W/m3, wind direction resolution of 5°, and recognition accuracy of 98.98% based on deep learning. [Display omitted] • The self-tuning strategy immensely expends wind speed and direction response ranges. • A two-step model employed hydromechanics details the dynamic gas-solid interaction. • Wind vector sensor achieves 98.98% recognition accuracy based on deep learning. [ABSTRACT FROM AUTHOR]