Machinery & Energetics

Mathematical modelling of solar power converters

Maksat Sadykov, Nazgul Temirbaeva, Maksat Narymbetov, Gulmira Shabikova, Ilyaz Turduev
Download article
Abstract

The study was conducted to develop a mathematical model of photovoltaic systems using artificial neural networks. A mathematical model of the photovoltaic system was established, a simulation process was implemented considering key parameters, and a maximum power point tracking method was applied to improve the efficiency of the system. In this research, a mathematical model of photovoltaic system based on artificial neural networks was developed and tested to improve the efficiency of solar panels. The model showed high accuracy in predicting the maximum power point under varying environmental conditions such as illumination and temperature. Analysis of the data demonstrated that the use of neural networks for maximum power point tracking significantly reduces energy losses compared to traditional tracking methods. Experimental results confirmed that the proposed approach provides more stable and accurate detection of the maximum power point in real time. The findings showed that the implementation of such a system could significantly improve the overall performance of PV plants, especially under erratic solar conditions, making it promising for applications in different climatic zones. In addition, the model was shown to be robust to changes in input data parameters, making it adaptive to different types of solar panels and operating conditions. It was also found that the neural network-based system reduces PV plant operation and maintenance costs by minimizing the need for manual calibration and monitoring. The resulting model will improve the accuracy and efficiency of maximum power tracking of solar panels under varying environmental conditions

Keywords

maximum power, illumination, temperature, prediction, neural networks

Suggested citation
Sadykov, M., Temirbaeva, N., Narymbetov, M., Shabikova, G., & Turduev, I. (2024). Mathematical modelling of solar power converters. Machinery & Energetics, 15(4), 118-135. https://doi.org/10.31548/machinery/4.2024.118
References

[1] Abouzeid, A.F., Eleraky, H., Kalas, A., Rizk, R., Elsakka, M.M., & Refaat, A. (2024). Experimental validation of a low-cost maximum power point tracking technique based on artificial neural network for photovoltaic systems. Scientific Reports, 14, article number 18280. doi: 10.1038/s41598-024-67306-0.

[2] Derakhshandeh, J.F., AlLuqman, R., Mohammad, S., AlHussain, H., AlHendi, G., AlEid, D., & Ahmad, Z. (2021). A comprehensive review of automatic cleaning systems of solar panels. Sustainable Energy Technologies and Assessments, 47, article number 101518. doi: 10.1016/j.seta.2021.101518.

[3] Elnozahy, A., Yousef, A.M., Abo-Elyousr, F.K., Mohamed, M., & Abdelwahab, S.A.M. (2021). Performance improvement of hybrid renewable energy sources connected to the grid using artificial neural network and sliding mode control. Journal of Power Electronics, 21, 1166-1179. doi: 10.1007/s43236-021-00242-8.

[4] Fatima, K., Minai, A.F., & Malik, H. (2022). Intelligent approach-based maximum power point tracking for renewable energy system: A review. In H. Malik, M.W. Ahmad & D. Kothari (Eds.), Intelligent data analytics for power and energy systems (pp. 373-405). Singapore: Springer. doi: 10.1007/978-981-16-6081-8_19.

[5] Ghadami, N., Gheibi, M., Kian, Z., Faramarz, M.G., Naghedi, R., Eftekhari, M., Fathollahi-Fard, A.M., Dulebenets, M.A., & Tian, G. (2021). Implementation of solar energy in smart cities using an integration of artificial neural network, photovoltaic system and classical Delphi methods. Sustainable Cities and Society, 74, article number 103149. doi: 10.1016/j.scs.2021.103149.

[6] González-Castaño, C., Restrepo, C., Kouro, S., & Rodriguez, J. (2021). MPPT algorithm based on artificial bee colony for PV system. IEEE Access, 9, 43121-43133. doi: 10.1109/ACCESS.2021.3066281.

[7] Gorjian, S., Calise, F., Kant, K., Ahamed, M.S., Copertaro, B., Najafi, G., Zhang, X., Aghaei, M., & Shamshiri, R.R. (2021). A review on opportunities for implementation of solar energy technologies in agricultural greenhouses. Journal of Cleaner Production, 285, article number 124807. doi: 10.1016/j.jclepro.2020.124807.

[8] Granata, F., & Di Nunno, F. (2021). Forecasting evapotranspiration in different climates using ensembles of recurrent neural networks. Agricultural Water Management, 255, article number 107040. doi: 10.1016/j.agwat.2021.107040.

[9] Hewitt, C.D., Guglielmo, F., Joussaume, S., Bessembinder, J., Christel, I., Doblas-Reyes, F.J., Djurdjevic, V., Garrett, N., Kjellström, E., Krzic, A., Costa, M.M., & St Clair, A.L. (2021). Recommendations for future research priorities for climate modeling and climate services. Bulletin of the American Meteorological Society, 102(3), E578-E588. doi: 10.1175/BAMS-D-20-0103.1.

[10] Jasiūnas, J., Lund, P.D., & Mikkola, J. (2021). Energy system resilience – a review. Renewable and Sustainable Energy Reviews, 150, article number 111476. doi: 10.1016/j.rser.2021.111476.

[11] Katche, M.L., Makokha, A.B., Zachary, S.O., & Adaramola, M.S. (2023). A comprehensive review of maximum power point tracking (MPPT) techniques used in solar PV systems. Energies, 16(5), article number 2206. doi: 10.3390/en16052206.

[12] Khan, S.U., Khan, N., Ullah, F.U.M., Kim, M.J., Lee, M.Y., & Baik, S.W. (2023). Towards intelligent building energy management: AI-based framework for power consumption and generation forecasting. Energy and Buildings, 279, article number 112705. doi: 10.1016/j.enbuild.2022.112705.

[13] Kiran, K.B.M., Indira, M.S., & S Nagaraja, R. (2021). Mathematical modeling and evaluation of performance characteristics of a hybrid solar PV and wind energy system. Journal of Applied Science and Engineering, 25(4), 785-797. doi: 10.6180/jase.202208_25(4).0014.

[14] Kudabayev, R., Mizamov, N., Zhangabay, N., Suleimenov, U., Kostikov, A., Vorontsova, A., Buganova, S., Umbitaliyev, A., Kalshabekovа, E., & Aldiyarov, Z. (2022). Construction of a model for an enclosing structure with a heat-accumulating material with phase transition taking into account the process of solar energy accumulation. Eastern-European Journal of Enterprise Technologies, 6(8-120), 26-37. doi: 10.15587/1729-4061.2022.268618.

[15] Li, G., Li, M., Taylor, R., Hao, Y., Besagni, G., & Markides, C.N. (2022). Solar energy utilisation: Current status and roll-out potential. Applied Thermal Engineering, 209, article number 118285. doi: 10.1016/j.applthermaleng.2022.118285.

[16] Mari, M.A., Memon, Z.A., Shaikh, P.H., Mirjat, N.H., & Soomro, M.I. (2021). A review study on mathematical modeling of solar parabolic dish‐Stirling system used for electricity generation. International Journal of Energy Research, 45(13), 18355-18391. doi: 10.1002/er.7000.

[17] Mellit, A., & Kalogirou, S. (2021). Artificial intelligence and internet of things to improve efficacy of diagnosis and remote sensing of solar photovoltaic systems: Challenges, recommendations and future directions. Renewable and Sustainable Energy Reviews, 143, article number 110889. doi: 10.1016/j.rser.2021.110889.

[18] Nabavi-Pelesaraei, A., Rafiee, S., Hosseini-Fashami, F., & Chau, K.W. (2021). Artificial neural networks and adaptive neuro-fuzzy inference system in energy modeling of agricultural products. In predictive modelling for energy management and power systems engineering (pp. 299-334). Amsterdam: Elsevier. doi: 10.1016/B978-0-12-817772-3.00011-2.

[19] Osmani, K., Haddad, A., Lemenand, T., Castanier, B., & Ramadan, M. (2021). An investigation on maximum power extraction algorithms from PV systems with corresponding DC-DC converters. Energy, 224, article number 120092. doi: 10.1016/j.energy.2021.120092.

[20] Pulvermüller, F., Tomasello, R., Henningsen-Schomers, M.R., & Wennekers, T. (2021). Biological constraints on neural network models of cognitive function. Nature Reviews Neuroscience, 22(8), 488-502. doi: 10.1038/s41583-021-00473-5.

[21] Qureshi, M.S., Umar, S., & Nawaz, M.U. (2024). Machine learning for predictive maintenance in solar farms. International Journal of Advanced Engineering Technologies and Innovations, 1(3), 27-49.

[22] Restrepo, C., Yanẽz-Monsalvez, N., González-Castaño, C., Kouro, S., & Rodriguez, J. (2021). A fast-converging hybrid MPPT algorithm based on ABC and P&O techniques for a partially shaded pv system. Mathematics, 9(18), article number 2228. doi: 10.3390/math9182228.

[23] Rexhaj, G. (2024). The role of Building Information Modelling in the implementation of sustainable, environmentally friendly, and social infrastructure projects. Architectural Studies, 10(1), 69-78. doi: 10.56318/as/1.2024.69.

[24] Roy, R.B., Rokonuzzaman, M., Amin, N., Mishu, M.K., Alahakoon, S., Rahman, S., Mithulananthan, N., Rahman, K.S., Shakeri, M., & Pasupuleti, J. (2021). A comparative performance analysis of ANN algorithms for MPPT energy harvesting in solar PV system. IEEE Access, 9, 102137-102152. doi: 10.1109/ACCESS.2021.3096864.

[25] Salem, A.A., Ismail, M.M., Zedan, H.A., & Elnaghi, B.E. (2024). Design of a perturb and observe and neural network algorithms-based maximum power point tracking for a gridconnected photovoltaic system. International Journal of Electrical & Computer Engineering, 14(4), article number 3674. doi: 10.11591/ijece.v14i4.pp3674-3687.

[26] Sarang, S.A., Raza, M.A., Panhwar, M., Khan, M., Abbas, G., Touti, E., Altamimi, A., & Wijaya, A.A. (2024). Maximizing solar power generation through conventional and digital MPPT techniques: A comparative analysis. Scientific Reports, 14, article number 8944. doi: 10.1038/s41598-024-59776-z.

[27] Shahini, E., Fedorchuk, M., Hruban, V., Fedorchuk, V., & Sadovoy, O. (2024). Renewable energy opportunities in Ukraine in the context of blackouts. International Journal of Environmental Studies, 81(1), 125-133. doi: 10.1080/00207233.2024.2320021.

[28] Sharma, P., Dhaked, D.K., & Sharma, A.K. (2021). Mathematical modeling and simulation of DC-DC converters using state-space approach. In D. Goyal, P. Chaturvedi, A.K. Nagar & S. Purohit (Eds.), Proceedings of second international conference on smart energy and communication: ICSEC 2020 (pp. 11-29). Singapore: Springer. doi: 10.1007/978-981-15-6707-0_2.

[28] Smyk, I., & Arkhypova, L. (2023). Analysis of influence of meteorological conditions on the efficiency of solar panels in Ivano-Frankivsk Region. Ecological Safety and Balanced Use of Resources, 14(1), 99-107. doi: 10.31471/2415-3184-2023-1(27)-99-107.

[29] Soomar, A.M., Hakeem, A., Messaoudi, M., Musznicki, P., Iqbal, A., & Czapp, S. (2022). Solar photovoltaic energy optimization and challenges. Frontiers in Energy Research, 10, article number 879985. doi: 10.3389/fenrg.2022.879985.

[30] Stoliarov, O. (2024). Big Data technologies in the process of forecasting electricity generation from solar photovoltaic power plants. Bulletin of Cherkasy State Technological University, 29(2), 79-92. doi: 10.62660/bcstu/2.2024.79.

[31] Szafraniec, A., Halko, S., Miroshnyk, O., Figura, R., Zharkov, A., & Vershkov, O. (2021). Magnetic field parameters mathematical modelling of windelectric heater. Przeglad Elektrotechniczny, 97(8), 36-41. doi: 10.15199/48.2021.08.07.

[32] Talaat, M., Said, T., Essa, M.A., & Hatata, A.Y. (2022). Integrated MFFNN-MVO approach for PV solar power forecasting considering thermal effects and environmental conditions. International Journal of Electrical Power & Energy Systems, 135, article number 107570. doi: 10.1016/j.ijepes.2021.107570.

[33] Villegas-Mier, C.G., Rodriguez-Resendiz, J., Álvarez-Alvarado, J.M., Rodriguez-Resendiz, H., Herrera-Navarro, A.M., & Rodríguez-Abreo, O. (2021). Artificial neural networks in MPPT algorithms for optimization of photovoltaic power systems: A review. Micromachines, 12(10), article number 1260. doi: 10.3390/mi12101260.

[34] Zafar, A., Aamir, M., Nawi, N.M., Arshad, A., Riaz, S., Alruban, A., Dutta, A.K., & Almotairi, S. (2022). A comparison of pooling methods for convolutional neural networks. Applied Sciences, 12(17), article number 8643. doi: 10.3390/app12178643.