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An Improved Data-Efficiency Algorithm Based on Combining Isolation Forest and Mean Shift for Anomaly Data Filtering in Wind Power Curve

Author

Listed:
  • Wei Wang

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Shiyou Yang

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Yankun Yang

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

Abstract

A wind turbine working in a harsh environment is prone to generate abnormal data. An efficient algorithm based on the combination of an Isolation Forest (I-Forest) and a mean-shift algorithm is proposed for data cleaning in wind power curves. The I-Forest is used for detecting the local anomalies in each power and wind speed interval after data preprocessing. The contamination of I-Forest can be flexibly adjusted according to the data distribution of the wind turbine data. The remaining stacked data is eliminated by the mean-shift algorithm. To verify the filtering performance of the proposed combined method, five different algorithms, including the quartile and k -means (QK), the quartile and density-based spatial clustering (QD), the mathematical morphology operation (MMO), the fast data cleaning algorithm (FA), and the proposed one, are applied to the wind power curves of a prototype wind farm for comparisons. The numerical results have positively confirmed the reliability of the universal framework provided by the proposed algorithm.

Suggested Citation

  • Wei Wang & Shiyou Yang & Yankun Yang, 2022. "An Improved Data-Efficiency Algorithm Based on Combining Isolation Forest and Mean Shift for Anomaly Data Filtering in Wind Power Curve," Energies, MDPI, vol. 15(13), pages 1-12, July.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:13:p:4918-:d:856215
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    References listed on IDEAS

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    1. Xiang, Ling & Yang, Xin & Hu, Aijun & Su, Hao & Wang, Penghe, 2022. "Condition monitoring and anomaly detection of wind turbine based on cascaded and bidirectional deep learning networks," Applied Energy, Elsevier, vol. 305(C).
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    Cited by:

    1. Qiang Zhou & Yanhong Ma & Qingquan Lv & Ruixiao Zhang & Wei Wang & Shiyou Yang, 2022. "Short-Term Interval Prediction of Wind Power Based on KELM and a Universal Tabu Search Algorithm," Sustainability, MDPI, vol. 14(17), pages 1-12, August.

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