Optimal efficiency control of synchronous reluctance motors-based ANN considering cross magnetic saturation and iron losses

This paper presents a new method by using the Artificial Neural Networks (ANNs) for estimating the parameters of the machine which achieving the optimal efficiency of the Synchronous Reluctance Motor (SynRM). This model take into consideration the magnetic saturation, cross-coupling and iron losses. With Finite Element Analysis (FEA), the characteristics of the SynRM including inductances and iron loss resistance are determined. Because of the non-linear characteristics, an ANN is trained to obtain the d-q inductances and the iron loss resistance from Id, Iq currents and rotor speed. After learning process, an analytical expression of the optimal currents is given thanks to Lagrange optimization. Therefore, the optimal currents will be obtained online in real time. This method can be achieved with maximum efficiency and high-precision torque control. Simulation and experimental results are presented to confirm the validity of the proposed method.