By Topic

A novel probabilistic neural network system for power quality classification based on different wavelet transform

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Wei-Bing Hu ; Huazhong Univ. of Sci. & Technol., Wuhan ; Kai-Cheng Li ; Dangjun Zhao

This paper proposed a power quality disturbances classification system based on wavelet transforms and novel probabilistic neural network (PNN). Wavelet transform is utilized to extract feature vectors for various power quality disturbances based on multi-resolution analysis. The decomposition signal is divided into 5 equal length bins in each level. Root mean square (RMS) value of the wavelet coefficients that fall within that bin is regarded as a dimension of feature vectors. These feature vectors are applied to a probabilistic neural network for training and testing. Evolutionary algorithm is used to in this paper as a multivariate optimization scheme for finding multiple sigma values in estimation of probabilistic density function. One of the major virtue of PNN stems from its modular architecture design, then it can be easily extended adapt to a changing environment by appropriate chromosomes and generations. We examined that different decomposition levels of wavelet transform are concerned with the classifier accuracy, and the performance of classification is minor distinction with different wavelet families under the condition of same decomposition level.

Published in:

Wavelet Analysis and Pattern Recognition, 2007. ICWAPR '07. International Conference on  (Volume:2 )

Date of Conference:

2-4 Nov. 2007