Original scientific paper

https://doi.org/10.7906/indecs.22.3.8

Improving Synchronous Motor Modelling with Artificial Intelligence

Petar Čisar orcid.org/0000-0001-8009-3347 ; University of Criminal Investigation and Police Studies, Belgrade, Serbia & John Von Neumann University, GAMF Faculty of Engineering and Computer Science, Kecskemét, Hungary
Sanja Maravić Čisar orcid.org/0000-0001-8131-9141 ; Subotica Tech-College of Applied Sciences, Subotica, Serbia *
Attila Pásztor orcid.org/0000-0001-7354-5114 ; John Von Neumann University, GAMF Faculty of Engineering and Computer Science, Kecskemét, Hungary

* Corresponding author.

Abstract

Synchronous motors are essential in various industrial and commercial applications because of their efficiency and constant speed operation. Accurate modelling of these motors is crucial for optimizing performance, control, and maintenance. Traditional modelling methods, such as the d-q reference frame method, often fall short in terms of complexity and accuracy, especially under dynamic conditions. This study aims to enhance synchronous motor modelling using machine learning algorithms, specifically focussing on predicting the excitation current, a critical parameter for motor performance.
In this research, a dataset comprising synchronous motor operational parameters was analysed using various machine learning techniques. The primary methods evaluated include regression and M5 algorithms. The evaluation criteria were the time required to build and test the models and the accuracy of their predictions. Our findings indicate that both the regression and M5 algorithms significantly outperform traditional methods, providing more precise and efficient models for synchronous motor behaviour under diverse operating conditions.

Keywords

synchronous motors; parameters; machine learning; prediction; excitation current

Hrčak ID:

318428

URI

https://hrcak.srce.hr/318428

Publication date:

30.6.2024.

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