Power quality disturbance classification based on wavelet transform and self-organizing learning neural network

Guangbin Ding; Lin Liu

doi:10.1117/12.718214

30 October 2006 Power quality disturbance classification based on wavelet transform and self-organizing learning neural network

Guangbin Ding, Lin Liu

Author Affiliations +

Proceedings Volume 6358, Sixth International Symposium on Instrumentation and Control Technology: Sensors, Automatic Measurement, Control, and Computer Simulation; 63584V (2006) https://doi.org/10.1117/12.718214
Event: Sixth International Symposium on Instrumentation and Control Technology, 2006, Beijing, China

Abstract

A novel approach for the power quality (PQ) disturbances classification based on the wavelet transform (WT) and selforganizing learning array (SOLAR) system is proposed. Wavelet transform is utilized to extract feature vectors for various PQ disturbances and the WT can accurately localizes the characteristics of a signal both in the time and frequency domains. These feature vectors then are applied to a SOLAR system for training and disturbance pattern classification. By comparing with a classic neural network, it is concluded that SOLAR has better data driven learning and local interconnections performance. The research results between the proposed method and the other existing method is discussed and the proposed method can provide accurate classification results. On the basis of hypothesis test of the averages, it is shown that corresponding to different wavelets selection, there is no statistically significant difference in performance of PQ disturbances classification and the relationship between the wavelet decomposition level and classification performance is discussed. The simulation results demonstrate the proposed method gives a new way for identification and classification of dynamic power quality disturbances.

Citation Download Citation

Guangbin Ding and Lin Liu "Power quality disturbance classification based on wavelet transform and self-organizing learning neural network", Proc. SPIE 6358, Sixth International Symposium on Instrumentation and Control Technology: Sensors, Automatic Measurement, Control, and Computer Simulation, 63584V (30 October 2006); https://doi.org/10.1117/12.718214

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
7 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Wavelets

Neurons

Wavelet transforms

Neural networks

Classification systems

Fuzzy systems

Picosecond phenomena

Show All Keywords

Keywords/Phrases

Search In:

Publication Years