Power quality problem classification using wavelet transformation and artificial neural networks

• Main Authors: Kanitpanyacharoean W., Premrudeepreechacharn S.
• Format: Conference or Workshop Item
• Language: English
• Published: 2014
• Online Access: http://www.scopus.com/inward/record.url?eid=2-s2.0-15944409505&partnerID=40&md5=1a295547cd4e28273c7a0e9dce50c282; http://cmuir.cmu.ac.th/handle/6653943832/1543
• Institution: Chiang Mai University

This paper presents a classification method for power quality problems in electrical power systems. To improve the electric power quality, sources of disturbances must be known and controlled. Power quality disturbance waveform recognition is often troublesome because it involves a broad range of disturbance categories or classes. This is a study of power quality problem classification using wavelet transformation and artificial neural networks. After training neural networks, the weight and bias is obtained for using to classify the power quality problems. The combined wavelet transformation with neural networks is able to classify all 6 types for power quality problems correctly. © 2004 IEEE.