TITLE:
Optimized Complex Power Quality Classifier Using One vs. Rest Support Vector Machines
AUTHORS:
David De Yong, Sudipto Bhowmik, Fernando Magnago
KEYWORDS:
Complex Power Quality, Optimal Feature Selection, One vs. Rest Support Vector Machine, Learning Algorithms, Wavelet Transform, Pattern Recognition
JOURNAL NAME:
Energy and Power Engineering,
Vol.9 No.10,
September
12,
2017
ABSTRACT: Nowadays, power quality issues are becoming a
significant research topic because of the increasing inclusion of very
sensitive devices and considerable renewable energy sources. In general, most
of the previous power quality classification techniques focused on single power
quality events and did not include an optimal feature selection process. This
paper presents a classification system that employs Wavelet Transform and the
RMS profile to extract the main features of the measured waveforms containing
either single or complex disturbances. A data mining process is designed to
select the optimal set of features that better describes each disturbance
present in the waveform. Support Vector Machine binary classifiers organized in
a “One Vs Rest” architecture are individually optimized to classify single and
complex disturbances. The parameters that rule the performance of each binary
classifier are also individually adjusted using a grid search algorithm that
helps them achieve optimal performance. This specialized process significantly
improves the total classification accuracy. Several single and complex
disturbances were simulated in order to train and test the algorithm. The
results show that the classifier is capable of identifying >99% of
single disturbances and >97% of complex disturbances.