Power quality disturbances classification using support vector machines with optimised time-frequency kernels

2012 ◽  
Vol 4 (2) ◽  
pp. 181 ◽  
Author(s):  
Mihir Narayan Mohanty ◽  
Aurobinda Routray ◽  
Ashok Kumar Pradhan ◽  
Prithviraj Kabisatpathy
Keyword(s):  
Support Vector Machines ◽  
Power Quality ◽  
Support Vector ◽  
Time Frequency ◽  
Vector Machines ◽  
Power Quality Disturbances

Transient Power Quality Disturbances Identification and Classification Using Wavelet and Support Vector Machines

2012 ◽  
Vol 433-440 ◽  
pp. 1071-1077
Author(s):  
Wen Sheng Sun ◽  
Xiang Ning Xiao ◽  
Shun Tao ◽  
Jian Wang
Keyword(s):  
Wavelet Transform ◽  
Support Vector Machines ◽  
Power Quality ◽  
Support Vector ◽  
Time Frequency ◽  
Vector Machines ◽  
Power Quality Disturbance ◽  
Time Frequency Localization ◽  
Power Quality Disturbances ◽  
Transient Power Quality

Based on wavelet transform and support vector machines, a method of recognition and classification of transient power quality disturbance is presented. Using wavelet transform time-frequency localization characteristics, according to the principle of modulus maxima, realize the automatic detection positioning. After multi-resolution signal decomposition of PQ disturbances, multi-scale information in frequency domain and time domain of the signal can be extracted as the characteristic vectors. After choose and optimization of the eigenvectors based on the method of F-score, support vector machines are used to classify these eigenvectors of power quality disturbances. Effectiveness of the proposed method is verified through Matlab simulation.

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