IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v198y2022icp936-946.html
   My bibliography  Save this article

Hybrid Gaussian process regression and Fuzzy Inference System based approach for condition monitoring at the rotor side of a doubly fed induction generator

Author

Listed:
  • Zhang, Shuo
  • Robinson, Emma
  • Basu, Malabika

Abstract

With the applications of Machine Learning (ML) in condition monitoring (CM) of wind turbines (WTs), regression-based approaches are mainly for WT power curve modelling to evaluate its performance. A fitted performance curve (PC) of power versus wind speed is prevailing and straightforward for WT performance monitoring. As WT operations could be affected by multiple parameters. It is difficult to identify the fault type merely by a power curve. Particularly at the rotor side of doubly fed induction generator (DFIG), there are miscellaneous electrical disturbances that have their distinct effects on current signals. Hence, by merely processing the rotor currents, a PC of standard deviation of currents versus wind speed can be employed for CM of rotor-side faults. A non-parametric regression model, Gaussian process regression (GPR), is used to predict the PC under normal operation, and afterwards, Fuzzy Inference System (FIS) is applied for fault diagnosis. Therefore, the paper proposes a hybrid GPR and FIS approach towards condition monitoring (CM) and fault diagnosis at the rotor side of a DFIG by merely processing rotor currents through various signal processing techniques, where miscellaneous electrical disturbances can be recognized and localized. The comparative results show that the hybrid approach has superiority over two common ML classifiers, support vector machine (SVM) and decision tree (DT), for fault diagnosis under both test cases including drift faults and excluding drift faults. By disregarding drift faults, this hybrid approach is validated to be efficient for a real-time application with overall promising performance metrics in terms of accuracy (98.29%), dependability (98.12%), and security (100%). The diagnosed results on individual faults and research shortcomings are also discussed.

Suggested Citation

  • Zhang, Shuo & Robinson, Emma & Basu, Malabika, 2022. "Hybrid Gaussian process regression and Fuzzy Inference System based approach for condition monitoring at the rotor side of a doubly fed induction generator," Renewable Energy, Elsevier, vol. 198(C), pages 936-946.
    • Handle: RePEc:eee:renene:v:198:y:2022:i:c:p:936-946
    DOI: 10.1016/j.renene.2022.08.080
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148122012460
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2022.08.080?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Jichuan Kang & Zihao Wang & C. Guedes Soares, 2020. "Condition-Based Maintenance for Offshore Wind Turbines Based on Support Vector Machine," Energies, MDPI, vol. 13(14), pages 1-17, July.
    2. Stetco, Adrian & Dinmohammadi, Fateme & Zhao, Xingyu & Robu, Valentin & Flynn, David & Barnes, Mike & Keane, John & Nenadic, Goran, 2019. "Machine learning methods for wind turbine condition monitoring: A review," Renewable Energy, Elsevier, vol. 133(C), pages 620-635.
    3. Md Liton Hossain & Ahmed Abu-Siada & S. M. Muyeen, 2018. "Methods for Advanced Wind Turbine Condition Monitoring and Early Diagnosis: A Literature Review," Energies, MDPI, vol. 11(5), pages 1-14, May.
    4. Dao, Phong B., 2022. "On Wilcoxon rank sum test for condition monitoring and fault detection of wind turbines," Applied Energy, Elsevier, vol. 318(C).
    5. Arabian-Hoseynabadi, H. & Oraee, H. & Tavner, P.J., 2010. "Wind turbine productivity considering electrical subassembly reliability," Renewable Energy, Elsevier, vol. 35(1), pages 190-197.
    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. Liu, Ding Peng & Ferri, Giulio & Heo, Taemin & Marino, Enzo & Manuel, Lance, 2024. "On long-term fatigue damage estimation for a floating offshore wind turbine using a surrogate model," Renewable Energy, Elsevier, vol. 225(C).

    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. Mohamed Benbouzid & Tarek Berghout & Nur Sarma & Siniša Djurović & Yueqi Wu & Xiandong Ma, 2021. "Intelligent Condition Monitoring of Wind Power Systems: State of the Art Review," Energies, MDPI, vol. 14(18), pages 1-33, September.
    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. Lorin Jenkel & Stefan Jonas & Angela Meyer, 2023. "Privacy-Preserving Fleet-Wide Learning of Wind Turbine Conditions with Federated Learning," Energies, MDPI, vol. 16(17), pages 1-29, September.
    4. Phong B. Dao, 2023. "On Cointegration Analysis for Condition Monitoring and Fault Detection of Wind Turbines Using SCADA Data," Energies, MDPI, vol. 16(5), pages 1-17, March.
    5. 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.
    6. 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.
    7. Wang, Anqi & Pei, Yan & Zhu, Yunyi & Qian, Zheng, 2023. "Wind turbine fault detection and identification through self-attention-based mechanism embedded with a multivariable query pattern," Renewable Energy, Elsevier, vol. 211(C), pages 918-937.
    8. Ademi, Sul & Jovanovic, Milutin, 2016. "Control of doubly-fed reluctance generators for wind power applications," Renewable Energy, Elsevier, vol. 85(C), pages 171-180.
    9. Reddy, Sohail R., 2021. "A machine learning approach for modeling irregular regions with multiple owners in wind farm layout design," Energy, Elsevier, vol. 220(C).
    10. Ahmed Al-Ajmi & Yingzhao Wang & Siniša Djurović, 2021. "Wind Turbine Generator Controller Signals Supervised Machine Learning for Shaft Misalignment Fault Detection: A Doubly Fed Induction Generator Practical Case Study," Energies, MDPI, vol. 14(6), pages 1-15, March.
    11. 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).
    12. Chen, Wanqiu & Qiu, Yingning & Feng, Yanhui & Li, Ye & Kusiak, Andrew, 2021. "Diagnosis of wind turbine faults with transfer learning algorithms," Renewable Energy, Elsevier, vol. 163(C), pages 2053-2067.
    13. Zhu, Yongchao & Zhu, Caichao & Tan, Jianjun & Tan, Yong & Rao, Lei, 2022. "Anomaly detection and condition monitoring of wind turbine gearbox based on LSTM-FS and transfer learning," Renewable Energy, Elsevier, vol. 189(C), pages 90-103.
    14. Boudy Bilal & Kaan Yetilmezsoy & Mohammed Ouassaid, 2024. "Benchmarking of Various Flexible Soft-Computing Strategies for the Accurate Estimation of Wind Turbine Output Power," Energies, MDPI, vol. 17(3), pages 1-36, February.
    15. Meng, Debiao & Yang, Shiyuan & Jesus, Abílio M.P. de & Zhu, Shun-Peng, 2023. "A novel Kriging-model-assisted reliability-based multidisciplinary design optimization strategy and its application in the offshore wind turbine tower," Renewable Energy, Elsevier, vol. 203(C), pages 407-420.
    16. Laura Schröder & Nikolay Krasimirov Dimitrov & David Robert Verelst & John Aasted Sørensen, 2022. "Using Transfer Learning to Build Physics-Informed Machine Learning Models for Improved Wind Farm Monitoring," Energies, MDPI, vol. 15(2), pages 1-21, January.
    17. Tian, Ai-Qing & Wang, Xiao-Yang & Xu, Heying & Pan, Jeng-Shyang & Snášel, Václav & Lv, Hong-Xia, 2024. "Multi-objective optimization model for railway heavy-haul traffic: Addressing carbon emissions reduction and transport efficiency improvement," Energy, Elsevier, vol. 294(C).
    18. Qian, XiaoYi & Sun, TianHe & Zhang, YuXian & Wang, BaoShi & Awad Gendeel, Mohammed Altayeb, 2023. "Wind turbine fault detection based on spatial-temporal feature and neighbor operation state," Renewable Energy, Elsevier, vol. 219(P1).
    19. Galih Bangga, 2022. "Progress and Outlook in Wind Energy Research," Energies, MDPI, vol. 15(18), pages 1-5, September.
    20. Zhu, Yongchao & Zhu, Caichao & Tan, Jianjun & Wang, Yili & Tao, Jianquan, 2022. "Operational state assessment of wind turbine gearbox based on long short-term memory networks and fuzzy synthesis," Renewable Energy, Elsevier, vol. 181(C), pages 1167-1176.

    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:renene:v:198:y:2022:i:c:p:936-946. 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.journals.elsevier.com/renewable-energy .

    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.