Power quality event characterization using support vector machine and optimization using advanced immune algorithm

Birendra Biswal

doi:10.1016/j.neucom.2012.08.031

Power quality event characterization using support vector machine and optimization using advanced immune algorithm

2013-03-01

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DOI：10.1016/j.neucom.2012.08.031

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This paper presents a time-time transform (TT-transform) variant for classification of non-stationary power signal disturbance patterns. The TT-transform variant is derived from the well known S-transform and employs a new window function whose width is inversely proportional to the frequency raised to a constant power with values within 0 and 1. Features are derived from the TT-transform result of the power signal patterns. These features are used for automatic recognition of types of disturbances with the help of kernel based support vector machine (SVM) based clustering. Further, the clustering performance of the TT-SVM based pattern recognizer is improved by a modified immune optimization algorithm. Several test cases are provided to demonstrate the improvement in classification accuracy while resulting in significant reduction of support vectors. (C) 2012 Elsevier B.V. All rights reserved.

Keywords:

Power signals

TT-transform

Support vector machine (SVM)

Radial basis function

Mexican hat wavelet kernel

Modified immune optimization algorithm (MIOA)

Power quality event characterization using support vector machine and optimization using advanced immune algorithm

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