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智能化与AI应用 ★ 4.0

压电式地震能量收集器的能量收集性能及其理论模型研究

A study on the energy harvesting performance and corresponding theoretical models of piezoelectric seismic energy harvesters

语言:

中文摘要

摘要 尽管地震蕴含巨大的能量,但迄今为止,地震传感器的自供电技术以及地震能量的有效利用仍未得到很好解决。鉴于此,本文研制了一系列压电式地震能量收集器(PSEHs),并在不同类型地震波激励下开展了相应的能量收集性能实验与仿真研究。研究并讨论了若干关键设计参数对PSEH输出电压和输出功率的影响。研究结果表明,U型PSEH在从不同地震波中收集能量方面具有良好的能力与理想的鲁棒性。例如,在峰值地面加速度(PGA)为0.024g的El-Centro波激励下,U型PSEH产生的均方根(RMS)电压和RMS功率分别为104 V和11.1 mW,该水平已具备为地震传感器供电的可行性。基于实验与仿真研究,本文推导出一系列理论模型,可用于预测不同设计参数及不同PGA条件下U型PSEH的输出电压与输出功率。这些理论模型为针对不同地震烈度区域的地震传感器匹配设计U型PSEH提供了可靠的指导依据。

English Abstract

Abstract The self-powered technology of earthquake sensors and the seismic energy utilization have not been solved well up to now although earthquake includes mega energy. In view of this, a series of piezoelectric seismic energy harvesters (PSEHs) are developed, and their corresponding experiments and simulations about energy harvesting performance are conducted in the excitation of different seismic waves . The effects of some important design parameters on the output voltage and power of PSEHs are studied and discussed. The research results show that U-shaped PSEH has a good ability and ideal robustness in energy harvesting from different seismic waves . For example, the root mean square (RMS) voltages and RMS powers from U-shaped PSEH are 104 V and 11.1 mW for El-Centro wave with a peak ground acceleration (PGA) of 0.024 g, which is feasible to supply an earthquake sensor. Based on the experiment and simulation research, a series of theoretical models are derived to predict the output voltage and power of U-shaped PSEH with different design parameters and different PGAs, these theoretical models give reliable instructions for the design of U-shaped PSEH to match the earthquake sensors in the area authorized by different earthquake intensities.
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SunView 深度解读

该压电地震能量采集技术对阳光电源储能系统和智能运维具有重要参考价值。研究中U型压电采集器在0.024g加速度下可输出104V/11.1mW,为自供电传感器提供可能。这启发ST系列储能变流器和PowerTitan系统可集成类似微能量采集技术,实现地震高发区储能站的振动监测传感器自供电,降低辅助电源依赖。其理论建模方法可应用于iSolarCloud平台的预测性维护算法,通过振动能量信号分析实现设备状态监测。该技术与阳光电源在功率变换和能量管理领域的核心能力高度契合,可拓展分布式微能量采集应用场景。