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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
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    References listed on IDEAS

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    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. Nielson, Jordan & Bhaganagar, Kiran & Meka, Rajitha & Alaeddini, Adel, 2020. "Using atmospheric inputs for Artificial Neural Networks to improve wind turbine power prediction," Energy, Elsevier, vol. 190(C).
    3. 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.
    4. 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).
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    Cited by:

    1. Chen, Yan & Ban, Guihua & Ding, Tingxiao, 2025. "Abnormal data recognition method for wind turbines based on alpha channel fusion," Applied Energy, Elsevier, vol. 396(C).

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