Power Quality Disturbances Classification using Discrete Wavelet Transform and Support Vector Machine

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    Power utility providers and power industry service providers face a significant challenge in identifying the type of Power Quality Disturbances (PQD) automatically. This paper discusses a method to classify PQD using signal decomposition, statistical analysis and machine learning. Firstly, Discrete Wavelet Transform (DWT) is applied on the generated PQD signals to decompose the signal to obtain its representation in time and frequency domain. Secondly, first and second order statistical parameters are computed on the selected sub-band of DWT. These parameters are used as features vector for the machine learning based classifier. Our database consists of 2400 generated signals of PQD, which were divided into train and test set. Another set of noise corrupted signal database was generated to evaluate the capability of the system. SVM using quadratic kernel was selected as the classifier of the Power Quality Disturbances feature vector. Comparisons were also made with other types of classifiers and other types of mother wavelet filter functions. The results show that the combination of DWT and SVM managed to classify Power Quality Disturbances with high accuracy and has a strong resistance towards noise.

     

     


  • Keywords


    Power Quality; Machine Learning; Support Vector Machine; Wavelet Transform; Statistical feature

  • References


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Article ID: 28298
 
DOI: 10.14419/ijet.v7i4.35.28298




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