Intelligent Classifiers for Islanding Detection in Microgrids and Modern Power Systems

Distributed generation (DG) has become an increasingly preferred technology for addressing the growing energy demand while ensuring environmental compliance. In this context, microgrids have emerged as attractive energy systems that facilitate the secure integration of distributed generators into the distribution network. As the deployment of microgrids becomes more widespread, the issue of islanding has gathered significant attention. Islanding detection is a critical aspect of microgrid operation, as it plays a vital role in maintaining system stability and security. A variety of methods have been proposed for islanding detection, each with distinct advantages and limitations. This study provides a concise overview of islanding detection methods while primarily focusing on the application of intelligent classification techniques in this domain. The paper explores the operational principles of islanding detection using intelligent classifiers and presents a literature review of various classification approaches. Notably, the selection of an appropriate classifier in the detection process is of paramount importance, as it directly influences cost-effectiveness, accuracy, and practical applicability.