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Phase flux linkage estimation of external rotor switched reluctance motor with NARX neural network

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Abstract

In this study, the stator phase flux linkage of a switched reluctance motor was estimated using a nonlinear autoregressive network with external input (NARX) neural network (NN). The application of artificial neural network (ANN) technique for phase flux linkage estimation yielded satisfactory results and the online phase resistance variation compensation requirement, which is an important problem in classical flux linkage calculation methodology, was eliminated. Using the NARX neural network model, which was trained with a set of experimental data, the phase flux linkage value was estimated by the NN from the phase current measurements during real-time operation. The stator phase current values measured under different speed and load conditions together with offline calculated flux linkage values composed the training dataset. The training data preparation methodology was explained in section 2. It was followed by an explanation of the NN training process and the training results were given. The NN based phase flux linkage estimation technique was compared with other techniques such as finite element analysis, torque balance method, voltage balance method in section 7 experimentally.

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Funding

This study was funded by TUBITAK (The Scientific and Technological Research Council of Turkiye) 1002 Short-Term R&D Funding Program (Project Number 119E020).

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Authors and Affiliations

  1. Electrical and Electronics Engineering Department, Karadeniz Technical University, 61080, Trabzon, Turkey

    Mustafa Aydemir & Halil Ibrahim Okumus

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  1. Mustafa Aydemir
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  2. Halil Ibrahim Okumus
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MA wrote the main manuscript text and Prof. HIO reviewed the manuscript.

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Correspondence to Mustafa Aydemir.

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Aydemir, M., Okumus, H.I. Phase flux linkage estimation of external rotor switched reluctance motor with NARX neural network. Electr Eng 105, 1223–1233 (2023). https://doi.org/10.1007/s00202-022-01726-x

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