Abstract
When thermoelectric generators can be used to convert heat into power, the efficiency of this process is low. The efficiency with which TEGs convert energy is improved via a new method called maximum power point tracking (MPPT). This study aims to examine the possibility of utilizing an artificial neural network (ANN) to monitor the amount of data that can be stored in PowerPoint. The neural network is trained using the error backpropagation method. One of the benefits of using a neural network is its speed and accuracy while tracking the Maximum PowerPoint. In order to test the suggested MPPT technique, MATLAB/SIMULINK software was used to simulate the TEG mode's performance. MPPT was developed and used to regulate SEPIC converter switching technology. The temperature is used as an input variable in this technique. Under a variety of temperature conditions, the proposed methodology for studying dynamic responses to track the TEG system's maximum power point and power generated. We found that the intelligent technique's output has high tracking speed, tracking accuracy, and minimized oscillation around the maximum power point (MPP).
This is a preview of subscription content, access via your institution.
Data availability
Data used in the paper is openly available. Enquiries for the data can be directed to authors.
Abbreviations
- SEPIC:
-
Single ended primary inductor converter
- TEG:
-
Thermoelectric generator
- MPPT:
-
Maximum power point tracking,
- ANN:
-
Artificial neural network
- MLP:
-
Multilayer perceptron
- VOC:
-
Voltage source
- RL:
-
Internal resistance
References
Ahmed EM, Shoyama M 2011 Stability study of variable step size incremental conductance/impedance MPPT for PV systems," 8th International conference on power electronics-ECCE Asia, Jeju, Korea (South), 386–392, https://doi.org/10.1109/ICPE.2011.5944555
Ali H, Yilbas BS (2017) Configuration of segmented leg for the enhanced performance of segmented thermoelectric generator. Int J Energy Res 41(2):274–288. https://doi.org/10.1002/er.3620
Bijukumar B, Raam AGK, Ganesan SI, Nagamani C (2018) A linear extrapolation-based MPPT algorithm for thermoelectric generators under dynamically varying temperature conditions. IEEE Trans Energy Convers 33:1641–1649. https://doi.org/10.1109/TEC.2018.2830796
Bond M, Park JD (2015) Current-sensor less power estimation and MPPT implementation for thermoelectric generators. IEEE Trans Industr Electron 62:5539–5548. https://doi.org/10.1109/TIE.2015.2414393
Cao Q, Luan W, Wang T (2018) Performance enhancement of heat pipes assisted thermoelectric generator for automobile exhaust heat recovery. Appl Therm Eng, 130:1472–1479
Chandrarathna SC, Lee JW (2019) A dual-stage boost converter using two-dimensional adaptive input-sampling MPPT for thermoelectric energy harvesting. IEEE Trans Circuits Syst I Regul Pap 66:4888–4900. https://doi.org/10.1109/TCSI.2019.2935221
Chen WH, Huang SR, Wang XD, Po-Hua Wu, Lin YL (2017) Performance of a thermoelectric generator intensified by temperature oscillation. Energy 133:257–269. https://doi.org/10.1016/j.energy.2017.05.091
Cheng F (2019) Fabrication of nanostructured skutterudite-based thermoelectric module and design of a maximum power point tracking system for the thermoelectric pile. IEEE Sens J 19:5885–5894
Hannoon NM, Chowdary PSR, Ananth DVN, Chakravarthy VVSSS (2022) DFIG HVDC based wind farms system profile improvement under grid faults. In: 2022 International conference on computing, communication and power technology (IC3P) (pp 57–64). IEEE
Ibrahim A, Rahnamayan S, Martin MV, Yilbas B (2014) Multi-objective thermal analysis of a thermoelectric device: influence of geometric features on device characteristics. Energy 77:305–317. https://doi.org/10.1016/j.energy.2014.08.041
Jang B, Han S, Kim JY (2011) Optimal design for micro-thermoelectric generators using finite element analysis. Microelectron Eng 88(5):775–778. https://doi.org/10.1016/j.mee.2010.06.025
Kim J, Kim C (2013) A DC–DC boost converter with variation-tolerant MPPT technique and efficient ZCS circuit for thermoelectric energy harvesting applications. IEEE Trans Power Electron 28:3827–3833. https://doi.org/10.1109/TPEL.2012.2231098
Kim R-Y, Lai J-S, York B, Koran A (2009) Analysis and design of maximum power point tracking scheme for thermoelectric battery energy storage system. IEEE Trans Industr Electron 56:3709–3716. https://doi.org/10.1109/TIE.2009.2025717
Kim TY, Negash A, Cho G (2017) Direct contact thermoelectric generator (DCTEG): a concept for removing the contact resistance between thermoelectric modules and heat source. Energy Convers Manage 142:20–27
Kramer LR, Maran ALO et al (2019) Analytical and numerical study for the determination of a thermoelectric generator’s internal resistance. Energies 12:16. https://doi.org/10.3390/en12163053
Lineykin S, Ben-Yaakov S (2007) Modeling and analysis of thermoelectric modules. IEEE Trans Ind Appl 43:505–512. https://doi.org/10.1109/TIA.2006.889813
Meng JH, Zhang XX, Wang XD (2014) Dynamic response characteristics of thermoelectric generator predicted by a three-dimensional heat-electricity coupled model. J Power Sour 245:262–269. https://doi.org/10.1016/j.jpowsour.2013.06.127
Montecucco A, Knox AR (2014) Accurate simulation of thermoelectric power generating systems. Appl Energy 118:166–172. https://doi.org/10.1016/j.apenergy.2013.12.028
Montecucco A, Knox AR (2015) Maximum power point tracking converter based on the open-circuit voltage method for thermoelectric generators. IEEE Trans Power Electron 30:828–839. https://doi.org/10.1109/TPEL.2014.2313294
Rezania A, Rosendahl LA, Yin H (2014) Parametric optimization of thermoelectric elements footprint for maximum power generation. J Power Sources 255:151–156. https://doi.org/10.1016/j.jpowsour.2014.01.002
Selvan KV, Mohamed Ali MS (2018) Copper–nickel and copper-cobalt thermoelectric generators: power-generating optimization through structural geometrics. IEEE Trans Electron Devices 65:3394–3400. https://doi.org/10.1109/TED.2018.2840105
Selvan KV, Rehman T, Saleh T, Ali MSM (2019) Copper–cobalt thermoelectric generators: power improvement through optimized thickness and sandwiched planar structure. IEEE Trans Electron Devices 66:3459–3465. https://doi.org/10.1109/TED.2019.2920898
Sequeira TN, Santos MS (2018) Renewable energy and politics: a systematic review and new evidence. J Clean Prod 192:553–568
Tsai HL, Lin JM (2010) Model building and simulation of thermoelectric module using Matlab/Simulink. J Electron Mater 39:2105–2111. https://doi.org/10.1007/s11664-009-0994-x
Willars-Rodríguez FJ, Chávez-Urbiola EA, Vorobiev P, Vorobiev YV (2017) Investigation of solar hybrid system with concentrating Fresnel lens, photovoltaic and thermoelectric generators. Int J Energy Res 41(3):377–388. https://doi.org/10.1002/er.3614
Xiao W, Dunford WG (2004) A modified adaptive hill climbing MPPT method for photovoltaic power systems, Proceedings of the 35th annual IEEE power electronics specialists conference PESC’04, Aachen, 20 1957–1963
Yanga Bo, Yub T, Shua H, Donga J, Jiang L (2018) Robust sliding-mode control of wind energy conversion systems for optimal power extraction via nonlinear perturbation observers. Appl Energy 210:711–723. https://doi.org/10.1016/j.apenergy.2017.08.027
Yilbas BS, Sahin AZ (2010) Thermoelectric device and optimum external load parameter and slenderness ratio. Energy 35(12):5380–5384. https://doi.org/10.1016/j.energy.2010.07.019
Zhao Y, Wang S, Ge M, Li Y, Liang Z (2017) Analysis of thermoelectric generation characteristics of flue gas waste heat from natural gas boiler. Energy Convers Manag 148(2017):820–829
Zhu DC, Su CQ, Deng YD (2018) The influence of the inner topology of cooling units on the performance of automotive exhaust-based thermoelectric generators. J Electron Mater 47:3320–3329. https://doi.org/10.1007/s11664-017-5959-x
Funding
No funding received for this work.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors have not disclosed any competing interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Jaswanth, N., RaamDheep, G. Thermoelectric maximum power point tracking by artificial neural networks. Soft Comput 27, 4041–4050 (2023). https://doi.org/10.1007/s00500-023-07948-w
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-023-07948-w
Keywords
- SEPIC
- TEG
- MPPT
- ANN