Online dynamic security assessment of microgrids before intentional islanding occurrence

Abstract

This paper presents a statistical learning-based method for security assessment of microgrids (MGs) in case of isolation from the main grid. Based on the stability criteria, the MG pre-islanding conditions are divided into secure and insecure regions. Critical system variables regarding the MG dynamic security are first selected via a feature selection procedure, known as minimum redundancy maximum relevance. An unsupervised learning method called pattern discovery method is then performed on the space of the critical features to extract the organization (patterns) among samples. Geometrically, the patterns are hyper-rectangles in the features space representing the system dynamic secure/insecure regions and can be effectively used for online MG security monitoring before islanding condition. Simulation results are carried out in the time domain, by using MATLAB, which demonstrate the effectiveness and accuracy of the proposed method in the MG security assessment