Estimation of Permanent Magnet Synchronous Generator Performance with Artificial Neural Network Models
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 source...
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| Format: | Article |
| Language: | English |
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Sakarya University
2020-04-01
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| Series: | Sakarya University Journal of Computer and Information Sciences |
| Subjects: | |
| Online Access: | https://dergipark.org.tr/tr/download/article-file/1077120 |
| _version_ | 1797351832689836032 |
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| author | Adem Dalcalı Onursal Çetin Feyzullah Temurtaş |
| author_facet | Adem Dalcalı Onursal Çetin Feyzullah Temurtaş |
| author_sort | Adem Dalcalı |
| collection | DOAJ |
| description | 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). |
| first_indexed | 2024-03-08T13:06:11Z |
| format | Article |
| id | doaj.art-6d7405d8731349a98fa28cdf5bc07e69 |
| institution | Directory Open Access Journal |
| issn | 2636-8129 |
| language | English |
| last_indexed | 2024-03-08T13:06:11Z |
| publishDate | 2020-04-01 |
| publisher | Sakarya University |
| record_format | Article |
| series | Sakarya University Journal of Computer and Information Sciences |
| spelling | doaj.art-6d7405d8731349a98fa28cdf5bc07e692024-01-18T16:44:35ZengSakarya UniversitySakarya University Journal of Computer and Information Sciences2636-81292020-04-0131607310.35377/saucis.03.01.72497628Estimation of Permanent Magnet Synchronous Generator Performance with Artificial Neural Network ModelsAdem Dalcalı0Onursal Çetin1Feyzullah Temurtaş2BANDIRMA ONYEDİ EYLÜL ÜNİVERSİTESİ, MÜHENDİSLİK VE DOĞA BİLİMLERİ FAKÜLTESİBANDIRMA ONYEDİ EYLÜL ÜNİVERSİTESİ, MÜHENDİSLİK VE DOĞA BİLİMLERİ FAKÜLTESİBANDIRMA ONYEDİ EYLÜL ÜNİVERSİTESİ, MÜHENDİSLİK VE DOĞA BİLİMLERİ FAKÜLTESİThe 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).https://dergipark.org.tr/tr/download/article-file/1077120multilayer neural networkelman neural networkradial basis function neural networkpermanent magnet synchronous generatorçok katmanlı sinir ağıelman sinir ağıradyal tabanlı fonksiyon sinir ağısabit mıknatıslı senkron generatör |
| spellingShingle | Adem Dalcalı Onursal Çetin Feyzullah Temurtaş Estimation of Permanent Magnet Synchronous Generator Performance with Artificial Neural Network Models Sakarya University Journal of Computer and Information Sciences multilayer neural network elman neural network radial basis function neural network permanent magnet synchronous generator çok katmanlı sinir ağı elman sinir ağı radyal tabanlı fonksiyon sinir ağı sabit mıknatıslı senkron generatör |
| title | Estimation of Permanent Magnet Synchronous Generator Performance with Artificial Neural Network Models |
| title_full | Estimation of Permanent Magnet Synchronous Generator Performance with Artificial Neural Network Models |
| title_fullStr | Estimation of Permanent Magnet Synchronous Generator Performance with Artificial Neural Network Models |
| title_full_unstemmed | Estimation of Permanent Magnet Synchronous Generator Performance with Artificial Neural Network Models |
| title_short | Estimation of Permanent Magnet Synchronous Generator Performance with Artificial Neural Network Models |
| title_sort | estimation of permanent magnet synchronous generator performance with artificial neural network models |
| topic | multilayer neural network elman neural network radial basis function neural network permanent magnet synchronous generator çok katmanlı sinir ağı elman sinir ağı radyal tabanlı fonksiyon sinir ağı sabit mıknatıslı senkron generatör |
| url | https://dergipark.org.tr/tr/download/article-file/1077120 |
| work_keys_str_mv | AT ademdalcalı estimationofpermanentmagnetsynchronousgeneratorperformancewithartificialneuralnetworkmodels AT onursalcetin estimationofpermanentmagnetsynchronousgeneratorperformancewithartificialneuralnetworkmodels AT feyzullahtemurtas estimationofpermanentmagnetsynchronousgeneratorperformancewithartificialneuralnetworkmodels |