Simscape PMSM start-up

In the first step, I try to simulate a PMSM start-up sequence without V/f, FOC, or anything else. Just a programmable 3-phase voltage source ramped up from 1 to 5120 Hz within a period of 5 s. Until approx. 1 s, the velocity increases as expected, but then it starts to oscillate and drops to 0 rpm. If I increase the ramp up periode to 30 s, this also happens but a little bit later. I used this motor as reference but with 8 pole pairs and a max. speed of 38 400 rpm: RPX22-042V24

Motor load is set to 0, only the rotor inertia is configured in the PMSM-Block.

I have 2 questions

1. I obviously did something wrong with the model. I am not very familar with motor parameters. Did I do something wrong in the paramters of the PMSM-Block? (e.g. the datasheet doesn't provide a stator mutual inductance, so I had to estimate a value)
2. If I want to use the fixed frequency voltage source, my expectation is that the motor will have a constant velocity after setting a rotor speed inital value, but it drops to 0 rpm. I am not sure why?

[Kai]

I tested it also with the default PMSM Model from the simscape toolbox and I can see the same behaviour. The frequency ramps up from 1 to 300 Hz within 4 s. The default PMSM has 6 pole pairs so it should reach 3000 rpm, but it stops at 1500 rpm and drops to zero.

How is it possible to reach nomial rpm with a programmable voltage source and a PMSM in matlab?

Answer 1

[Joel Van Sickel]

In your initial model, you say that you use a motor with a max speed of 400 rpm. However, in your simulation, the simulation is failing closer to 6000 RPM, so you are driving the motor significantly faster than it was desigend to handle. In general you couldn't spin a PMSM this fast just by hooking it up to a constant voltage changing frequency. Specialized control is required for high speed motors. Also, this is a lumped parameter motor model, so it is effectifly acting linearly, and motors pushed to their extremes never act linearly, so extrapolating out this far is also likely to provide poor results. There isn't anything wrong with your model, you are expecting behavior that you should not see in this scenario.