caa a22 4500 445842482 CHVBK 20180317145349.0 cr unu---uuuuu 170323e20110301xx s 000 0 eng 10.1007/s11047-010-9204-2 doi (NATIONALLICENCE)springer-10.1007/s11047-010-9204-2 An immune system inspired clustering and classification method to detect critical areas in electrical power networks [Elektronische Daten] [N. Woolley, J. Milanović] Identifying critical, failure prone areas in a power system network are often a difficult and computationally intensive task. Artificial Immune System (AIS) algorithms have been shown to be capable of generalization and learning to identify previously unseen patterns. In this paper, a method is developed that uses artificial immune system classification and clustering algorithms to identify critical areas in the network. The algorithm identifies areas of the power system network that are prone to voltage collapse and areas with overloaded lines. The applicability of AIS for this particular task is demonstrated on test electrical power system networks. Its accuracy is compared with an optimised support vector machine (SVM) algorithm and k nearest neighbours algorithm (kNN) across 3 different power system networks. Springer Science+Business Media B.V., 2010 Clustering nationallicence Classification nationallicence Artificial immune systems nationallicence AIS nationallicence Power systems nationallicence Critical network areas nationallicence Overloaded lines nationallicence Woolley N. School of Electrical and Electronic Engineering, The University of Manchester, Ferranti Building, M601QD, Manchester, UK aut Milanović J. School of Electrical and Electronic Engineering, The University of Manchester, Ferranti Building, M601QD, Manchester, UK aut Natural Computing Springer Netherlands 10/1(2011-03-01), 305-333 1567-7818 10:1<305 2011 10 11047 https://doi.org/10.1007/s11047-010-9204-2 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11047-010-9204-2 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Woolley N. School of Electrical and Electronic Engineering, The University of Manchester, Ferranti Building, M601QD, Manchester, UK aut NATIONALLICENCE 700 1- Milanović J. School of Electrical and Electronic Engineering, The University of Manchester, Ferranti Building, M601QD, Manchester, UK aut NATIONALLICENCE 773 0- Natural Computing Springer Netherlands 10/1(2011-03-01), 305-333 1567-7818 10:1<305 2011 10 11047 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer