Application of AIS Based Classification Algorithms to Detect Overloaded Areas in Power System Networks

Authors:
N. C. Woolley;J. V. Milanović
Affiliations:
School of Electrical and Electronic Engineering, The University of Manchester, Manchester, United Kingdom;School of Electrical and Electronic Engineering, The University of Manchester, Manchester, United Kingdom
Venue:
ICARIS '09 Proceedings of the 8th International Conference on Artificial Immune Systems
Year:
2009

Citing 6
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Abstract

The identification of voltage collapse prone areas in a power system network is 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, an AIS algorithm - Support Vector Machine (AIS-SVM) hybrid algorithm is developed to identify voltage collapse prone areas and overloaded lines in the power system network. The applicability of AIS for this particular task is demonstrated on a 30 bus electrical power system network and its accuracy compared to a conventional un-optimised SVM algorithm across 3 different power system networks.