Powering autonomous sensors with miniaturized piezoelectric based energy harvesting devices operating at very low frequency

<p>Harvesting few amount of charge from environmental ambient sources namely, wind, thermal, heat, vibration, solar utilizing micro scale energy harvesting devices, offers vast view of powering for numerous portable low power electronic devices. However, power harvesting using piezoelectric crystal from low power ambient source nowdays has increasing popularity with the advantages of low cost, long life time, stability and clean energy. Recent trends have shown that most researchers are interested in designing a low resonance frequency vibration based energy harvesting devices despite of its challenges ahead. In this paper, a low frequency based rectangular shape piezoelectric cantilever beam has been developed for energy harvesting applications. The proposed vibration based low frequency cantilever beam using piezoelectric element has been developed by finite element analysis (FEA) employing COMSOL Multiphysics platform. The main goal of the study is to analyze the outcome of geometric model of a piezoelectric cantilever beam and to calculate the resonance frequency of the structure according to its length. The material of PZT-5H, has been considered to enhance the efficiency of the low frequency based cantilever beam. Finally, the proposed result is compared with other existing works.</p>

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Ball-impact Piezoelectric Converter for Multi-degree-of-freedom Energy Harvesting from Broadband Low-frequency Vibrations in Autonomous Sensors

Procedia Engineering ◽

10.1016/j.proeng.2014.11.590 ◽

2014 ◽

Vol 87 ◽

pp. 1529-1532 ◽

Cited By ~ 3

Author(s):

Davide Alghisi ◽

Simone Dalola ◽

Marco Ferrari ◽

Vittorio Ferrari

Keyword(s):

Energy Harvesting ◽

Low Frequency ◽

Degree Of Freedom ◽

Ball Impact ◽

Piezoelectric Converter ◽

Autonomous Sensors

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Vibration-Based Energy Harvesting With Essential Nonlinearities

Volume 1: 21st Biennial Conference on Mechanical Vibration and Noise, Parts A, B, and C ◽

10.1115/detc2007-35457 ◽

2007 ◽

Cited By ~ 10

Author(s):

D. Dane Quinn ◽

Alexander F. Vakakis ◽

Lawrence A. Bergman

Keyword(s):

Power Generation ◽

Energy Harvesting ◽

Numerical Study ◽

Low Frequency ◽

Dominant Frequency ◽

Low Level ◽

Portable Electronics ◽

Operational Capabilities ◽

Performance Gains ◽

Energy Harvesting Devices

The implementation of energy harvesting devices enables and extends the operational capabilities of a wide variety of devices, including portable electronics and inaccessible sensors. While linear energy harvesting devices have been shown to be effective for power generation in some environments, they must be tuned to the single dominant frequency of the excitations. In contrast, this work investigates the energy harvesting capabilities of attachments based on essentially nonlinear elements. Although primarily a numerical study, the results suggest that the use of essentially nonlinear attachments for harvesting energy low-level broadband vibration sources is not only possible but also efficacious, leading to performance gains that may be orders of magnitude superior to that of corresponding linear devices for broadband low-frequency excitations.

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The Attenuation of Very Low Frequency Brain Oscillations in Transitions from a Rest State to Active Attention

Journal of Psychophysiology ◽

10.1027/0269-8803.23.4.191 ◽

2009 ◽

Vol 23 (4) ◽

pp. 191-198 ◽

Cited By ~ 26

Author(s):

Suzannah K. Helps ◽

Samantha J. Broyd ◽

Christopher J. James ◽

Anke Karl ◽

Edmund J. S. Sonuga-Barke

Keyword(s):

Brain Activity ◽

Sampling Rate ◽

Low Frequency ◽

Future Research ◽

Adhd Symptoms ◽

Default Mode ◽

Very Low Frequency ◽

Wide Range ◽

Interference Hypothesis ◽

Mode Interference

Background: The default mode interference hypothesis ( Sonuga-Barke & Castellanos, 2007 ) predicts (1) the attenuation of very low frequency oscillations (VLFO; e.g., .05 Hz) in brain activity within the default mode network during the transition from rest to task, and (2) that failures to attenuate in this way will lead to an increased likelihood of periodic attention lapses that are synchronized to the VLFO pattern. Here, we tested these predictions using DC-EEG recordings within and outside of a previously identified network of electrode locations hypothesized to reflect DMN activity (i.e., S3 network; Helps et al., 2008 ). Method: 24 young adults (mean age 22.3 years; 8 male), sampled to include a wide range of ADHD symptoms, took part in a study of rest to task transitions. Two conditions were compared: 5 min of rest (eyes open) and a 10-min simple 2-choice RT task with a relatively high sampling rate (ISI 1 s). DC-EEG was recorded during both conditions, and the low-frequency spectrum was decomposed and measures of the power within specific bands extracted. Results: Shift from rest to task led to an attenuation of VLFO activity within the S3 network which was inversely associated with ADHD symptoms. RT during task also showed a VLFO signature. During task there was a small but significant degree of synchronization between EEG and RT in the VLFO band. Attenuators showed a lower degree of synchrony than nonattenuators. Discussion: The results provide some initial EEG-based support for the default mode interference hypothesis and suggest that failure to attenuate VLFO in the S3 network is associated with higher synchrony between low-frequency brain activity and RT fluctuations during a simple RT task. Although significant, the effects were small and future research should employ tasks with a higher sampling rate to increase the possibility of extracting robust and stable signals.

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