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首页> 外文期刊>Sustainable Energy, IEEE Transactions on >A Data-Driven Residual-Based Method for Fault Diagnosis and Isolation in Wind Turbines
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A Data-Driven Residual-Based Method for Fault Diagnosis and Isolation in Wind Turbines

机译：一种用于风力涡轮机的故障诊断和隔离的数据驱动的基于残余方法

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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，减少操作和维护铸造，重要的是有效地检测在恶劣环境中运行的风力涡轮机的早期断层。本文提出了一种用于风力涡轮机的数据驱动故障诊断和隔离方法，其实现了用于残留发电机的长短期存储网络，并应用随机林算法进行决策。该方法已经在风力涡轮机基准模拟模型中评估，与基于四种模型的算法和四种数据驱动方法相比，结果表明，所提出的方法实现了最高精度。此外，已经进行了广泛的评估以分析所提出的方法的稳健性，实验结果验证了提出的方法在诊断风力涡轮机故障中的稳定性。

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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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  • 原文格式 PDF
  • 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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