The problem of estimating the parameters of induction motor models is
considered, using the data measured by a circuit breaker equipped with
industrial sensors. The measured data pertain to direct-on-line motor startups,
during which the breaker acquires three-phase stator voltage and current
derivative. This setup is novel with respect to previous contributions in the
literature, where voltage and current (and possibly also rotor speed) are
considered. The collected data are used to formulate a parameter identification
problem, where the cost function penalizes the discrepancy between simulated
and measured derivatives of the stator currents. The resulting nonlinear
program is solved via numerical optimization, and a number of algorithmic
improvements with respect to the literature are proposed. In order to evaluate
the goodness of the obtained results, an experimental rig has been built, where
the motor's voltages and currents are simultaneously acquired also by accurate
sensors, and the corresponding identification results are compared with those
obtained with the circuit breaker. The presented experimental results indicate
that the considered industrial circuit breaker is able to provide data with
high-enough quality to carry out model-based nonlinear identification of
induction machines. The identified models can then be used for several further
applications within a smart grid scenario
