CLASSIFICATION OF THE COIL PITCHES OF INDUCTION MOTORS USING KNN MODEL

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Noğay H. S.

1. ULUSLARARASI MÜHENDİSLİK BİLİMLERİ VE MULTİDİSİPLİNER YAKLAŞIMLAR KONGRESİ, İstanbul, Türkiye, 23 - 24 Şubat 2021, ss.80-86

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Basıldığı Şehir: İstanbul
  • Basıldığı Ülke: Türkiye
  • Sayfa Sayıları: ss.80-86
  • Kayseri Üniversitesi Adresli: Evet

Özet

Increasing the performance of induction motors, which are widely used in the industry, depends on the improvements to be made in some design steps. One of the most important of these design steps is to create the stator winding structure and decide on the coil pitches. With appropriate stator coil pitches structures, some single-row harmonics can be suppressed in the induction motors and motor performance can be improved by keeping motor losses at a certain level. In this study, K – Nearest Neighbor (KNN) model, which is a machine learning method, was designed to classify and predict the stator coil pitches and tested with five-fold cross-validation. As a result of this experimental study, a significant success was achieved by estimating both 100o and 120o coil pitches with 99.7% accuracy and all winding steps with 92.3% average accuracy. It has been revealed that KNN algorithms are a machine learning method that can be used effectively in a way that can contribute to electric machine design.