Improving Synchronous Motor Modelling with Artificial Intelligence

Petar Cisar, Sanja Maravic Cisar () and Attila Pásztor
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Petar Cisar: University of Criminal Investigation and Police Studies, Belgrade, Serbia
Sanja Maravic Cisar: Subotica Tech-College of Applied Sciences, Subotica, Serbia
Attila Pásztor: John Von Neumann University, GAMF Faculty of Engineering and Computer Science, Kecskemét, Hungary

Interdisciplinary Description of Complex Systems - scientific journal, 2024, vol. 22, issue 3, 329-340

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 (search for similar items in EconPapers)
JEL-codes: C45 C63 (search for similar items in EconPapers)
Date: 2024
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