IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v150y2020icp598-606.html
   My bibliography  Save this article

Hidden Markov model based principal component analysis for intelligent fault diagnosis of wind energy converter systems

Author

Listed:
  • Kouadri, Abdelmalek
  • Hajji, Mansour
  • Harkat, Mohamed-Faouzi
  • Abodayeh, Kamaleldin
  • Mansouri, Majdi
  • Nounou, Hazem
  • Nounou, Mohamed

Abstract

Fault Detection and Diagnosis (FDD) for overall modern Wind Energy Conversion (WEC) systems, particularly its converter, is still a challenge due to the high randomness to their operating environment. This paper presents an advanced FDD approach aims to increase the availability, reliability and required safety of WEC Converters (WECC) under different conditions. The developed FDD approach must be able to detect and correctly diagnose the occurrence of faults in WEC systems. The developed approach exploits the benefits of the machine learning (ML)-based Hidden Markov model (HMM) and the principal component analysis (PCA) model. The PCA technique is used for efficiently extracting and selecting features to be fed to HMM classifier. The effectiveness and higher classification accuracy of the developed PCA-based HMM approach are demonstrated via simulated data collected from the WEC.

Suggested Citation

  • Kouadri, Abdelmalek & Hajji, Mansour & Harkat, Mohamed-Faouzi & Abodayeh, Kamaleldin & Mansouri, Majdi & Nounou, Hazem & Nounou, Mohamed, 2020. "Hidden Markov model based principal component analysis for intelligent fault diagnosis of wind energy converter systems," Renewable Energy, Elsevier, vol. 150(C), pages 598-606.
    • Handle: RePEc:eee:renene:v:150:y:2020:i:c:p:598-606
    DOI: 10.1016/j.renene.2020.01.010
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148120300112
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2020.01.010?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. Yang, Zhimin & Chai, Yi, 2016. "A survey of fault diagnosis for onshore grid-connected converter in wind energy conversion systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 345-359.
    2. 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.
    3. Robert M. Losee, 2000. "When information retrieval measures agree about the relative quality of document rankings," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 51(9), pages 834-840.
    4. Zappalá, D. & Sarma, N. & Djurović, S. & Crabtree, C.J. & Mohammad, A. & Tavner, P.J., 2019. "Electrical & mechanical diagnostic indicators of wind turbine induction generator rotor faults," Renewable Energy, Elsevier, vol. 131(C), pages 14-24.
    5. Sabri Boughorbel & Fethi Jarray & Mohammed El-Anbari, 2017. "Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric," PLOS ONE, Public Library of Science, vol. 12(6), pages 1-17, June.
    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. Amal Hichri & Mansour Hajji & Majdi Mansouri & Kamaleldin Abodayeh & Kais Bouzrara & Hazem Nounou & Mohamed Nounou, 2022. "Genetic-Algorithm-Based Neural Network for Fault Detection and Diagnosis: Application to Grid-Connected Photovoltaic Systems," Sustainability, MDPI, vol. 14(17), pages 1-14, August.
    2. Majdi Mansouri & Khaled Dhibi & Hazem Nounou & Mohamed Nounou, 2022. "An Effective Fault Diagnosis Technique for Wind Energy Conversion Systems Based on an Improved Particle Swarm Optimization," Sustainability, MDPI, vol. 14(18), pages 1-11, September.
    3. Hubert Szczepaniuk & Edyta Karolina Szczepaniuk, 2022. "Applications of Artificial Intelligence Algorithms in the Energy Sector," Energies, MDPI, vol. 16(1), pages 1-24, December.
    4. Michael W. Hopwood & Lekha Patel & Thushara Gunda, 2022. "Classification of Photovoltaic Failures with Hidden Markov Modeling, an Unsupervised Statistical Approach," Energies, MDPI, vol. 15(14), pages 1-12, July.
    5. Dhibi, Khaled & Mansouri, Majdi & Bouzrara, Kais & Nounou, Hazem & Nounou, Mohamed, 2022. "Reduced neural network based ensemble approach for fault detection and diagnosis of wind energy converter systems," Renewable Energy, Elsevier, vol. 194(C), pages 778-787.
    6. Sondes Gharsellaoui & Majdi Mansouri & Shady S. Refaat & Haitham Abu-Rub & Hassani Messaoud, 2020. "Multivariate Features Extraction and Effective Decision Making Using Machine Learning Approaches," Energies, MDPI, vol. 13(3), pages 1-16, January.
    7. Zahra Yahyaoui & Mansour Hajji & Majdi Mansouri & Kamaleldin Abodayeh & Kais Bouzrara & Hazem Nounou, 2022. "Effective Fault Detection and Diagnosis for Power Converters in Wind Turbine Systems Using KPCA-Based BiLSTM," Energies, MDPI, vol. 15(17), pages 1-19, August.
    8. Mahsa Dehghan Manshadi & Majid Ghassemi & Seyed Milad Mousavi & Amir H. Mosavi & Levente Kovacs, 2021. "Predicting the Parameters of Vortex Bladeless Wind Turbine Using Deep Learning Method of Long Short-Term Memory," Energies, MDPI, vol. 14(16), pages 1-17, August.
    9. Arpad Gellert & Stefan-Alexandru Precup & Alexandru Matei & Bogdan-Constantin Pirvu & Constantin-Bala Zamfirescu, 2022. "Real-Time Assembly Support System with Hidden Markov Model and Hybrid Extensions," Mathematics, MDPI, vol. 10(15), pages 1-21, August.
    10. Yanning Sun & Wei Qin & Zilong Zhuang & Hongwei Xu, 2021. "An adaptive fault detection and root-cause analysis scheme for complex industrial processes using moving window KPCA and information geometric causal inference," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 2007-2021, October.
    11. Zhang, Jinchun & Xv, Feiyu & Hou, Jinxiu, 2023. "Degradation recognition and residual life analysis of gasifier firebrick in service using Hidden Semi-Markov Model," Energy, Elsevier, vol. 264(C).
    12. James Roetzer & Xingjie Li & John Hall, 2024. "Review of Data-Driven Models in Wind Energy: Demonstration of Blade Twist Optimization Based on Aerodynamic Loads," Energies, MDPI, vol. 17(16), pages 1-20, August.
    13. Shi, Jihao & Li, Junjie & Usmani, Asif Sohail & Zhu, Yuan & Chen, Guoming & Yang, Dongdong, 2021. "Probabilistic real-time deep-water natural gas hydrate dispersion modeling by using a novel hybrid deep learning approach," Energy, Elsevier, vol. 219(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. Jia Tian & Xingqin Zhang & Shuangqing Zheng & Zhiyong Liu & Changshu Zhan, 2024. "Synergising an Advanced Optimisation Technique with Deep Learning: A Novel Method in Fault Warning Systems," Mathematics, MDPI, vol. 12(9), pages 1-25, April.
    2. Mingzhu Tang & Wei Chen & Qi Zhao & Huawei Wu & Wen Long & Bin Huang & Lida Liao & Kang Zhang, 2019. "Development of an SVR Model for the Fault Diagnosis of Large-Scale Doubly-Fed Wind Turbines Using SCADA Data," Energies, MDPI, vol. 12(17), pages 1-15, September.
    3. Feng, Chenlong & Liu, Chao & Jiang, Dongxiang, 2023. "Unsupervised anomaly detection using graph neural networks integrated with physical-statistical feature fusion and local-global learning," Renewable Energy, Elsevier, vol. 206(C), pages 309-323.
    4. Zhang, Chen & Yang, Tao, 2021. "Optimal maintenance planning and resource allocation for wind farms based on non-dominated sorting genetic algorithm-ΙΙ," Renewable Energy, Elsevier, vol. 164(C), pages 1540-1549.
    5. Christian Kauten & Ashish Gupta & Xiao Qin & Glenn Richey, 2022. "Predicting Blood Donors Using Machine Learning Techniques," Information Systems Frontiers, Springer, vol. 24(5), pages 1547-1562, October.
    6. Xu, Qifa & Fan, Zhenhua & Jia, Weiyin & Jiang, Cuixia, 2020. "Fault detection of wind turbines via multivariate process monitoring based on vine copulas," Renewable Energy, Elsevier, vol. 161(C), pages 939-955.
    7. Guilherme V. Hollweg & Shahid A. Khan & Shivam Chaturvedi & Yaoyu Fan & Mengqi Wang & Wencong Su, 2023. "Grid-Connected Converters: A Brief Survey of Topologies, Output Filters, Current Control, and Weak Grids Operation," Energies, MDPI, vol. 16(9), pages 1-31, April.
    8. Meng Li & Shuangxin Wang, 2019. "Dynamic Fault Monitoring of Pitch System in Wind Turbines using Selective Ensemble Small-World Neural Networks," Energies, MDPI, vol. 12(17), pages 1-20, August.
    9. David Cemernek & Sandra Cemernek & Heimo Gursch & Ashwini Pandeshwar & Thomas Leitner & Matthias Berger & Gerald Klösch & Roman Kern, 2022. "Machine learning in continuous casting of steel: a state-of-the-art survey," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1561-1579, August.
    10. Jastrzebska, Agnieszka & Morales Hernández, Alejandro & Nápoles, Gonzalo & Salgueiro, Yamisleydi & Vanhoof, Koen, 2022. "Measuring wind turbine health using fuzzy-concept-based drifting models," Renewable Energy, Elsevier, vol. 190(C), pages 730-740.
    11. Neha Dimri & Himanshu Kaul & Daya Gupta, 2018. "MetaXplorer: an intelligent and adaptable metasearch engine using a novel ordered weighted averaging operator," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 9(6), pages 1315-1325, December.
    12. 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.
    13. Matthew Boeding & Kelly Boswell & Michael Hempel & Hamid Sharif & Juan Lopez & Kalyan Perumalla, 2022. "Survey of Cybersecurity Governance, Threats, and Countermeasures for the Power Grid," Energies, MDPI, vol. 15(22), pages 1-22, November.
    14. Paweł Knes & Phong B. Dao, 2024. "Machine Learning and Cointegration for Wind Turbine Monitoring and Fault Detection: From a Comparative Study to a Combined Approach," Energies, MDPI, vol. 17(20), pages 1-21, October.
    15. Artigao, Estefania & Martín-Martínez, Sergio & Honrubia-Escribano, Andrés & Gómez-Lázaro, Emilio, 2018. "Wind turbine reliability: A comprehensive review towards effective condition monitoring development," Applied Energy, Elsevier, vol. 228(C), pages 1569-1583.
    16. Golnary, Farshad & Moradi, Hamed, 2022. "Identification of the dynamics of the drivetrain and estimating its unknown parts in a large scale wind turbine," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 192(C), pages 50-69.
    17. Chen, Hansi & Liu, Hang & Chu, Xuening & Liu, Qingxiu & Xue, Deyi, 2021. "Anomaly detection and critical SCADA parameters identification for wind turbines based on LSTM-AE neural network," Renewable Energy, Elsevier, vol. 172(C), pages 829-840.
    18. Yolanda Vidal & Francesc Pozo & Christian Tutivén, 2018. "Wind Turbine Multi-Fault Detection and Classification Based on SCADA Data," Energies, MDPI, vol. 11(11), pages 1-18, November.
    19. Zhang, Chen & Gao, Wei & Yang, Tao & Guo, Sheng, 2019. "Opportunistic maintenance strategy for wind turbines considering weather conditions and spare parts inventory management," Renewable Energy, Elsevier, vol. 133(C), pages 703-711.
    20. Schade, Philipp & Schuhmacher, Monika C., 2023. "Predicting entrepreneurial activity using machine learning," Journal of Business Venturing Insights, Elsevier, vol. 19(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:eee:renene:v:150:y:2020:i:c:p:598-606. 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.