IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i20p6601-d655314.html
   My bibliography  Save this article

Investigation of Isolation Forest for Wind Turbine Pitch System Condition Monitoring Using SCADA Data

Author

Listed:
  • Conor McKinnon

    (Wind and Marine Energy Systems CDT, University of Strathclyde, Glasgow G1 1RD, UK
    Current address: Wind and Marine Energy Systems CDT, University of Strathclyde, Glasgow G1 1RD, UK.)

  • James Carroll

    (Wind and Marine Energy Systems CDT, University of Strathclyde, Glasgow G1 1RD, UK)

  • Alasdair McDonald

    (Wind and Marine Energy Systems CDT, University of Strathclyde, Glasgow G1 1RD, UK)

  • Sofia Koukoura

    (Wind and Marine Energy Systems CDT, University of Strathclyde, Glasgow G1 1RD, UK)

  • Charlie Plumley

    (Cubico Sustainable Investments, Harman House, 1 George St, Uxbridge UB8 1QQ, UK)

Abstract

Wind turbine pitch system condition monitoring is an active area of research, and this paper investigates the use of the Isolation Forest Machine Learning model and Supervisory Control and Data Acquisition system data for this task. This paper examines two case studies, turbines with hydraulic or electric pitch systems, and uses an Isolation Forest to predict failure ahead of time. This novel technique compared several models per turbine, each trained on a different number of months of data. An anomaly proportion for three different time-series window lengths was compared, to observe trends and peaks before failure. The two cases were compared, and it was found that this technique could detect abnormal activity roughly 12 to 18 months before failure for both the hydraulic and electric pitch systems for all unhealthy turbines, and a trend upwards in anomalies could be found in the immediate run up to failure. These peaks in anomalous behaviour could indicate a future failure and this would allow for on-site maintenance to be scheduled. Therefore, this method could improve scheduling planned maintenance activity for pitch systems, regardless of the pitch system employed.

Suggested Citation

  • Conor McKinnon & James Carroll & Alasdair McDonald & Sofia Koukoura & Charlie Plumley, 2021. "Investigation of Isolation Forest for Wind Turbine Pitch System Condition Monitoring Using SCADA Data," Energies, MDPI, vol. 14(20), pages 1-20, October.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:20:p:6601-:d:655314
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/20/6601/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/20/6601/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Giovanni Rinaldi & Philipp R. Thies & Lars Johanning, 2021. "Current Status and Future Trends in the Operation and Maintenance of Offshore Wind Turbines: A Review," Energies, MDPI, vol. 14(9), pages 1-28, April.
    2. Conor McKinnon & James Carroll & Alasdair McDonald & Sofia Koukoura & David Infield & Conaill Soraghan, 2020. "Comparison of New Anomaly Detection Technique for Wind Turbine Condition Monitoring Using Gearbox SCADA Data," Energies, MDPI, vol. 13(19), pages 1-19, October.
    3. Angel Gil & Miguel A. Sanz-Bobi & Miguel A. Rodríguez-López, 2018. "Behavior Anomaly Indicators Based on Reference Patterns—Application to the Gearbox and Electrical Generator of a Wind Turbine," Energies, MDPI, vol. 11(1), pages 1-15, January.
    4. Lin, Zhenjia & Chen, Haoyong & Wu, Qiuwei & Li, Weiwei & Li, Mengshi & Ji, Tianyao, 2020. "Mean-tracking model based stochastic economic dispatch for power systems with high penetration of wind power," Energy, Elsevier, vol. 193(C).
    5. Zhao, Hongshan & Liu, Huihai & Hu, Wenjing & Yan, Xihui, 2018. "Anomaly detection and fault analysis of wind turbine components based on deep learning network," Renewable Energy, Elsevier, vol. 127(C), pages 825-834.
    6. Cong Yang & Zheng Qian & Yan Pei & Lu Wei, 2018. "A Data-Driven Approach for Condition Monitoring of Wind Turbine Pitch Systems," Energies, MDPI, vol. 11(8), pages 1-17, August.
    7. Nielsen, Jannie Jessen & Sørensen, John Dalsgaard, 2011. "On risk-based operation and maintenance of offshore wind turbine components," Reliability Engineering and System Safety, Elsevier, vol. 96(1), pages 218-229.
    8. Marc-Alexander Lutz & Stephan Vogt & Volker Berkhout & Stefan Faulstich & Steffen Dienst & Urs Steinmetz & Christian Gück & Andres Ortega, 2020. "Evaluation of Anomaly Detection of an Autoencoder Based on Maintenace Information and Scada-Data," Energies, MDPI, vol. 13(5), pages 1-18, February.
    9. Davide Astolfi & Francesco Castellani & Andrea Lombardi & Ludovico Terzi, 2021. "Multivariate SCADA Data Analysis Methods for Real-World Wind Turbine Power Curve Monitoring," Energies, MDPI, vol. 14(4), pages 1-18, February.
    10. Pinar Pérez, Jesús María & García Márquez, Fausto Pedro & Tobias, Andrew & Papaelias, Mayorkinos, 2013. "Wind turbine reliability analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 23(C), pages 463-472.
    11. Cho, Seongpil & Choi, Minjoo & Gao, Zhen & Moan, Torgeir, 2021. "Fault detection and diagnosis of a blade pitch system in a floating wind turbine based on Kalman filters and artificial neural networks," Renewable Energy, Elsevier, vol. 169(C), pages 1-13.
    12. Yonglong Yan & Jian Li & David Wenzhong Gao, 2014. "Condition Parameter Modeling for Anomaly Detection in Wind Turbines," Energies, MDPI, vol. 7(5), pages 1-17, May.
    13. Cho, Seongpil & Gao, Zhen & Moan, Torgeir, 2018. "Model-based fault detection, fault isolation and fault-tolerant control of a blade pitch system in floating wind turbines," Renewable Energy, Elsevier, vol. 120(C), pages 306-321.
    14. Ren, Zhengru & Verma, Amrit Shankar & Li, Ye & Teuwen, Julie J.E. & Jiang, Zhiyu, 2021. "Offshore wind turbine operations and maintenance: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Fengjuan Zhang & Xiaohui Zhang & Zhilei Xu & Keliang Dong & Zhiwei Li & Yubo Liu, 2024. "Cleaning of Abnormal Wind Speed Power Data Based on Quartile RANSAC Regression," Energies, MDPI, vol. 17(22), pages 1-14, November.
    2. 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.
    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. Junshuai Yan & Yongqian Liu & Xiaoying Ren & Li Li, 2023. "Wind Turbine Gearbox Condition Monitoring Using Hybrid Attentions and Spatio-Temporal BiConvLSTM Network," Energies, MDPI, vol. 16(19), pages 1-22, September.

    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. Conor McKinnon & James Carroll & Alasdair McDonald & Sofia Koukoura & David Infield & Conaill Soraghan, 2020. "Comparison of New Anomaly Detection Technique for Wind Turbine Condition Monitoring Using Gearbox SCADA Data," Energies, MDPI, vol. 13(19), pages 1-19, October.
    2. McMorland, J. & Collu, M. & McMillan, D. & Carroll, J. & Coraddu, A., 2023. "Opportunistic maintenance for offshore wind: A review and proposal of future framework," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
    3. McMorland, Jade & Flannigan, Callum & Carroll, James & Collu, Maurizio & McMillan, David & Leithead, William & Coraddu, Andrea, 2022. "A review of operations and maintenance modelling with considerations for novel wind turbine concepts," Renewable and Sustainable Energy Reviews, Elsevier, vol. 165(C).
    4. 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.
    5. 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.
    6. Truong, Hoai Vu Anh & Dang, Tri Dung & Vo, Cong Phat & Ahn, Kyoung Kwan, 2022. "Active control strategies for system enhancement and load mitigation of floating offshore wind turbines: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).
    7. Alan Turnbull & Conor McKinnon & James Carrol & Alasdair McDonald, 2022. "On the Development of Offshore Wind Turbine Technology: An Assessment of Reliability Rates and Fault Detection Methods in a Changing Market," Energies, MDPI, vol. 15(9), pages 1-20, April.
    8. 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.
    9. Pliego Marugán, Alberto & García Márquez, Fausto Pedro & Pinar Pérez, Jesús María, 2022. "A techno-economic model for avoiding conflicts of interest between owners of offshore wind farms and maintenance suppliers," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    10. Cho, Seongpil & Choi, Minjoo & Gao, Zhen & Moan, Torgeir, 2021. "Fault detection and diagnosis of a blade pitch system in a floating wind turbine based on Kalman filters and artificial neural networks," Renewable Energy, Elsevier, vol. 169(C), pages 1-13.
    11. Igba, Joel & Alemzadeh, Kazem & Durugbo, Christopher & Henningsen, Keld, 2015. "Performance assessment of wind turbine gearboxes using in-service data: Current approaches and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 144-159.
    12. Mazare, Mahmood & Taghizadeh, Mostafa, 2022. "Uncertainty estimator-based dual layer adaptive fault-tolerant control for wind turbines," Renewable Energy, Elsevier, vol. 188(C), pages 545-560.
    13. Shafiee, Mahmood & Sørensen, John Dalsgaard, 2019. "Maintenance optimization and inspection planning of wind energy assets: Models, methods and strategies," Reliability Engineering and System Safety, Elsevier, vol. 192(C).
    14. Guodong You & Tao Xu & Honglin Su & Xiaoxin Hou & Jisheng Li, 2019. "Fault-Tolerant Control for Actuator Faults of Wind Energy Conversion System," Energies, MDPI, vol. 12(12), pages 1-16, June.
    15. Aleksandra Grzesiek & Radosław Zimroz & Paweł Śliwiński & Norbert Gomolla & Agnieszka Wyłomańska, 2021. "A Method for Structure Breaking Point Detection in Engine Oil Pressure Data," Energies, MDPI, vol. 14(17), pages 1-24, September.
    16. Moghaddass, Ramin & Sheng, Shuangwen, 2019. "An anomaly detection framework for dynamic systems using a Bayesian hierarchical framework," Applied Energy, Elsevier, vol. 240(C), pages 561-582.
    17. Sadeghian, Omid & Mohammadpour Shotorbani, Amin & Mohammadi-Ivatloo, Behnam & Sadiq, Rehan & Hewage, Kasun, 2021. "Risk-averse maintenance scheduling of generation units in combined heat and power systems with demand response," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    18. Jin, Xin & Zhang, Zhaolong & Shi, Xiaoqiang & Ju, Wenbin, 2014. "A review on wind power industry and corresponding insurance market in China: Current status and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 38(C), pages 1069-1082.
    19. Kumarasamy Palanimuthu & Ganesh Mayilsamy & Ameerkhan Abdul Basheer & Seong-Ryong Lee & Dongran Song & Young Hoon Joo, 2022. "A Review of Recent Aerodynamic Power Extraction Challenges in Coordinated Pitch, Yaw, and Torque Control of Large-Scale Wind Turbine Systems," Energies, MDPI, vol. 15(21), pages 1-27, November.
    20. Wang, Anqi & Pei, Yan & Qian, Zheng & Zareipour, Hamidreza & Jing, Bo & An, Jiayi, 2022. "A two-stage anomaly decomposition scheme based on multi-variable correlation extraction for wind turbine fault detection and identification," Applied Energy, Elsevier, vol. 321(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:14:y:2021:i:20:p:6601-:d:655314. 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.