IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i6p1381-d1095188.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/6/1381/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/11/6/1381/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kanwal, S. & Khan, B. & Ali, S.M. & Mehmood, C.A., 2018. "Gaussian process regression based inertia emulation and reserve estimation for grid interfaced photovoltaic system," Renewable Energy, Elsevier, vol. 126(C), pages 865-875.
    2. Md Tariqul Islam & M. J. Hossain, 2023. "Artificial Intelligence for Hosting Capacity Analysis: A Systematic Literature Review," Energies, MDPI, vol. 16(4), pages 1-33, February.
    3. Shuyu Dai & Dongxiao Niu & Yaru Han, 2018. "Forecasting of Energy-Related CO 2 Emissions in China Based on GM(1,1) and Least Squares Support Vector Machine Optimized by Modified Shuffled Frog Leaping Algorithm for Sustainability," Sustainability, MDPI, vol. 10(4), pages 1-17, March.
    4. Zhou, Yi & Zhou, Nanrun & Gong, Lihua & Jiang, Minlin, 2020. "Prediction of photovoltaic power output based on similar day analysis, genetic algorithm and extreme learning machine," Energy, Elsevier, vol. 204(C).
    5. Kaloop, Mosbeh R. & Bardhan, Abidhan & Kardani, Navid & Samui, Pijush & Hu, Jong Wan & Ramzy, Ahmed, 2021. "Novel application of adaptive swarm intelligence techniques coupled with adaptive network-based fuzzy inference system in predicting photovoltaic power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 148(C).
    6. Xu, Lei & Hou, Lei & Zhu, Zhenyu & Li, Yu & Liu, Jiaquan & Lei, Ting & Wu, Xingguang, 2021. "Mid-term prediction of electrical energy consumption for crude oil pipelines using a hybrid algorithm of support vector machine and genetic algorithm," Energy, Elsevier, vol. 222(C).
    7. Manoj Verma & Harish Kumar Ghritlahre, 2023. "Forecasting of Wind Speed by Using Three Different Techniques of Prediction Models," Annals of Data Science, Springer, vol. 10(3), pages 679-711, June.
    8. Moreno, Sinvaldo Rodrigues & dos Santos Coelho, Leandro, 2018. "Wind speed forecasting approach based on Singular Spectrum Analysis and Adaptive Neuro Fuzzy Inference System," Renewable Energy, Elsevier, vol. 126(C), pages 736-754.
    9. Ifaei, Pouya & Nazari-Heris, Morteza & Tayerani Charmchi, Amir Saman & Asadi, Somayeh & Yoo, ChangKyoo, 2023. "Sustainable energies and machine learning: An organized review of recent applications and challenges," Energy, Elsevier, vol. 266(C).
    10. Wang, Zheng-Xin & Li, Qin & Pei, Ling-Ling, 2018. "A seasonal GM(1,1) model for forecasting the electricity consumption of the primary economic sectors," Energy, Elsevier, vol. 154(C), pages 522-534.
    11. Hammed Olabisi Omotoso & Abdullrahman A. Al-Shamma’a & Mohammed Alharbi & Hassan M. Hussein Farh & Abdulaziz Alkuhayli & Akram M. Abdurraqeeb & Faisal Alsaif & Umar Bawah & Khaled E. Addoweesh, 2023. "Machine Learning Supervisory Control of Grid-Forming Inverters in Islanded Mode," Sustainability, MDPI, vol. 15(10), pages 1-19, May.
    12. Kelachukwu J. Iheanetu, 2022. "Solar Photovoltaic Power Forecasting: A Review," Sustainability, MDPI, vol. 14(24), pages 1-31, December.
    13. Farid Saberi-Movahed & Mohammad Najafzadeh & Adel Mehrpooya, 2020. "Receiving More Accurate Predictions for Longitudinal Dispersion Coefficients in Water Pipelines: Training Group Method of Data Handling Using Extreme Learning Machine Conceptions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(2), pages 529-561, January.
    14. Li, Chengdong & Zhou, Changgeng & Peng, Wei & Lv, Yisheng & Luo, Xin, 2020. "Accurate prediction of short-term photovoltaic power generation via a novel double-input-rule-modules stacked deep fuzzy method," Energy, Elsevier, vol. 212(C).
    15. Mahmoodi, Kumars & Nepomuceno, Erivelton & Razminia, Abolhassan, 2022. "Wave excitation force forecasting using neural networks," Energy, Elsevier, vol. 247(C).
    16. Ladislav Zjavka, 2021. "Photovoltaic Energy All-Day and Intra-Day Forecasting Using Node by Node Developed Polynomial Networks Forming PDE Models Based on the L-Transformation," Energies, MDPI, vol. 14(22), pages 1-14, November.
    17. Dongxiao Niu & Shuyu Dai, 2017. "A Short-Term Load Forecasting Model with a Modified Particle Swarm Optimization Algorithm and Least Squares Support Vector Machine Based on the Denoising Method of Empirical Mode Decomposition and Gre," Energies, MDPI, vol. 10(3), pages 1-20, March.
    18. Muhammad Naveed Akhter & Saad Mekhilef & Hazlie Mokhlis & Ziyad M. Almohaimeed & Munir Azam Muhammad & Anis Salwa Mohd Khairuddin & Rizwan Akram & Muhammad Majid Hussain, 2022. "An Hour-Ahead PV Power Forecasting Method Based on an RNN-LSTM Model for Three Different PV Plants," Energies, MDPI, vol. 15(6), pages 1-21, March.
    19. Du, Pei & Wang, Jianzhou & Yang, Wendong & Niu, Tong, 2018. "Multi-step ahead forecasting in electrical power system using a hybrid forecasting system," Renewable Energy, Elsevier, vol. 122(C), pages 533-550.
    20. Xiaomei Wu & Chun Sing Lai & Chenchen Bai & Loi Lei Lai & Qi Zhang & Bo Liu, 2020. "Optimal Kernel ELM and Variational Mode Decomposition for Probabilistic PV Power Prediction," Energies, MDPI, vol. 13(14), pages 1-21, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:11:y:2023:i:6:p:1381-:d:1095188. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.