• CSCD核心库收录期刊
  • 中文核心期刊
  • 中国科技核心期刊

Electric Power Construction ›› 2017, Vol. 38 ›› Issue (5): 38-.doi: 10.3969/j.issn.1000-7229.2017.05.005

Previous Articles     Next Articles

 Reliability Analysis of Business in Grid Cyber Physical System Considering the Factors of Cyber Attacks
 

 RU Yeqi1, ZHOU Bin2, WU Yibei1, LI June3,4, YUAN Kai1, LIU Kaipei1   

  1.  1. School of Electrical Engineering, Wuhan University, Wuhan 430072, China;
    2. NARI Technology Development Co., Ltd., Nanjing 211106, China;3. School of Computer, Wuhan University, 
    Wuhan 430072, China; 4. Key Laboratory of Aerospace Information Security and Trusted 
    Computing of Ministry of Education(Wuhan University), Wuhan 430072, China
  • Online:2017-05-01
  • Supported by:
     Project supported by National Natural Science Foundation of China(51377122)

Abstract:  The deep integration and interaction between cyber space and power space is the main feature of grid cyber physical system (GCPS). According to the wide-area real-time protection and control business in smart grid, this paper firstly studies the way of the impacts of information business on the reliability of physical system, and establishes the model of business reliability associated with information equipment. On this basis, this paper introduces the reliability factor of equipment, proposes the reliability evaluation indexes for information equipment considering cyber attack, and presents a quantitative evaluation method of business reliability with combining analytic hierarchy process (AHP) and Bayesian network. Finally, through the case study, this paper identifies the weak link of the information system considering cyber attack and analyses the impact of information systems with different levels of redundancy on business reliability. Meanwhile, compared with the existing method, the result verifies the feasibility and rationality of the proposed method.

 

Key words:  grid cyber physical system (GCPS), business reliability, cyber attack, analytic hierarchy process (AHP), Bayesian network

CLC Number: