Squirrel-cage induction motors are widely used in industrial applications. Their electrical parameters often need to be estimated for high-performance motor drives using field oriented control. The estimated parameters can also provide indispensable information for the purpose of motor condition monitoring, diagnosis, and protection. For example, an accurate estimate of the rotor temperature in a model-reference adaptive system (“MRAS”) requires precise knowledge of a motor's stator inductance and total leakage factor.
There are typically four major approaches to obtaining the induction motor electrical parameters. The first approach involves locked-rotor and no-load tests according to an IEEE standard test procedure for polyphase induction motors and generators. Such tests require interruption of normal motor operation. Therefore, it is often difficult, if not impossible, to apply these tests to motors that have already been installed. Consequently, various estimation methods have been proposed for in-service motors based on a steady-state induction motor equivalent circuit model. According to this model, these estimation methods collect voltage and current measurements from a line-connected motor operated at various load levels and use equations to calculate the induction motor electrical parameters without actually having to stop the motor. A ratio of the stator leakage inductance to the magnetizing inductance is assumed during calculations to simplify the estimation process.
The second approach is based on signal injection techniques and is often used for inverter-fed motors. By controlling the inverter switches, different voltage waveforms are generated and applied to the stator terminals. Induction motor electrical parameters, such as the stator and rotor resistances, as well as, the transient and magnetizing inductances, are then extracted from stator voltage and current measurements. Some signal injection techniques require the rotor to remain stationary during the commissioning process, while other injection methods do not have such a restriction.
Although reasonably accurate estimates of induction motor electrical parameters are usually obtainable using this approach, in practice, it is rather impractical to implement this approach for line-connected motors because separate electronic circuits are required to modify the stator voltage waveforms.
The third approach involves iterative tuning of induction motor electrical parameters to minimize certain error indices. However, while certain schemes in this approach are based on the steady-state induction motor equivalent circuit model, others still require spectrally rich excitation signals be injected into the motor. It is possible that they do not work well for line-connected motors under dynamic motor operations, such as the applications where motors are connected to time-varying loads of reciprocating compressors or pumps.
The fourth approach is based on a dynamic induction motor equivalent circuit model. This approach estimates the induction motor electrical parameters by computing a least-squares solution. This least-squares solution computation technique requires the knowledge of a rotor speed, usually obtained from a mechanical speed sensor attached to the shaft of a motor. However, because of the cost and fragile nature of such a speed sensor, and because of the difficulty of installing the sensor in many motor applications, speed-sensorless schemes based on induction motor magnetic saliency are preferred.
What is needed is a cost-effective induction motor condition monitoring, diagnosis, and protection system that can accurately and reliably determine induction motor electrical parameters from motor nameplate data plus voltage and current measurements. What is also needed is an induction motor condition monitoring, diagnosis, and protection system that is capable of producing such estimates during steady-state and/or dynamic conditions.