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基于模型与BPNN感应电机定子匝间故障定位方法

, PP. 336-345

Keywords: 感应电机,匝间短路故障定位,相位移,BP神经网络,遗传算法优化

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Abstract:

为准确确定三相感应电机定子绕组匝间短路故障发生相,本文采用基于电机匝间故障模型与神经网络相结合的方法,对故障相有效检测并自动定位。通过分析,三相电流的有效值、电流与电压的相位移均可作为诊断系统神经网络的输入,但采用电流与电压的相位移,无论训练速度还是输出精度都比三相电流有效值更具优越性。因此,选择三相电流与电压相位移作为匝间故障诊断系统BP神经网络的输入,采用遗传算法优化BPNN的权值,仅通过少量样本训练就能完成学习。一旦神经网络训练成功就能正确运行,仿真和试验均证明,该方法能有效、准确地定位定子绕组匝间短路故障位置,有利于电机有针对性的维修。

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