Recent advances in fault detection, classification and isolation techniques for smart grids: a review

Recent advances in fault detection, classification and isolation techniques for smart grids: a review

Every electric power system necessarily consists of one or more power sources, transmission lines and loads. Conventional and renewable power sources are part of the electric system. The word“smart grid”got attention in in the early 2000s and research in said domain started with development in renewable energy sources and digital communication technologies. Interest in this field grew swiftly with the increased focus on efficient energy sources, grid reliability, and distributed energy resources integration. Initial stage pilot projects and foundational studies with large-scale implementations emerged around 2010, coinciding with policy support and funding initiatives worldwide. The world is giving attention to transitioning to renewable sources of energy to slow down the adverse effect of climate change and minimize the use of polluting material and fossil fuels. Artificial intelligence and machine learning-powered smart grid effectively integrates renewable resources, manages the demand-supply and empowers the consumers to dynamically participate in the energy ecosystem to promote a culture of energy conservation and improved sustainability. The aim of the study is to have a thorough survey of existing artificial intelligence and machine learning methods and their implementa¬tion in smart grids that enhance the efficiency and sustainability of the system and promote green energy deployment.