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Proceedings Paper

Optimized MPPT-based converter for TEG energy harvester to power wireless sensor and monitoring system in nuclear power plant
Author(s): Shaoxu Xing; Isil Anakok; Lei Zuo
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Paper Abstract

Accidents like Fukushima Disasters push people to improve the monitoring systems for the nuclear power plants. Thus, various types of energy harvesters are designed to power these systems and the Thermoelectric Generator (TEG) energy harvester is one of them. In order to enhance the amount of harvested power and the system efficiency, the power management stage needs to be carefully designed. In this paper, a power converter with optimized Maximum Power Point Tracking (MPPT) is proposed for the TEG Energy Harvester to power the wireless sensor network in nuclear power plant. The TEG Energy Harvester is installed on the coolant pipe of the nuclear plant and harvests energy from its heat energy while the power converter with optimized MPPT can make the TEG Energy Harvester output the maximum power, quickly response to the voltage change and provide sufficient energy for wireless sensor system to monitor the operation of the nuclear power plant. Due to the special characteristics of the Single-Ended Primary Inductor Converter (SEPIC) when it is working in the Discontinuous Inductor Current Mode (DICM) and Continuous Conduction Mode (CCM), the MPPT method presented in this paper would be able to control the converter to achieve the maximum output power in any working conditions of the TEG system with a simple circuit. The optimized MPPT algorithm will significantly reduce the cost and simplify the system as well as achieve a good performance. Experiment test results have shown that, comparing to a fixed- duty-cycle SEPIC which is specifically designed for the working on the secondary coolant loop in nuclear power plant, the optimized MPPT algorithm increased the output power by 55%.

Paper Details

Date Published: 19 April 2017
PDF: 12 pages
Proc. SPIE 10171, Smart Materials and Nondestructive Evaluation for Energy Systems 2017, 1017105 (19 April 2017); doi: 10.1117/12.2259809
Show Author Affiliations
Shaoxu Xing, Virginia Polytechnic Institute and State Univ. (United States)
Isil Anakok, Virginia Polytechnic Institute and State Univ. (United States)
Lei Zuo, Virginia Polytechnic Institute and State Univ. (United States)

Published in SPIE Proceedings Vol. 10171:
Smart Materials and Nondestructive Evaluation for Energy Systems 2017
Norbert G. Meyendorf, Editor(s)

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