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An Assessment of the Condition of Distribution Network Equipment Based on Large Data Fuzzy Decision-Making

Author

Listed:
  • Ning Wang

    (School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071000, China)

  • Fei Zhao

    (School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071000, China)

Abstract

As one of the most important components of power grid, a distribution network is the most vulnerable part in the face of various uncertainties, and influences the stability and economy of a power system. In this paper, the operational information, hardware information and human factors were considered, and a state evaluation model of multi-source information fusion was established. Based on big data fuzzy iteration method and a weighted expert library, a weighted distribution of multi-source information was obtained, and an equipment condition assessment was carried out reasonably. Taking the distribution transformer as an example, the assessment showed that fusion of multi-source information presented in this paper is more comprehensive, and has the ability to reflect the state of equipment. The method proposed in this paper can accurately judge the running state of distribution equipment based on all kinds of information, and provides a reference for the follow-up power marketing for the status assessment of the user equipment.

Suggested Citation

  • Ning Wang & Fei Zhao, 2020. "An Assessment of the Condition of Distribution Network Equipment Based on Large Data Fuzzy Decision-Making," Energies, MDPI, vol. 13(1), pages 1-13, January.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:1:p:197-:d:304089
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    References listed on IDEAS

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    1. Fang Yuan & Jiang Guo & Zhihuai Xiao & Bing Zeng & Wenqiang Zhu & Sixu Huang, 2019. "A Transformer Fault Diagnosis Model Based on Chemical Reaction Optimization and Twin Support Vector Machine," Energies, MDPI, vol. 12(5), pages 1-18, March.
    2. Fenglan Tian & Zhongzhao Jing & Huan Zhao & Enze Zhang & Jiefeng Liu, 2019. "A Synthetic Condition Assessment Model for Power Transformers Using the Fuzzy Evidence Fusion Method," Energies, MDPI, vol. 12(5), pages 1-17, March.
    3. Lingjie Sun & Yingyi Liu & Boyang Zhang & Yuwei Shang & Haiwen Yuan & Zhao Ma, 2016. "An Integrated Decision-Making Model for Transformer Condition Assessment Using Game Theory and Modified Evidence Combination Extended by D Numbers," Energies, MDPI, vol. 9(9), pages 1-22, August.
    4. Jiake Fang & Hanbo Zheng & Jiefeng Liu & Junhui Zhao & Yiyi Zhang & Ke Wang, 2018. "A Transformer Fault Diagnosis Model Using an Optimal Hybrid Dissolved Gas Analysis Features Subset with Improved Social Group Optimization-Support Vector Machine Classifier," Energies, MDPI, vol. 11(8), pages 1-18, July.
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    Cited by:

    1. Dan Liu & Yiqun Kang & Heng Luo & Xiaotong Ji & Kan Cao & Hengrui Ma, 2023. "A Grid Status Analysis Method with Large-Scale Wind Power Access Using Big Data," Energies, MDPI, vol. 16(12), pages 1-12, June.
    2. Qiang Li & Feng Zhao & Li Zhuang & Qiulin Wang & Chenzhou Wu, 2023. "Steady-State Risk Prediction Analysis of Power System Based on Power Digital Twinning," Sustainability, MDPI, vol. 15(3), pages 1-20, January.
    3. Akhyurna Swain & Elmouatamid Abdellatif & Ahmed Mousa & Philip W. T. Pong, 2022. "Sensor Technologies for Transmission and Distribution Systems: A Review of the Latest Developments," Energies, MDPI, vol. 15(19), pages 1-37, October.

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