A Comprehensive Review of Intelligent Islanding Schemes and Feature Selection Techniques for Distributed Generation System

Detection of unintentional islanding, defined as inadvertently separation of distributed generators (DGs) from the utility grid, is a major challenging issue for modern distribution networks. Islanding detection becomes problematic especially when the local generation matches or closely matches the local load. Therefore, there are strict requirements for accurate, fast, and reliable islanding detection of renewables and DG-based systems. Various islanding schemes have been proposed in the literature, which can be categorized as remote, local, and intelligent-classifier-based schemes. Recently, intelligent schemes have gained attention due to their superior properties and advantages relative to traditional approaches. This paper overviews the shift in research from traditional schemes to intelligent islanding schemes. It also highlights the major obstacles, challenges, advantages and disadvantages, and future research directions of intelligent schemes. In this study, the intelligent-classifier-based islanding detection schemes presented over the last decade are analyzed objectively and comprehensively from all aspects of islanding detection. This research further highlights feature selection schemes and the most common parameters used for islanding detection. Finally, based on a detailed and critical analysis, the findings and potential recommendations are presented.