MRAS-Based Induction Machine Magnetizing Inductance Estimator With Included Effect of Iron Losses and Load

Although still widely used due to its robustness, reliability, and low cost, induction motor (IM) has a disadvantage of more complicated mathematical description than permanent magnet AC machines. In high-demanding applications, the decoupled control of the machine’s flux and torque along with the proper function of selected efficiency-improving and flux-weakening algorithms can be achieved only if the IM parameters are known with sufficient accuracy. For parameter estimation, many algorithms have been proposed in the literature so far. Due to its simple and straightforward implementation, one of the popular estimation strategies is the model reference adaptive system (MRAS). However, MRAS-based algorithms for a specific parameter estimation tend to be sensitive to other machine parameters. For instance, most of the proposed MRAS algorithms do not consider the influence of the phenomena such as iron losses and load-dependent saturation. Since one of the most performance-decisive parameters of the popular rotor flux-oriented control (RFOC) are the magnetizing inductance and the rotor resistance, this paper aims to present a novel MRAS-based magnetizing inductance estimator (Lm-MRAS) with the included effect of iron losses. Furthermore, to enable the identification of the load-dependent saturation, another MRAS with included iron losses based on reactive power is proposed to work parallelly with Lm-MRAS, since under load conditions, the rotor resistance mismatch causes RFOC detuning. The adaptation law of the Lm-MRAS is obtained using the Lyapunov function approach and further examined using small-signal analysis. The proposed algorithms are verified on a 3.6 kW IM drive both in simulations and experiments.