IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v215y2025ics1364032125002461.html
   My bibliography  Save this article

Artificial intelligence techniques framework in the design and optimisation phase of the doubly fed induction generator's power electronic converters: A review of current status and future trends

Author

Listed:
  • Behara, Ramesh Kumar
  • Saha, Akshay Kumar

Abstract

The advancement of power supply systems is necessary for experiencing growth, stability, technological progress, reliability, choice of design, and dynamic response. This study investigated the increasing utilisation of artificial intelligence (AI) and machine learning (ML) in renewable energy systems (RES), their diverse applications, and an outlook for the future direction of research in the field. These applications include the wind turbine (WT)-driven doubly fed induction generator (DFIG) arrangements, particularly grid-tied DFIG; power converter design efficiency; and digital twin systems and economics. The use of AI in grid-tied DFIG and its power converter design and optimisation is being fueled by an increasing amount of pertinent RES operational data sets, high-performance computer resources, improved AI tools, and advancements in predicting control. These factors enable an increasing return on investment for system owners and operators. Modern AI technologies are driven by four principal methodologies: expert systems, fuzzy logic systems, metaheuristic systems, and machine and deep learning. The value maximization of WT-driven DFIG systems will soon depend on an AI architecture that will synergize the power converter electronic device, the edge (DFIG system/array controllers), and the cloud (for AI training and ML support). Including ML capabilities in the DFIG array controllers will help enable continuous advancements in power generation optimisation and open up more advanced grid-interactive features. These controllers will gather real-time data for AI inference engines (perhaps based on predictive neural logic) and the device-level inverters and transport the data to the cloud for more sophisticated ML tasks. This review briefly discusses how the growing interest in AI/ML is utilised in power converter design and its optimisation to overcome the grid-tied wind turbine-driven DFIG availability and efficiency issues. It concludes with some of the benefits and challenges to its widespread use.

Suggested Citation

  • Behara, Ramesh Kumar & Saha, Akshay Kumar, 2025. "Artificial intelligence techniques framework in the design and optimisation phase of the doubly fed induction generator's power electronic converters: A review of current status and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 215(C).
  • Handle: RePEc:eee:rensus:v:215:y:2025:i:c:s1364032125002461
    DOI: 10.1016/j.rser.2025.115573
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032125002461
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.rser.2025.115573?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Ayrir, W. & Ourahou, M. & El Hassouni, B. & Haddi, A., 2020. "Direct torque control improvement of a variable speed DFIG based on a fuzzy inference system," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 167(C), pages 308-324.
    2. Li Wang & Jiguang Yue & Yongqing Su & Feng Lu & Qiang Sun, 2017. "A Novel Remaining Useful Life Prediction Approach for Superbuck Converter Circuits Based on Modified Grey Wolf Optimizer-Support Vector Regression," Energies, MDPI, vol. 10(4), pages 1-22, April.
    3. Dahlia Byles & Salman Mohagheghi, 2023. "Sustainable Power Grid Expansion: Life Cycle Assessment, Modeling Approaches, Challenges, and Opportunities," Sustainability, MDPI, vol. 15(11), pages 1-25, May.
    4. Pierre Hansen & Nenad Mladenović & Raca Todosijević & Saïd Hanafi, 2017. "Variable neighborhood search: basics and variants," EURO Journal on Computational Optimization, Springer;EURO - The Association of European Operational Research Societies, vol. 5(3), pages 423-454, September.
    5. Ramesh Kumar Behara & Akshay Kumar Saha, 2023. "Neural Network Predictive Control for Improved Reliability of Grid-Tied DFIG-Based Wind Energy System under the Three-Phase Fault Condition," Energies, MDPI, vol. 16(13), pages 1-47, June.
    6. Sungwoo Jo & Sunkyu Jung & Taemoon Roh, 2021. "Battery State-of-Health Estimation Using Machine Learning and Preprocessing with Relative State-of-Charge," Energies, MDPI, vol. 14(21), pages 1-16, November.
    7. Helena R. Lourenço & Olivier C. Martin & Thomas Stützle, 2010. "Iterated Local Search: Framework and Applications," International Series in Operations Research & Management Science, in: Michel Gendreau & Jean-Yves Potvin (ed.), Handbook of Metaheuristics, chapter 0, pages 363-397, Springer.
    8. Joachim Zietz, 2007. "Dynamic Programming: An Introduction by Example," The Journal of Economic Education, Taylor & Francis Journals, vol. 38(2), pages 165-186, April.
    9. Chen-Fu Chien & Stéphane Dauzère-Pérès & Woonghee Tim Huh & Young Jae Jang & James R. Morrison, 2020. "Artificial intelligence in manufacturing and logistics systems: algorithms, applications, and case studies," International Journal of Production Research, Taylor & Francis Journals, vol. 58(9), pages 2730-2731, May.
    10. Matej Žnidarec & Zvonimir Klaić & Damir Šljivac & Boris Dumnić, 2019. "Harmonic Distortion Prediction Model of a Grid-Tie Photovoltaic Inverter Using an Artificial Neural Network," Energies, MDPI, vol. 12(5), pages 1-19, February.
    11. Tielens, Pieter & Van Hertem, Dirk, 2016. "The relevance of inertia in power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 999-1009.
    12. Bo Pang & Hui Dai & Feng Li & Heng Nian, 2019. "Coordinated Control of RSC and GSC for DFIG System under Harmonically Distorted Grid Considering Inter-Harmonics," Energies, MDPI, vol. 13(1), pages 1-15, December.
    13. Ramesh Kumar Behara & Akshay Kumar Saha, 2022. "Artificial Intelligence Control System Applied in Smart Grid Integrated Doubly Fed Induction Generator-Based Wind Turbine: A Review," Energies, MDPI, vol. 15(17), pages 1-56, September.
    14. Gang Lei & Jianguo Zhu & Youguang Guo & Chengcheng Liu & Bo Ma, 2017. "A Review of Design Optimization Methods for Electrical Machines," Energies, MDPI, vol. 10(12), pages 1-31, November.
    15. Ramesh Kumar Behara & Akshay Kumar Saha, 2022. "Artificial Intelligence Methodologies in Smart Grid-Integrated Doubly Fed Induction Generator Design Optimization and Reliability Assessment: A Review," Energies, MDPI, vol. 15(19), pages 1-39, September.
    16. Kevin Leahy & Colm Gallagher & Peter O’Donovan & Dominic T. J. O’Sullivan, 2019. "Issues with Data Quality for Wind Turbine Condition Monitoring and Reliability Analyses," Energies, MDPI, vol. 12(2), pages 1-22, January.
    17. Rohit Gupta & Krishna Teerth Chaturvedi, 2023. "Adaptive Energy Management of Big Data Analytics in Smart Grids," Energies, MDPI, vol. 16(16), pages 1-19, August.
    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. Ramesh Kumar Behara & Akshay Kumar Saha, 2025. "Optimised Neural Network Model for Wind Turbine DFIG Converter Fault Diagnosis," Energies, MDPI, vol. 18(13), pages 1-31, June.
    2. Ramesh Kumar Behara & Akshay Kumar Saha, 2023. "Neural Network Predictive Control for Improved Reliability of Grid-Tied DFIG-Based Wind Energy System under the Three-Phase Fault Condition," Energies, MDPI, vol. 16(13), pages 1-47, June.
    3. Ramesh Kumar Behara & Akshay Kumar Saha, 2022. "Artificial Intelligence Methodologies in Smart Grid-Integrated Doubly Fed Induction Generator Design Optimization and Reliability Assessment: A Review," Energies, MDPI, vol. 15(19), pages 1-39, September.
    4. Cabrera-Tobar, Ana & Bullich-Massagué, Eduard & Aragüés-Peñalba, Mònica & Gomis-Bellmunt, Oriol, 2016. "Review of advanced grid requirements for the integration of large scale photovoltaic power plants in the transmission system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 971-987.
    5. Cuesta, Jokin & Leturiondo, Urko & Vidal, Yolanda & Pozo, Francesc, 2025. "A review of prognostics and health management techniques in wind energy," Reliability Engineering and System Safety, Elsevier, vol. 260(C).
    6. Guerra, K. & Haro, P. & Gutiérrez, R.E. & Gómez-Barea, A., 2022. "Facing the high share of variable renewable energy in the power system: Flexibility and stability requirements," Applied Energy, Elsevier, vol. 310(C).
    7. Pablo González-Inostroza & Claudia Rahmann & Ricardo Álvarez & Jannik Haas & Wolfgang Nowak & Christian Rehtanz, 2021. "The Role of Fast Frequency Response of Energy Storage Systems and Renewables for Ensuring Frequency Stability in Future Low-Inertia Power Systems," Sustainability, MDPI, vol. 13(10), pages 1-16, May.
    8. Nicolas Bernard & Linh Dang & Luc Moreau & Salvy Bourguet, 2022. "A Pre-Sizing Method for Salient Pole Synchronous Reluctance Machines with Loss Minimization Control for a Small Urban Electrical Vehicle Considering the Driving Cycle," Energies, MDPI, vol. 15(23), pages 1-19, December.
    9. Izquierdo, J. & Márquez, A. Crespo & Uribetxebarria, J. & Erguido, A., 2020. "On the importance of assessing the operational context impact on maintenance management for life cycle cost of wind energy projects," Renewable Energy, Elsevier, vol. 153(C), pages 1100-1110.
    10. Camila Correa-Jullian & Sergio Cofre-Martel & Gabriel San Martin & Enrique Lopez Droguett & Gustavo de Novaes Pires Leite & Alexandre Costa, 2022. "Exploring Quantum Machine Learning and Feature Reduction Techniques for Wind Turbine Pitch Fault Detection," Energies, MDPI, vol. 15(8), pages 1-29, April.
    11. Bogdanov, Dmitrii & Toktarova, Alla & Breyer, Christian, 2019. "Transition towards 100% renewable power and heat supply for energy intensive economies and severe continental climate conditions: Case for Kazakhstan," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    12. Chaimae Dardabi & Santiago Cóbreces Álvarez & Abdelouahed Djebli, 2025. "An Artificial-Neural-Network-Based Direct Power Control Approach for Doubly Fed Induction Generators in Wind Power Systems," Energies, MDPI, vol. 18(8), pages 1-23, April.
    13. Angel A. Juan & Helena Ramalhinho-Lourenço & Manuel Mateo & Quim Castellà & Barry B. Barrios, 2012. "ILS-ESP: An efficient, simple, and parameter-free algorithm for solving the permutation flow-shop problem," Economics Working Papers 1319, Department of Economics and Business, Universitat Pompeu Fabra.
    14. Daniele Linaro & Federico Bizzarri & Davide Giudice & Cosimo Pisani & Giorgio M. Giannuzzi & Samuele Grillo & Angelo M. Brambilla, 2023. "Continuous estimation of power system inertia using convolutional neural networks," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    15. Ester Vasta & Tommaso Scimone & Giovanni Nobile & Otto Eberhardt & Daniele Dugo & Massimiliano Maurizio De Benedetti & Luigi Lanuzza & Giuseppe Scarcella & Luca Patanè & Paolo Arena & Mario Cacciato, 2023. "Models for Battery Health Assessment: A Comparative Evaluation," Energies, MDPI, vol. 16(2), pages 1-34, January.
    16. Pan, Quan-Ke & Ruiz, Rubén, 2012. "Local search methods for the flowshop scheduling problem with flowtime minimization," European Journal of Operational Research, Elsevier, vol. 222(1), pages 31-43.
    17. Fernández-Guillamón, Ana & Gómez-Lázaro, Emilio & Muljadi, Eduard & Molina-García, Ángel, 2019. "Power systems with high renewable energy sources: A review of inertia and frequency control strategies over time," Renewable and Sustainable Energy Reviews, Elsevier, vol. 115(C).
    18. Albert Poulose & Soobae Kim, 2023. "Transient Stability Analysis and Enhancement Techniques of Renewable-Rich Power Grids," Energies, MDPI, vol. 16(5), pages 1-30, March.
    19. Kanwal, S. & Khan, B. & Ali, S.M. & Mehmood, C.A., 2018. "Gaussian process regression based inertia emulation and reserve estimation for grid interfaced photovoltaic system," Renewable Energy, Elsevier, vol. 126(C), pages 865-875.
    20. Bulhões, Teobaldo & Subramanian, Anand & Erdoğan, Güneş & Laporte, Gilbert, 2018. "The static bike relocation problem with multiple vehicles and visits," European Journal of Operational Research, Elsevier, vol. 264(2), pages 508-523.

    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:eee:rensus:v:215:y:2025:i:c:s1364032125002461. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600126/description#description .

    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.