SCIRP Mobile Website
Paper Submission

Why Us? >>

  • - Open Access
  • - Peer-reviewed
  • - Rapid publication
  • - Lifetime hosting
  • - Free indexing service
  • - Free promotion service
  • - More citations
  • - Search engine friendly

Free SCIRP Newsletters>>

Add your e-mail address to receive free newsletters from SCIRP.

 

Contact Us >>

WhatsApp  +86 18163351462(WhatsApp)
   
Paper Publishing WeChat
Book Publishing WeChat
(or Email:book@scirp.org)

Article citations

More>>

Mahela, O.P., Shaik, A.G. and Gupta, N. (2015) A Critical Review of Detection and Classification of Power Quality Events. Renewable and Sustainable Energy Reviews, 41, 495-505.
https://doi.org/10.1016/j.rser.2014.08.070

has been cited by the following article:

  • TITLE: Optimized Complex Power Quality Classifier Using One vs. Rest Support Vector Machines

    AUTHORS: David De Yong, Sudipto Bhowmik, Fernando Magnago

    KEYWORDS: Complex Power Quality, Optimal Feature Selection, One vs. Rest Support Vector Machine, Learning Algorithms, Wavelet Transform, Pattern Recognition

    JOURNAL NAME: Energy and Power Engineering, Vol.9 No.10, September 12, 2017

    ABSTRACT: Nowadays, power quality issues are becoming a significant research topic because of the increasing inclusion of very sensitive devices and considerable renewable energy sources. In general, most of the previous power quality classification techniques focused on single power quality events and did not include an optimal feature selection process. This paper presents a classification system that employs Wavelet Transform and the RMS profile to extract the main features of the measured waveforms containing either single or complex disturbances. A data mining process is designed to select the optimal set of features that better describes each disturbance present in the waveform. Support Vector Machine binary classifiers organized in a “One Vs Rest” architecture are individually optimized to classify single and complex disturbances. The parameters that rule the performance of each binary classifier are also individually adjusted using a grid search algorithm that helps them achieve optimal performance. This specialized process significantly improves the total classification accuracy. Several single and complex disturbances were simulated in order to train and test the algorithm. The results show that the classifier is capable of identifying >99% of single disturbances and >97% of complex disturbances.