The extent in the estimation of frequency and rate of change of frequency (RoCoF) plays an important role in providing power system stability, preventing equipment damage, and thus ensuring reliability of the grid. The inevitable shift from synchronous generators to inverter dependent generation has substantially reduced the overall system inertia which causes rapid frequency variations reflecting in the RoCoF where it fluctuates up to ±1 Hz/s. This range of fluctuations can cause relay malfunctions leading to cascading failures in the system. This chapter discusses two advanced methodologies to address such challenges, namely the interpolated discrete Fourier transform (IpDFT) with Kalman Filtering (KF) approach and second approach discusses about the observer-based adaptive detection methodology (O-ADM). The IpDFT-KF method integrates spectral analysis with dynamic state estimation, utilizing the computational efficiency of the IpDFT for estimation of power system variables like frequency, phase, and magnitude. The KF ensures noise is filtered while the RoCoF is estimated. The O-ADM alternatively employs a Lyapunov-based algorithm for designing adaptive observer framework to offset the measurement distortions, such as harmonics, ensuring high accuracy and robustness even under non-Gaussian noise and extreme grid conditions. Both these methods have been validated on a 16-machine, 68-bus benchmark system, demonstrating their effectiveness in handling noise and sudden large disturbances. The errors in the frequency and RoCoF estimation were reducing as low as 0.5% even under high noise levels up to 15%. Thus, the proposed approaches can be used as viable solutions for real-time power system monitoring, enhancing grid resilience, and improving the performance of control and protection frameworks in the current networks dominated by power-electronics.