Vacuum-Packaged Piezoelectric Energy Harvester for Powering Smart Grid Monitoring Devices

Abasian, Alireza; Tabesh, Ahmadreza; Rezaei-Hosseinabadi, Nasrin; Nezhad, Abolghasem Zeidaabadi; Bongiorno, Massimo; Khajehoddin, S. Ali

doi:10.1109/tie.2018.2860557

Cited by 29 publications

(14 citation statements)

References 20 publications

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“…Secondly, the theoretical framework and hyperparameter estimation method of block sparse Bayesian learning and reconstruction algorithm are studied, and the use of block sparse Bayesian learning for compressed sensing reconstruction of rotating machinery vibration signals is proposed [25]. The compression sensing method is used to process the bearing vibration signal, and the performance of different reconstruction algorithms is compared, which proves that the block sparse Bayesian learning method has better reconstruction accuracy than the traditional compression sensing reconstruction algorithm [26,27].…”

Section: Methodsmentioning

confidence: 99%

Research on mechanical vibration monitoring based on wireless sensor network and sparse Bayes

Lei¹,

Wu²

2020

J Wireless Com Network

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Mechanical vibration monitoring for rotating mechanical equipment can improve the safety and reliability of the equipment. The traditional wired monitoring technology faces problems such as high-frequency signal pickup and high-precision data collection. Therefore, this paper proposes optimization techniques for mechanical vibration monitoring and signal processing based on wireless sensor networks. First, the hardware design uses high-performance STM32 as the control center and Si4463 as the wireless transceiver core. The monitoring node uses a high-precision MEMS acceleration sensor with a 16-bit resolution ADC acquisition chip to achieve high-frequency, high-precision acquisition of vibration signals. Then, the bearing vibration signal optimization method is studied, and the sparse Bayes algorithm is proposed as a compressed sensing reconstruction algorithm. Finally, the difference in reconstruction accuracy between this method and the traditional reconstruction algorithm is compared through experiments and the effect of this method on the reconstruction performance is analyzed when different parameters are selected.

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Section: Methodsmentioning

confidence: 99%

Research on mechanical vibration monitoring based on wireless sensor network and sparse Bayes

Lei¹,

Wu²

2020

J Wireless Com Network

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“…Environments involving electrical currents include industrial plant areas, large machinery, power distribution lines, electrical installations of buildings and vehicle power networks. Piezoelectric [16][17][18][19][20][21][22] electrostatic [23][24][25][26], and inductive [27][28][29][30][31][32][33][34] coupling methods have already been studied for this purpose, demonstrating that adequate power density can be achieved by non-invasive coupling, to support wireless sensors with continuous power.…”

Section: Introductionmentioning

confidence: 99%

Power Supply Based on Inductive Harvesting From Structural Currents

Kiziroglou

Wright

Yeatman

2022

IEEE Internet Things J.

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Monitoring infrastructure offers functional optimisation, lower maintenance cost, security, stability and data analysis benefits. Sensor nodes require some level of energy autonomy for reliable and cost-effective operation, and energy harvesting methods have been developed in the last two decades for this purpose. Here, a power supply that collects, stores and delivers regulated power from the stray magnetic field of currentcarrying structures is presented. In cm-scale structures the skin effect concentrates current at edges at frequencies even below 1 kHz. A coil-core inductive transducer is designed. A fluxfunnelling soft magnetic core shape is used, multiplying power density by the square of funnelling ratio. A power management circuit combining reactance cancellation, voltage doubling, rectification, super-capacitor storage and switched inductor voltage boosting and regulation is introduced. The power supply is characterised in house and on a full-size industrial setup, demonstrating a power reception density of 0.36 mW/cm 3 , 0.54 mW/cm 3 and 0.73 mW/cm 3 from a 25 A RMS structural current at 360 Hz, 500 Hz and 800 Hz respectively, corresponding to the frequency range of aircraft currents. The regulated output is tested under various loads and cold starting is demonstrated. The introduced method may enable power autonomy to wireless sensors deployed in current-carrying infrastructure.

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“…The VPH was similar to the design previously which was consisted of a piezoelectric bimorph with the dimension of 28.6 × 12.7 × 0.508 mm 3 and stiffness of K = 760 N/m. The VPH generated output power of 90.3 µW at the frequency of 50 Hz [17].…”

Section: Introductionmentioning

confidence: 99%

Air cavity-based vibrational piezoelectric energy harvesters

Yusoff

Ahmad

Sulaiman

et al. 2021

Electrical Engineering & Electromechanics

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Introduction. Known vibrational energy harvesting methods use a source of vibration to harvest electric energy. Piezoelectric material works as a sensing element converted mechanical energy (vibration) to electrical energy (electric field). The existing piezoelectric energy harvesting (PEHs) devices have low sensitivity, low energy conversion, and low bandwidth. The novelty of the proposed work consists of the design of PEH’s structure. Air cavity was implemented in the design where it is located under the sensing membrane to improve sensitivity. Another novelty is also consisting in the design structure where the flexural membrane was located at the top of electrodes. The third novelty is a new design structure of printed circuit board (PCB). The purpose of improvised design is to increase the stress in between the edges of PEH and increase energy conversion. With the new structure of PCB, it will work as a substrate that absorbs surrounding vibration energy and transfers it to sensing element. Methods. Three techniques were successfully designed in PEH and fabricated namely PEH A, PEH B, and PEH C were characterized by two experiments: load and vibration. The load experiment measured load pressure towards the PEH, whereas the vibration experiment measured stress towards the PEH. Results. PEH C has the highest induced voltage for a weight of 5.2 kg at the frequency of 50 Hz and the highest stored voltage for a period of 4 min. The three techniques applied in PEHs were showed improvement in transducer sensitivity and energy conversion. Practical value. A piezoelectric acoustic generator was used in the experiment to compare the performance of the designed PEH with available piezoelectric transducers in the market. The new flexible membrane worked as a sensing element was worked as a cantilever beam. PVDF was used as a sensing element due to the flexibility of the polymer material, which is expected to improve sensitivity and operating bandwidth.

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Vacuum-Packaged Piezoelectric Energy Harvester for Powering Smart Grid Monitoring Devices

Cited by 29 publications

References 20 publications

Research on mechanical vibration monitoring based on wireless sensor network and sparse Bayes

Research on mechanical vibration monitoring based on wireless sensor network and sparse Bayes

Power Supply Based on Inductive Harvesting From Structural Currents

Air cavity-based vibrational piezoelectric energy harvesters

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