ARTIFICIAL INTELLIGENCE APPLICATIONS FOR GRID-CONNECTED SOLAR INVERTERS

Authors

Utkirjon Ubaydullaev, Tashkent State Technical University. Address: University st. 2, 100095, Tashkent city, Republic of Uzbekistan. E-mail: utkir2005@gmail.com, Phone: +998-90-319-86-00;Follow
Sarvinoz Mirzaeva, Tashkent State Technical University. Address: University st. 2, 100095, Tashkent city, Republic of Uzbekistan. E-mail: sarvinozmirzaeva86@gmail.com, Phone: + 998-93-950-01-55;Follow
Hasan Mustafoev, Tashkent State Technical University. Address: University st. 2, 100095, Tashkent city, Republic of Uzbekistan. E-mail: hasanboymustafayev381@gmail.com, Phone: + 998-88-971-02-25.Follow

Abstract

The increasing global demand for renewable energy has highlighted the importance of grid-connected solar inverters in ensuring efficient and stable power conversion. However, challenges such as fluctuations in solar energy generation, grid disturbances, and power quality issues necessitate advanced control strategies. The integration of artificial intelligence (AI) into solar inverters presents a transformative solution, enhancing performance, adaptability, and reliability in real-world applications.

This review explores the role of AI techniques, including machine learning (ML), deep learning (DL), fuzzy logic, and reinforcement learning (RL), in optimizing key inverter functionalities such as maximum power point tracking (MPPT), fault detection, power quality enhancement, and grid synchronization. AI-driven MPPT algorithms significantly improve energy extraction efficiency, while deep learning-based fault detection methods enable early anomaly identification, reducing system failures and maintenance costs. Furthermore, fuzzy logic and RL enhance grid-inverter interactions, ensuring seamless synchronization and real-time adaptive control. AI-powered power quality management mitigates voltage fluctuations and harmonic distortions, maintaining compliance with grid standards.

Despite the evident benefits, challenges such as computational complexity, real-time implementation constraints, and data availability hinder widespread adoption. This review provides an in-depth analysis of AI applications in grid-connected solar inverters, discussing existing solutions, challenges, and future research directions. Emphasis is placed on the potential of AI in advancing intelligent and resilient renewable energy systems through emerging technologies such as edge computing, blockchain, digital twins, and IoT-enabled smart grids.

First Page

33

Last Page

39

References

1. Guerrero, J. M., Chandorkar, M., Lee, T.-L., & Loh, P. C. (2013). Advanced control architectures for intelligent microgrids—Part I: Decentralized and hierarchical control. IEEE Transactions on Industrial Electronics, 60(4), 1254–1262. https://doi.org/10.1109/TIE.2012.2194969.
2. Kurukuru, V. S. B., Haque, A., Khan, M. A., Sahoo, S., Malik, A., & Blaabjerg, F. (2021). A review on artificial intelligence applications for grid-connected solar photovoltaic systems. Energies, 14(15), 4690. https://doi.org/10.3390/en14154690.
3. Mahendravarman, I., Elankurusil, S., Ragavendiran, A. (2022). Artificial intelligent based grid-connected PV system using cascaded multilevel inverter. 17(4).
4. Alam, M. M., Hossain, M. J., Habib, M. A., Arafat, M. Y., Hannan, M. A. (2025). Artificial intelligence integrated grid systems: Technologies, potential frameworks, challenges, and research directions. Renewable and Sustainable Energy Reviews, 211, 115251. https://doi.org/10.1016/j.rser.2024.115251.
5. Sreedhar, R., Karunanithi, K., & Ramesh, S. (2024). Design, implementation and empirical analysis of a cascaded hybrid MPPT controller for grid tied solar photovoltaic systems under partial shaded conditions. Measurement: Sensors, 31, 100961. https://doi.org/10.1016/j.measen.2023.100961.
6. Abdelshafy, A. M., Jurasz, J., Hassan, H., Mohamed, A. M. (2020). Optimized energy management strategy for grid connected double storage (pumped storage-battery) system powered by renewable energy resources. Energy, 192, 116615. https://doi.org/10.1016/j.energy.2019.116615.
7. Guerrero, J. M., Loh, P. C., Lee, T.-L., & Chandorkar, M. (2013). Advanced control architectures for intelligent microgrids. Part II: Power quality, energy storage, and AC/DC microgrids. IEEE Transactions on Industrial Electronics, 60(4), 1263-1270. https://doi.org/10.1109/TIE.2012.2196889.
8. Azghandi, M. A., Barakati, S. M., Wu, B. (2008). Dynamic modeling and control of grid-connected photovoltaic systems based on amplitude-phase transformation.
9. Puttgen, H. B., MacGregor, P. R., & Lambert, F. C. (2003). Distributed generation: Semantic hype or the dawn of a new era? IEEE Power and Energy Magazine, 1(1), 22-29. https://doi.org/10.1109/MPAE.2003.1180357.
10. Khatib, T., Mohamed, A., Sopian, K. (2013). A review of photovoltaic systems size optimization techniques. Renewable and Sustainable Energy Reviews, 22, 454-465. https://doi.org/10.1016/j.rser.2013.02.023.
11. Marweni, M., Fezai, R., Hajji, M., Mansouri, M. (2022, May). Deep learning for fault detection and diagnosis: Application to photovoltaic system. In 2022 19th International Multi-Conference on Systems, Signals & Devices (SSD). IEEE. 829-834. https://doi.org/10.1109/SSD54932.2022.9955680.
12. Ulzhayev, E., Ubaydullayev, U., & Abdulhamidov, A. (2024). Neyronnye texnologii raspoznavaniya i klassifikatsii stepeni raskrytiya xlopkovyx korobochek [Neural technologies for recognition and classification of cotton boll opening degree]. Al-Fargoniy Avlodlari, 1(1). https://doi.org/10.5281/ZENODO.10867098
13. Sabareesh, S.U., Aravind, K.S.N., Chowdary, K.B., Syama, S., Devi, K.V.S. (2023). LSTM based 24 hours ahead forecasting of solar PV system for standalone household system. Procedia Computer Science, 218, 1304-1313. https://doi.org/10.1016/j.procs.2023.01.109.
14. Wang, M.-H., Lin, Z.-H., Lu, S.-D. (2022). A fault detection method based on CNN and symmetrized dot pattern for PV modules. Energies, 15(17), 6449. https://doi.org/10.3390/en15176449.
15. Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353. https://doi.org/10.1016/S0019-9958(65)90241-X.
16. Mnih, V., et al. (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), 529-533. https://doi.org/10.1038/nature14236.
17. Elkholy, A.M., Taha, I.B.M., Kamel, S., El-Nemr, M.K. (2022). Grid synchronization enhancement of distributed generators using an adaptive phase-locked loop tuning system. Electronics, 11(5), 702. https://doi.org/10.3390/electronics11050702.
18. El-Habrouk, M., Darwish, M. K., Mehta, P. (2000). Active power filters: A review. IEE Proceedings - Electric Power Applications, 147(5), 403. https://doi.org/10.1049/ip-epa:20000522.
19. Famous, G., Igbinovia, F.O., Fandi, G., Svec, J., Muller, Z., Tlusty, J. (2015, May). Comparative review of reactive power compensation technologies. In 2015 16th International Scientific Conference on Electric Power Engineering (EPE). IEEE. 2-7. https://doi.org/10.1109/EPE.2015.7161066.
20. Duan, J., et al. (2020). Deep-reinforcement-learning-based autonomous voltage control for power grid operations. IEEE Transactions on Power Systems, 35(1), 814-817. https://doi.org/10.1109/TPWRS.2019.2941134.
21. Petrusev, A., Putratama, M. A., Rigo-Mariani, R., Debusschere, V., Reignier, P., Hadjsaid, N. (2023). Reinforcement learning for robust voltage control in distribution grids under uncertainties. Sustainable Energy, Grids and Networks, 33, 100959. https://doi.org/10.1016/j.segan.2022.100959.
22. Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61, 85-117. https://doi.org/10.1016/j.neunet.2014.09.003.
23. Matetić, I., Štajduhar, I., Wolf, I., & Ljubic, S. (2022). A review of data-driven approaches and techniques for fault detection and diagnosis in HVAC systems. Sensors, 23(1), 1. https://doi.org/10.3390/s23010001.
24. Strbac, G. (2008). Demand side management: Benefits and challenges. Energy Policy, 36(12), 4419-4426. https://doi.org/10.1016/j.enpol.2008.09.030.
25. Ulzhayev, E., Ubaydullayev, U. M., & Ulzhayev, Z. E. (2014). Electronic on-board control system of operational and technological parameters of cotton harvesting units. Traktory i selkhozmashiny, 81(10), 11-14. https://doi.org/10.17816/0321-4443-65490.

Recommended Citation

Ubaydullaev, Utkirjon; Mirzaeva, Sarvinoz; and Mustafoev, Hasan (2025) "ARTIFICIAL INTELLIGENCE APPLICATIONS FOR GRID-CONNECTED SOLAR INVERTERS," Chemical Technology, Control and Management: Vol. 2025: Iss. 2, Article 5.
DOI: https://doi.org/10.59048/2181-1105.1661