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Area of Science:

  • Electrical Engineering
  • Power Systems Analysis
  • Artificial Intelligence

Background:

  • Traditional power grids rely on synchronous generators for stable inertia.
  • Increasing renewable energy integration reduces system inertia, posing stability risks.
  • Accurate inertia estimation is vital for managing modern power grids.

Purpose of the Study:

  • To develop a framework for continuous inertia estimation in power systems.
  • To investigate the application of artificial intelligence (AI) for inertia estimation.
  • To understand the input features required for AI-based inertia estimation.

Main Methods:

  • Utilized state-of-the-art artificial intelligence techniques.
  • Performed power spectra analysis and input-output correlation analysis.
  • Validated the approach on a heterogeneous power network.

Main Results:

  • Developed a novel framework for continuous inertia estimation.
  • Identified key input features for AI-driven inertia estimation.
  • Demonstrated distinct spectral footprints for different power system components.

Conclusions:

  • The AI framework enables reliable continuous inertia estimation.
  • Understanding spectral footprints is essential for transmission system operators.
  • This approach enhances online network stability analyses in grids with diverse generation sources.