Determination of leakage reactance in monophase transformers using by cascaded neural network

Selcuk NOGAY

Article Details

Determination of leakage reactance in monophase transformers using by cascaded neural network

Authors:

Selcuk NOGAY

Publisher:

Balkan Yayın

Main Topics:

Applied physics

Journal:

Balkan Journal of Electrical and Computer Engineering 2016 Vol.4 Issue 2, pp.89-96

Document Type:

Article

Publication Date:

9/2016

Document Language:

English

Document ID (Org No.):

2000927524

Frequency:

Quarterly

pISSN:

2147-284X

Abstract:

 


In this study, the artificial neural networks method was used in order to determine the leakage induction coefficient using known constants of the transformer. It is important to reach the leakage reactance through guessing. For this purpose, two types of ANN models were used in the study and were compared to one another. These two ANN models are cascaded ANN model and the conventional model. Testing data were used to measure the efficiency of these two models. Testing data are the same for both models. When the models were compared to each other, it was concluded that Cascaded ANN model was more successful. However, it is a fact that both models produce estimation around 99%. The main reason why the ANN model is used in the study is to ensure a more practical and quicker attainment of leakage reactance or leakage induction coefficient by looking at the fixed and measurable values of the transformer than calculation method.

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Article Details

Determination of leakage reactance in monophase transformers using by cascaded neural network

Authors: Selcuk NOGAY

Publisher: Balkan Yayın

Main Topics: Applied physics

Journal: Balkan Journal of Electrical and Computer Engineering 2016 Vol.4 Issue 2, pp.89-96

Document Type: Article

Publication Date: 9/2016

Document Language: English

Document ID (Org No.): 2000927524

Frequency: Quarterly

pISSN: 2147-284X

Issues

Authors:

Selcuk NOGAY

Publisher:

Balkan Yayın

Main Topics:

Applied physics

Journal:

Balkan Journal of Electrical and Computer Engineering 2016 Vol.4 Issue 2, pp.89-96

Document Type:

Article

Publication Date:

9/2016

Document Language:

English

Document ID (Org No.):

2000927524

Frequency:

Quarterly

pISSN:

2147-284X