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Integrated dynamic spiking neural P systems for fault line selection in distribution network

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A Correction to this article was published on 24 August 2024

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Abstract

Due to the compensating function of neutral grounded arc suppression coil, fault line selection in distribution network is still facing challenges: the classical models have insufficient learning ability in extracting fault features, and there is an imbalance in the original data used, resulting in low accuracy in fault line selection. In order to address this issue, this paper proposes a novel variant of spiking neural P (SNP) systems called integrated dynamic SNP (IDSNP) systems, which consist of gated neurons, rule neurons, and weighed neurons with different designed rules. Furthermore, according to the IDSNP systems, an IDSNP(FL) model is developed for fault line selection in distribution network, where the number of layers for transmitting weighted neuron spiking information could be dynamically changeable depending on the complexity of the number of stations in the power system. Finally, the proposed model is evaluated on a real-time dispatch dataset of a real power system. The experimental results show that the IDSNP(FL) model achieves the best performance compared with several classical models in deep learning, verifying the effectiveness of the proposed model for fault line selection tasks in distribution network.

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Acknowledgements

The authors acknowledge the above funds for supporting this research and the editor and reviewers for their comments and suggestions.

Funding

Sichuan Provincial Science & Technology Department under Grant (No.2023NSFSC1985, No.2023YFG0046, No.2021YFG0133), School Project of Chengdu University of Information Technology (No.KYTZ202148, No.KYTZ202149), International Joint Research Center of Robots and Intelligence Program (No.JQZN2022-001), Ministry of Education industry-school cooperative education project Grant 220900882063927, Grant 220900882293657, Opening Fund of Key Laboratory of Natural Disaster Monitoring, Early Warning and Assessment of Jiangxi Province(Jiangxi Normal University)(JXZRZH202304), Opening Fund of Sichuan Research Center of Electronic Commerce and Modern Logistics (DSWL23–36, DSWL23–35).

Author information

Authors and Affiliations

  1. National and Local Joint Engineering Laboratory for Renewable Energy Access to Grid Technology, Hefei University of Technology, No. 193 Tunxi Road, Hefei, 230009, Anhui, China

    Song Ma & Fei Li

  2. School of Automation, Chengdu University of Information Technology, No. 24 Block 1, Xuefu Road, Chengdu, 610225, Anhui, China

    Song Ma, Qiang Yang & Gexiang Zhang

  3. Key Laboratory of Natural Disaster Monitoring & Early Warning and Assessment of Jiangxi Province, Jiangxi Normal University, No. 99 Ziyang Avenue, Nanchang, 330022, Jiangxi, China

    Qiang Yang

  4. School of Computer Science and Cyber Security, Chengdu University of Technology, No. 1, East Third Road, Erxianqiao, Chenghua District, Chengdu, 610059, Sichuan, China

    Fan Yu

  5. Business School, Shandong Normal University, No. 88 East Wenhua Road, Lixia District, Shandong, Jinan, China, 250014

    Xiu Yin

Authors
  1. Song Ma
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  2. Qiang Yang
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  3. Gexiang Zhang
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  4. Fei Li
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Contributions

Song Ma: Performed the experiments and designed the study, Wrote the paper. Qiang Yang: Conceived and designed the study, Wrote the paper. Gexiang Zhang: Reviewed and edited the manuscript.  Fei Li: Reviewed and edited the manuscript. Fan Yu: Performed the experiments. Xiu Yin: Collected and pre-processed data.

Corresponding author

Correspondence to Qiang Yang.

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The original online version of this article was revised due to the author's name Fei Li was incorrectly written as Fan Li.

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Ma, S., Yang, Q., Zhang, G. et al. Integrated dynamic spiking neural P systems for fault line selection in distribution network. Nat Comput (2024). https://doi.org/10.1007/s11047-024-09995-0

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