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

• Autorzy: Kosten Mehmet Muzaffer , Emlek Alper , Yildiz Recep , Barut Murat
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

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

• Wydawca: De Gruyter
• Czasopismo: Power Electronics and Drives
• Rocznik: 2023
• Tom: Vol. 8 (43)
• Strony: 310--324
• Opis fizyczny: Bibliogr. 31 poz., rys., tab.

Twórcy

autor

Kosten Mehmet Muzaffer

• Department of Electrical and Electronic Engineering, Nigde Omer Halisdemir University, Nigde, Türkiye
• Koç Bilgi ve Savunma Teknolojileri A.Ş., Middle East Technical University Technopolis, Ankara, Türkiye

autor

Emlek Alper

• Department of Electrical and Electronic Engineering, Nigde Omer Halisdemir University, Nigde, Türkiye

• https://orcid.org/0000-0001-8161-7181

autor

Yildiz Recep

• Department of Electrical and Electronic Engineering, Nigde Omer Halisdemir University, Nigde, Türkiye

• https://orcid.org/0000-0002-8167-321X

autor

Barut Murat

• Department of Electrical and Electronic Engineering, Nigde Omer Halisdemir University, Nigde, Türkiye

• https://orcid.org/0000-0001-6798-0654

Bibliografia

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Uwagi

Special Section - Artificial Intelligent Based Designs and Applications for the Control of Electrical Drives

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).