Data-driven resilience analysis of power grids

Qicong Sun, Yan Li, Jason Philhower

Research output: Contribution to journalArticlepeer-review


With the integration of renewable energy resources, the inertia of power systems significantly reduces, thereby making the system sensitive to operational disturbances. A disturbance-based method is presented herein to estimate inertia, uncovering the influence of renewables on system-resilient operations. The Gaussian process regression method is then used to predict the power system trajectory after disturbance. Extensive tests demonstrate the data-driven method mathematically estimates the inertia of the system as well as predicts the dynamics operations of power grids subject to disturbances. Numerical results also offer insights into the enhancement of system resilience by strategically designing the inertia of power systems.

Original languageEnglish (US)
Pages (from-to)104-114
Number of pages11
JournalGlobal Energy Interconnection
Issue number1
StatePublished - Feb 2021

All Science Journal Classification (ASJC) codes

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Automotive Engineering
  • Renewable Energy, Sustainability and the Environment

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