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An immune system inspired clustering and classification method to detect critical areas in electrical power networks.

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Title: An immune system inspired clustering and classification method to detect critical areas in electrical power networks.
Authors: Woolley, N. C.1, Milanović, J. V.1 nick.woolley@postgrad.manchester.ac.uk
Source: Natural Computing. Mar2011, Vol. 10 Issue 1, p305-333. 29p.
Subjects: Computer simulation of immune system, Electric power systems, Algorithms, Support vector machines, Nearest neighbor analysis (Statistics), Kernel functions
Abstract: 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. [ABSTRACT FROM AUTHOR]
Copyright of Natural Computing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: 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. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Natural Computing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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