Machine learning for parameter identification of electric induction machines
This thesis is concerned with the application of simulated evolution (SE) to the
steady-state parameter identification problem of a simulated and real 3-phase
induction machine, over the no-load direct-on-line start period.
In the case of the simulated 3-phase induction machine, the Kron's two-axis
dynamic mathematical model was used to generate the real and simulated system
responses where the induction machine parameters remain constant over
the entire range of slip. The model was used in the actual value as well as
the per-unit system, and the parameters were estimated using both the genetic
algorithm (GA) and the evolutionary programming (EP) from the machine's
dynamic response to a direct-on-line start. Two measurement vectors represented
the dynamic responses and all the parameter identification processes
were subject to five different levels of measurement noise.
For the case of the real 3-phase induction machine, the real system responses
were generated by the real 3-phase induction machine whilst the simulated
system responses were generated by the Kron's model. However, the
real induction machine's parameters are not constant over the range of slip,
because of the nonlinearities caused by the skin effect and saturation. Therefore,
the parameter identification of a real3-phase induction machine, using EP
from the machine's dynamic response to a direct-on-line start, was not possible
by applying the same methodology used for estimating the parameters of the
simulated, constant parameters, 3-phase induction machine.