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

Abstract

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.

Optimal voltage angle for maximum torque per voltage control of induction machine in deep field‐weakening region

“…The sampling time is synchronised with the PWM up‐down counters, operating at 10 kHz. The implemented code utilised a direct rotor flux linkage‐oriented (DRFOC) approach with algorithms for magnetizing inductance (Lm‐MRAS) and rotor resistance (Q‐MRAS) estimation described in [17]. The utilised configuration does not represent any practical control scheme but is used solely for the experimental verification of the theoretical and numerical results.…”

“…Before each set of measurements, the load torque was briefly increased close to the nominal value, and Q‐MRAS measured rotor resistance with the active Lm‐MRAS for the magnetizing inductance estimation as described in [17]. The reason for setting the higher torque is the better estimation accuracy of the Q‐MRAS algorithm.…”

“…The block diagram of the experimental setup and the implemented DSP code is depicted in Figure 10, and a picture of the experimental setup is shown in Figure 11. It is worth noting that, compared to [17], the iron losses were not considered since they were omitted in the analytically derived expressions in this paper. Also, the most significant inverter nonlinearities were compensated, as described in [17].…”

Long-Horizon Robust Direct Model Predictive Control for Medium-Voltage Induction Motor Drives With Reduced Computational Complexity

“…An alternative is to employ system identification algorithms. Such methods are either white-box model-based approaches, since they assume full knowledge of the system [17]- [20], or they do not depend on the model at all, i.e., they are black-box methods [21], [22]. The former techniques, however, cannot simultaneously estimate all the system parameters, meaning that combinations of different sources of uncertainties/model mismatches are usually not considered.…”

“…As a result, the performance of such methods is not the most desired for a wide range of operating conditions. For example, the fidelity of the model reference adaptive system (MRAS) approach adopted in [20] to estimate the mutual inductance degrades linearly with the increasing IM load torque. Moreover, as the MRAS adaptive model depends, among others, on the rotor resistance, a reactive power MRAS (Q-MRAS) scheme is implemented in parallel to improve the estimation accuracy of the machine parameters.…”