IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i19p7317-d933904.html
   My bibliography  Save this article

Applying Intelligent Multi-Agents to Reduce False Alarms in Wind Turbine Monitoring Systems

Author

Listed:
  • Weldon Carlos Elias Teixeira

    (Coordination of Electrotechnology, Federal Institute of Pará, Marabá 68508-970, PA, Brazil)

  • Miguel Ángel Sanz-Bobi

    (Department of Telematics and Computer Science, Institute for Research in Technology (IIT), Comillas Pontifical University, 28015 Madrid, Spain)

  • Roberto Célio Limão de Oliveira

    (Institute of Technology, School of Electrical Engineering, Federal University of Pará, Belém 66075-110, PA, Brazil)

Abstract

This study proposes a method for improving the capability of a data-driven multi-agent system (MAS) to perform condition monitoring and fault detection in industrial processes. To mitigate the false fault-detection alarms, a co-operation strategy among software agents is proposed because it performs better than the individual agents. Few steps transform this method into a valuable procedure for improving diagnostic certainty. First, a failure mode and effects analysis are performed to select physical monitoring signals of the industrial process that allow agents to collaborate via shared signals. Next, several artificial neural network (ANN) models are generated based on the normal behavior operation conditions of various industrial subsystems equipped with monitoring sensors. Thereafter, the agents use the ANN-based expected behavior models to prevent false alarms by continuously monitoring the measurement samples of physical signals that deviate from normal behavior. Finally, this method is applied to a wind turbine. The system and tests use actual data from a wind farm in Spain. The results show that the collaboration among agents facilitates the effective detection of faults and can significantly reduce false alarms, indicating a notable advancement in the industrial maintenance and monitoring strategy.

Suggested Citation

  • Weldon Carlos Elias Teixeira & Miguel Ángel Sanz-Bobi & Roberto Célio Limão de Oliveira, 2022. "Applying Intelligent Multi-Agents to Reduce False Alarms in Wind Turbine Monitoring Systems," Energies, MDPI, vol. 15(19), pages 1-28, October.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:19:p:7317-:d:933904
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/19/7317/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/19/7317/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Li, He & Teixeira, Angelo P. & Guedes Soares, C., 2020. "A two-stage Failure Mode and Effect Analysis of offshore wind turbines," Renewable Energy, Elsevier, vol. 162(C), pages 1438-1461.
    2. Pinjia Zhang & Delong Lu, 2019. "A Survey of Condition Monitoring and Fault Diagnosis toward Integrated O&M for Wind Turbines," Energies, MDPI, vol. 12(14), pages 1-22, July.
    3. Rubert, T. & McMillan, D. & Niewczas, P., 2018. "A decision support tool to assist with lifetime extension of wind turbines," Renewable Energy, Elsevier, vol. 120(C), pages 423-433.
    4. Jun-Hyeok Kim & Jong-Man Joung & Byung-Sung Lee, 2022. "A Study on the Preprocessing Method for Power System Applications Based on Polynomial and Standard Patterns," Energies, MDPI, vol. 15(4), pages 1-12, 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. Han Peng & Songyin Li & Linjian Shangguan & Yisa Fan & Hai Zhang, 2023. "Analysis of Wind Turbine Equipment Failure and Intelligent Operation and Maintenance Research," Sustainability, MDPI, vol. 15(10), pages 1-35, May.
    2. Rubert, T. & Zorzi, G. & Fusiek, G. & Niewczas, P. & McMillan, D. & McAlorum, J. & Perry, M., 2019. "Wind turbine lifetime extension decision-making based on structural health monitoring," Renewable Energy, Elsevier, vol. 143(C), pages 611-621.
    3. Alessandro Murgia & Robbert Verbeke & Elena Tsiporkova & Ludovico Terzi & Davide Astolfi, 2023. "Discussion on the Suitability of SCADA-Based Condition Monitoring for Wind Turbine Fault Diagnosis through Temperature Data Analysis," Energies, MDPI, vol. 16(2), pages 1-20, January.
    4. Brooks, Sam & Mahmood, Minhal & Roy, Rajkumar & Manolesos, Marinos & Salonitis, Konstantinos, 2023. "Self-reconfiguration simulations of turbines to reduce uneven farm degradation," Renewable Energy, Elsevier, vol. 206(C), pages 1301-1314.
    5. Jannie Sønderkær Nielsen & Lindsay Miller-Branovacki & Rupp Carriveau, 2021. "Probabilistic and Risk-Informed Life Extension Assessment of Wind Turbine Structural Components," Energies, MDPI, vol. 14(4), pages 1-16, February.
    6. Luis M. Abadie & Nestor Goicoechea, 2021. "Old Wind Farm Life Extension vs. Full Repowering: A Review of Economic Issues and a Stochastic Application for Spain," Energies, MDPI, vol. 14(12), pages 1-24, June.
    7. Benjamin Pakenham & Anna Ermakova & Ali Mehmanparast, 2021. "A Review of Life Extension Strategies for Offshore Wind Farms Using Techno-Economic Assessments," Energies, MDPI, vol. 14(7), pages 1-23, March.
    8. Dominik McInnis & Massimiliano Capezzali, 2020. "Managing Wind Turbine Generators with a Profit Maximized Approach," Sustainability, MDPI, vol. 12(17), pages 1-16, September.
    9. Phong B. Dao, 2021. "A CUSUM-Based Approach for Condition Monitoring and Fault Diagnosis of Wind Turbines," Energies, MDPI, vol. 14(11), pages 1-19, June.
    10. Wilkie, David & Galasso, Carmine, 2020. "Impact of climate-change scenarios on offshore wind turbine structural performance," Renewable and Sustainable Energy Reviews, Elsevier, vol. 134(C).
    11. Jorge Maldonado-Correa & Sergio Martín-Martínez & Estefanía Artigao & Emilio Gómez-Lázaro, 2020. "Using SCADA Data for Wind Turbine Condition Monitoring: A Systematic Literature Review," Energies, MDPI, vol. 13(12), pages 1-21, June.
    12. Ruiwang Sun & Longfei Guan & Naizhe Diao, 2024. "rlaNet: A Residual Convolution Nested Long-Short-Term Memory Model with an Attention Mechanism for Wind Turbine Fault Diagnosis," Mathematics, MDPI, vol. 12(22), pages 1-17, November.
    13. Li, He & Diaz, H. & Guedes Soares, C., 2021. "A developed failure mode and effect analysis for floating offshore wind turbine support structures," Renewable Energy, Elsevier, vol. 164(C), pages 133-145.
    14. Leite, Gustavo de Novaes Pires & Weschenfelder, Franciele & Farias, João Gabriel de & Kamal Ahmad, Muhammad, 2022. "Economic and sensitivity analysis on wind farm end-of-life strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    15. de Simón-Martín, Miguel & Ciria-Garcés, Tomás & Rosales-Asensio, Enrique & González-Martínez, Alberto, 2022. "Multi-dimensional barrier identification for wind farm repowering in Spain through an expert judgment approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    16. Nicola Grieve & Abbas Mehrad Kazemi Amiri & William E. Leithead, 2022. "A Straightforward Approach to Site-Wide Assessment of Wind Turbine Tower Lifetime Extension Potential," Energies, MDPI, vol. 15(9), pages 1-30, May.
    17. Szumilas-Kowalczyk, H. & Pevzner, N. & Giedych, R., 2020. "Long-term visual impacts of aging infrastructure: Challenges of decommissioning wind power infrastructure and a survey of alternative strategies," Renewable Energy, Elsevier, vol. 150(C), pages 550-560.
    18. Jordi Burriel-Valencia & Ruben Puche-Panadero & Javier Martinez-Roman & Angel Sapena-Baño & Martin Riera-Guasp & Manuel Pineda-Sánchez, 2019. "Multi-Band Frequency Window for Time-Frequency Fault Diagnosis of Induction Machines," Energies, MDPI, vol. 12(17), pages 1-18, August.
    19. Sergen Tumse & Mehmet Bilgili & Alper Yildirim & Besir Sahin, 2024. "Comparative Analysis of Global Onshore and Offshore Wind Energy Characteristics and Potentials," Sustainability, MDPI, vol. 16(15), pages 1-28, August.
    20. Junshuai Yan & Yongqian Liu & Xiaoying Ren, 2023. "An Early Fault Detection Method for Wind Turbine Main Bearings Based on Self-Attention GRU Network and Binary Segmentation Changepoint Detection Algorithm," Energies, MDPI, vol. 16(10), pages 1-23, May.

    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:15:y:2022:i:19:p:7317-:d:933904. 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.