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Distribution Network Fault Location Based on Intelligent Algorithm Research

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Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2021) (AICV 2021)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1377))

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Abstract

With the development of science and technology, many scholars study artificial intelligence technology in distribution network fault location. In recent times, there have been many achievements made by many researchers, of which some were the application of particle swarm optimization [1, 2], genetic algorithm [3, 4], ant colony algorithm [5]. This paper describes the application of a bi-state binary particle swarm optimization algorithm, multiple population genetic algorithm, and hybrid ant colony algorithm, the optimized intelligent algorithm in the distribution network's fault location. In distribution network fault location, traditional algorithms have more limitations, which need further improvement to make them more perfect—comparing the simulation data obtained before and after optimization shows that the optimized algorithm has faster convergence speed, higher accuracy, and higher fault location efficiency.

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Author information

Authors and Affiliations

  1. Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, China

    Kuo-Chi Chang, Rongsheng Zhang & Hui-Qiong Deng

  2. School of Information Science and Engineering, Fujian University of Technology, Fuzhou, China

    Kuo-Chi Chang, Rongsheng Zhang & Hui-Qiong Deng

  3. Department of Tourism, Shih-Hsin University, Taipei, Taiwan

    Fu-Hsiang Chang

  4. Institute of Environmental Engineering, National Taiwan University, Taipei, Taiwan

    Hsiao-Chuan Wang

  5. College of Mechanical and Electrical Engineering, National Taipei University of Technology, Taipei, Taiwan

    Kuo-Chi Chang

  6. Department of Business Administration, North Borneo University College, Sabah, Malaysia

    Kuo-Chi Chang & Tsui- Lien Hsu

  7. Smart Manufacturing Laboratory of Inherently Safer Design and Applications, ISD Mechanical and Electrical Engineering Co., Ltd., New Taipei, Taiwan

    Kuo-Chi Chang

Authors
  1. Kuo-Chi Chang
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  2. Rongsheng Zhang
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  3. Hui-Qiong Deng
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  4. Fu-Hsiang Chang
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  5. Hsiao-Chuan Wang
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  6. Tsui- Lien Hsu
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Editor information

Editors and Affiliations

  1. Information Technology Department, Faculty of Computers and Artificial Intelligence, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. Faculty of Sciences and Techniques, Hassan 1st University, Settat, Morocco

    Abdelkrim Haqiq

  3. School of Medicine, University of Missouri, Columbia, MO, USA

    Peter J. Tonellato

  4. ISAE-ENSMA, Futuroscope Chasseneuil Cedex, France

    Ladjel Bellatreche

  5. British University Vietnam, Hung Yen, Vietnam

    Sam Goundar

  6. Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt

    Ahmad Taher Azar

  7. NEST Research Group, ENSEM, Hassan II University of Casablanca, Casablanca, Morocco

    Essaid Sabir

  8. High National School for Computer Science and Systems Analysis (ENSIAS), Mohammed V University, Rabat, Morocco

    Driss Bouzidi

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Chang, KC., Zhang, R., Deng, HQ., Chang, FH., Wang, HC., Hsu, T.L. (2021). Distribution Network Fault Location Based on Intelligent Algorithm Research. In: Hassanien, A.E., et al. Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2021). AICV 2021. Advances in Intelligent Systems and Computing, vol 1377. Springer, Cham. https://doi.org/10.1007/978-3-030-76346-6_17

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