Analysis and application of two recursive parametric estimation algorithms for an asynchronous machine

In this communication, two recursive parametric estimation algorithms are analyzed and applied to an squirrel-cage asynchronous machine located at the research “Unit of Automatic Control” (UCA) at ENIS. The first algorithm which, use the transfer matrix mathematical model, is based on the gradient principle. The second algorithm, which use the state-space mathematical model, is based on the minimization of the estimation error. These algorithms are applied as a key technique to estimate asynchronous machine with unknown, but constant or time-varying parameters. Stator voltage and current are used as measured data. The proposed recursive parametric estimation algorithms are validated on the experimental data of an asynchronous machine under normal operating condition as full load. The results show that these algorithms can estimate effectively the machine parameters with reliability.