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首页> 外文期刊>Mathematical Problems in Engineering >Diagnosis of Short-Circuit Fault in Large-Scale Permanent-Magnet Wind Power Generator Based on CMAC
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Diagnosis of Short-Circuit Fault in Large-Scale Permanent-Magnet Wind Power Generator Based on CMAC

机译：基于CMAC的大型永磁风力发电机短路故障诊断

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摘要

This study proposes a method based on the cerebellar model arithmetic controller (CMAC) for fault diagnosis of large-scale permanent-magnet wind power generators and compares the results with Error Back Propagation (EBP). The diagnosis is based on the short-circuit faults in permanent-magnet wind power generators, magnetic field change, and temperature change. Since CMAC is characterized by inductive ability, associative ability, quick response, and similar input signals exciting similar memories, it has an excellent effect as an intelligent fault diagnosis implement. The experimental results suggest that faults can be diagnosed effectively after only training CMAC 10 times. In comparison to training 151 times for EBP, CMAC is better than EBP in terms of training speed.
机译：这项研究提出了一种基于小脑模型算术控制器（CMAC）的大型永磁风力发电机故障诊断方法，并将结果与​​误差反向传播（EBP）进行了比较。该诊断基于永磁风力发电机的短路故障，磁场变化和温度变化。由于CMAC具有感应能力，联想能力，快速响应能力和类似输入信号激发相似记忆的特点，因此作为智能故障诊断工具具有出色的效果。实验结果表明，仅对CMAC进行10次训练就可以有效地诊断故障。与对EBP进行151次训练相比，CMAC在训练速度上优于EBP。

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  • 来源
    《Mathematical Problems in Engineering》 |2013年第1期|935048.1-935048.7|共7页
  • 作者

    Chin-Tsung Hsieh; Her-Terng Yau; Jen Shiu;

  • 作者单位

    Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung 411, Taiwan;

    Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung 411, Taiwan;

    Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung 411, Taiwan;

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