基于参数辨识的异步电动机温度在线监测方法

提出了一种基于参数辨识技术的异步电动机定转子温度在线监测方法，即首先辨识异步电动机定转子电阻，随后根据金属电阻与其温度之间具有严格线性关系这一原理，计算异步电动机定转子温度，进而实现在线监测。数字仿真及实验结果证明该方法是正确可行的，其突出优点是仅根据异步电动机定子电压、定子电流及有功功率即可实现定转子温度在线监测，而不必使用温度传感器。同时，为了避免使用转速传感器，应用ＢＰ神经网络确定了异步电动机定子电压、定子电流、功率因数角与其转子转速之间的映射关系。基于参数辨识技术的异步电动机定转子温度在线监测方法简捷实用，可集成于异步电动机状态监测与故障诊断系统，无需任何额外的硬件投资，同时，软件开销也很有限。

Temperature monitoring for asynchronous motors based on parameter identification technique

The stator and rotor temperatures are of concern in condition monitoring and faults diagnosis system for asynchronous motors. A novel temperature monitoring method for asynchronous motors based on parameter identification technique is presented in this paper. The stator and rotor resistance are identified on-line, and then the corresponding temperatures are calculated according to the principle that the metal resistance linearly depends on its temperature. Results of numerical simulations and experimental tests show that the method is valid and effective. It is the prominent advantage that only the stator voltage, stator current and active power information is necessary for the proposed method to realize the temperature monitoring. That means the temperature transducers, which are expensive and delicate, are avoidable. At the same time, the mapping relationship between the stator voltage, stator current, active power information and the rotor speed is determined using the BP neural network. So, the speed transducer is also evitable. It is easy to integrate the proposed method with the condition monitoring and faults diagnosis system for asynchronous motors.