Physics-Informed Neural Networks in Grid-Connected Inverters: A Review

Ekram Al Mahdouri, Said Al-Abri (), Hassan Yousef, Ibrahim Al-Naimi and Hussein Obeid
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Ekram Al Mahdouri: Department of Electrical and Computer Engineering, College of Engineering, Sultan Qaboos University, Muscat 123, Oman
Said Al-Abri: Department of Electrical and Computer Engineering, College of Engineering, Sultan Qaboos University, Muscat 123, Oman
Hassan Yousef: Department of Electrical and Computer Engineering, College of Engineering, Sultan Qaboos University, Muscat 123, Oman
Ibrahim Al-Naimi: Department of Electrical and Computer Engineering, College of Engineering, Sultan Qaboos University, Muscat 123, Oman
Hussein Obeid: Department of Mechanical and Industrial Engineering, College of Engineering, Sultan Qaboos University, Muscat 123, Oman

Energies, 2025, vol. 18, issue 20, 1-19

Abstract: Physics-Informed Neural Networks (PINNs) have emerged as a powerful tool for modeling and controlling complex energy systems by embedding physical laws into deep learning architectures. This review paper highlights the application of PINNs in grid-connected inverter systems (GCISs), categorizing them by key tasks: parameter estimation, state estimation, control strategies, fault diagnosis and detection, and system identification. Particular focus is given to the use of PINNs in enabling accurate parameter estimation for aging and degradation monitoring. Studies show that PINN-based approaches can outperform purely data-driven models and traditional methods in both computational efficiency and accuracy. However, challenges remain, mainly related to high training costs and limited uncertainty quantification. To address these, emerging strategies such as advanced PINN frameworks are explored. The paper also explores emerging solutions and outlines future research directions to support the integration of PINNs into practical inverter design and operation.

Keywords: physics-informed neural networks; data-driven modeling; grid-connected inverters; parameter estimation; state estimation; control strategies; fault detection and diagnosis; system identification; smart grid (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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