A Novel Selection Algorithm of a Wavelet-Based Transformer Differential Current Features

Ghunem R. A., El-Shatshat R., Özgönenel O.

IEEE TRANSACTIONS ON POWER DELIVERY, cilt.29, sa.3, ss.1120-1126, 2014 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 29 Sayı: 3
  • Basım Tarihi: 2014
  • Doi Numarası: 10.1109/tpwrd.2013.2293976
  • Dergi Adı: IEEE TRANSACTIONS ON POWER DELIVERY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.1120-1126
  • Anahtar Kelimeler: Entropy criterion, feature selection, internal fault, magnetization inrush, minimum description length criterion, stepwise regression, transformer differential current, wavelet multiresolution analysis, POWER TRANSFORMERS, IMPROVED OPERATION, PROTECTION, CLASSIFICATION
  • Ondokuz Mayıs Üniversitesi Adresli: Evet

Özet

In this paper, a novel selection algorithm of wavelet-based transformer differential current features is proposed. The minimum description length with entropy criteria are employed for an initial selection of the mother wavelet and the resolution level, respectively; whereas stepwise regression is applied for obtaining the most statistically significant features. Dimensionality reduction is accordingly achieved, with an acceptable accuracy maintained for classification. The validity of the proposed algorithm is tested through a neuro-wavelet-based classifier of transformer inrush and internal fault differential currents. The proposed algorithm highlights the potential of utilizing synergism of integrating multiple feature selection techniques as opposed to an individual technique, which ensures optimal selection of the features.