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首页> 外文期刊>Journal of applied mathematics >Practical Aspects of Broken Rotor Bars Detection in PWM Voltage-Source-Inverter-Fed Squirrel-Cage Induction Motors
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Practical Aspects of Broken Rotor Bars Detection in PWM Voltage-Source-Inverter-Fed Squirrel-Cage Induction Motors

机译:PWM电压源-逆变器馈电鼠笼式感应电动机中转子断条检测的实践方面

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Broken rotor bars fault detection in inverter-fed squirrel cage induction motors is still as difficult as the dynamics introduced by the control system or the dynamically changing excitation (stator) frequency. This paper introduces a novel fault diagnosis techniques using motor current signature analysis (MCSA) to solve the problems. Switching function concept and frequency modulation theory are firstly used to model fault current signal. The competency of the amplitude of the sideband components at frequencies (1±2s)fsas indices for broken bars recognition is subsequently studied in the controlled motor via open-loop constant voltage/frequency control method. The proposed techniques are composed of five modules of anti-aliasing signal acquisition, optimal-slip-estimation based on torque-speed characteristic curve of squirrel cage motor with different load types, fault characteristic frequency determination, nonparametric spectrum estimation, and fault identification for achieving MCSA efficiently. Experimental and simulation results obtained on 3 kW three-phase squirrel-cage induction motors show that the model and the proposed techniques are effective and accurate.
机译:逆变器供电的鼠笼式感应电动机中转子条故障的检测仍然与控制系统引入的动力学或动态变化的励磁(定子)频率一样困难。本文介绍了一种使用电动机电流特征分析(MCSA)的新型故障诊断技术来解决该问题。首先使用开关功能概念和调频理论对故障电流信号进行建模。随后通过开环恒压/频率控制方法,在受控电动机中研究了频率为(1±2s)fsas的边带分量的振幅能力,以识别折线。所提出的技术由抗混叠信号采集,基于不同负载类型的鼠笼电动机转矩-速度特性曲线的最优滑移估计,故障特征频率确定,非参数频谱估计以及故障识别五个模块组成,以实现MCSA有效。在3 kW三相鼠笼式感应电动机上进行的实验和仿真结果表明,该模型和所提出的技术是有效且准确的。

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