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

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dc.authoridYILDIZ, RECEP/0000-0002-8167-321X
dc.authoridEMLEK, Alper/0000-0001-8161-7181
dc.contributor.authorKosten, Mehmet Muzaffer
dc.contributor.authorEmlek, Alper
dc.contributor.authorYildiz, Recep
dc.contributor.authorBarut, Murat
dc.date.accessioned2024-11-07T13:31:20Z
dc.date.available2024-11-07T13:31:20Z
dc.date.issued2023
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractIn 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.
dc.identifier.doi10.2478/pead-2023-0021
dc.identifier.endpage324
dc.identifier.issn2451-0262
dc.identifier.issn2543-4292
dc.identifier.issue1
dc.identifier.startpage310
dc.identifier.urihttps://doi.org/10.2478/pead-2023-0021
dc.identifier.urihttps://hdl.handle.net/11480/14761
dc.identifier.volume8
dc.identifier.wosWOS:001080390000005
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.language.isoen
dc.publisherSciendo
dc.relation.ispartofPower Electronics and Drives
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241106
dc.subjectlong short-term memory
dc.subjectdeep neural network
dc.subjectelectrical vehicle
dc.subjectinduction motor
dc.subjectstate and parameter estimation
dc.titleRotor Speed and Load Torque Estimations of Induction Motors via LSTM Network
dc.typeArticle

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