JCEEES
Journal of Computer & Electrical and Electronics Engineering Sciences

JCEEES aims to publish original articles covering the theoretical foundations of major computer, electronic and electrical engineering sciences, as well as academic, commercial and educational aspects that propose new ideas for the application and design of artificial intelligence, software and information systems. In addition to wide-ranging regular topics, JCEEES also makes it a principle to include special topics covering specific topics in all areas of interest mainly in computational medicine, artificial intelligence, computer science, and electrical & electronic engineering science.

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Original Article
Determination of I-V curves for photovoltaic generator by an analytical method; Akbaba model
Abstract
In order to maximize the amount of electrical energy generated and ensure effective use, the conventional I-V or V-I characteristics derived from the diode model of Photovoltaic (PV) Solar Panels are important. Unfortunately, these features cannot be extracted using linear equations. It follows that the answer requires extremely drawn-out, time-consuming procedures. Many studies focus on calculating maximum power extraction. The analytical solutions to these equations are also nonexistent. Programming on computers can assist in solving the equations. The Akbaba Model was created specifically for the PVG to address these challenges. The Akbaba Model relies heavily on coefficients A, B, and C in this equation. The diode model serves as the true model in this case, and the coefficients A, B, and C are calculated. Another advantage of this model is that, because its qualities have partially deteriorated with time, the genuine I-V characteristics of the PV Panels can be recovered by measuring again.

Keywords : Electrical power generation, maximum power, photovoltaic, renewable energy, solar panel

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1. NREL (National Renewable Energy Laboratory), “http://www.nrel.gov”.
2. Akbaba, M. (2006). Optimum matching parameters of an MPPT unit used for a PVG-powered water pumping system for maximum power transfer. Int J Energy Res, 30(6), 395-409.
3. Brambilla, A., Gambarra, M., Garutti, A., & Ronchi, F. Optimum matching parameters of an MPPT unit used for a PVG- powered water pumping system for maximum power transfer. In proceedings of the 30th Power Electronics Specialists Conference, Charleston, USA, 1999, vol. 2, pp. 632-637.
4. Kulaksiz, A.A., & Akkaya, R. (2012). Training data optimization for ANNs using genetic algorithms to enhance MPPT efficiency of a stand-alone PV system. Turk J Electr Engineer Comput Sci, 20(2),241-254.
5. Ali, A., Almutairi, K., Padmanaban, S., Tirth, V., Algarni, S., Irshad, K., ... & Malik, M. Z. (2020). Investigation of MPPT techniques under uniform and non-uniform solar irradiation condition-a retrospection. Ieee Access, 8,127368-127392.
6. Motahhir, S., El Hammoumi, A., & El Ghzizal, A. (2020). The most used MPPT algorithms: review and the suitable low-cost embedded board for each algorithm. J Clean Product, 246,118983.
7. Yang, B., Zhu, T., Wang, J., Shu, H., Yu, T., Zhang, X., ... & Sun, L. (2020). Comprehensive overview of maximum power point tracking algorithms of PV systems under partial shading condition. J Clean Product, 268,121983.
8. Shankar, N., & Saravana Kumar, N. (2020). Reduced partial shading effect in multiple PV array configuration model using MPPT based enhanced particle swarm optimization technique. Microprocessor Microsyst, 103287.
9. Karami, N., Moubayed, N., & Outbib, R. (2017). General review and classification of different MPPT Techniques. Renewabl Sustainabl Energy Rev, 68,1-18.
10. Rezk, H., Fathy, A., & Abdelaziz, A.Y. (2017). A comparison of different global MPPT techniques based on meta-heuristic algorithms for photovoltaic system subjected to partial shading conditions. Renewabl Sustainabl Energy Rev, 74,377-386.
11. Hasanah, R.N., Setyawan, A.B., Maulana, E., Nurwati, T., & Taufik, T. (2020). Computer-based solar tracking system for PV energy yield improvement. Int J Power Electr Drive Syst, 11(2),743.
12. Zegrar, M., Benmessaoud, M.T., & Zerhouni, F.Z. (2021). Design and implementation of an IV curvetracer dedicated to characterize PV panels. Int J Electr Comput Engineer, 11(3),2011.
13. De Riso, M., Matacena, I., Guerriero, P., & Daliento, S. (2024). A wireless self-powered I-V curve tracer for on-line characterization of individual PV panels. IEEE Transact Industr Electr, 71(9),11508-11518.
14. Akbaba, M. (2007). Matching induction motors to PVG for maximum power transfer. Desalination, 209(1-3),31-38.
15. Türk, F. (2024). Investigation of machine learning algorithms on heart disease through dominant feature detection and feature selection. Signal Imag Video Proces, 18(4),3943-3955.
16. Turk, F. (2024). RNGU-NET: a novel efficient approach in segmenting tuberculosis using chest X-Ray images. Peer J Comput Sci, 10,e1780.
17. Voutsinas, S., Karolidis, D., Voyiatzis, I., & Samarakou, M. (2023). Development of a machine-learning-based method for early fault detection in photovoltaic systems. J Engineer App Sci, 70(1),27.
18. González, I., Portalo, J.M., & Calderón, A.J. (2021). Configurable IoT open-source hardware and software IV curve tracer for photovoltaic generators. Sensors, 21(22),7650.
19. Sardar, R.H., Bera, A., Chattopadhyay, S., Ali, S.I., Pramanik, S., & Mandal, A.C. (2024). The impact of series (Rs) and shunt resistances (Rsh) on solar cell parameters to enhance the photovoltaic performance of f-PSCs. Optical Materials, 155,115818.
20. Hocine, L., Samira, K.M., Tarek, M., Salah, N., & Samia, K. (2021). Automatic detection of faults in a photovoltaic power plant based on the observation of degradation indicators. Renewable Energy, 164,603-617.
21. Oliva, D., Cuevas, E., & Pajares, G. (2014). Parameter identification of solar cells using artificial bee colony optimization. Energy, 72,93-102.
22. Appelbaum, J., & Sarma, M.S. (1989). The operation of permanent magnet DC motors powered by a common source of solar cells. IEEE Transact Energy Convers, 4(4),635-642.
23. Saied, M.M. (1988). Matching of DC motors to photovoltaic generators for maximum daily gross mechanical energy. IEEE Transact Energy Convers, 3(3),465-472.
24. López-Lapeña, O., Penella, M.T., & Gasulla, M. (2009). A new MPPT method for low-power solar energy harvesting. IEEE Transact Industr Electr, 57(9),3129-3138.
25. Akbaba, M., & Alattawi, M.A. (1995). A new model for I-V characteristic of solar cell generators and its applications. Solar Energy Mater Solar Cells, 37(2),123-132.
26. Goksenli, N., & Akbaba, M. (2016). Development of a new microcontroller based MPPT method for photovoltaic generators using Akbaba Model with implementation and simulation. Solar Energy, 136,622-628.
Article available in :
Volume 2, Issue 2, 2024
Page : 52-55
https://doi.org/10.51271/JCEEES-0018
Article Information
Received : 29 Jul 2024
Accepted : 07 Oct 2024
Published : 29 Oct 2024
Corresponding Author :

Nurettin Gökşenli: Department of Electronics and Automation, Faculty of Engineering, Çankırı Karatekin University, Çankırı, Turkiye

[email protected]
Citation : Gökşenli, N., Bektaş, E., & Tümay, M. (2024) Determination of I-V curves for photovoltaic generator by an analytical method: Akbaba Model. J Comp Electr Electron Eng Sci, 2(2), 52-55.

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