A self-powered and self-sensing hierarchical triboelectric-electromagnetic hybrid generator for forest monitoring

Abstract

As an environmentally friendly primary renewable energy source, wind energy has enormous potential for development and research. In the field of forest ecosystem monitoring, it can provide sustainable clean energy support for the monitoring system in this domain. Traditional monitoring equipment has significant shortcomings, which are specifically manifested in strong dependence on continuous external energy supply, poor environmental adaptability, and insufficient sustainability of energy supply. To address these issues, this study designed and proposed a centrifugal force-driven conversion structure triboelectric-electromagnetic hybrid generator and triboelectric wind sensor (CDCS-TEH-TWS). Through an innovative multi-level structural design, the device enables adaptive three-level regulation of the triboelectric wind sensor (TWS), triboelectric nanogenerator (TENG), and electromagnetic generator (EMG), all within a compact form factor. The design effectively integrates the complementary strengths of TENG and EMG across various energy harvesting conditions, enhancing power generation efficiency via a wind-speed-matching mechanism, thereby addressing the variable nature of wind in forested areas. Additionally, by combining the device's electrical signals with a long short-term memory (LSTM) deep learning model, the TWS achieves real-time wind speed monitoring and supports a self-sustaining sensing capability. Experimental results showed that under the wind speed condition of 12 m/s, the open-circuit peak voltage of the TENG unit reached 780 V, with an output power of 33.8 mW; the open-circuit peak voltage of the EMG was 15.6 V, and the output power reached 3.042 W. In addition, the CDCS-TEH-TWS system could continuously drive multiple sensors to operate and successfully achieved dynamic monitoring of conventional parameters such as ambient temperature and humidity. Notably, the TWS module, tested based on 8400 sets of sample data, had a detection accuracy of up to 96.43 % for external wind speed and could accurately capture wind speed changes. These results fully confirmed the practical value of the device in constructing a self-powered forest environmental monitoring system and provided a reliable technical solution for related application scenarios.