Naslov
Estimation of Excitation Current of a Synchronous Machine Using Machine Learning Methods

Autori
Glučina, Matko ; Anđelić, Nikola ; Lorencin, Ivan ; Car, Zlatan

Izvornik
Computers (Basel) (2073-431X) 12 (2023), 1; 1-25

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
artificial intelligence algorithms ; excitation current ; regression algorithms ; synchronous machine
(algoritmi umjetne inteligencije ; uzbudna struja ; regresijski algoritmi ; sinkroni stroj ;)

Sažetak
A synchronous machine is an electro-mechanical converter consisting of a stator and a rotor. The stator is the stationary part of a synchronous machine that is made of phase-shifted armature windings in which voltage is generated and the rotor is the rotating part made using permanent magnets or electromagnets. The excitation current is a significant parameter of the synchronous machine, and it is of immense importance to continuously monitor possible value changes to ensure the smooth and high-quality operation of the synchronous machine itself. The purpose of this paper is to estimate the excitation current on a publicly available dataset, using the following input parameters: Iy: load current ; PF: power factor ; e: power factor error ; and df : changing of excitation current of synchronous machine, using artificial intelligence algorithms. The algorithms used in this research were: k- nearest neighbors, linear, random forest, ridge, stochastic gradient descent, support vector regressor, multi-layer perceptron, and extreme gradient boost regressor, where the worst result was elasticnet, with R2 = −0.0001, MSE = 0.0297, and MAPE = 0.1442 ; the best results were provided by extreme boosting regressor, with R2 = 0.9963, MSE = 0.0001, and MAPE = 0.0057, respectively.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika, Računarstvo, Temeljne tehničke znanosti, Interdisciplinarne tehničke znanosti

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