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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神经网络的涡轮发电机转子绕组短路的故障诊断

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摘要

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 F{sub}f is constant in a fixed condition whereas the exciting current I{sub}f 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:涡轮发电机的电磁特性和转子振动特性进行分析当转子绕组绕组短路故障发生时。本文揭示了激励磁力F {Sub} F在固定状态下是恒定的,而励磁电流I {Sub} F在转子变频故障的情况下增加。本文还发现了相关的特征参数。根据理论,我们可以在不进行破坏性测试的情况下获得培训模式。然后BP(反向传播)神经网络可以充分训练和诊断转子绕组绕组短路。 BP神经网络是独立于涡轮发电机的数学模型和参数。最后几乎采用了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,方法有效准确。

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