Dynamic State Estimation for Inverter-Based Resources: A Control-Physics Dual Estimation Framework

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

Research output: Contribution to journalArticlepeer-review


As Inverter-Based Resources (IBRs) gradually replace conventional synchronous generators (SGs), Dynamic State Estimation (DSE) techniques must be extended for the monitoring of IBRs. The key difference between IBRs and SGs is that the dynamics of IBRs comprise a heavy mix of physical processes and digital controller computations. This paper develops a generic framework of Control-Physics Dynamic State Estimation (CPDSE) for IBRs. First, a control-physics state-space representation of IBRs is presented. Noting the symmetry of the control and physical state spaces, it is proposed to use two dual estimators to track the states of the physical inverter subsystem and the digital controller subsystem, respectively. The CPDSE framework has the capability of suppressing errors in both measurement signals and control signals flowing between the two subsystems and the potential to distinguish between cyber and physical events. The advantages and versatility of the proposed CPDSE framework are validated on a variety of IBR systems (solar, wind, and storage), control strategies (grid-following and grid-forming), and both transmission and distribution systems.

Original languageEnglish (US)
Pages (from-to)1-12
Number of pages12
JournalIEEE Transactions on Power Systems
StateAccepted/In press - 2024


  • adaptive cubature Kalman filter
  • Aerospace electronics
  • Control systems
  • Dynamic scheduling
  • dynamic state estimation
  • grid-forming control
  • inverter-based resources
  • Inverters
  • Kalman filters
  • Mathematical models
  • Power system dynamics
  • renewable energy
  • situational awareness

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering


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