Dynamic-State-Estimation-Based Cyber Attack Detection for Inverter-Based Resources

Avinash Kumar, Yuzhang Lin, Heqing Huang, Xiaonan Lu, Yue Zhao

Research output: Chapter in Book/Report/Conference proceedingConference contribution


The cyber security of Inverter Based Resources (IBRs) has received increasing attention in recent years. In this paper, a cyber attack detection method is proposed based on dynamic state estimation for IBRs. The state-space models of the physical inverter system and the digital controller are separately derived and then coupled by the data flows in between, i.e., measurement signals and control signals. Based on Kalman Filtering (KF), two dual dynamic state estimators are developed for tracking the cyber and physical state variables, respectively. By checking the model-data consistency of both the physical and cyber layers via hypothesis testing, the proposed method features the capability of distinguishing between false data in measurement signals and in control signals. Simulation results in an IEEE 13 node test feeder demonstrate the effectiveness of the proposed method.

Original languageEnglish (US)
Title of host publication2023 IEEE Power and Energy Society General Meeting, PESGM 2023
PublisherIEEE Computer Society
ISBN (Electronic)9781665464413
StatePublished - 2023
Event2023 IEEE Power and Energy Society General Meeting, PESGM 2023 - Orlando, United States
Duration: Jul 16 2023Jul 20 2023

Publication series

NameIEEE Power and Energy Society General Meeting
ISSN (Print)1944-9925
ISSN (Electronic)1944-9933


Conference2023 IEEE Power and Energy Society General Meeting, PESGM 2023
Country/TerritoryUnited States


  • Cyber security
  • Dynamic state estimation
  • Event detection
  • Inverter based resources
  • Kalman filter

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Nuclear Energy and Engineering
  • Renewable Energy, Sustainability and the Environment
  • Electrical and Electronic Engineering


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