<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In this study, a long short-term memory (LSTM) based estimator using rotating axis components of the stator voltages and currents as inputs is designed to perform estimations of rotor mechanical speed and load torque values of the induction motor (IM) for electrical vehicle (EV) applications. For this aim, first of all, an indirect vector controlled IM drive is implemented in simulation to collect both training and test datasets. After the initial training, a fine-tuning process is applied to increase the robustness of the proposed LSTM network. Furthermore, the LSTM parameters, layer size, and hidden size are also optimised to increase the estimation performance. The proposed LSTM network is tested under two different challenging scenarios including the operation of the IM with linear and step-like load torque changes in a single direction and in both directions. To force the proposed LSTM network, it is also tested under the variation of stator and rotor resistances for the both-direction scenario. The obtained results confirm the highly satisfactory estimation performance of the proposed LSTM network and its applicability for the EV applications of the IMs.</p>
</abstract>

In this study, a long short-term memory (LSTM) based estimator using rotating axis components of the stator voltages and currents as inputs is designed to perform estimations of rotor mechanical speed and load torque values of the induction motor (IM) for electrical vehicle (EV) applications. For this aim, first of all, an indirect vector controlled IM drive is implemented in simulation to collect both training and test datasets. After the initial training, a fine-tuning process is applied to increase the robustness of the proposed LSTM network. Furthermore, the LSTM parameters, layer size, and hidden size are also optimised to increase the estimation performance. The proposed LSTM network is tested under two different challenging scenarios including the operation of the IM with linear and step-like load torque changes in a single direction and in both directions. To force the proposed LSTM network, it is also tested under the variation of stator and rotor resistances for the both-direction scenario. The obtained results confirm the highly satisfactory estimation performance of the proposed LSTM network and its applicability for the EV applications of the IMs.

Rotor Speed and Load Torque Estimations of Induction Motors via LSTM Network

Department of Electrical and Electronic Engineering

Middle East Technical University Technopolis

Power Electronics and Drives

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Rotor Speed and Load Torque Estimations of Induction Motors via LSTM Network"}

In this study, a long short-term memory (LSTM) based estimator using rotating axis components of the stator voltages and currents as inputs is designed to...

Rotor Speed and Load Torque Estimations of Induction Motors via LSTM Network

Online veröffentlicht: 01. Okt. 2023

Seitenbereich: 310 - 324

Eingereicht: 14. Juni 2023

Akzeptiert: 02. Sept. 2023

DOI: https://doi.org/10.2478/pead-2023-0021

Schlüsselwörter
long short-term memory, deep neural network, electrical vehicle, induction motor, state and parameter estimation

© 2023 Mehmet Muzaffer Kosten et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Rotor Speed and Load Torque Estimations of Induction Motors via LSTM Network

Online veröffentlicht: 01. Okt. 2023

Seitenbereich: 310 - 324

Eingereicht: 14. Juni 2023

Akzeptiert: 02. Sept. 2023

DOI: https://doi.org/10.2478/pead-2023-0021

Schlüsselwörterlong short-term memory, deep neural network, electrical vehicle, induction motor, state and parameter estimation

© 2023 Mehmet Muzaffer Kosten et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
long short-term memory, deep neural network, electrical vehicle, induction motor, state and parameter estimation