Harmonic estimation based support vector machine for typical power systems

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ÖZDEMİR S., Demirtaş M., AYDIN Ş.

Neural Network World, cilt.26, sa.3, ss.233-252, 2016 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 26 Sayı: 3
  • Basım Tarihi: 2016
  • Doi Numarası: 10.14311/nnw.2016.26.013
  • Dergi Adı: Neural Network World
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.233-252
  • Anahtar Kelimeler: ANN, Harmonic, LR, Power distribution system, Power quality, Support Vector Machine, THD
  • Manisa Celal Bayar Üniversitesi Adresli: Evet

Özet

The power quality in electrical energy systems is very important and harmonic is the vital criterion. Traditionally Fast Fourier Transform (FFT) and Discrete Fourier Transform (DFT) have been used for the harmonic distortion analysis and in the literature harmonic estimations have been made using di erent methods. As an alternative method, this paper suggested using Support Vector Machine (SVM) for harmonic estimation. The real power energy distribution system has been examined and the estimation results have been compared with measured real data. The proposed solution approach was comparatively evaluated with the ANN and LR estimation methods. Comparison results show that THD estimation values that were obtained by the SVM method are close to the THD estimation values obtained from ANN (Artificial Neural Network) and LR (Linear regression) methods. The numerical results clearly showed that the SVM method is valid for THD estimation in the power system.