Machine Learning Techniques Applied to the Harmonic Analysis of Railway Power Supply

Manuela Panoiu (), Caius Panoiu, Sergiu Mezinescu, Gabriel Militaru and Ioan Baciu
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Manuela Panoiu: Department of Electrical Engineering and Industrial Informatics, University Polytechnica Timisoara, 331128 Hunedoara, Romania
Caius Panoiu: Department of Electrical Engineering and Industrial Informatics, University Polytechnica Timisoara, 331128 Hunedoara, Romania
Sergiu Mezinescu: Department of Electrical Engineering and Industrial Informatics, University Polytechnica Timisoara, 331128 Hunedoara, Romania
Gabriel Militaru: Department of Electrical Engineering and Industrial Informatics, University Polytechnica Timisoara, 331128 Hunedoara, Romania
Ioan Baciu: Department of Electrical Engineering and Industrial Informatics, University Polytechnica Timisoara, 331128 Hunedoara, Romania

Mathematics, 2023, vol. 11, issue 6, 1-20

Abstract: Harmonic generation in power system networks presents significant issues that arise in power utilities. This paper describes a machine learning technique that was used to conduct a research study on the harmonic analysis of railway power stations. The research was an investigation of a time series whose values represented the total harmonic distortion (THD) for the electric current. This study was based on information collected at a railway power station. In an electrified substation, measurements of currents and voltages were made during a certain interval of time. From electric current values, the THD was calculated using a fast Fourier transform analysis (FFT) and the results were used to train an adaptive ANN—GMDH (artificial neural network–group method of data handling) algorithm. Following the training, a prediction model was created, the performance of which was investigated in this study. The model was based on the ANN—GMDH method and was developed for the prediction of the THD. The performance of this model was studied based on its parameters. The model’s performance was evaluated using the regression coefficient (R), root-mean-square error (RMSE), and mean absolute error (MAE). The model’s performance was very good, with an RMSE (root-mean-square error) value of less than 0.01 and a regression coefficient value higher than 0.99. Another conclusion from our research was that the model also performed very well in terms of the training time (calculation speed).

Keywords: harmonic analysis; total harmonic distortion; computational techniques; prediction; machine learning; electrified railway; GMDH (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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