| 引用本文: | 刘一江,易理刚.人工神经网络方法实现自动判别失磁类型及失磁深度.[J].国防科技大学学报,1999,21(4):125-128.[点击复制] |
| Liu Yijiang,Yi Ligang.The Study of Application of Artifical Neural Network on Failure-Mode-Recognition and Loss-Degree-Predition[J].Journal of National University of Defense Technology,1999,21(4):125-128[点击复制] |
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| 人工神经网络方法实现自动判别失磁类型及失磁深度 |
| 刘一江, 易理刚 |
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(湖南大学 湖南 长沙 410082)
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| 摘要: |
| 利用失磁后Et.q、Efq.q衰减时间常数Tq仅取决于发电机参数及失磁类型的结论, 根据失磁故障的分类, 采用一个三层前向神经网络得出Tq,使微机失磁保护能够在失磁发生的瞬间自动判别出失磁类型并预测失磁深度。数字仿真、动模试验以及实际运行证明了本文所提出的方法。 |
| 关键词: 失磁保护, 神经网络 |
| DOI： |
| 投稿日期：1999-04-30 |
| 基金项目: |
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| The Study of Application of Artifical Neural Network on Failure-Mode-Recognition and Loss-Degree-Predition |
| Liu Yijiang, Yi Ligang |
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(Hunan University, Changsha, 410082)
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| Abstract: |
| Using a conclusion of time-constant-attenuation after loss-of-excitaion only decided by generator parameters and failure-modes, the paper according to the classify of loss-of-excitation faults, adopts a three-lay feed-forward neural network to obtain“Tq”,Which makes microcomputer-based Loss-of-Excitation protection in the twinking of fault happening failure-mode-recognition and loss-degree-prediction. Figure simulating, trend imitate testing and action moving proved that the method raised is correct. |
| Keywords: loss-of-excitation protection, neural network |
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