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Data Mining and Neural Networks Based Self-Adaptive Protection Strategies for Distribution Systems with DGs and FCLs

Author

Listed:
  • Wen-Jun Tang

    (Department of Electrical Engineering, National Cheng Kung University, East Dist., Tainan City 701, Taiwan)

  • Hong-Tzer Yang

    (Department of Electrical Engineering, National Cheng Kung University, East Dist., Tainan City 701, Taiwan)

Abstract

In light of the development of renewable energy and concerns over environmental protection, distributed generations (DGs) have become a trend in distribution systems. In addition, fault current limiters (FCLs) may be installed in such systems to prevent the short-circuit current from exceeding the capacity of the power apparatus. However, DGs and FCLs can lead to problems, the most critical of which is miscoordination in protection system. This paper proposes overcurrent protection strategies for distribution systems with DGs and FCLs. Through the proposed approach, relays with communication ability can determine their own operating states with the help of an operation setting decision tree and topology-adaptive neural network model based on data processed through continuous wavelet transform. The performance and effectiveness of the proposed protection strategies are verified by the simulation results obtained from various system topologies with or without DGs, FCLs, and load variations.

Suggested Citation

  • Wen-Jun Tang & Hong-Tzer Yang, 2018. "Data Mining and Neural Networks Based Self-Adaptive Protection Strategies for Distribution Systems with DGs and FCLs," Energies, MDPI, vol. 11(2), pages 1-13, February.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:2:p:426-:d:131627
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    Citations

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    Cited by:

    1. Pejman Peidaee & Akhtar Kalam & Juan Shi, 2020. "Integration of a Heuristic Multi-Agent Protection System into a Distribution Network Interconnected with Distributed Energy Resources," Energies, MDPI, vol. 13(20), pages 1-25, October.
    2. Carlos Morón & Jorge Pablo Diaz & Daniel Ferrández & Pablo Saiz, 2018. "Design, Development and Implementation of a Weather Station Prototype for Renewable Energy Systems," Energies, MDPI, vol. 11(9), pages 1-13, August.
    3. Cristian Cepeda & Cesar Orozco-Henao & Winston Percybrooks & Juan Diego Pulgarín-Rivera & Oscar Danilo Montoya & Walter Gil-González & Juan Carlos Vélez, 2020. "Intelligent Fault Detection System for Microgrids," Energies, MDPI, vol. 13(5), pages 1-21, March.
    4. Liangwen Yan & Fengfeng Qian & Wei Li, 2018. "Research on Key Parameters Operation Range of Central Air Conditioning Based on Binary K-Means and Apriori Algorithm," Energies, MDPI, vol. 12(1), pages 1-13, December.
    5. Krzysztof Lowczowski & Jozef Lorenc & Jozef Zawodniak & Grzegorz Dombek, 2020. "Detection and Location of Earth Fault in MV Feeders Using Screen Earthing Current Measurements," Energies, MDPI, vol. 13(5), pages 1-24, March.

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