Accurate knowledge and estimation of the electro-mechanical modes in the power system are of great importance since a system-wide outage can be caused by one single unstable mode of oscillation. Most of these unstable mode of oscillations are inter-area oscillations which typically are in the range of 0.15Hz to 1Hz. Generally oscillation identification and damping are performed based on Model-based frequency studies. However, the stochastic nature of modern power grid with the advent of renewables and changing load dynamics, and nonlinear interactions makes the oscillation study with apriori models inaccurate and inefficient. Due to these factors, recently, measurement-based power grid mode estimation has attracted great attention.
In this research work, a series of measurement-based oscillation identification methods are proposed. First, a comparison of various measurement-based electro-mechanical oscillation mode detection methods is studied. Among the measurement-based techniques, Prony analysis, Eigenvalue Realization Algorithm (ERA), and Matrix Pencil (MP) methods are found to be very useful. These methods have successfully been used to determine low-frequency oscillatory modes from measurement data. Recently, Subspace Identification (SSI) methods have become popular as they are robust to variations, and can be represented in state-space form, thus making it easier for designing time-domain control approaches. Thus, second, an online wide-area direct coordinated control architecture for power grid transient stability enhancement based on subspace identification and Lyapunov energy functions has been designed and studied. Third, a novel hybrid deterministic-stochastic online measurement-based identification framework using subspace theory is introduced. The novelty of the design using a fully recursive algorithm and the effectiveness of combined treatment are further discussed. For controlling electro-mechanical oscillations of the power system effectively, identifying the location of the oscillation source is very important. Thus, fourth, an approach for power system oscillatory mode estimation and classification and source location identification based on Lyapunov energy functions are proposed. This new method is then compared with the most commonly used method known as dissipated energy flow (DEF). Finally, this work explores grid following and grid forming control architecture of battery energy storage and the use of identification methods to observe low-frequency oscillation with Distributed Energy Resource (DER) connections.