Detection and localization of biased load attacks in smart grids via interval observer

Xinyu Wang, Xiaoyuan Luo, Mingyue Zhang, Zhongping Jiang, Xinping Guan

Research output: Contribution to journalArticlepeer-review


The biased load attacks pose enormous security risks to smart grids, due to the characteristics of spoofing attack. To handle the risks, a novel scheme for detecting and localizing biased load attacks is developed. Firstly, an unknown input interval observer is designed to mitigate the influences of disturbances and regional interconnection information, contributing to an accurate estimation of the interval state. Secondly, considering the feature of interval residuals, a novel detection criterion is developed to eliminate the limitation resulted by the prior threshold in the existing detection techniques. In addition, a logic judgment matrix is established based on the combination of sensor set, addressing the problem of attack detection and localization under structural vulnerability. Finally, the simulation results indicate that the developed scheme can detect and localize the biased load attacks effectively. Also, the developed scheme shows superior performance than state-of-the-art techniques.

Original languageEnglish (US)
Pages (from-to)291-309
Number of pages19
JournalInformation Sciences
StatePublished - Apr 2021


  • Biased load attack
  • Detection and localization
  • Interval observer
  • Security

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computer Science Applications
  • Information Systems and Management
  • Artificial Intelligence


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