Affiliations: Research Scholar, Faculty of Electrical and Electronics Engineering, Sathyabama University, Chennai, India | R&D Head, ESAB Group, Sriperumbudur, Kanchipuram, India
Note: [] Corresponding author. D. Susitra, Research Scholar, Faculty of Electrical and Electronics Engineering, Sathyabama University, Chennai 600 119, India. E-mail: susitradhanraj@gmail.com
Abstract: A Non-linear flux linkage model of a Switched reluctance Machine (SRM) has been developed using two different real time applicable modelling techniques and presented in this paper. The techniques are based on Multivariate nonlinear regression (MVNLR) and adaptive neuro fuzzy inference system (ANFIS). The best features of MVNLR and ANFIS are utilized in this research to develop the computationally efficient flux linkage model for SRM. Mathematical models for the phase flux linkage ψ(i, θ) using MVNLR and ANFIS have been successfully arrived, tested and presented for various values of phase currents (Iph) and rotor positions (θ) of a non linear SRM. It is observed that MVNLR and ANFIS are highly suitable for flux linkage ψ(i, θ) modelling of SRM which is tested to be in good agreement with the training data used for modelling.
