An evaluation of a medium-power grid-Integrated Solar Photovoltaic System MPPT Energy Capturing Capability Using ANN

• Autorzy: Joddumahanthi Vijaychandra , Kakinada Vedaprakash , Bammidi Vanajakshi , Knypiński Łukasz

Identyfikatory

DOI

10.24425/ijet.2025.153630

• Języki publikacji: EN

Abstrakty

EN

Technologies centered around renewable energy are now feasible options for providing everyone with quick and dependable access to electricity. Solar energy, in which photovoltaic (PV) cells can convert directly into electricity, is one of the most efficient renewable energy sources. The sun irradiation and temperature are the two factors that affect how much powerphoto voltaic systems can produce. In order to increase power, maximum power point tracking techniques have been developed and applied to PV systems. The proposed model was trained on a total of 1000 datasets containing information on voltage, temperature, and sun irradiation. Training, validation, and testing are the three categories into which the data are divided. The Artificial Neural Network (ANN) model was compared with the classical method like the perturb and observe method (P&O).

Słowa kluczowe

EN

Artificial Neural Network; Maximum power point tracking; solar PV system; Perturb; Observe method; DC-DC converter

• Wydawca: Polish Academy of Sciences, Committee of Electronics and Telecommunication
• Czasopismo: International Journal of Electronics and Telecommunications
• Rocznik: 2025
• Tom: Vol. 71, No. 3
• Strony: 24
• Opis fizyczny: Bibliogr. 13 poz., tab., rys.

Twórcy

autor

Joddumahanthi Vijaychandra

• Lendi Institute of Engineering and Technology, Vizianagaram, AP, India

autor

Kakinada Vedaprakash

• Lendi Institute of Engineering and Technology, Vizianagaram, AP, India

autor

Bammidi Vanajakshi

• Lendi Institute of Engineering and Technology, Vizianagaram, AP, India

autor

Knypiński Łukasz

• Poznan University of Technology

• https://orcid.org/0000-0001-5741-9548

Bibliografia

• [1] T. V. Dixit, A. Yadav and S. Gupta, “Experimental assessment of maximum power extraction from solar panel with different converter topologies”, Int. Trans. Electr. Energy Syst., vol. 29, no. 2, pp. 1-33, 2019, https://doi.org/10.1002/etep.2712
• [2] P. Neves, D. Gonçalves, J. G. Pinto, R. Alves and J. L. Afonso, “Single-phase shunt active filter interfacing renewable energy sources with the power grid”, IECON Proc. (Industrial Electron. Conf., pp. 3264-3269, 2009, https://doi.org/10.1109/IECON.2009.5415208
• [3] B. Tarroja, B. Shaffer and S. Samuelsen, “The importance of grid integration for achievable greenhouse gas emissions reductions from alternative vehicle technologies”, Energy, vol. 87, pp. 504-519, 2015, https://doi.org/10.1016/j.energy.2015.05.012
• [4] J. Khanam and F. Sy, “Modeling of a Photovoltaic Array in MATLAB Simulink and Maximum Power Point Tracking Using Neural Network”, J. Electr. Electron. Syst., vol. 07, no. 03, pp. 1-8, 2018, https://doi.org/10.4172/2332-0796.1000263
• [5] C. H. Basha and C. Rani, “Different conventional and soft computing MPPT techniques for solar PV systems with high step-up boost converters: A comprehensive analysis”, Energies, vol. 13, no. 2, 2020, https://doi.org/10.3390/en13020371
• [6] M. Khatun Mishu, M. Rokonuzzaman, J. Pasupuleti, M. Shakeri, K. Sajedur Rahman, S. Binzaid, S. Kiong Tiong and N. Amin “An Adaptive TE-PV Hybrid Energy Harvesting System for Self-Powered IoT Sensor Applications”, Sensors, vol. 21, no. 8, pp. 1-21, 2021, https://doi.org/10.3390/s21082604
• [7] P. Sivakumar, A. Abdul Kader, Y. Kaliavaradhan and M. Arutchelvi, “Analysis and enhancement of PV efficiency with incremental conductance MPPT technique under non-linear loading conditions”, Renew. Energy, vol. 81, pp. 543-550, 2015, https://doi.org/10.1016/j.renene.2015.03.062
• [8] N. Karami, N. Moubayed and R. Outbib, “General review and classification of different MPPT Techniques”, Renew. Sustain. Energy Rev., vol. 68, 2015, pp. 1-18, 2017, https://doi.org/10.1016/j.rser.2016.09.132
• [9] M. G. De Giorgi, P. M. Congedo and M. Malvoni, “Photovoltaic power forecasting using statistical methods: Impact of weather data”, IET Sci. Meas. Technol., vol. 8, no. 3, pp. 90-97, 2014, https://doi.org/10.1049/iet-smt.2013.0135
• [10] L. Chen and X. Wang, “Enhanced MPPT method based on ANN-assisted sequential Monte-Carlo and quickest change detection”, IET Smart Grid, vol. 2, no. 4, pp. 635-644, 2019, https://doi.org/10.1049/iet-stg.2019.0012
• [11] I. Chtouki, P. Wira, and M. Zazi, “Comparison of several neural network perturb and observe MPPT methods for photovoltaic applications”, Proc. IEEE Int. Conf. Ind. Technol., vol. 2018-Febru, pp. 909-914, 2018, https://doi.org/10.1109/ICIT.2018.8352299
• [12] C. Vimalarani, N. Kamaraj and B. Chitti Babu, “Improved method of maximum power point tracking of photovoltaic (PV) array using hybrid intelligent controller”, Optik, vol. 168, no. 2010, pp. 403-415, 2018, https://doi.org/10.1016/j.ijleo.2018.04.114
• [13] M. Khatun Mishu, M. Rokonuzzaman, J. Pasupuleti, M. Shakeri, K. Sajedur Rahman, F. Azlee Hamid, S. Kiong Tiong, N. Amin, “Prospective efficient ambient energy harvesting sources for IOT-equipped sensor applications”, Electron., vol. 9, no. 9, pp. 1-22, 2020, https://doi.org/10.3390/electronics9091345.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).