Abstract

The concentration of abundant wind energy on the high-voltage transmission tower with the feature of high height not only provides a promising solution for powering distributed sensors in smart grid, but also enables a potential feasibility for the real-time sensing of the multi-directional wind to evaluate the transmission line aeolian vibration induced by wind excitation. In this paper, the comprehensive strategy appropriately hybridizes the ingenious integration of a rotating-type TENG for efficient wind energy harvesting and a flapping-type TENG for multi-directional wind vector sensing to establish the self-powered multi-directional wind-driven triboelectric nanogenerator (SMW-TENG). The basic D-TENG and F-TENG in terms of structural parameters and response characteristics are systematically optimized to realize an efficient energy harvesting with the peak power of 3.9 mW, a smooth output in the wind speed of 3.7–16.3 m s−1 and sensitive response to eight-direction wind speed ranging from 2.6 to 13.5 m s−1. Hybrid power supply strategy based on collaborative working of UD-TENG as a standby power and LD-TENG as a main power is innovatively proposed to prolong the continues power supply of a Bluetooth temperature and humidity sensor by 70% and 126.7% compared to the single module, respectively. Importantly, the F-TENG equipped with an eight-channel signal acquisition and processing is developed to achieve the multi-directional wind vector information in response to the aeolian vibration. Furthermore, a self-powered ambient weather and multichannel wind vector sensing system based on the SMW-TENG, a hybrid power supply circuit, an eight-channel signal acquisition circuit and a Bluetooth module is developed to comprehensively realize the real-time monitoring of temperature, humidity, wind speed and direction. The abnormal wire vibration alarm can be realized through monitoring the wind direction angle on the transmission line cross-section, thereby exhibiting a great potential for developing self-powered sensing in the field of smart grids.

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