KMITL

Permanent URI for this communityhttps://dspace.kmitl.ac.th/handle/123456789/1

Browse

Settings

Author: search.filters.author.Pahasa, JonglakSubject: search.filters.subject.Multiresolution analysis

Search Results

Now showing 1 - 2 of 2
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    PSO based kernel principal component analysis and multi-class support vector machine for power quality problem classification
    (2012-03-01)
    Pahasa, Jonglak
    ;
    Ngamroo, Issarachai
    Electric power quality (PQ) problems are very important aspects due to the increase in the number of loads which are sensitive to power disturbances. One of the important issues in the PQ problems is to detect and classify disturbance waveforms auto-matically in an efficient approach, because the possible solutions can be determined after the disturbance types are detected. This paper proposes a particle swarm optimization (PSO) based kernel principal component analysis (KPCA) and support vector machine (SVM) for PQ problem classification. Wavelet based multiresolution analysis (MRA) is utilized to extract features for various PQ disturbances. Dimension of these features are then reduced by KPCA so that the noise has less impact on the classification results. The multi-class SVM is used to classify the PQ problem using the dominant KPCA. The PSO is applied to optimize the KPCA and SVM parameters in order to improve the classification performance. The classification process implemented with various PQ events shows that the proposed technique provides more accuracy than the conventional technique under both noisy and noiseless environments. © 2012 ISSN 1349-4198.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Kernel principal component analysis for power quality problem classification
    (2010-07-30)
    Pahasa, Jonglak
    ;
    Ngamroo, Issarachai
    This paper proposes the application of kernel principal component analysis (KPCA) for power quality (PQ) problem classification. First, the features of PQ signal are extracted using wavelet-multiresolution analysis. Then, KPCA captures the dominant nonlinear properties of the extracted features by transforming to a high dimensional feature space. The dimension of extracted features produced by KPCA can be reduced without loss of information of the original features. Finally, support vector machines (SVMs) are used to classify the PQ problem using the dominant components of KPCA. Simulation results with six types of PQ problem demonstrate that the proposed KPCA-based SVMs provides the superior classification performance of PQ problem to the conventional SVMs.