Estimation of Permanent Magnet Synchronous Generator Performance with Artificial Neural Network Models

Onursal Çetin; Adem Dalcalı; Feyzullah Temurtaş

doi:10.35377/saucis.03.01.724976

EN TR

Estimation of Permanent Magnet Synchronous Generator Performance with Artificial Neural Network Models

Abstract

The interest in renewable energy sources has grown with the increase of environmental pollution and the decrease of fossil fuels. It is possible to provide energy supply security and diversity by using renewable energy sources. In this regard, wind energy, which is one of the renewable energy sources whose share in energy production increases day by day, emerges as a local and environmentally friendly solution. Many different types of generators are used in wind turbines and these have advantages and disadvantages according to each other. Permanent magnet synchronous generators (PMSG) are preferred because of their advantages such as high efficiency, high power density and being used directly in wind turbines without the need for gear system. In this study, the performance of the 2,5 kW PMSG, with a 14-pole surface placement, internal rotor, suitable for use in wind turbines, has been examined by changing the physical structure of the magnet. For this purpose, performance parameters such as total magnet consumption, efficiency, power loss have been successfully estimated using single and double hidden layered multi layer neural network (MLNN), elman neural network (ENN) and radial basis function neural network (RBFNN).

Keywords

multilayer neural network,elman neural network,radial basis function neural network,permanent magnet synchronous generator

Sabit Mıknatıslı Senkron Generatör Performansının Yapay Sinir Ağı Modelleri ile Kestirimi

Öz

Çevre kirliliğinin artması ve fosil yakıtların azalması ile yenilenebilir enerji kaynaklarına ilgi artmıştır. Yenilenebilir enerji kaynaklarını kullanarak enerji arz güvenliğini ve çeşitliliğini sağlamak mümkündür. Bu bağlamda, enerji üretiminde payı her geçen gün artan yenilenebilir enerji kaynaklarından biri olan rüzgâr enerjisi, yerel ve çevre dostu bir çözüm olarak ortaya çıkmaktadır. Rüzgâr türbinlerinde birçok farklı tipte jeneratör kullanılmaktadır ve bunlar birbirlerine göre avantaj ve dezavantajlara sahiptir. Sabit mıknatıslı senkron generatörler (SMSG) yüksek verim, yüksek güç yoğunluğu ve dişli sistemine gerek olmadan direkt olarak rüzgâr türbinlerinde kullanılma gibi avantajlarından dolayı tercih edilmektedirler. Bu çalışmada, rüzgâr türbinlerinde kullanıma uygun 14-kutuplu yüzey yerleştirmeli, içten rotorlu, 2,5 kW SMSG’nin performansı mıknatısın fiziksel yapısı değiştirilerek incelenmiştir. Bu amaçla toplam kullanılan mıknatıs miktarı, verim, güç kaybı gibi performans parametreleri tek ve iki gizli katmana sahip çok katmanlı sinir ağı (MLNN), elman sinir ağı (ENN) ve radyal tabanlı fonksiyon sinir ağı (RBFNN) kullanılarak başarılı bir şekilde kestirilmiştir.

Anahtar Kelimeler

çok katmanlı sinir ağı,elman sinir ağı,radyal tabanlı fonksiyon sinir ağı,sabit mıknatıslı senkron generatör

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Details

Primary Language

English

Subjects

Electrical Engineering

Journal Section

Research Article

Authors

Onursal Çetin ^*
0000-0001-5220-3959
Türkiye

Adem Dalcalı
0000-0002-9940-0471
Türkiye

Feyzullah Temurtaş
0000-0002-3158-4032
Türkiye

Publication Date

April 30, 2020

Submission Date

April 21, 2020

Acceptance Date

April 27, 2020

Published in Issue

Year 2020 Volume: 3 Number: 1

DOI

https://doi.org/10.35377/saucis.03.01.724976

IZ

https://izlik.org/JA42CX62WY

Cite

RIS / Bibtex

APA

Çetin, O., Dalcalı, A., & Temurtaş, F. (2020). Estimation of Permanent Magnet Synchronous Generator Performance with Artificial Neural Network Models. Sakarya University Journal of Computer and Information Sciences, 3(1), 60-73. https://doi.org/10.35377/saucis.03.01.724976

AMA

1.Çetin O, Dalcalı A, Temurtaş F. Estimation of Permanent Magnet Synchronous Generator Performance with Artificial Neural Network Models. SAUCIS. 2020;3(1):60-73. doi:10.35377/saucis.03.01.724976

Chicago

Çetin, Onursal, Adem Dalcalı, and Feyzullah Temurtaş. 2020. “Estimation of Permanent Magnet Synchronous Generator Performance With Artificial Neural Network Models”. Sakarya University Journal of Computer and Information Sciences 3 (1): 60-73. https://doi.org/10.35377/saucis.03.01.724976.

EndNote

Çetin O, Dalcalı A, Temurtaş F (April 1, 2020) Estimation of Permanent Magnet Synchronous Generator Performance with Artificial Neural Network Models. Sakarya University Journal of Computer and Information Sciences 3 1 60–73.

IEEE

[1]O. Çetin, A. Dalcalı, and F. Temurtaş, “Estimation of Permanent Magnet Synchronous Generator Performance with Artificial Neural Network Models”, SAUCIS, vol. 3, no. 1, pp. 60–73, Apr. 2020, doi: 10.35377/saucis.03.01.724976.

ISNAD

Çetin, Onursal - Dalcalı, Adem - Temurtaş, Feyzullah. “Estimation of Permanent Magnet Synchronous Generator Performance With Artificial Neural Network Models”. Sakarya University Journal of Computer and Information Sciences 3/1 (April 1, 2020): 60-73. https://doi.org/10.35377/saucis.03.01.724976.

JAMA

1.Çetin O, Dalcalı A, Temurtaş F. Estimation of Permanent Magnet Synchronous Generator Performance with Artificial Neural Network Models. SAUCIS. 2020;3:60–73.

MLA

Çetin, Onursal, et al. “Estimation of Permanent Magnet Synchronous Generator Performance With Artificial Neural Network Models”. Sakarya University Journal of Computer and Information Sciences, vol. 3, no. 1, Apr. 2020, pp. 60-73, doi:10.35377/saucis.03.01.724976.

Vancouver

1.Onursal Çetin, Adem Dalcalı, Feyzullah Temurtaş. Estimation of Permanent Magnet Synchronous Generator Performance with Artificial Neural Network Models. SAUCIS. 2020 Apr. 1;3(1):60-73. doi:10.35377/saucis.03.01.724976