In modern power grids, an increasing number of renewable sources are integrated via inverters, affecting the inertia of the overall power system. Thus, a low-inertia system formed is potentially vulnerable to fast grid collapse and catastrophic failures in case of any disturbances. The study of low-inertia systems is an important aspect of the stability and control of the power grid. The stability studies require system model representation in the time or frequency domain. The traditional modeling method is the state-space approach which requires detailed system knowledge that may not be available always. The impedance-based approach is computationally efficient but fails to provide information about the internal states of the system. To overcome these drawbacks, we develop a transfer function-based approach. The purpose of this study is to present a computationally efficient modeling method for a converter system that incorporates internal knowledge of the system. It provides flexibility in understanding the stability of the overall system through Bode, Nyquist, and pole-zero plots. The proposed Transfer Function-based approach is applied to the generalized model of the converter consisting of two types of inverters Grid Forming (GFM) and Grid Following (GFL), and its stability assessment is carried out in MATLAB.

In transmission and distribution substations, power transformers account for the majority of capital investment. As they are expensive, effective thermal performance monitoring is necessary for life extension. The hot-spot temperature value is among the most significant factors affecting a transformer's life expectancy. As HST evaluation require some Top-oil Temperature model parameters, accurate estimations of TOT are required to analyze the thermal performance and lifespan of transformers. Conventionally, the TOT parameters from the Resistance-Capacitance network are determined using the input-output data. While the regressor signals meet the Persistence of Excitation criterion in the Gradient Estimator a parametric estimate error approaches zero and the parameter converges to their true value. The Design of Experiment is often carried out in the test center to meet the PE requirement. As a consequence, actual operating transformer data is utilized for the identification and estimation of the parameters associated with the TOT model by using finite-time estimators. The detected PE issue, the impact of DoE, and the efficiency of FTEs with different filters for non-PE data obtained from the actual operating transformers' thermal model are demonstrated by experimental analysis using MATLAB.