Long Short-Term Memory Network-based Speed Estimation Model of an Asynchronous Motor

Abstract

In this paper, an effective deep rotor speed estimation model of an asynchronous motor is presented. The estimation model is based on the long short-term memory (LSTM) network which is one of the deep learning models. The designed model includes three main steps as the preprocessing, training of the deep speed estimation model, and evaluation of the model with testing. The dataset of the asynchronous motor model is obtained in MATLAB/Simulink environment under variable step speed references. The input parameters of the network are the dq-axis currents (id, iq) and voltages (vd, vq). The output is selected as the rotor speed (wr). The whole data is normalized to increase the estimation performance and then randomly divided into the training and validation. For the testing stage, different test data is also constructed. In the training process, the variation of the network performance is analyzed according to the neuron number increasing and optimum neuron number is achieved. The obtained results show that the proposed model is robust and efficient under the variable step speed references.