Simulation of Load Behavior Based on Perturb-Observation Method in Non-Isolated Boost Converter for Maximum Power Point Tracking of Thermoelectric Generators

Yıl 2023, , 70 - 77, 31.03.2023

Hayati Mamur , Çiğdem Akyıldız Mehmet Ali Üstüner

https://doi.org/10.30516/bilgesci.1201697

Öz

The efficiency of thermoelectric generators (TEGs) is quite low. To operate the TEGs at the maximum power point (MPP), the internal resistance of the connected load and the TEG must be equal. This is not always possible. For this, converters containing the maximum power point tracking (MPPT) algorithm tracking MPP are placed between the TEG and the load. These converters cannot perform MPPT on every connected load value. The aim of this study is to investigate at which load values MPPT can be performed in non-isolated boost converters by using perturb & observation (P&O) method. For this purpose, a 50 W converter was designed with a 45.76 W TEG in MATLAB/Simulink environment. Load resistance value has been increased starting from the minimum value up to 5.84 ohm being the internal resistance value of the TEG. For this case, the amount of error in MPPT was large up to the internal resistance value of the TEG. In other words, the P&O algorithm could not perform MPPT. When the load resistance value started from 5.84 ohms and increased to larger values, MPPT could be performed by means of the non-isolated boost converter with the P&O algorithm.

Anahtar Kelimeler

Thermoelectric generator, MPPT, Boost converter, Load limit

Destekleyen Kurum

Manisa Celal Bayar University

Proje Numarası

2022-027

Teşekkür

This work was supported by Research Project Coordination Unit of The Manisa Celal Bayar University (Project Number 2022-027). In addition, this study was published in full text at the International Conference on Science and Technology (ICONST 2022, 7-9 September, Budva, Montenegro).

Kaynakça

• Ahmad, M. E., Numan, A. H., Mahmood, D. Y. (2022). A comparative study of perturb and observe (P&O) and incremental conductance (INC) PV MPPT techniques at different radiation and temperature conditions. Engineering and Technology Journal, 40(02), 376-385. http://doi.org/10.30684/etj.v40i2.2189
• Al-Diab, A., Sourkounis, C. (2010, May). Variable step size P&O MPPT algorithm for PV systems. In 2010 12th International conference on optimization of electrical and electronic equipment (pp. 1097-1102). IEEE. http://doi.org/10.1109/OPTIM.2010.5510441
• Armin Razmjoo, A., Sumper, A., Davarpanah, A. (2020). Energy sustainability analysis based on SDGs for developing countries. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 42(9), 1041-1056. http://doi.org/10.1080/15567036.2019.1602215
• Attar, A., Lee, H., Snyder, G. J. (2020). Optimum load resistance for a thermoelectric generator system. Energy Conversion and Management, 226, 113490. http://doi.org/10.1016/j.enconman.2020.113490
• Benhadouga, S., Meddad, M., Eddiai, A., Boukhetala, D., Khenfer, R. (2019). Sliding Mode Control for MPPT of a Thermogenerator. Journal of Electronic Materials, 48, 2103-2111. https://doi.org/10.1007/s11664-019-06997-y
• Bijukumar, B., Raam, A. G. K., Ganesan, S. I., Nagamani, C., Reddy, M. J. B. (2019). MPPT algorithm for thermoelectric generators based on parabolic extrapolation. IET Generation, Transmission & Distribution, 13(6), 821-828. http://doi.org/10.1049/iet-gtd.2017.2007
• Bhuiyan, M. R. A., Mamur, H., Üstüner, M. A., Dilmaç, Ö. F., (2022). Current and future trend opportunities of thermoelectric generator applications in waste heat recovery. Gazi University Journal of Science, 896-915. http://doi.org/10.35378/gujs.934901
• Bond, M., Park, J. D. (2015). Current-sensorless power estimation and MPPT implementation for thermoelectric generators. IEEE Transactions on Industrial Electronics, 62(9), 5539-5548. http://doi.org/10.1109/TIE.2015.2414393
• Dileep, G., Singh, S. N. (2017). Selection of non-isolated DC-DC converters for solar photovoltaic system. Renewable and Sustainable Energy Reviews, 76, 1230-1247. http://doi.org/10.1016/j.rser.2017.03.130
• Jouhara, H., Khordehgah, N., Almahmoud, S., Delpech, B., Chauhan, A., Tassou, S. A. (2018). Waste heat recovery technologies and applications. Thermal Science and Engineering Progress, 6, 268-289. http://doi.org/10.1016/j.tsep.2018.04.017
• Khan, M. K., Zafar, M. H., Mansoor, M., Mirza, A. F., Khan, U. A., & Khan, N. M. (2022). Green energy extraction for sustainable development: A novel MPPT technique for hybrid PV-TEG system. Sustainable Energy Technologies and Assessments, 53, 102388. https://doi.org/10.1016/j.seta.2022.102388
• Mamur, H., Ahiska, R. (2015). Application of a DC–DC boost converter with maximum power point tracking for low power thermoelectric generators. Energy conversion and management, 97, 265-272. http://doi.org/10.1016/j.enconman.2015.03.068
• Mamur, H., Coban, Y. (2020a). Detailed modeling of a thermoelectric generator for maximum power point tracking. Turkish Journal of Electrical Engineering & Computer Sciences, 28(1), 124-139. http://doi.org/10.3906/elk-1907-166
• Mamur, H., Çoban, Y. (2020b). Termoelektrik jeneratörler için alçaltan-yükselten çeviricili maksimum güç noktası takibi benzetimi. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 26(5), 916-926. http://doi.org/10.5505/pajes.2019.92488
• Mamur, H., Dilmaç, Ö. F., Begum, J., Bhuiyan, M. R. A. (2021). Thermoelectric generators act as renewable energy sources. Cleaner Materials, 2, 100030.
• Mamur, H., Üstüner, M. A. (2021) Improved perturb and observe maxımum power poınt trackıng method wıth thermoelectrıc generator model. International Scientific Conferance, Gabrova.
• Mamur, H., Üstüner, M. A., Bhuiyan, M. R. A., (2022). Future perspective and current situation of maximum power point tracking methods in thermoelectric generators. Sustainable Energy Technologies and Assessments, 50, 101824. http://doi.org/10.1016/j.clema.2021.100030
• Montecucco, A., Knox, A. R. (2014). Maximum power point tracking converter based on the open-circuit voltage method for thermoelectric generators. IEEE Transactions on Power Electronics, 30(2), 828-839. http://doi.org/10.1109/TPEL.2014.2313294
• Pilakkat, D., Kanthalakshmi, S. (2019). An improved P&O algorithm integrated with artificial bee colony for photovoltaic systems under partial shading conditions. Solar Energy, 178, 37-47. http://doi.org/10.1016/j.solener.2018.12.008
• Sarbu, I., Sebarchievici, C. (2018). A comprehensive review of thermal energy storage. Sustainability, 10(1), 191. http://doi.org/10.3390/su10010191
• Taghvaee, M. H., Radzi, M. A. M., Moosavain, S. M., Hizam, H., Marhaban, M. H., (2013). A current and future study on non-isolated DC–DC converters for photovoltaic applications. Renewable and Sustainable Energy Reviews, 17, 216-227. http://doi.org/10.1016/j.rser.2012.09.023
• Tsai, H. L., Lin, J. M. (2010). Model building and simulation of thermoelectric module using Matlab/Simulink. Journal of Electronic Materials, 39(9), 2105. http://doi.org/10.1007/s11664-009-0994-x
• Qasim, A. M., Alwan, T. N., PraveenKumar, S., Velkin, V. I., Agyekum, E. B. (2021). A New maximum power point tracking technique for thermoelectric generator modules. Inventions, 6(4), 88. http://doi.org/10.3390/inventions6040088
• Mamur, H., Çiğdem, A., Üstüner, M.A. (2022, Sep). Investigation of Load Dependent Behavior of Boost Converter with Perturb and Observe Maximum Power Point Tracking for Thermoelectric Generators, In International Conferences on Science and Technology Engineering Sciences and Technology (ICONST EST 2022).
• Zafar, M. H., Khan, N. M., Mansoor, M., & Khan, U. A. (2022). Towards green energy for sustainable development: Machine learning based MPPT approach for thermoelectric generator. Journal of Cleaner Production, 351, 131591. https://doi.org/10.1016/j.jclepro.2022.131591