基于模型与BPNN感应电机定子匝间故障定位方法

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摘要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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	王旭红
	何怡刚
	王昆洋

关键词 ：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

Abstract：In order to detect and locate an inter-turn short circuit fault on the stator windings of induction motors, an approach based on faulty model and BP neural network(BPNN)is presented. The diagnostic process can be achieved through monitoring both simultaneously the values of the three-phase current root mean square(rms)and three-phase shifts between the line current and the phase voltage of the machine. By analysis, it is rapid and accurate to choose three-phase shifts as inputs for the BPNN of the diagnostic system. For improving dynamical processing ability, genetic algorithm is adopted to optimize the BP neural network parameters. Simulation and experimental results have verified the validity of the method.

Key words： keywords：Induction motor inter-turn short circuit fault location phase shift BP neural network genetic algorithm

收稿日期: 2012-09-10 出版日期: 2013-12-12

PACS:

TM351

作者简介: 王旭红女，1969年生，博士，教授，主要从事电力设备在线监测与故障诊断方面的研究。何怡刚男，1966年生，教授，博士生导师，主要从事复杂电气工程自动测试与诊断装备、复杂电磁分析与检测方面的研究。

引用本文:

王旭红, 何怡刚, 王昆洋. 基于模型与BPNN感应电机定子匝间故障定位方法[J]. 电工技术学报, 2013, 28(1增): 336-345. Wang Xuhong, He Yigang, Wang Kunyang. An Approach for Location of Stator Interturn Faults in Induction Motor Based on the Model and Neural Network. Transactions of China Electrotechnical Society, 2013, 28(1增): 336-345.

链接本文:

http://dgjsxb.ces-transaction.com/CN/Y2013/V28/I1增/336

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