Comparative Study of Data-driven Area Inertia Estimation Approaches on WECC Power Systems

TL;DR

This work addresses the challenge of declining grid inertia from increasing inverter-based resources by evaluating area inertia estimation methods on the WECC 240-bus system. It compares three paradigms—system identification, measurement reconstruction via dynamic mode decomposition, and electromechanical-oscillation analysis—finding the system identification approach most robust and accurate in realistic conditions, despite a minor bias from neglecting damping. The study provides a rigorous benchmark across noise and time-varying operation, demonstrating that a higher-order system-identification model with N4SID reliably captures area inertia from tie-line power disturbances $ΔP_{e,i}$ and COI frequency dynamics $Δω_{COI,i}$. The findings have practical impact for real-time inertia monitoring and stability assessment in large, multi-area grids like WECC, where uneven inertia distribution requires reliable, distributed inertia estimates.

Abstract

With the increasing integration of inverter-based resources into the power grid, there has been a notable reduction in system inertia, potentially compromising frequency stability. To assess the suitability of existing area inertia estimation techniques for real-world power systems, this paper presents a rigorous comparative analysis of system identification, measurement reconstruction, and electromechanical oscillation-based area inertia estimation methodologies, specifically applied to the large-scale and multi-area WECC 240-bus power system. Comprehensive results show that the system identification-based approach exhibits superior robustness and accuracy relative to its counterparts.

Figures (6)

• Figure 1: Diagram of the multi-area power system.
• Figure 2: One-line diagram of WECC 240-bus power system.
• Figure 3: Maximum area inertia estimation error under different hyperparameters for various methods. (a) Measurement reconstruction-based method; (b) System identification-based method; (c) Electromechanical oscillation-based method.
• Figure 4: Area inertia estimation error for various methods. (a) System identification-based method; (b) Measurement reconstruction-based method.
• Figure 5: Inertia estimation results of system identification-based method for each area from January 1st to January 14th, 2019.