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

<p>In modern areas of knowledge related to electric drive automation, there is often a need to predict the state variables of the drive system state variables, such as phase current and voltage, electromagnetic torque, stator and rotor flux, and others. This need arises mainly from the use of predictive control algorithms but also from the need to monitor the state of the drive to diagnose possible faults that have not yet occurred but may occur in the future. This paper presents a method for predicting stator phase current signals using a network composed of long-short-term memory units, allowing the simultaneous prediction of two signals. The developed network was trained on a set of current signals generated by software. Its operation was verified by simulation tests in a direct rotor flux-oriented control (DRFOC) structure for an induction motor drive in the Matlab/Simulink environment. An important property of this method is the possibility of obtaining a filtering action on the output of the network, whose intensity can be controlled by varying the sampling frequency of the training signals.</p>
</abstract>

In modern areas of knowledge related to electric drive automation, there is often a need to predict the state variables of the drive system state variables, such as phase current and voltage, electromagnetic torque, stator and rotor flux, and others. This need arises mainly from the use of predictive control algorithms but also from the need to monitor the state of the drive to diagnose possible faults that have not yet occurred but may occur in the future. This paper presents a method for predicting stator phase current signals using a network composed of long-short-term memory units, allowing the simultaneous prediction of two signals. The developed network was trained on a set of current signals generated by software. Its operation was verified by simulation tests in a direct rotor flux-oriented control (DRFOC) structure for an induction motor drive in the Matlab/Simulink environment. An important property of this method is the possibility of obtaining a filtering action on the output of the network, whose intensity can be controlled by varying the sampling frequency of the training signals.

Analysis of the Stator Current Prediction Capabilities in Induction Motor Drive Using the LSTM Network

Department of Electrical Machines, Drives and Measurements

Wrocław University of Science and Technology

Power Electronics and Drives

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

In modern areas of knowledge related to electric drive automation, there is often a need to predict the state variables of the drive system state variables,...

Analysis of the Stator Current Prediction Capabilities in Induction Motor Drive Using the LSTM Network

Online veröffentlicht: 17. Jan. 2023

Seitenbereich: 31 - 52

Eingereicht: 06. Nov. 2022

Akzeptiert: 13. Dez. 2022

DOI: https://doi.org/10.2478/pead-2023-0003

Schlüsselwörter
current sensors, faults, signal prediction, LSTM, induction motor drive

© 2023 Krystian Teler et al., published by Sciendo

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

Analysis of the Stator Current Prediction Capabilities in Induction Motor Drive Using the LSTM Network

Online veröffentlicht: 17. Jan. 2023

Seitenbereich: 31 - 52

Eingereicht: 06. Nov. 2022

Akzeptiert: 13. Dez. 2022

DOI: https://doi.org/10.2478/pead-2023-0003

Schlüsselwörtercurrent sensors, faults, signal prediction, LSTM, induction motor drive

© 2023 Krystian Teler et al., published by Sciendo

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

Schlüsselwörter
current sensors, faults, signal prediction, LSTM, induction motor drive