Estimating Synchronous Inertia in Power Systems Using Equal Area Criterion and Artificial Intelligence

In this paper, an advanced algorithm is presented that utilizes artificial neural networks (ANN) for estimating the inertia of synchronous generators (SGs). The algorithm is enhanced by integrating a modified equal area criterion method along with the kinetic energy concept to compute the maximum mechanical power (Pmax). This calculated Pmax is introduced as a novel input feature to the ANN model. Beyond the contributions already discussed, a supplementary proposal involves the development of a novel index designed to identify the most optimal fault location in power systems. The efficiency of the proposed algorithm was rigorously evaluated using simulations on the IEEE 39-bus New England test system. The simulation results demonstrated the algorithm’s high accuracy, with an estimation error of less than 1% in determining the inertia of the generators. These findings underscore the algorithm’s potential for enhancing the performance of power system stability analysis.