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Fault Classifications in Distribution Systems Consisting of Wind Power as Distributed Generation Using Discrete Wavelet Transforms

Author

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  • Theerasak Patcharoen

    (Faculty of Industrial Technology, Rajabhat Rajanagarindra University, Chachoengsao 24000, Thailand)

  • Atthapol Ngaopitakkul

    (Faculty of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand)

Abstract

This paper proposed a fault type classification algorithm in a distribution system consisting of multiple distributed generations (DGs). The study also discussed the changing of signal characteristics in the distribution system with DGs during the occurrence of different fault types. Discrete Wavelet Transform (DWT)-based signal processing has been used to construct a classification algorithm and a decision tree to classify fault types. The input data for the algorithm is extracted from the three-phase current signal under normal conditions and during fault occurrence. These signals are recorded from the substation, load, and DG bus. The performance of the proposed classifying algorithm has been tested on a simulation system that was modeled after part of Thailand’s 22 kV distribution system, with a 2-MW wind power generation as the DG, connected to the distribution line by PSCAD software. The parameters that were taken into consideration consisted of the fault type, location of the fault, location of DG(s), and the number of DGs, to evaluate the performance of the proposed algorithm under various conditions. The result of the simulation indicated significant changes in current signal characteristics when installing DGs. In addition, the proposed algorithm has achieved a satisfactory accuracy in terms of identifying and classifying fault types when applied to a distribution system with multiple DGs.

Suggested Citation

  • Theerasak Patcharoen & Atthapol Ngaopitakkul, 2019. "Fault Classifications in Distribution Systems Consisting of Wind Power as Distributed Generation Using Discrete Wavelet Transforms," Sustainability, MDPI, vol. 11(24), pages 1-16, December.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:24:p:7209-:d:298465
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    References listed on IDEAS

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    1. Mehigan, L. & Deane, J.P. & Gallachóir, B.P.Ó. & Bertsch, V., 2018. "A review of the role of distributed generation (DG) in future electricity systems," Energy, Elsevier, vol. 163(C), pages 822-836.
    2. El-Naggar, Ahmed & Erlich, Istvàn, 2016. "Analysis of fault current contribution of Doubly-Fed Induction Generator Wind Turbines during unbalanced grid faults," Renewable Energy, Elsevier, vol. 91(C), pages 137-146.
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    Cited by:

    1. Younis M. Nsaif & Molla Shahadat Hossain Lipu & Aini Hussain & Afida Ayob & Yushaizad Yusof & Muhammad Ammirrul A. M. Zainuri, 2022. "A Novel Fault Detection and Classification Strategy for Photovoltaic Distribution Network Using Improved Hilbert–Huang Transform and Ensemble Learning Technique," Sustainability, MDPI, vol. 14(18), pages 1-19, September.

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