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Research on Smart Grid Comprehensive Development Level Based on the Improved Cloud Matter Element Analysis Method

  • Chengze SongEmail author
  • Junyong Wu
  • Meiyang Shao
  • Liangliang Hao
  • Lin Liu
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 925)

Abstract

According to the characteristics of the smart grid, in order to solve the problem of subjective hierarchical boundary cloud model and the problem of real-time data collection, this paper proposes an objective cloud matter element analysis based on cluster analysis. According to the development and planning, this paper puts forward an index system of the smart grid comprehensive development level. Because of non-national-standard data, this paper classifies the data into several levels according to cluster analysis, and then gets each hierarchical boundary cloud model of each index by calculating, then the correlation degree between the sample data and the hierarchical boundary cloud model is calculated; By combining ANP and principal component analysis method, the integrated weight vector is obtained and the weighted evaluation result is obtained. By multiple operations, the evaluation is ordered by using the standard deviation. The model illustrates the validity and superiority of the method.

Keywords

Cloud matter element theory Objective Smart grid Comprehensive development level Evaluation 

References

  1. 1.
    Wang, M.J.: Smart grid and smart energy resource grid. Power Syst. Technol. 34, 1–5 (2010). (in Chinese)Google Scholar
  2. 2.
    Xu, X.H.: Smart Grid Introduction. China Electric Power Press, Beijing (2009). (in Chinese)Google Scholar
  3. 3.
    Chen, S.Y., Song, S.F., Li, L.X., et al.: Survey on smart grid technology. Power Syst. Technol. 33, 1–7 (2009). (in Chinese)Google Scholar
  4. 4.
    Zhang, D.X., Yao, L.Z., Ma, W.Y.: Development strategies of smart grid in China and abroad. Proc. CSEE. 33, 1–14 (2013). (in Chinese)Google Scholar
  5. 5.
    Xia, F., Fan, L., et al.: A comprehensive evaluation model of power quality based on the theory of cloud element analysis. Power Syst. Prot. Control 40, 6–10 (2012). (in Chinese)Google Scholar
  6. 6.
    Dai, Z.Y., Zhang, W.L., et al.: The application of cloud matter-element in information security risk assessment. In: 3rd International Conference on Information Management, pp. 218–222 (2017)Google Scholar
  7. 7.
    Li, P., et al.: Study on fault diagnosis for power transformer based on cloud matter element analysis principle and DGA. In: Proceedings of the 9th International Conference on Properties and Applications of Dielectric Materials, Harbin, pp. 244–248 (2009)Google Scholar
  8. 8.
    Liu, W.: Principle and Application of K-harmonic Mean Clustering Analysis. Shanxi Medical University (2014). (in Chinese)Google Scholar
  9. 9.
    Wu, Y.H.: Cluster algorithm overview. Comput. Sci. 42, 491–542 (2015). (in Chinese)Google Scholar
  10. 10.
    Wang, Z.H., Li, H., et al.: Smart grid evaluation index system. Power Syst. Technol. 33, 14–18 (2009). (in Chinese)Google Scholar
  11. 11.
    Liu, Z.Y.: Global Energy Internet. China Electric Power Press, Beijing (2015). (in Chinese)Google Scholar
  12. 12.
    State Grid Energy Research Institute: Global Energy Analysis and Outlook, State Grid Corporation (2016). (in Chinese)Google Scholar
  13. 13.
    Xu, Q.H.: A Comprehensive Evaluation Study on Intelligent Transmission and Distribution Network Based on Grey Association and Combination. North China Electric Power University (2015). (in Chinese)Google Scholar
  14. 14.
    Jiang, H., Zhang, Q.L., Peng, J.C.: Evaluation of wind power quality based on improved cloud matter model. Power Syst. Technol. 38, 205–210 (2014). (in Chinese)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Chengze Song
    • 1
    Email author
  • Junyong Wu
    • 1
  • Meiyang Shao
    • 1
  • Liangliang Hao
    • 1
  • Lin Liu
    • 2
  1. 1.Electrical Engineering School of Beijing Jiaotong UniversityBeijingChina
  2. 2.State Grid Energy Research Institute, Future Science and Technology CityBeijingChina

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