Machine Learning
Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
An Evolutionary Immune Network for Data Clustering
SBRN '00 Proceedings of the VI Brazilian Symposium on Neural Networks (SBRN'00)
Asymptotic behaviors of support vector machines with Gaussian kernel
Neural Computation
Artificial Immune Recognition System (AIRS): An Immune-Inspired Supervised Learning Algorithm
Genetic Programming and Evolvable Machines
Computational Techniques for Voltage Stability Assessment and Control (Power Electronics and Power Systems)
Artificial immune systems---today and tomorrow
Natural Computing: an international journal
Cluster Analysis
ICARIS '09 Proceedings of the 8th International Conference on Artificial Immune Systems
Improved heterogeneous distance functions
Journal of Artificial Intelligence Research
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
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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.