Power Disturbance Classifier Using Wavelet-Based Neural Network

Author

Hongkyun Kim ; Jinmok Lee ; Jaeho Choi ; Gyo-Bum Chung

Author_Institution

Sch. of Electr. & Comput. Eng., Chung-Buk Nat. Univ., Cheongju

fYear

2006

fDate

18-22 June 2006

Firstpage

1

Lastpage

6

Abstract

This paper presents a wavelet-based neural network technology for the detection and classification of the various types of power quality disturbances. For the detection and classification of the PQ transient signals, it should be done at the same time and the automatic methodology is recommended. In this paper, the hardware and software of the power quality data acquisition system (PQDAS) is proposed. In this system, the auto-classifying system combines the properties of the wavelet transform and the advantages of neural networks. Especially, the additional feature extraction to improve the recognition rate is considered. The configuration of the hardware of PQDAS and some case studies are also described

Keywords

data acquisition; feature extraction; neural nets; pattern classification; power engineering computing; power supply quality; wavelet transforms; autoclassifying system; feature extraction; power disturbance classifier; power quality data acquisition system; power quality disturbances; wavelet transform; wavelet-based neural network technology; Computer networks; Discrete wavelet transforms; Hardware; Multi-layer neural network; Neural networks; Power engineering computing; Power quality; Signal resolution; Wavelet analysis; Wavelet transforms;

fLanguage

English

Publisher

ieee

Conference_Titel

Power Electronics Specialists Conference, 2006. PESC '06. 37th IEEE

Conference_Location

Jeju

ISSN

0275-9306

Print_ISBN

0-7803-9716-9

Type

conf

DOI

10.1109/PESC.2006.1711939

Filename

1711939