IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i7p1843-d1628803.html
   My bibliography  Save this article

Wind Power Plant Expert System Diagnostic Knowledge Base Creation

Author

Listed:
  • Radosław Duer

    (Independent Researcher, Koszalin, 75-620 Koszalin, Poland)

  • Stanisław Duer

    (Department of Energy, Faculty of Mechanical Engineering and Power Engineering, Technical University of Koszalin, 5-17 Raclawicka St., 75-620 Koszalin, Poland)

  • Konrad Zajkowski

    (Department of Energy, Faculty of Mechanical Engineering and Power Engineering, Technical University of Koszalin, 5-17 Raclawicka St., 75-620 Koszalin, Poland)

  • Marek Woźniak

    (Doctoral School, Technical University of Koszalin, 2 Sniadeckich St., 75-620 Koszalin, Poland)

  • Dariusz Bernatowicz

    (Faculty of Electronic and Informatics, Technical University of Koszalin, 2 Sniadeckich St., 75-620 Koszalin, Poland)

  • Jacek Paś

    (Faculty of Electronic, Military University of Technology of Warsaw, 2 Urbanowicza St., 00-908 Warsaw, Poland)

  • Marek Stawowy

    (Department of Transport Telecommunication, Faculty of Transport, Warsaw University of Technology, Koszykowa St. 75, 00-662 Warsaw, Poland)

  • Atif Iqbal

    (School of Mechanical Engineering, Hangzhou City University, Hangzhou 310015, China
    School of Mechanical and Energy Engineering, Zhejiang University of Science and Technology, Hangzhou 310012, China
    School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Marta Harničárová

    (Department of Mechanical Engineering, Faculty of Technology, Institute of Technology and Business in České Budějovice, Okružní 10, 370 01 České Budějovice, Czech Republic
    Faculty of Engineering, Institute of Electrical Engineering, Automation, Informatics and Physics, Slovak University of Agriculture in Nitra, Tr. A. Hlinku 2, 949 76 Nitra, Slovakia)

Abstract

Under certain weather circumstances, wind farms might lose their operational integrity. The study describes the process for building a WPP (Wind Power Plant) expert knowledge base for the WPPES system. The article presents the creation of an expert knowledge base on wind farm equipment that is the basis for the functioning of an intelligent expert system. For this purpose, a functional and diagnostic model of wind farm equipment was presented and described. Functional diagnostic models of objects are the basis for obtaining diagnostic information about the study object (set of facts). The operating conditions of the wind farm equipment and their surroundings have been characterized and described. On this basis, admissible and boundary conditions for the functioning of the tested technical object were determined. The above information specifically supplements the diagnostic information set when building the set of facts and rules. An important part of the article is to describe the principles and conditions for developing a set of diagnostic rules for the knowledge base of an expert system diagnosing wind farm equipment. Building expert knowledge bases is an extremely complex process of transforming diagnostic object information sets into the form of knowledge that is required by an expert system. To this end, an analytical relationship was developed and described as the basis for building a set of inference rules for the examined object. The effectiveness of the developed expert knowledge base is presented in the aspect of its introduction to the expert system knowledge module.

Suggested Citation

  • Radosław Duer & Stanisław Duer & Konrad Zajkowski & Marek Woźniak & Dariusz Bernatowicz & Jacek Paś & Marek Stawowy & Atif Iqbal & Marta Harničárová, 2025. "Wind Power Plant Expert System Diagnostic Knowledge Base Creation," Energies, MDPI, vol. 18(7), pages 1-20, April.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:7:p:1843-:d:1628803
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/7/1843/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/7/1843/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Toshio Nakagawa, 2005. "Maintenance Theory of Reliability," Springer Series in Reliability Engineering, Springer, number 978-1-84628-221-8, April.
    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. Hashemi, M. & Asadi, M. & Zarezadeh, S., 2020. "Optimal maintenance policies for coherent systems with multi-type components," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    2. Chin-Chih Chang, 2023. "Optimal maintenance policy for a k-out-of-n system with replacement first and last," Annals of Operations Research, Springer, vol. 323(1), pages 31-43, April.
    3. Finkelstein, Maxim & Cha, Ji Hwan & Langston, Amy, 2023. "Improving classical optimal age-replacement policies for degrading items," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    4. Ali, Sajid & Pievatolo, Antonio, 2018. "Time and magnitude monitoring based on the renewal reward process," Reliability Engineering and System Safety, Elsevier, vol. 179(C), pages 97-107.
    5. Torrado, Nuria, 2022. "Optimal component-type allocation and replacement time policies for parallel systems having multi-types dependent components," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    6. Ji Hwan Cha & Maxim Finkelstein, 2020. "On optimal life extension for degrading systems," Journal of Risk and Reliability, , vol. 234(3), pages 487-495, June.
    7. Safaei, Fatemeh & Taghipour, Sharareh, 2024. "Integrated degradation-based burn-in and maintenance model for heterogeneous and highly reliable items," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    8. Zheng, Junjun & Okamura, Hiroyuki & Dohi, Tadashi, 2021. "Age replacement with Markovian opportunity process," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    9. Zhao, Xufeng & Qian, Cunhua & Nakagawa, Toshio, 2013. "Optimal policies for cumulative damage models with maintenance last and first," Reliability Engineering and System Safety, Elsevier, vol. 110(C), pages 50-59.
    10. Cha, Ji Hwan & Finkelstein, Maxim, 2024. "Preventive maintenance for the constrained multi-attempt minimal repair," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    11. M D Pandey & T Cheng & J A M van der Weide, 2011. "Finite-time maintenance cost analysis of engineering systems affected by stochastic degradation," Journal of Risk and Reliability, , vol. 225(2), pages 241-250, June.
    12. Fu-Min Chang & Yu-Hung Chien, 2012. "Optimal Discrete-Time Periodic Replacement Policy For Repairable Products Under Free Minimal Repair Warranty," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 29(03), pages 1-14.
    13. Doostparast, Mohammad & Kolahan, Farhad & Doostparast, Mahdi, 2014. "A reliability-based approach to optimize preventive maintenance scheduling for coherent systems," Reliability Engineering and System Safety, Elsevier, vol. 126(C), pages 98-106.
    14. Abdolsaeed Toomaj & Antonio Di Crescenzo, 2020. "Connections between Weighted Generalized Cumulative Residual Entropy and Variance," Mathematics, MDPI, vol. 8(7), pages 1-27, July.
    15. Eryilmaz, Serkan & Ozkut, Murat, 2020. "Optimization problems for a parallel system with multiple types of dependent components," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    16. Hyunju Lee & Ji Hwan Cha & Maxim Finkelstein, 2022. "A Preventive Replacement Policy for a System Subject to Bivariate Generalized Polya Failure Process," Mathematics, MDPI, vol. 10(11), pages 1-15, May.
    17. Sheu, Shey-Huei & Liu, Tzu-Hsin & Zhang, Zhe-George & Tsai, Hsin-Nan, 2018. "The generalized age maintenance policies with random working times," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 503-514.
    18. Badía, F.G. & Berrade, M.D. & Cha, Ji Hwan & Lee, Hyunju, 2018. "Optimal replacement policy under a general failure and repair model: Minimal versus worse than old repair," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 362-372.
    19. Michael Patriksson & Ann-Brith Strömberg & Adam Wojciechowski, 2015. "The stochastic opportunistic replacement problem, part II: a two-stage solution approach," Annals of Operations Research, Springer, vol. 224(1), pages 51-75, January.
    20. Zhang, Qin & Fang, Zhigeng & Cai, Jiajia, 2021. "Preventive replacement policies with multiple missions and maintenance triggering approaches," Reliability Engineering and System Safety, Elsevier, vol. 213(C).

    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:7:p:1843-:d:1628803. 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 (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.