Detection of induction machine winding faults using genetic algorithm

M. Alamyal, S. Gadoue, B. Zahawi
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引用次数: 26

Abstract

In this paper, an identification technique for fault detection of induction machines using Genetic Algorithm (GA) is investigated. The condition monitoring technique proposed in this paper indicates the presence of a winding fault and provides information about its nature and location. The data required for the proposed method are motor terminal voltages, stator currents and rotor speed obtained during steady state operation. The data is then processed off-line using an induction motor model in conjunction with GA to determine the effective motor parameters. The proposed technique is demonstrated using experimental data obtained from a 1.5 kW wound rotor three-phase induction machine with both stator and rotor winding faults considered. Results confirm the effectiveness of GA to properly identify the type and location of the fault without the need for knowledge of various fault signatures.
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感应电机绕组故障的遗传算法检测
本文研究了一种基于遗传算法的感应电机故障识别技术。本文提出的状态监测技术可以指示绕组故障的存在,并提供故障的性质和位置信息。所提出的方法所需的数据是在稳态运行时获得的电机端子电压、定子电流和转子转速。然后使用感应电机模型结合遗传算法离线处理数据,以确定有效的电机参数。在考虑定子和转子绕组故障的情况下,利用1.5 kW绕线转子三相感应电机的实验数据对该技术进行了验证。结果证实了遗传算法在不需要了解各种故障特征的情况下正确识别故障类型和位置的有效性。
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