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A Data-Driven Residual-Based Method for Fault Diagnosis and Isolation in Wind Turbines

机译：一种基于数据驱动残差的风力发电机组故障诊断与隔离方法

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摘要

In order to improve the reliability of wind turbines, avoid serious accidents, and reduce operation and maintenance casts, it is important to effectively detect early faults of wind turbines operating in harsh environment. This paper proposes a data-driven fault diagnosis and isolation method for wind turbines, which implements long short-term memory networks for residual generator and applies the random forest algorithm for decision making. The method has been evaluated in a wind turbine benchmark Simulink model, in comparison with four model-based algorithms and four data-driven methods, and the results have shown that the proposed method achieves the highest accuracy. Moreover, extensive evaluation has been conducted to analyze the robustness of proposed method, and the experimental results have verified the stability of the proposed method in diagnosis of wind turbine faults.
机译：为了提高风力涡轮机的可靠性，避免严重事故并减少操作和维护工作量，有效检测在恶劣环境下运行的风力涡轮机的早期故障非常重要。本文提出了一种数据驱动的风机故障诊断与隔离方法，该方法为残差发电机实现了长短期记忆网络，并采用随机森林算法进行决策。与四种基于模型的算法和四种数据驱动方法相比，该方法已在风力涡轮机基准Simulink模型中进行了评估，结果表明该方法达到了最高的精度。此外，已经进行了广泛的评估以分析该方法的鲁棒性，并且实验结果证明了该方法在风力涡轮机故障诊断中的稳定性。

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  • 来源
    《Sustainable Energy, IEEE Transactions on》 |2019年第2期|895-904|共10页
  • 作者

    Li Mengshi; Yu Da; Chen Ziming; Xiahou Kaishun; Ji Tianyao; Wu Q. H.;

  • 作者单位

    South China Univ Technol, Sch Elect Power Engn, Guangzhou 510000, Guangdong, Peoples R China;

    South China Univ Technol, Sch Elect Power Engn, Guangzhou 510000, Guangdong, Peoples R China|Guangdong Power Grid Co Ltd, Elect Power Res Inst, Guangzhou 510080, Guangdong, Peoples R China;

    South China Univ Technol, Sch Elect Power Engn, Guangzhou 510000, Guangdong, Peoples R China;

    South China Univ Technol, Sch Elect Power Engn, Guangzhou 510000, Guangdong, Peoples R China;

    South China Univ Technol, Sch Elect Power Engn, Guangzhou 510000, Guangdong, Peoples R China;

    South China Univ Technol, Sch Elect Power Engn, Guangzhou 510000, Guangdong, Peoples R China;

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  • 正文语种 eng
  • 中图分类
  • 关键词

    Wind turbine; fault diagnosis and isolation; Long Short-Term Memory networks; random forest; data-driven;

    机译：风力涡轮机;故障诊断和隔离;长短期内存网络;随机森林;数据驱动;

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