caa a22 4500 606159460 CHVBK 20210128100624.0 cr unu---uuuuu 210128e20150401xx s 000 0 eng 10.1007/s00521-014-1706-x doi (NATIONALLICENCE)springer-10.1007/s00521-014-1706-x Online dynamic security assessment of microgrids before intentional islanding occurrence [Elektronische Daten] [M. Sanjari, A. Yatim, G. Gharehpetian] 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. The Natural Computing Applications Forum, 2014 Microgrid nationallicence Islanding occurrence nationallicence Pattern discovery method nationallicence Security assessment nationallicence Sanjari M. Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Johor Bahru, Malaysia aut Yatim A. Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Johor Bahru, Malaysia aut Gharehpetian G. Electrical Engineering Department, Amirkabir University of Technology, Tehran, Iran aut Neural Computing and Applications Springer London 26/3(2015-04-01), 659-668 0941-0643 26:3<659 2015 26 521 https://doi.org/10.1007/s00521-014-1706-x text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00521-014-1706-x text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Sanjari M. Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Johor Bahru, Malaysia aut NATIONALLICENCE 700 1- Yatim A. Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Johor Bahru, Malaysia aut NATIONALLICENCE 700 1- Gharehpetian G. Electrical Engineering Department, Amirkabir University of Technology, Tehran, Iran aut NATIONALLICENCE 773 0- Neural Computing and Applications Springer London 26/3(2015-04-01), 659-668 0941-0643 26:3<659 2015 26 521