NEURAL NETWORK ESTIMATION OF MUTUAL INDUCTANCE VARIATION FOR A SHADED-POLE INDUCTION MOTOR

Yıl 2018, Cilt: 3 Sayı: 2, 36 - 45, 01.01.2019

Emre Çelik

Öz

Shaded-pole
induction motors (SPIMs) are often preferred in small power applications owing
to their ability to work with single-phase power source, simple structure and
low-cost properties. Such motors are within the class easy to manufacture, but
the most difficult to analyze mathematically due to the fact that they have a
variable air gap and elliptical rotating magnetic field, which leads to highly
complex inductance calculations. Considering that the identification accuracy
of phase variables is directly related to the correct knowledge of inductances
in AC machines, the authors of this article attempt to realize a neural network
(NN)-based inductance estimation in-between the stator and rotor, and also
in-between the shading ring (shaded-pole winding) and rotor loop for an
industrial SPIM. For this aim, corresponding inductance measurements are made
first experimentally in terms of each 3.6º electrical position, and as such, a
total of 101 data samples have been collected. %70 of them are considered as
training data to train the NN while the remainder is adopted for testing the
generation capability of NN. Results in comparison with the actual values have
affirmed the excellence performance of the introduced NN in simultaneous
estimation of the concerned two important inductances.

Anahtar Kelimeler

Shaded-pole induction motors, shaded-pole, rotor loop, neural network, inductance estimation

Kaynakça

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