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

Açıkgöz, Hakan; Korkmaz, Deniz

doi:10.1109/atee52255.2021.9425346

Cited by 3 publications

(1 citation statement)

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“…One of the reasons why the present research might be considered relatively unique is that it involves the evaluation of both different number layers and different hidden sizes over the estimations. Compared to Acikgoz and Korkmaz (2021) and Ilten et al (2022), both the train data and test data contain an operation condition in both directions of rotation along with the challenging load torque variations. Therefore, the operation of the IM in both directions is examined in this paper.…”

Section: Introductionmentioning

confidence: 99%

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

Kosten,

Emlek,

Yildiz

et al. 2023

Power Electronics and Drives

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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.

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Section: Introductionmentioning

confidence: 99%