Risk Prediction of Power Data Network Based on Entropy Weight-Gray Model

被引:0
作者
Li W.-J. [1 ]
Li M. [1 ]
Xing N.-Z. [2 ]
Ji Y.-T. [2 ]
Zeng X.-J. [1 ]
机构
[1] State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing
[2] State Grid Jibei Electric Power Company Limited Information & Communication Dispatch, Beijing
来源
| 2018年 / Beijing University of Posts and Telecommunications卷 / 41期
关键词
Entropy method; Gray prediction; Power data network; Risk warning;
D O I
10.13190/j.jbupt.2017-125
中图分类号
学科分类号
摘要
Aiming at the present situation that risk prediction model of power data network cannot effectively predict the risk, a risk prediction mechanism of power data network based on entropy weight-gray model is proposed. This paper focuses on risk prediction of the entire network. Firstly, the gray model is used to predict the risk indexes of power data network, and the individual risk index value is determined. Then, the dynamic weight of each index is obtained by entropy weight method. Finally, it can calculate the risk value of the network according to the risk index value and the weight. The simulation results show that the proposed model can guarantee predictive accuracy of dynamic real-time network. © 2018, Editorial Department of Journal of Beijing University of Posts and Telecommunications. All right reserved.
引用
收藏
页码:39 / 45
页数:6
相关论文
共 12 条
  • [1] Gupta S., Waghmare S., Kazi F., Et al., Blackout risk analysis in Smart grid WAMPAC system using KL divergence approach, International Conference on Power Systems, pp. 1-6, (2016)
  • [2] Zhang Y., Zhang T., Song X., Et al., Research on risk warning system of distribution network, International Conference on Power System Technology, pp. 1509-1514, (2014)
  • [3] Gu T., Janssen J., Tazelaar E., Et al., Risk prediction in distribution networks based on the relation between weather and (underground) component failure, CIRED-Open Access Proceedings Journal, pp. 1442-1445, (2017)
  • [4] Hafeez K., Khan S., Risk management analysis with the help of lightning strike mapping around 500 k-v grid station using artificial intelligence technique, International Conference of Robotics and Artificial Intelligence, pp. 165-168, (2012)
  • [5] Li J., Zhao Y., Li J., Power grid safety evaluation based on rough set neural network, International Conference on Risk Management & Engineering Management, pp. 245-249, (2008)
  • [6] Li Z., Shang Y., Cui N., Et al., The prediction method for battery open circuit voltage based on GM (1, 1) grey model, Chinese Control Conference (CCC), pp. 1902-1906, (2015)
  • [7] Li X., Jing Z., Wu Q., Application of improved GM(1, N) models in annual electricity demand forecasting, Innovative Smart Grid Technologies-Asia, pp. 1-6, (2015)
  • [8] Ding K., Feng L., Wang X., Et al., Forecast of PV power generation based on residual correction of Markov chain, International Conference on Control, Automation and Information Sciences, pp. 355-359, (2015)
  • [9] Li M., Li W., Yu P., Et al., Risk prediction of the SCADA communication network based on entropy-gray model, International Conference on Network and Service Management (CNSM), pp. 1-5, (2017)
  • [10] Li M., Li W., Zeng X., Et al., A decision-making mechanism of network risk control based on grey relation, IEEE/IFIP Network Operations and Management Symposium (NOMS), pp. 1-4, (2018)