Estimation of Flux Saturation Model for SynRMs Using Artificial Neural Network

Abstract

This paper proposes a flux saturation model of synchronous reluctance machines (SynRMs) using an artificial neural network. The proposed ANN model is structured with current as the input and flux as the output, and supervised learning is conducted using the current and flux data. Considering test conditions of motor drive systems, flux data were acquired through two methods based on the voltage equation. For the first one, flux data were obtained in a stationary state using hysteresis current control. In the other, flux data were acquired in a rotating state. Since the acquired data are influenced by sensor noise and inverter non-linearity, data preprocessing was performed through cleansing and normalization. Furthermore, the model was designed to be light-weighted for applications in motor drive systems. To verify the feasibility of two ANN models which were trained with different datasets, the outputs of the ANN models were compared with the current-flux data obtained in the rotating state. Moreover, the proposed ANN models were verified using model-based sensorless drives in a 1.5 kW SynRM.