Application of multi-class support vector machines for power
system on-line static security assessment using DT - based feature and data
selection algorithms
Affiliations: No. 424, Hafez Ave, Electrical Engineering Department,
Amirkabir University of Technology, 15914, Tehran, Iran
Note: [] Corresponding author: M. Mohammadi, No. 424, Hafez Ave,
Electrical Engineering Department, Amirkabir University of Technology, 15914,
Tehran, Iran. Tel.: +98 2164543504; Fax: +98 2164543504; E-mail:
m.mohammadi@aut.ac.ir
Abstract: This paper presents a multi-class Support Vector Machine (SVM) based
algorithm for on-line static security assessment of the power systems. The
proposed SVM based security assessment algorithm has a very small training time
and space in comparison with the traditional machine learning methods such as
Artificial Neural Networks (ANN) based algorithms. In addition, the proposed
algorithm is faster than existing algorithms. One of the main points, to apply
a machine learning method is feature selection. In this paper, a new Decision
Tree (DT) based feature selection algorithm has been presented. The proposed
SVM algorithm has been applied to New England 39-bus power system. The
simulation results show the effectiveness and the stability of the proposed
method for on-line static security assessment. The effectiveness of the
proposed feature selection algorithm has been investigated, too. The proposed
feature selection algorithm has been compared with different feature selection
algorithms. The simulation results demonstrate the effectiveness of the
proposed feature algorithm.
Keywords: Machine learning, multi-class Support Vector Machines (SVM), feature selection, power system security
