A Simple Modeling of MPPT-based ANN for Photovoltaic System

Evi Nafiatus Sholikhah; Aulia Rahma Annisa; Muhammad Rizani Rusli; Mentari Putri Jati

doi:10.52435/complete.v6i1.684

Authors

Evi Nafiatus Sholikhah Department of Marine Engineering, Politeknik Perkapalan Negeri Surabaya, Surabaya, Indonesia
Aulia Rahma Annisa Department of Marine Electrical Engineering, Politeknik Perkapalan Negeri Surabaya, Surabaya, Indonesia
Muhammad Rizani Rusli Department of Electrical Engineering, Politeknik Elektronika Negeri Surabaya, Surabaya, Indonesia
Mentari Putri Jati Department of Electro-Optical Engineering, National Taipei University of Technology, Taipei City, Taiwan

DOI:

https://doi.org/10.52435/complete.v6i1.684

Keywords:

MPPT-based ANN, PV System, uniform irradiation

Abstract

This research describes a simple modeling technique for Maximum Power Point Tracking based on Artificial Neural Network (MPPT-based ANN) for photovoltaic (PV) systems. The proposed ANN model utilizes a feed-forward backpropagation architecture. The PV system was developed and tested in a simulation environment under uniform irradiation levels of 1000 W/m², 800 W/m², and 600 W/m², and rapidly varying irradiation changes. The simulation results demonstrate that the MPPT-based ANN accurately tracks the MPP, achieving stable power outputs of 98.36 W, 79 W, and 57.45 W, respectively. Although the system experiences initial transient oscillations during the tracking phase, it stabilizes within 80 milliseconds, showcasing rapid convergence and high steady-state accuracy. Under dynamic conditions, the MPPT-based ANN adapts effectively to fast-changing irradiation, restarting the algorithm to track and maintain the system at the updated MPP accurately. These results highlight the reliability, adaptability, and suitability of the MPPT-based ANN for real-time applications in dynamic environments. Nonetheless, further improvements to the ANN model are suggested to minimize transient oscillations and enhance overall performance.

References

R. J. Mustafa, M. R. Gomaa, M. Al-Dhaifallah, and H. Rezk, “Environmental Impacts on the Performance of Solar Photovoltaic Systems,” Sustainability, vol. 12, no. 2, p. 608, Jan. 2020, doi: 10.3390/su12020608.

K. Hasan, S. B. Yousuf, M. S. H. K. Tushar, B. K. Das, P. Das, and Md. S. Islam, “Effects of different environmental and operational factors on the PV performance: A comprehensive review,” Energy Sci. Eng., vol. 10, no. 2, pp. 656–675, Feb. 2022, doi: 10.1002/ese3.1043.

Moch. F. Fathul Yaqin, “Implementasi dan Optimalisasi Solar PV sebagai Sumber Tenaga Listrik untuk Memenuhi Beban Elektronik di Kampung Oase Ondomohen Surabaya,” J. Comput. Electron. Telecommun., vol. 4, no. 1, Aug. 2023, doi: 10.52435/complete.v4i1.370.

M. Mao, L. Cui, Q. Zhang, K. Guo, L. Zhou, and H. Huang, “Classification and summarization of solar photovoltaic MPPT techniques: A review based on traditional and intelligent control strategies,” Energy Rep., vol. 6, pp. 1312–1327, Nov. 2020, doi: 10.1016/j.egyr.2020.05.013.

B. Yang et al., “Comprehensive overview of maximum power point tracking algorithms of PV systems under partial shading condition,” J. Clean. Prod., vol. 268, p. 121983, Sep. 2020, doi: 10.1016/j.jclepro.2020.121983.

F. Belhachat and C. Larbes, “Comprehensive review on global maximum power point tracking techniques for PV systems subjected to partial shading conditions,” Sol. Energy, vol. 183, pp. 476–500, May 2019, doi: 10.1016/j.solener.2019.03.045.

R. B. Roy et al., “A Comparative Performance Analysis of ANN Algorithms for MPPT Energy Harvesting in Solar PV System,” IEEE Access, vol. 9, pp. 102137–102152, 2021, doi: 10.1109/ACCESS.2021.3096864.

M. Rasheed, O. Y. Mohammed, S. Shihab, and A. Al-Adili, “A comparative Analysis of PV Cell Mathematical Model,” J. Phys. Conf. Ser., vol. 1795, no. 1, p. 012042, Mar. 2021, doi: 10.1088/1742-6596/1795/1/012042.

D. W. Hart, Power electronics. New York: McGraw-Hill, 2011.

I. Achmad and A. T. Nugraha, “Implementasi Buck-Boost Converter pada Hybrid Turbin Angin Savonius dan Panel Surya,” J. Comput. Electron. Telecommun., vol. 3, no. 2, Dec. 2022, doi: 10.52435/complete.v3i2.192.

E. N. Sholikhah, M. N. Habibi, N. A. Windarko, and D. O. Anggriawan, “Abnormal Detection in Photovoltaic Array Based on Artificial Neural Network,” in 2020 10th Electrical Power, Electronics, Communications, Controls and Informatics Seminar (EECCIS), Malang, Indonesia: IEEE, Aug. 2020, pp. 59–64. doi: 10.1109/EECCIS49483.2020.9263457.

M. N. Habibi, N. Ayub Windarko, and A. Tjahjono, “Hybrid Maximum Power Point Tracking Using Artificial Neural Network-Incremental Conduction With Short Circuit Current of Solar Panel,” in 2019 International Electronics Symposium (IES), Surabaya, Indonesia: IEEE, Sep. 2019, pp. 63–69. doi: 10.1109/ELECSYM.2019.8901535.