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Improvement of Distance Protection with SVM on PV-Fed Transmission Lines in Infeed Conditions

Author

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  • Yasar Beyazit Yoldas

    (4th Regional Directorate, Turkish Electricity Transmission Corporation, Istanbul 34762, Türkiye
    Department of Electrical Engineering, Yildiz Technical University, Istanbul 34220, Türkiye)

  • Recep Yumurtacı

    (Department of Electrical Engineering, Yildiz Technical University, Istanbul 34220, Türkiye)

Abstract

Photovoltaic (PV) power plants have comparatively weak infeed characteristics, unlike conventional synchronous generators. The controllability of grid-connected inverters and the limited overcurrent capability of power electronic devices means that the characteristics of faults on transmission lines fed by PV power stations are substantially different than those on transmission lines fed by conventional sources. Operating performances of distance relays on PV-fed transmission line are unveiled. This paper analyses the impact of PV-fed transmission lines in infeed conditions on distance protection. Fault signals on the transmission line were generated by Digsilent PowerFactory software. Then, fault signals were analyzed by discrete Fourier transformation (DFT) with MATLAB software. The measured current and voltage signals were preprocessed first with DFT, and then machine learning via a support vector machine (SVM) was used for regression. This research proposes an improvement on distance protection with SVM for preventing maloperation in infeed conditions on PV fed transmission lines. The average accuracy was up to 95.6% in this study. The simulation was performed at different locations along the transmission line with different types of fault on a given power system model with the PV power plant.

Suggested Citation

  • Yasar Beyazit Yoldas & Recep Yumurtacı, 2023. "Improvement of Distance Protection with SVM on PV-Fed Transmission Lines in Infeed Conditions," Energies, MDPI, vol. 16(6), pages 1-18, March.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:6:p:2587-:d:1092258
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    References listed on IDEAS

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    1. Aleksandr Kulikov & Anton Loskutov & Dmitriy Bezdushniy, 2022. "Relay Protection and Automation Algorithms of Electrical Networks Based on Simulation and Machine Learning Methods," Energies, MDPI, vol. 15(18), pages 1-19, September.
    2. Yingyu Liang & Guanjun Xu & Wenting Zha & Cong Wang, 2019. "Adaptability Analysis of Fault Component Distance Protection on Transmission Lines Connected to Photovoltaic Power Stations," Energies, MDPI, vol. 12(8), pages 1-19, April.
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