Fuzzy Logic-Based Degradation Fault Identification in Photovoltaic Arrays

  • Ainil Abidah Department of Electrical Engineering, Politeknik Negeri Bengkalis, Bengkalis, Riau, Indonesia
  • Jefri Lianda Department of Electrical Engineering, Politeknik Negeri Bengkalis, Bengkalis, Riau, Indonesia
  • Adam Adam Department of Electrical Engineering, Politeknik Negeri Bengkalis, Bengkalis, Riau, Indonesia
Keywords: Photovoltaic Array, Degradation Fault, Fuzzy Logic, Voltage, Temperature, Blynk

Abstract

The development of renewable energy technology, particularly photovoltaic (PV) systems, has increased due to the growing demand for environmentally friendly energy sources. However, PV performance may decrease due to degradation faults caused by changes in electrical characteristics and environmental conditions. This study aims to identify degradation faults in a photovoltaic array using the Fuzzy Logic method based on voltage and temperature parameters. The experimental data were collected using a sensor-based monitoring system integrated with the Blynk platform. The results demonstrate that the proposed Fuzzy Logic method can effectively classify PV operating conditions into normal, warning, and degradation fault categories. Therefore, the developed system can support real-time monitoring and improve the reliability of PV system maintenance

References

Mellit, A., & Kalogirou, S. A. (2008). Artificial intelligence techniques for photovoltaic applications: A review. Progress in Energy and Combustion Science, 34(5), 574–632. https://doi.org/10.1016/j.pecs.2008.01.001
Green, M. A., Dunlop, E. D., Hohl-Ebinger, J., Yoshita, M., Kopidakis, N., & Hao, X. (2024). Solar cell efficiency tables (Version 63). Progress in Photovoltaics: Research and Applications, 32(1), 3–13.
Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X
Madeti, S. R., & Singh, S. N. (2017). A comprehensive study on different types of faults and detection techniques for solar photovoltaic system. Solar Energy, 158, 161–185.
Silvestre, S., Chouder, A., Guasch, D., & Karatepe, E. (2014). New procedure for fault detection in grid-connected PV systems based on the evaluation of current and voltage indicators. Energy Conversion and Management, 86, 241–249.
Wohlgemuth, J. H. (2008). Long-term photovoltaic module reliability. National Renewable Energy Laboratory (NREL).
Dhimish, M., & Holmes, V. (2016). Fault detection algorithm for grid-connected photovoltaic plants. Solar Energy, 137, 236–245.
Skoczek, A., Sample, T., & Dunlop, E. D. (2009). The results of performance measurements of field-aged crystalline silicon photovoltaic modules. Progress in Photovoltaics: Research and Applications, 17(4), 227–240.
Jordan, D. C., & Kurtz, S. R. (2013). Photovoltaic degradation rates—An analytical review. Progress in Photovoltaics: Research and Applications, 21(1), 12–29.
Chine, W., Mellit, A., Pavan, A. M., & Kalogirou, S. A. (2016). Fault detection method for photovoltaic systems using artificial neural networks and fuzzy logic: A review. Renewable and Sustainable Energy Reviews, 59, 1114–1124
Published
2026-06-21
How to Cite
Abidah, A., Lianda, J., & Adam, A. (2026). Fuzzy Logic-Based Degradation Fault Identification in Photovoltaic Arrays. International Journal of Education, Vocational and Social Science , 5(03), 2250-2257. https://doi.org/10.63922/ijevss.v5i03.4418