IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i22p5697-d1520952.html
   My bibliography  Save this article

Cleaning of Abnormal Wind Speed Power Data Based on Quartile RANSAC Regression

Author

Listed:
  • Fengjuan Zhang

    (College of Electrical Engineering, Henan University of Technology, Zhengzhou 450001, China)

  • Xiaohui Zhang

    (College of Electrical Engineering, Henan University of Technology, Zhengzhou 450001, China)

  • Zhilei Xu

    (College of Electrical Engineering, Henan University of Technology, Zhengzhou 450001, China)

  • Keliang Dong

    (College of Electrical Engineering, Henan University of Technology, Zhengzhou 450001, China)

  • Zhiwei Li

    (College of Electrical Engineering, Henan University of Technology, Zhengzhou 450001, China)

  • Yubo Liu

    (College of Electrical Engineering, Henan University of Technology, Zhengzhou 450001, China)

Abstract

The combined complexity of wind turbine systems and harsh operating conditions pose significant challenges to the accuracy of operational data in Supervisory Control and Data Acquisition (SCADA) systems. Improving the precision of data cleaning for high proportions of stacked abnormalities remains an urgent problem. This paper deeply analyzes the distribution characteristics of abnormal data and proposes a novel method for abnormal data cleaning based on a classification processing framework. Firstly, the first type of abnormal data is cleaned based on operational criteria; secondly, the quartile method is used to eliminate sparse abnormal data to obtain a clearer boundary line; on this basis, the Random Sample Consensus (RANSAC) algorithm is employed to eliminate stacked abnormal data; finally, the effectiveness of the proposed algorithm in cleaning abnormal data with a high proportion of stacked abnormalities is verified through case studies, and evaluation indicators are introduced through comparative experiments to quantitatively assess the cleaning effect. The research results indicate that the algorithm excels in cleaning effectiveness, efficiency, accuracy, and rationality of data deletion. The cleaning accuracy improvement is particularly significant when dealing with a high proportion of stacked anomaly data, thereby bringing significant value to wind power applications such as wind power prediction, condition assessment, and fault detection.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:22:p:5697-:d:1520952
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/22/5697/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/22/5697/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. Long, Huan & Xu, Shaohui & Gu, Wei, 2022. "An abnormal wind turbine data cleaning algorithm based on color space conversion and image feature detection," Applied Energy, Elsevier, vol. 311(C).
    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. 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.
    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. Tobias Mueller & Steven Gronau, 2023. "Fostering Macroeconomic Research on Hydrogen-Powered Aviation: A Systematic Literature Review on General Equilibrium Models," Energies, MDPI, vol. 16(3), pages 1-33, February.
    4. Becky Corley & Sofia Koukoura & James Carroll & Alasdair McDonald, 2021. "Combination of Thermal Modelling and Machine Learning Approaches for Fault Detection in Wind Turbine Gearboxes," Energies, MDPI, vol. 14(5), pages 1-14, March.
    5. Francisco Bilendo & Angela Meyer & Hamed Badihi & Ningyun Lu & Philippe Cambron & Bin Jiang, 2022. "Applications and Modeling Techniques of Wind Turbine Power Curve for Wind Farms—A Review," Energies, MDPI, vol. 16(1), pages 1-38, December.
    6. Adaiton Oliveira-Filho & Monelle Comeau & James Cave & Charbel Nasr & Pavel Côté & Antoine Tahan, 2024. "Wind Turbine SCADA Data Imbalance: A Review of Its Impact on Health Condition Analyses and Mitigation Strategies," Energies, MDPI, vol. 18(1), pages 1-23, December.
    7. 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.
    8. Mu, Yunfei & Xu, Yurui & Cao, Yan & Chen, Wanqing & Jia, Hongjie & Yu, Xiaodan & Jin, Xiaolong, 2022. "A two-stage scheduling method for integrated community energy system based on a hybrid mechanism and data-driven model," Applied Energy, Elsevier, vol. 323(C).
    9. Hou, Guolian & Wang, Junjie & Fan, Yuzhen & Zhang, Jianhua & Huang, Congzhi, 2024. "A novel wind power deterministic and interval prediction framework based on the critic weight method, improved northern goshawk optimization, and kernel density estimation," Renewable Energy, Elsevier, vol. 226(C).
    10. 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.
    11. Liang, Guoyuan & Su, Yahao & Wu, Xinyu & Ma, Jiajun & Long, Huan & Song, Zhe, 2023. "Abnormal data cleaning for wind turbines by image segmentation based on active shape model and class uncertainty," Renewable Energy, Elsevier, vol. 216(C).
    12. 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.
    13. Davide Astolfi & Silvia Iuliano & Antony Vasile & Marco Pasetti & Salvatore Dello Iacono & Alfredo Vaccaro, 2024. "Wind Turbine Static Errors Related to Yaw, Pitch or Anemometer Apparatus: Guidelines for the Diagnosis and Related Performance Assessment," Energies, MDPI, vol. 17(24), pages 1-34, December.
    14. Xiao Chen & Martin A. Eder & Asm Shihavuddin & Dan Zheng, 2021. "A Human-Cyber-Physical System toward Intelligent Wind Turbine Operation and Maintenance," Sustainability, MDPI, vol. 13(2), pages 1-10, January.
    15. Waqar Muhammad Ashraf & Ghulam Moeen Uddin & Muhammad Farooq & Fahid Riaz & Hassan Afroze Ahmad & Ahmad Hassan Kamal & Saqib Anwar & Ahmed M. El-Sherbeeny & Muhammad Haider Khan & Noman Hafeez & Arman, 2021. "Construction of Operational Data-Driven Power Curve of a Generator by Industry 4.0 Data Analytics," Energies, MDPI, vol. 14(5), pages 1-18, February.
    16. Qiao, Yanhui & Han, Shuang & Zhang, Yajie & Liu, Yongqian & Yan, Jie, 2024. "A multivariable wind turbine power curve modeling method considering segment control differences and short-time self-dependence," Renewable Energy, Elsevier, vol. 222(C).
    17. Chang Cai & Jicai Guo & Xiaowen Song & Yanfeng Zhang & Jianxin Wu & Shufeng Tang & Yan Jia & Zhitai Xing & Qing’an Li, 2023. "Review of Data-Driven Approaches for Wind Turbine Blade Icing Detection," Sustainability, MDPI, vol. 15(2), pages 1-20, January.
    18. Davide Astolfi & Raymond Byrne & Francesco Castellani, 2020. "Analysis of Wind Turbine Aging through Operation Curves," Energies, MDPI, vol. 13(21), pages 1-21, October.
    19. Yao, Qingtao & Zhu, Haowei & Xiang, Ling & Su, Hao & Hu, Aijun, 2023. "A novel composed method of cleaning anomy data for improving state prediction of wind turbine," Renewable Energy, Elsevier, vol. 204(C), pages 131-140.
    20. 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.

    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:17:y:2024:i:22:p:5697-:d:1520952. 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.