Study on Intelligent Power Electronics Dominated Grid Via Machine Learning Techniques

Abstract

Intelligent power electronics and machine learning algorithms are gradually reshaping the context of the more progressive electrical power grids today’s world. This abstract looks into how intelligent power electronics can be incorporated into grid systems and how control using machine learning can be applied. By using of complex algorithms, real-time analysis, these technologies improve temporal flexibility of the grid, its ability to prevent disruptions, and optimize the usage of renewable sources of energy. Intelligent power electronics can join forces with machine learning to provide completely new ways of managing the much-needed grid stability and low energy losses.  The rapid emergence and evolution of power electronics has presented various challenges and opportunities in modern electrical grids. These include their ability to enhance grid flexibility and efficiency, but also their potential to introduce complex stability and control issues. This paper proposes a framework for addressing these issues using machine learning. The paper presents a comprehensive review of the current state of the art in machine learning and its potential to improve the stability and control of electrical grids. It proposes a framework that will help facilitate the transition to a more resilient and smart electrical system.