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Non-Intrusive Load Monitoring Applied to AC Railways

Author

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  • Andrea Mariscotti

    (DITEN, University of Genova, 16145 Genova, Italy)

Abstract

Non-intrusive load monitoring takes place in residential and industrial contexts to disaggregate and identify loads connected to a distribution grid. This work studies the applicability and effectiveness for AC railways, considering the highly dynamic behavior of rolling stock as an electric load, immersed in varying contexts of moving loads. Both voltage–current diagrams and harmonic spectra were considered for identification and extraction of features relevant to classification and clustering. Principal components were extracted, approaching the problem using principal component analysis (PCA) and partial least square regression (PLSR). Clustering methods were then discussed, verifying separability performance and applicability to the railway context, checking the performance by means of the balanced accuracy index. Based on more than one hundred measured spectra, PLSR has been confirmed with superior performance and lower complexity. Independent verification based on dispersion and correlation were used to spot relevant spectrum components to use as clustering features and confirm the PLSR outcome.

Suggested Citation

  • Andrea Mariscotti, 2022. "Non-Intrusive Load Monitoring Applied to AC Railways," Energies, MDPI, vol. 15(11), pages 1-27, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:11:p:4141-:d:831769
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    References listed on IDEAS

    as
    1. Andrea Mariscotti & Leonardo Sandrolini, 2021. "Detection of Harmonic Overvoltage and Resonance in AC Railways Using Measured Pantograph Electrical Quantities," Energies, MDPI, vol. 14(18), pages 1-22, September.
    2. Jin Wang & Zhongping Yang & Fei Lin & Junci Cao, 2013. "Harmonic Loss Analysis of the Traction Transformer of High-Speed Trains Considering Pantograph-OCS Electrical Contact Properties," Energies, MDPI, vol. 6(11), pages 1-21, November.
    3. Zhengyou He & Zheng Zheng & Haitao Hu, 2016. "Power quality in high-speed railway systems," International Journal of Rail Transportation, Taylor & Francis Journals, vol. 4(2), pages 71-97, June.
    4. Hamed Jafari Kaleybar & Morris Brenna & Federica Foiadelli & Seyed Saeed Fazel & Dario Zaninelli, 2020. "Power Quality Phenomena in Electric Railway Power Supply Systems: An Exhaustive Framework and Classification," Energies, MDPI, vol. 13(24), pages 1-35, December.
    5. Yljon Seferi & Steven M. Blair & Christian Mester & Brian G. Stewart, 2020. "Power Quality Measurement and Active Harmonic Power in 25 kV 50 Hz AC Railway Systems," Energies, MDPI, vol. 13(21), pages 1-17, October.
    6. Ruixuan Yang & Fulin Zhou & Kai Zhong, 2020. "A Harmonic Impedance Identification Method of Traction Network Based on Data Evolution Mechanism," Energies, MDPI, vol. 13(8), pages 1-15, April.
    7. Antonio Ruano & Alvaro Hernandez & Jesus Ureña & Maria Ruano & Juan Garcia, 2019. "NILM Techniques for Intelligent Home Energy Management and Ambient Assisted Living: A Review," Energies, MDPI, vol. 12(11), pages 1-29, June.
    8. Jie Zhang & Jing Shang & Zhixue Zhang, 2019. "Optimization and Control on High Frequency Resonance of Train-Network Coupling Systems," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-10, January.
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    Cited by:

    1. Yongtao Shi & Xiaodong Zhao & Fan Zhang & Yaguang Kong, 2022. "Non-Intrusive Load Monitoring Based on Swin-Transformer with Adaptive Scaling Recurrence Plot," Energies, MDPI, vol. 15(20), pages 1-18, October.
    2. Adam Szeląg & Mladen Nikšić, 2023. "Advances in Electric Traction System—Special Issue," Energies, MDPI, vol. 16(3), pages 1-5, 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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