Ultra low-power photovoltaic MPPT technique for indoor and outdoor wireless sensor nodes

Weddell, Alex S.; Merrett, Geoff V.; Al-Hashimi, Bashir M.

doi:10.1109/date.2011.5763302

Cited by 17 publications

(10 citation statements)

References 8 publications

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“…Similarly, other researchers in Refs. [23,24] have proposed their solar energy harvesting models with various simulation parameters considered as shown in Table 4. Finally, our proposed solar energy-harvesting model has the highest efficiency of 96.06% as compared to the other simulation works reported by the various authors as presented in Table 4.…”

Section: Lm2575 Buck Converter Based Energy Harvesting Systemmentioning

confidence: 99%

Modeling and Optimisation of a Solar Energy Harvesting System for Wireless Sensor Network Nodes

Sharma

Haque

Jaffery

2018

JSAN

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The Wireless Sensor Networks (WSN) are the basic building blocks of today’s modern internet of Things (IoT) infrastructure in smart buildings, smart parking, and smart cities. The WSN nodes suffer from a major design constraint in that their battery energy is limited and can only work for a few days depending upon the duty cycle of operation. The main contribution of this research article is to propose an efficient solar energy harvesting solution to the limited battery energy problem of WSN nodes by utilizing ambient solar photovoltaic energy. Ideally, the Optimized Solar Energy Harvesting Wireless Sensor Network (SEH-WSN) nodes should operate for an infinite network lifetime (in years). In this paper, we propose a novel and efficient solar energy harvesting system with pulse width modulation (PWM) and maximum power point tracking (MPPT) for WSN nodes. The research focus is to increase the overall harvesting system efficiency, which further depends upon solar panel efficiency, PWM efficiency, and MPPT efficiency. Several models for solar energy harvester system have been designed and iterative simulations were performed in MATLAB/SIMULINK for solar powered DC-DC converters with PWM and MPPT to achieve optimum results. From the simulation results, it is shown that our designed solar energy harvesting system has 87% efficiency using PWM control and 96% efficiency ( η s y s ) by using the MPPT control technique. Finally, an experiment for PWM controlled SEH-WSN is performed using Scientech 2311 WSN trainer kit and a Generic LM2575 DC-DC buck converter based solar energy harvesting module for validation of simulation results.

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Section: Lm2575 Buck Converter Based Energy Harvesting Systemmentioning

confidence: 99%

Modeling and Optimisation of a Solar Energy Harvesting System for Wireless Sensor Network Nodes

Sharma

Haque

Jaffery

2018

JSAN

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“…In [28], the author adopted an a-Si 55mm x40mm Sanyo AM1815/16 module series PV cell and in [29] they used a Schott Solar 1116929 amorphous silicon PV cell placed on an office desk under 200-700 lux and the size of the adopted PV modules is greater than 20cm.…”

Section: Dimensioning Of Pv Harvester To Supply a Sensor Nodementioning

confidence: 99%

Modeling of WSN Energy Consumption Supplied by iPV Microsystem

Kessebi¹,

Rezig²

2018

ujam

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Current researches on wireless sensor networks (WSN) are focusing on reducing the total amount of energy consumption and extending the network lifetime. WSN have many application fields like industry, health care, commercial and residential applications. The main goal of this paper is to evaluate, using MATLAB/Simulink simulations, the average amount of energy consumed in a WSN based on a clustering topology; to predict the optimal number of clusters in order to optimize the energy and also to study the cloud integration effects on lifetime and energy time dependency. The clustering topology is based on defining K clusters with N/k nodes, each node transmits data to the cluster head which will collect data from all of its own nodes, compute it and send it to the base station (BS). In our simulation, we have focused on many principal metrics in WSN such as optimal number of clusters, duty cycle, lifetime and distance to BS. Finally we have studied the effect of integrating cloud into classic WSN, the effect in terms of energy consumption and sensor node lifetime.

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“…In the steady-state solution of (6), the amplitude of the tip displacement before the step change is (9) and after the step change, the oscillation should settle to the new steady-state solution with a tip displacement amplitude of…”

Section: Response To Step Change In Dampingmentioning

confidence: 99%

“…They are commonly used to maintain temperature-dependent photovoltaic cells at their peak power point [8], [9], and offer the ability to optimally load an energy harvester, compensating for both variations in optimal load and variations in converter output conditions. However, when used with systems with significant stored energy (like the kinetic energy harvesters investigated here) extra care must be taken to ensure correct operation.…”

Section: Introductionmentioning

confidence: 99%

Maximum Power Transfer Tracking for Ultralow-Power Electromagnetic Energy Harvesters

Szarka

Burrow

Proynov

et al. 2014

IEEE Trans. Power Electron.

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This paper describes the design and operation of power conditioning system with maximum power transfer tracking (MPTT) for low-power electromagnetic energy harvesters. The system is fully autonomous, starts up from zero stored energy, and actively rectifies and boosts the harvester voltage. The power conditioning system is able to operate the harvester at the maximum power point against varying excitation and load conditions, resulting in significantly increased power generation when the load current waveform has a high peak-to-mean ratio. First, the paper sets out the argument for MPTT, alongside the discussion on the dynamic effects of varying electrical damping on the mechanical structure. With sources featuring stored energy, such as a resonant harvester, maximum power point control can become unstable in certain conditions, and thus, a method to determine the maximum rate of change of electrical damping is presented. The complete power conditioning circuit is tested with an electromagnetic energy harvester that generates 600 mV rm s ac output at 870 μW under optimum load conditions, at 3.75 m·s −2 excitation. The digital MPTT control circuit is shown to successfully track the optimum operating conditions, responding to changes in both excitation and the load conditions. At 2 V d c output, the total current consumption of the combined ancillary and control circuits is just 22 μA. The power conditioning system is capable of transferring up to 70% of the potentially extractable power to the energy storage.

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Ultra low-power photovoltaic MPPT technique for indoor and outdoor wireless sensor nodes

Cited by 17 publications

References 8 publications

Modeling and Optimisation of a Solar Energy Harvesting System for Wireless Sensor Network Nodes

Modeling and Optimisation of a Solar Energy Harvesting System for Wireless Sensor Network Nodes

Modeling of WSN Energy Consumption Supplied by iPV Microsystem

Maximum Power Transfer Tracking for Ultralow-Power Electromagnetic Energy Harvesters

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