Detection and classification of complex power quality disturbances using S-transform amplitude matrix-based decision tree for different noise levels

2016 ◽  
Vol 27 (4) ◽  
pp. e2286 ◽  
Author(s):  
Arun Kumar Puliyadi Kubendran ◽  
Ashok Kumar Loganathan
Keyword(s):  
Decision Tree ◽  
Power Quality ◽  
Noise Levels ◽  
S Transform ◽  
Complex Power ◽  
Power Quality Disturbances ◽  
Amplitude Matrix

scholarly journals Recognition of Complex Power Quality Disturbances Using S-Transform Based Ruled Decision Tree

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 173530-173547
Author(s):  
Om Prakash Mahela ◽  
Abdul Gafoor Shaik ◽  
Baseem Khan ◽  
Rajendra Mahla ◽  
Hassan Haes Alhelou
Keyword(s):  
Decision Tree ◽  
Power Quality ◽  
S Transform ◽  
Complex Power ◽  
Power Quality Disturbances

Three-Layer Bayesian Network for Classification of Complex Power Quality Disturbances

2018 ◽  
Vol 14 (9) ◽  
pp. 3997-4006 ◽  
Author(s):  
Yi Luo ◽  
Kaicheng Li ◽  
Yuanzheng Li ◽  
Delong Cai ◽  
Chen Zhao ◽  
...  
Keyword(s):  
Bayesian Network ◽  
Power Quality ◽  
Complex Power ◽  
Power Quality Disturbances

Power Quality Disturbances Classification Based on Modified S Transform and Decision Tree

2013 ◽  
Vol 860-863 ◽  
pp. 1891-1894
Author(s):  
Ji Liang Yi ◽  
Ou Yang Qin
Keyword(s):  
Decision Tree ◽  
Power Quality ◽  
Statistical Features ◽  
Time Frequency ◽  
S Transform ◽  
Novel Method ◽  
Power Quality Disturbances ◽  
Frequency Properties

A novel method for power quality disturbances classification is presented using modified S transform (MST) and decision tree. The time-frequency properties of power quality disturbances are analyzed and the effects of window-wide parameter g on the properties are discussed. Four statistical features are extracted from the MST module time-frequency matrix and a decision tree is utilized to recognize 9 power quality disturbances. The simulations are made to illustrate the validity of the method proposed for power quality disturbances recognition.

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