首页> 外文会议>11th International Conference on Electrical Machines and Systems(第11届国际电机与系统会议)论文集 >Fault Diagnosis of Rotor Winding Inter-turn Short Circuit in Turbine-Generator Based on BP Neural Network
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Fault Diagnosis of Rotor Winding Inter-turn Short Circuit in Turbine-Generator Based on BP Neural Network

机译:基于BP神经网络的汽轮发电机转子绕组匝间短路故障诊断。

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The electromagnetic characteristic and rotor vibration characteristic of turbine-generator are analyzed when rotor winding inter-turn short circuit fault has happened. This paper reveals that exciting magnetic force Ff is constant in a fixed condition whereas the exciting current If increases in case of rotor inter-turn fault. This paper also finds relevant characteristic parameters. Based on the theory, we can get training patterns without doing destructive tests. Then BP (back propagation) neural network can be adequately trained and diagnosis rotor winding inter-turn short circuit. BP neural network is independent on mathematic models and parameters of turbine-generator. Finally practically acquired dynamic experiment data of the MJF-30-6 generator, the results of verification show that the theory analysis is right and the method is efficient and accurate.
机译:分析了发生转子绕组匝间短路故障的水轮发电机的电磁特性和转子振动特性。本文揭示了励磁磁力Ff在固定条件下是恒定的,而励磁电流If在转子匝间故障的情况下会增加。本文还找到了相关的特征参数。基于该理论,无需进行破坏性测试即可获得训练模式。然后可以对BP(反向传播)神经网络进行适当的训练,并诊断转子绕组匝间短路。 BP神经网络独立于涡轮发电机的数学模型和参数。最终实际获得了MJF-30-6发电机的动态实验数据,验证结果表明理论分析是正确的,该方法高效,准确。

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