A new Approach of Power Quality Disturbance Classification Based on Rough Membership Neural Networks

2010 ◽  
Author(s):  
Lixia Wang ◽  
Zhengyou He ◽  
Jing Zhao
Keyword(s):  
Neural Networks ◽  
Power Quality ◽  
New Approach ◽  
Power Quality Disturbance

Classification of Power Quality Disturbance Based on Continuous S-Transform-Windowing Technique (CST-WT) and ANOVA as a Feature Selection

2015 ◽  
Vol 785 ◽  
pp. 368-372 ◽  
Author(s):  
Kamarulazhar Daud ◽  
Ahmad Farid Abidin ◽  
Harapajan Singh Nagindar Singh ◽  
Mohd Najib Mohd Hussain
Keyword(s):  
Feature Selection ◽  
Power Quality ◽  
Transient Signal ◽  
New Approach ◽  
Power Quality Monitoring ◽  
S Transform ◽  
Power Quality Disturbance ◽  
Before And After ◽  
Windowing Technique

This paper was conducted in order to identify and classify the different types of Power Quality Disturbances (PQD) based on a new approach the Analysis Of Variance (ANOVA). ANOVA is used as feature selection for the PQD parameters. The datum of PQD from the PSCAD/EMTDC® simulation and Power Quality Monitoring has been validated before feature extraction analysis can be commenced. The obtained datum is then analyzed by using Windowing Technique (WT) based on Continuous S-Transform (CST) to extract the features and its characteristics. Moreover, the study focuses an important issue concerning the identification of PQD selection, detection and classification. The feature and characteristics of three types of signal such as sag, swell, and transient signal are obtained. The outcome of the analysis shows that a new approach framework ANOVA-Based Before and After Neural Network (NN) classification has a slightly increases to 15-25% in term of classification of PQD.

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