Revitalizing the Electric Grid: A Machine Learning Paradigm for Ensuring Stability in the U.S.A

Abstract

The electric grid entails a diverse range of components with pervasive heterogeneity. Conventional electricity models in the U.S.A. encounter challenges in terms of affirming the stability and security of the power system, particularly, when dealing with unexpected incidents. This study explored various electric grid models adopted in various nations and their shortcomings. To resolve these challenges, the research concentrated on consolidating machine learning algorithms as an optimization strategy for the electricity power grid. As such, this study proposed Ensemble Learning with a Feature Engineering Model which exemplified promising outputs, with the voting classifier performing well as compared to the rainforest classifier model. Particularly, the accuracy of the voting classifier was ascertained to be 94.57%, illustrating that approximately 94.17% of its predictions were correct as contrasted to the Random Forest. Besides, the precision of the voting classifier was ascertained to be 93.78%, implying that it correctly pinpointed positive data points 93.78% of the time. Remarkably, the Voting Classifier for the Ensemble Learning with Feature Engineering Model technique surpassed the performance of most other techniques, demonstrating an accuracy rate of 94.57%. These techniques provide protective and preventive measures to resolve the vulnerabilities and challenges faced by geographically distributed power systems.