## 1 Introduction
Additionally, methods utilizing electromechanical oscillation parameters are considered, which, despite being robust against the location of faults and RES penetration levels, are not suitable for ambient data and face performance issues in the presence of noise and non-stationary oscillations. The review also covers the second derivative of frequency data methods, recognized for their speed and accuracy but requiring a complete dynamical model. In recent times, the development of Wide Area Monitoring Systems (WAMS) has enabled massive real-time measurements to ensure monitoring and control applications for wide areas [13]. This paper applies an approach for estimating inertia that utilizes Phasor Measurement Units (PMUs) during large disturbances in the power system. In a steady state, the frequency across a power system is consistent, but disturbances cause varying frequencies across different parts of the grid for some time. Disturbance effects show up quicker in areas closer to the disturbances [14; 15]. To accurately assess the system's average frequency, it is essential to gather frequency measurements from various grid sections, ideally from each generator. The Center of Inertia (CoI) serves as a representation of this average frequency, acting as a hypothetical hub connecting all system generators. While current systems lack measurements from each generator, the introduction of PMUs enables quicker and more precise data collection. In [16], available measurements of electrical and mechanical power from generators are employed to derive estimates for the RoCoF and inertia constants within a three-generator test system, utilizing the frequency of the CoI. The scientific paper [17] introduces a novel method for estimating the RoCoF in power systems, utilizing Interpolated Discrete Fourier Transform (IDFT) and Kalman filtering. This approach aims to enhance accuracy and reduce latency in RoCoF estimation, outperforming traditional methods. While the method offers advantages such as increased accuracy and reduced estimation latency, potential challenges include its implementation complexity and the need for specific hardware or software capabilities. Its effectiveness may also vary across different power system configurations. The paper [18] explores the impact of oscillations on RoCoF curve quality, employing curve fitting and filtering to enhance data reliability. Selecting an optimal analysis range is crucial due to the inertia estimation method's sensitivity. Despite these challenges, the method demonstrates reduced error rates and improved accuracy when appropriate data ranges and processing techniques are utilized. This scientific paper [19] categorizes inertia estimation methods into three groups: DFT-based methods, Kalman Filter techniques, and other approaches like the orthogonal phase-locked method. While these methods are recognized for their robustness and accuracy in frequency tracking, they face challenges in real-time applications due to accuracy and computational demands. The paper further identifies key areas for future research, emphasizing the development of AI techniques for improved RoCoF prediction, the refinement of DFT and Kalman Filter methods, and the need for enhanced real-time RoCoF tracking. In [20], they present a method for estimating inertia in power systems, utilizing the eigenstructure analysis of linearized dynamic equations to elucidate the relationship between inertia, eigenvalues, and eigenvectors. It incorporates Dynamic Mode Decomposition (DMD) to extract these factors from synchronized measurements, notably from PMU. The methodology challenges include the complexity of its implementation, reliance on accurate PMU data, limited testing in diverse systems, high computational demands, and a possible overdependence on theoretical models. Despite these challenges, the method presents a sophisticated solution for inertia estimation, though it necessitates further refinement and broader real-world testing.