Parameters Identification and Dynamical Modeling of Excitation System and Generator in a Steam Power Plant

Khodadadi, Abolfazl; Pishkesh, M. Nakhaee; Zaker, Behrooz; Karrari, M.

doi:10.1109/ceit.2018.8751800

Cited by 3 publications

(1 citation statement)

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“…Electrical machines (EM) are built from a stator, which is the static part of the EM consisting of phase-shifted coiled poles, and a rotor, which is the rotary part constructed depending on the tasks for which the machine is intended. AMs are most often used as motor-driven machines [3][4][5] in the automotive industry [6,7], construction [8], elevators [9], etc., while SMs are most often used to produce electricity in fossil fuel power plants [10][11][12] or in renewable energy plants, such as hydroelectric power [13,14] and wind power plants [15,16]. For energy to be transmitted from the producer to the final consumer, it must be of an alternating current (AC) [17].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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.

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