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

Author

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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)

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).

Suggested Citation

  • Manuela Panoiu & Caius Panoiu & Sergiu Mezinescu & Gabriel Militaru & Ioan Baciu, 2023. "Machine Learning Techniques Applied to the Harmonic Analysis of Railway Power Supply," Mathematics, MDPI, vol. 11(6), pages 1-20, March.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:6:p:1381-:d:1095188
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    References listed on IDEAS

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    1. Eslami, Ahmadreza & Negnevitsky, Michael & Franklin, Evan & Lyden, Sarah, 2022. "Review of AI applications in harmonic analysis in power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
    2. Matej Žnidarec & Zvonimir Klaić & Damir Šljivac & Boris Dumnić, 2019. "Harmonic Distortion Prediction Model of a Grid-Tie Photovoltaic Inverter Using an Artificial Neural Network," Energies, MDPI, vol. 12(5), pages 1-19, February.
    3. José Manuel Gámez Medina & Jorge de la Torre y Ramos & Francisco Eneldo López Monteagudo & Leticia del Carmen Ríos Rodríguez & Diego Esparza & Jesús Manuel Rivas & Leonel Ruvalcaba Arredondo & Alejand, 2022. "Power Factor Prediction in Three Phase Electrical Power Systems Using Machine Learning," Sustainability, MDPI, vol. 14(15), pages 1-14, July.
    4. Vaclav Kus & Bohumil Skala & Pavel Drabek, 2021. "Complex Design Method of Filtration Station Considering Harmonic Components," Energies, MDPI, vol. 14(18), pages 1-17, September.
    5. Zhengwei Huang & Jin Huang & Jintao Min, 2022. "SSA-LSTM: Short-Term Photovoltaic Power Prediction Based on Feature Matching," Energies, MDPI, vol. 15(20), pages 1-16, October.
    6. De Giorgi, M.G. & Malvoni, M. & Congedo, P.M., 2016. "Comparison of strategies for multi-step ahead photovoltaic power forecasting models based on hybrid group method of data handling networks and least square support vector machine," Energy, Elsevier, vol. 107(C), pages 360-373.
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    Cited by:

    1. Manuela Panoiu & Caius Panoiu & Petru Ivascanu, 2024. "Power Factor Modelling and Prediction at the Hot Rolling Mills’ Power Supply Using Machine Learning Algorithms," Mathematics, MDPI, vol. 12(6), pages 1-26, March.
    2. Anca-Elena Iordan, 2024. "An Optimized LSTM Neural Network for Accurate Estimation of Software Development Effort," Mathematics, MDPI, vol. 12(2), pages 1-22, January.
    3. Rafael S. Salles & Sarah K. Rönnberg, 2023. "Review of Waveform Distortion Interactions Assessment in Railway Power Systems," Energies, MDPI, vol. 16(14), pages 1-33, July.

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