Title :
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
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;
Conference_Titel :
Power Electronics Specialists Conference, 2006. PESC '06. 37th IEEE
Conference_Location :
Jeju
Print_ISBN :
0-7803-9716-9
DOI :
10.1109/PESC.2006.1711939
