Method for Monitoring and Identifying PV (Photovoltaic) System Failures Using Machine Learning
DOI:
https://doi.org/10.59890/ijist.v1i5.686Keywords:
Artificial intelligence, Green energy supplies, Observing automated Intelligential, Sustainable power sources, ControlAbstract
Artificial intelligence techniques have been utilized to address intricate practical challenges in various domains and are gaining popularity in the contemporary era. The principal aim of this article is to assess the prediction of power generation in three distinct photovoltaic configurations and the surveillance of measurement sensors, employing artificial intelligence and data extraction, to conform to the behavior of environmental factors in the examined region. Additionally, it encompasses the incorporation of the resulting models into the SCADA system using benchmarks, allowing the operator to actively monitor the power grid. Furthermore, it provides a method for real-time simulation and anticipation of photovoltaic systems and measurement detector within the framework of intelligent system.
References
https://jeas.springeropen.com/articles/10.1186/s44147-023-00200-0
C. Voyant and et al, “Machine learning methods for solar radiation forecasting: A review,” Renewable Energy, vol. 105, May 2017. [Online]. Available: https://doi.org/10.1016/j.renene.2016.12.095 .
S. Theocharides, G. Makrides, G. E. Georghiou, and A. Kyprianou, “Machine learning algorithms for photovoltaic system power output prediction,” in 2018 IEEE International Energy Conference (ENERGYCON), Limassol, Cyprus, 2018.
C. Kurien and A. K. Srivastava, “Scope of artificial intelligence techniques for exhaust emission prediction of CI engines and renewable energy applications„” International Journal of Engineering Research in Computer Science and Engineering, vol. 5, no. 2, pp. 456–461, Feb 2018.
S. Preda, S. Vasilica, A. Bâra, and A. Belciu, “PV forecasting using support vector machine learning in a big data analytics context,” Symmetry, vol. 10, no. 12 December 2018. [Online]. Available: https://doi.org/10.3390/sym10120748