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Power quality disturbance classification using fuzzy C-means algorithm and adaptive particle swarm optimization / Biswal, B. in IEEE transactions on industrial electronics, Vol. 56 N°1 (Janvier 2009) 

Public ISBD [article]  in IEEE transactions on industrial electronics > Vol. 56 N°1 (Janvier 2009) . - pp. 212 - 220 Titre : Power quality disturbance classification using fuzzy C-means algorithm and adaptive particle swarm optimization Type de document : texte imprimé Auteurs : Biswal, B., Auteur ; Dash, P.K., Auteur ; Panigrahi, K.B., Auteur Année de publication : 2009 Article en page(s) : pp. 212 - 220 Note générale : electronics Langues : Anglais (eng) Mots-clés : feature  extraction;  fuzzy  set  theory;  genetic  algorithms;  particle  swarm  optimisation;  pattern  classification;  pattern  clustering;  power  supply  quality Résumé : This paper presents a new approach for the visual localization, detection, and classification of various nonstationary power signals using a variety of windowing techniques. Among the various windows used earlier like sine, cosine, tangent, hyperbolic tangent, Gaussian, bi-Gaussian, and complex, the modified Gaussian window is found to provide excellent normalized frequency contours of the power signal disturbances suitable for accurate detection, localization, and classification. Various nonstationary power signals are processed through the generalized S-transform with modified Gaussian window to generate time-frequency contours for extracting relevant features for pattern classification. The extracted features are clustered using fuzzy C-means algorithm, and finally, the algorithm is extended using either particle swarm optimization or genetic algorithm to refine the cluster centers. En ligne : http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4559369&sortType%3Das [...] [article] Power quality disturbance classification using fuzzy C-means algorithm and adaptive particle swarm optimization [texte imprimé] / Biswal, B., Auteur ; Dash, P.K., Auteur ; Panigrahi, K.B., Auteur . - 2009 . - pp. 212 - 220. electronics Langues : Anglais (eng) in IEEE transactions on industrial electronics > Vol. 56 N°1 (Janvier 2009) . - pp. 212 - 220 Mots-clés : feature  extraction;  fuzzy  set  theory;  genetic  algorithms;  particle  swarm  optimisation;  pattern  classification;  pattern  clustering;  power  supply  quality Résumé : This paper presents a new approach for the visual localization, detection, and classification of various nonstationary power signals using a variety of windowing techniques. Among the various windows used earlier like sine, cosine, tangent, hyperbolic tangent, Gaussian, bi-Gaussian, and complex, the modified Gaussian window is found to provide excellent normalized frequency contours of the power signal disturbances suitable for accurate detection, localization, and classification. Various nonstationary power signals are processed through the generalized S-transform with modified Gaussian window to generate time-frequency contours for extracting relevant features for pattern classification. The extracted features are clustered using fuzzy C-means algorithm, and finally, the algorithm is extended using either particle swarm optimization or genetic algorithm to refine the cluster centers. En ligne : http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4559369&sortType%3Das [...]