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State Perception Method of Intelligent Substation Secondary System Based on FCE and DCNN

  • Hongbing LiEmail author
  • Junyong Zhu
  • Ling Luo
Conference paper
First Online:
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Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 585)

Abstract

Aiming at second equipment lacks comprehensive and effective state detection and simple and reliable evaluation method, a state perception method of intelligent substation secondary system based on fuzzy comprehensive evaluation (FCE) and deep convolutional neural network (DCNN) is proposed. Firstly, combining with the factors of auxiliary equipment state evaluation, the FCE method is adopted to evaluate the influence degree of each secondary equipment. Secondly, DCNN was used to learn the regional and edge features respectively, and the significance and non-significance confidence of the detected region was obtained. Finally, combined with the influence degree of each secondary equipment and the significant and non-significant confidence level, the state of the secondary equipment in intelligent substation is evaluated. The experiment results indicate that the proposed method can effectively solve the deficiency of the corresponding equipment status detection and evaluation method of intelligent substation.

Keywords

Deep convolutional neural network Intelligent substation Secondary system Method of state perception State detection Fuzzy comprehensive evaluation 
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Notes

Acknowledgements

This work is supported by Science and Technology Project of State Grid Chongqing Electric Power Company in 2018. The project name is “Integrated Operational Support Technology of Intelligent Substations Based on Total Service Data” (No. 2018#35).

References

  1. 1.
    Wen B, Hong M, Wu D et al (2018) Research on SCD management and control technology in smart substation. In: IOP conference series: earth and environmental science, vol 170(4). IOP Publishing, p 042130Google Scholar
  2. 2.
    Jiang HT, Chen C, Bu J (2014) Design of performance analysis system of secondary circuit in intelligent substation based on NI platform. Adv Mater Res 986–987:1934–1937CrossRefGoogle Scholar
  3. 3.
    Ciwei G, Xiaojun C, Huaguang Y, Weihua S (2017) Energy management of data center and prospect for participation in demand side resource scheduling. Autom Electr Power Syst 41(23):1–7Google Scholar
  4. 4.
    Haijun X, Haozhong C, Jingfei Y et al (2016) Distribution network expansion planning considering multiple active management strategies. Autom Electr Power Syst 40(23):70–76,167Google Scholar
  5. 5.
    Wang H, Zhou B, Zhang X (2018) Research on the remote maintenance system architecture for the rapid development of smart substation in China. IEEE Trans Power Delivery 33(4):1845–1852CrossRefGoogle Scholar
  6. 6.
    Zhang S, Cheng P, Wang B et al (2019) Research on coordinated attack protection method based on global time synchronization system of intelligent substation. J Netw Comput Appl 4:14–20Google Scholar
  7. 7.
    Zheng Z, Yan Z, Yu T (2018) Design of conformance verification system for intelligent substation configuration file. In: 2018 Chinese control and decision conference (CCDC). IEEE. pp 4307–4310Google Scholar
  8. 8.
    Yang N, Pouget J, Letrouvé T et al (2019) Techno-economic design methodology of hybrid energy systems connected to electrical grid: An application of hybrid railway power substation. Math Comput Simul 158:107–119MathSciNetCrossRefGoogle Scholar
  9. 9.
    Wu T, Liu S, Ni M et al (2018) Model design and structure research for integration system of energy, information and transportation networks based on ANP-fuzzy comprehensive evaluation. Global Energy Interconnection 1(2):137–144Google Scholar
  10. 10.
    Wang Q, Han R, Huang Q et al (2018) Research on energy conservation and emissions reduction based on AHP-fuzzy synthetic evaluation model: a case study of tobacco enterprises. J Clean Prod 201:88–97CrossRefGoogle Scholar
  11. 11.
    Zhao H, Guo S, Zhao H (2019) Comprehensive assessment for battery energy storage systems based on fuzzy-MCDM considering risk preferences. Energy 168:450–461CrossRefGoogle Scholar
  12. 12.
    Feng L, Cui C, Ma R et al (2018) Deep learning algorithm for preliminary siting of substations considering various features in distribution network planning. In: IOP conference series: earth and environmental science, vol 192 (1). IOP Publishing, p 012032Google Scholar
  13. 13.
    Fu CZ, Si W R, Huang H et al (2018) Research on a detection and recognition algorithm for high-voltage switch cabinet based on deep learning with an improved YOLOv2 Network. In: 2018 11th international conference on intelligent computation technology and automation (ICICTA). IEEE, pp 346–350Google Scholar
  14. 14.
    Li Q, Han B, Yu M et al (2018) Modeling of multiple heating substations based on long short-term memory networks. In: International conference on smart city and intelligent building. Springer, Singapore, pp 515–524Google Scholar
  15. 15.
    Zhinong W, Yu C, Wenjin H et al (2018) Optimal allocation model for multi-energy capacity of virtual power plant considering conditional value-at-risk. Autom Electr Power Syst 42(4):39–46Google Scholar

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© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.State Grid Chongqing Electric Power CompanyChongqingChina

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