IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i20p5441-d1772156.html

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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Ekram Al Mahdouri & Said Al-Abri & Hassan Yousef & Ibrahim Al-Naimi & Hussein Obeid, 2025. "Physics-Informed Neural Networks in Grid-Connected Inverters: A Review," Energies, MDPI, vol. 18(20), pages 1-19, October.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:20:p:5441-:d:1772156
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/20/5441/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/20/5441/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ngo, Quang-Ha & Nguyen, Bang L.H. & Vu, Tuyen V. & Zhang, Jianhua & Ngo, Tuan, 2024. "Physics-informed graphical neural network for power system state estimation," Applied Energy, Elsevier, vol. 358(C).
    2. Fahad M. Almasoudi, 2023. "Enhancing Power Grid Resilience through Real-Time Fault Detection and Remediation Using Advanced Hybrid Machine Learning Models," Sustainability, MDPI, vol. 15(10), pages 1-21, May.
    3. Subhash Lakshminarayana & Saurav Sthapit & Carsten Maple, 2022. "Application of Physics-Informed Machine Learning Techniques for Power Grid Parameter Estimation," Sustainability, MDPI, vol. 14(4), pages 1-14, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Qiao, Xinzhu & Liu, Renpeng & Xie, Ruoxi & Xie, Qiang, 2026. "Seismic resilience assessment and post-earthquake recovery strategy of substations based on maximum flow theory," Reliability Engineering and System Safety, Elsevier, vol. 267(PA).
    2. Chen, Dongyu & Lin, Xiaojie & Qiao, Yiyuan, 2025. "Perspectives for artificial intelligence in sustainable energy systems," Energy, Elsevier, vol. 318(C).
    3. Jian Tiong Lim & Achnaf Habibullah & Eddie Yin Kwee Ng, 2025. "Towards a Digital Twin for Gas Turbines: Thermodynamic Modeling, Critical Parameter Estimation, and Performance Optimization Using PINN and PSO," Energies, MDPI, vol. 18(14), pages 1-28, July.
    4. Ren, Chao & Han, Jie & Sun, Lin & Yang, Chunhua, 2025. "A deposition–removal-informed hybrid temporal model for online fouling estimation of industrial heat exchangers under parameter variability and nonstationarity," Energy, Elsevier, vol. 337(C).
    5. Ulaa AlHaddad & Abdullah Basuhail & Maher Khemakhem & Fathy Elbouraey Eassa & Kamal Jambi, 2023. "Towards Sustainable Energy Grids: A Machine Learning-Based Ensemble Methods Approach for Outages Estimation in Extreme Weather Events," Sustainability, MDPI, vol. 15(16), pages 1-19, August.
    6. Wu, Shutan & Wang, Qi & Hu, Jianxiong & Ye, Yujian & Tang, Yi, 2025. "Attack-resilient state estimation for cyber-physical power systems: A dynamic spatial-temporal redundancy reconfiguration framework for FDIA detection," Applied Energy, Elsevier, vol. 397(C).
    7. Sayed, Aya Nabil & Himeur, Yassine & Varlamis, Iraklis & Bensaali, Faycal, 2025. "Continual learning for energy management systems: A review of methods and applications, and a case study," Applied Energy, Elsevier, vol. 384(C).
    8. Zhang, Jinxian & Zhao, Junbo & Cheng, Gang & Rouhani, Alireza & Chen, Xiao, 2025. "Explainable multi-fidelity Bayesian neural network for distribution system state estimation," Applied Energy, Elsevier, vol. 392(C).
    9. Amer Farea & Olli Yli-Harja & Frank Emmert-Streib, 2025. "Using Physics-Informed Neural Networks for Modeling Biological and Epidemiological Dynamical Systems," Mathematics, MDPI, vol. 13(10), pages 1-23, May.
    10. Cambier van Nooten, Charlotte & van de Poll, Tom & Füllhase, Sonja & Heres, Jacco & Heskes, Tom & Shapovalova, Yuliya, 2025. "Graph neural networks for assessing the reliability of the medium-voltage grid," Applied Energy, Elsevier, vol. 384(C).
    11. Kamil Prokop & Andrzej Bień & Szymon Barczentewicz, 2023. "Compression Techniques for Real-Time Control and Non-Time-Critical Big Data in Smart Grids: A Review," Energies, MDPI, vol. 16(24), pages 1-26, December.
    12. Javadi, Amir Bahador & Pong, Philip, 2025. "A review on symbolic regression in power systems: Methods, applications, and future directions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 224(C).
    13. Dwivedi, Divyanshi & Mitikiri, Sagar Babu & Babu, K. Victor Sam Moses & Yemula, Pradeep Kumar & Srinivas, Vedantham Lakshmi & Chakraborty, Pratyush & Pal, Mayukha, 2024. "Technological advancements and innovations in enhancing resilience of electrical distribution systems," International Journal of Critical Infrastructure Protection, Elsevier, vol. 46(C).
    14. Ximing Zhang & Xiyuan Ma & Yun Yu & Duotong Yang & Zhida Lin & Changcheng Zhou & Huan Xu & Zhuohuan Li, 2025. "Model-Data Hybrid-Driven Real-Time Optimal Power Flow: A Physics-Informed Reinforcement Learning Approach," Energies, MDPI, vol. 18(13), pages 1-20, July.
    15. Zhang, Jiahao & Peng, Ruo & Lu, Chenbei & Wu, Chenye, 2025. "Computationally efficient data synthesis for AC-OPF: Integrating Physics-Informed Neural Network solvers and active learning," Applied Energy, Elsevier, vol. 378(PA).
    16. Yun Li & Tunan Chen & Jianzhao Liu & Zhaohua Hu & Yuchen Qi & Ye Guo, 2025. "An Interpretable Data-Driven Dynamic Operating Envelope Calculation Method Based on an Improved Deep Learning Model," Energies, MDPI, vol. 18(10), pages 1-16, May.
    17. Mubarak Alrumaidhi & Mohamed M. G. Farag & Hesham A. Rakha, 2023. "Comparative Analysis of Parametric and Non-Parametric Data-Driven Models to Predict Road Crash Severity among Elderly Drivers Using Synthetic Resampling Techniques," Sustainability, MDPI, vol. 15(13), pages 1-30, June.
    18. Shuaibo Wang & Xinyuan Xiang & Jie Zhang & Zhuohang Liang & Shufang Li & Peilin Zhong & Jie Zeng & Chenguang Wang, 2025. "A Multi-Task Spatiotemporal Graph Neural Network for Transient Stability and State Prediction in Power Systems," Energies, MDPI, vol. 18(6), pages 1-17, March.
    19. Abouyehia, Mohamed & Egea-Àlvarez, Agustí & Ahmed, Khaled H., 2025. "Evaluating inertia estimation methods in low-inertia power systems: A comprehensive review with analytic hierarchy process-based ranking," Renewable and Sustainable Energy Reviews, Elsevier, vol. 217(C).
    20. Li, Pengchao & Guo, Fang & Li, Yongfei & Yang, Xuejing & Yang, Xudong, 2025. "Physics-informed neural network for real-time thermal modeling of large-scale borehole thermal energy storage systems," Energy, Elsevier, vol. 315(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:18:y:2025:i:20:p:5441-:d:1772156. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager The email address of this maintainer does not seem to be valid anymore. Please ask MDPI Indexing Manager to update the entry or send us the correct address (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.