Intelligent pattern classification approach to power quality events

• Type: Article in International Conference Proceedings Book
• Date: 2012

Authors

Mohanty, SR (Author) Other; Kishor, N (Author) Other; Ray, PK (Author) Other; Catalao, JPS (Author) Other

Conference proceedings International

Title: INES 2012 - IEEE 16th International Conference on Intelligent Engineering Systems, Proceedings

Pages: 567-572

Indexing

Scopus - 0 Citations

Other information

Authenticus ID: P-008-6A4

DOI: 10.1109/ines.2012.6249898

Abstract (EN): This paper presents the classification of power quality (PQ) disturbances using modular probabilistic neural network (MPNN), support vector machines (SVMs) and least square support vector machines (LS-SVMs) in grid-connected wind energy systems. Different types of sag and swell disturbances due to the change in load and wind speed are created using MATLAB/Simulink. Classification scheme encompasses suitable features extracted by S-transform (ST) and is subsequently trained with MPNN, SVM and LS-SVM to effectively classify the PQ disturbances. The accuracy and reliability of the proposed classifier are also validated on signals with noise content. A comparative study is also carried out to determine the efficacy of the proposed techniques. © 2012 IEEE.

Language: English

Type (Professor's evaluation): Scientific