Title: A novel parameter estimation method for permanent magnet synchronous motor drive

Authors: Pilla Ramana; Karlapudy Alice Mary; Munagala Surya Kalavathi

Addresses: Department of Electrical and Electronics Engineering, GMR Institute of Technology, Rajam, Andhra Pradesh, 532127, India ' Department of Electrical and Electronics Engineering, Gudlavalleru Engineering College, Gudlavalleru, Andhra Pradesh, 521356, India ' Department of Electrical and Electronics Engineering, JNTUH College of Engineering, Hyderabad, Telangana, 500085, India

Abstract: This paper proposes a novel parameter estimation method for permanent magnet synchronous motor (PMSM) drive based on Hartley modulating function (HMF). The speed and torque of a PMSM depend on currents directly and winding resistance, inductance indirectly. So, in order to achieve a good performance drive system, an accurate knowledge of machine parameters is important. Most of the existing techniques for parameter estimation of PMSM use a linear model of the system in order to estimate the parameters. However, PMSM is inherently nonlinear, and linear modelling is applicable for restricted operating region. The proposed estimation is free from boundary conditions and gives rise to linear-in-parameters model. Using HMF method, electrical parameters such as armature resistance (Ra), q-axis inductance (lqs), d-axis inductance (lds) and mechanical parameters such as the moment of inertia (J), viscous friction coefficient (β) are estimated with a fair amount of accuracy. The estimates have been found to be quite sensitive to the choice of sampling time and size. The proposed method is verified by MATLAB simulation, and shows better performance in the estimation process.

Keywords: permanent magnet synchronous motor; PMSM; rotor reference frame; parameter estimation; Hartley modulating function; HMF.

DOI: 10.1504/IJPEC.2018.092690

International Journal of Power and Energy Conversion, 2018 Vol.9 No.3, pp.295 - 310

Received: 24 Jun 2016
Accepted: 02 Oct 2016

Published online: 28 Jun 2018 *

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