Comparative Analysis of Machine Learning Approaches to Assess Stability in Micro Grid

Traditionally, electricity generation was localized, with a single power plant supplying power to surrounding towns and using only fossil fuels, but as modernization began, increasing electricity demand meant many Blackouts, distribution imbalances, and unavailability of power supply all these issues have resulted in high energy costs also in terms of pollution. The idea of a microgrid is growing in popularity as a means of addressing environmental pollution and raising energy demands. The features of Micro grid differ significantly from those of the traditional grid because distributed energy sources (DERs) are typically interfaced with the utility grid by inverters. In this work algorithms like Deep Learning, and LSTM, are compared with KNN a Machine learning Algorithm and both are used to evaluate for transient, frequency, and small signal stability in an 18-bus test system while comparing.