Öz
Bu çalışmada, esnek
aa iletim sistemi cihazlarının ana elemanlarından biri olan tristör kontrollü
reaktörün sinirsel bulanık denetim esaslı reaktif güç kontrolü yapılmıştır.
Sinirsel bulanık denetim mimarisi olarak adaptif sinirsel bulanık çıkarım
sistemi (ANFIS) kullanılmıştır. Önerilen denetleyicinin başarımını incelemek
için MATLAB/Simulink ortamında benzetim modeli oluşturulmuştur. Bununla
birlikte ANFIS denetim esaslı TKR’nin farklı çalışma durumlarındaki referans
reaktif gücü takip etme başarımı klasik PI tipli denetleyici ile
karşılaştırılmıştır.
Anahtar Kelimeler
Reaktif güç Tristör kontrollü reaktör Sinirsel bulanık denetleyici
Kaynakça
- [1] Pekdemir, A., Yildiz, A.B. Analysis and modelling of FC-TCR based on static VAR compensator, 5th International Conference on Electrical and Electronic Engineering (ICEEE) 2018.
- [2] Jamnani, J.G., Pandya, M. Coordination of SVC and TCSC for Management of Power Flow by Particle Swarm Optimization, Energy Procedia, vol. 156, pp:321-326, 2019
- [3] Kececioglu, O.F., Gani, A., Sekkeli, M. A performance comparison of static VAr compensator based on Goertzel and FFT algorithm and experimental validation, SpringerPlus, 5(1): 391, 2016.
- [4] Jang, J.S.R Neuro-Fuzzy Modeling: Architectures,Analyses, and Applications, Ph.D. Dissertation, Univ. of California at Berkeley, 1992.
- [5] Kılıç, E., Yılmaz, Ş., Özçalık, H.R., Şit, S. A comparative analysis of FLC and ANFIS controller for vector controlled induction motor drive, 2015 Intl Aegean Conference on Electrical Machines & Power Electronics (ACEMP), 2015 Intl Conference on Optimization of Electrical & Electronic Equipment (OPTIM) & 2015 Intl Symposium on Advanced Electromechanical Motion Systems (ELECTROMOTION), Side, 2015, pp. 102-106.doi: 10.1109/OPTIM.2015.7426971.
- [6] Kang Y., Chen Y.W., Chang Y.P., Chu M.H. The Direct Neural Control Applied to the Position Control in Hydraulic Servo System. In: Sun F., Zhang J., Tan Y., Cao J., Yu W. (eds) Advances in Neural Networks - ISNN 2008. ISNN 2008. Lecture Notes in Computer Science, vol 5264. Springer, Berlin, Heidelberg. 2008.
- [7] Jang, J.S.R. ANFIS, Adaptive-Network-based Fuzzy Inference Systems, IEEE Transactions on System, Man, and Cybernetics, Vol. 23, No. 5, pp. 665-685, May 1993.
- [8] Jang, J.S.R., Sun, C.T. Neuro-Fuzzy Modeling and Control, Proceeding of the IEEE Transactions, Vol. 83, No. 3, pp. 378-406, Mar 1995.
- [9] Tuncer, S., Dandil, B. Adaptive neuro‐fuzzy current control for multilevel inverter fed induction motor, COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, 27(3), 668-681,2008.
- [10] Coteli, R., Acikgoz, H., Ucar, F., Dandil B. Design and implementation of Type-2 fuzzy neural system controller for PWM rectifiers, International Journal of Hydrogen Energy, 42(32), 20759-20771, 2017.
- [11] Üstün, S.V., Nur, A., Kaya, M. PI Katsayıları ve Sistem Performansı Arasındaki İlişkinin Çıkarılmasında ANFIS ve YSA Yöntemlerinin Karşılaştırılması, Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 21(4), 322-328, 2018.
- [12] Acikgoz, H., Kececioglu, O.F., Karadol, I., Gani, A., Sekkeli M. Adaptive Control of Solid State Transformer Using Type-2 Fuzzy Neural System, Studies in Informatics and Control, 26(2), 171-181, 2017.
- [13] Kılıç, E., Şit, S., Gani, A., Şekkeli, M., Özçalık, H.R. Neuro-Fuzzy Based Model Reference Adaptive Control for Induction Motor Drive, Turkish Journal of Fuzzy Systems(TJFS), 8(2), 63-72, 2017.
- [14] Tür, M.D., Aydoğmuş, Z. İletim Hatları için Bulanık Mantık Tabanlı Direk Tipi Seçimi, Gazi Üniversitesi Fen Bilimleri Dergisi Part:C, Tasarım Ve Teknoloji, 2(4), 299-308, 2014.
Öz
In this paper, the
reactive power of thyristor controlled reactor (TCR) that is fundamental
element of flexible ac transmission system devices is controlled using
neuro-fuzzy controller. Adaptive neuro fuzzy inference system (ANFIS) is used
as neuro-fuzzy control architecture. A simulation model was developed in MATLAB
/ Simulink environment to examine the performance of the proposed controller.
In addition to this, the tracking performance of the reference reactive power
in different simulation cases of ANFIS controller based TCR is compared with
the conventional PI controller.
Anahtar Kelimeler
Reactive Power Thyristor controlled reactor Neuro-fuzzy controller
Kaynakça
- [1] Pekdemir, A., Yildiz, A.B. Analysis and modelling of FC-TCR based on static VAR compensator, 5th International Conference on Electrical and Electronic Engineering (ICEEE) 2018.
- [2] Jamnani, J.G., Pandya, M. Coordination of SVC and TCSC for Management of Power Flow by Particle Swarm Optimization, Energy Procedia, vol. 156, pp:321-326, 2019
- [3] Kececioglu, O.F., Gani, A., Sekkeli, M. A performance comparison of static VAr compensator based on Goertzel and FFT algorithm and experimental validation, SpringerPlus, 5(1): 391, 2016.
- [4] Jang, J.S.R Neuro-Fuzzy Modeling: Architectures,Analyses, and Applications, Ph.D. Dissertation, Univ. of California at Berkeley, 1992.
- [5] Kılıç, E., Yılmaz, Ş., Özçalık, H.R., Şit, S. A comparative analysis of FLC and ANFIS controller for vector controlled induction motor drive, 2015 Intl Aegean Conference on Electrical Machines & Power Electronics (ACEMP), 2015 Intl Conference on Optimization of Electrical & Electronic Equipment (OPTIM) & 2015 Intl Symposium on Advanced Electromechanical Motion Systems (ELECTROMOTION), Side, 2015, pp. 102-106.doi: 10.1109/OPTIM.2015.7426971.
- [6] Kang Y., Chen Y.W., Chang Y.P., Chu M.H. The Direct Neural Control Applied to the Position Control in Hydraulic Servo System. In: Sun F., Zhang J., Tan Y., Cao J., Yu W. (eds) Advances in Neural Networks - ISNN 2008. ISNN 2008. Lecture Notes in Computer Science, vol 5264. Springer, Berlin, Heidelberg. 2008.
- [7] Jang, J.S.R. ANFIS, Adaptive-Network-based Fuzzy Inference Systems, IEEE Transactions on System, Man, and Cybernetics, Vol. 23, No. 5, pp. 665-685, May 1993.
- [8] Jang, J.S.R., Sun, C.T. Neuro-Fuzzy Modeling and Control, Proceeding of the IEEE Transactions, Vol. 83, No. 3, pp. 378-406, Mar 1995.
- [9] Tuncer, S., Dandil, B. Adaptive neuro‐fuzzy current control for multilevel inverter fed induction motor, COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, 27(3), 668-681,2008.
- [10] Coteli, R., Acikgoz, H., Ucar, F., Dandil B. Design and implementation of Type-2 fuzzy neural system controller for PWM rectifiers, International Journal of Hydrogen Energy, 42(32), 20759-20771, 2017.
- [11] Üstün, S.V., Nur, A., Kaya, M. PI Katsayıları ve Sistem Performansı Arasındaki İlişkinin Çıkarılmasında ANFIS ve YSA Yöntemlerinin Karşılaştırılması, Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 21(4), 322-328, 2018.
- [12] Acikgoz, H., Kececioglu, O.F., Karadol, I., Gani, A., Sekkeli M. Adaptive Control of Solid State Transformer Using Type-2 Fuzzy Neural System, Studies in Informatics and Control, 26(2), 171-181, 2017.
- [13] Kılıç, E., Şit, S., Gani, A., Şekkeli, M., Özçalık, H.R. Neuro-Fuzzy Based Model Reference Adaptive Control for Induction Motor Drive, Turkish Journal of Fuzzy Systems(TJFS), 8(2), 63-72, 2017.
- [14] Tür, M.D., Aydoğmuş, Z. İletim Hatları için Bulanık Mantık Tabanlı Direk Tipi Seçimi, Gazi Üniversitesi Fen Bilimleri Dergisi Part:C, Tasarım Ve Teknoloji, 2(4), 299-308, 2014.
Ayrıntılar
| Birincil Dil | Türkçe |
|---|---|
| Konular | Mühendislik |
| Bölüm | Tasarım ve Teknoloji |
| Yazarlar | |
| Yayımlanma Tarihi | 11 Haziran 2019 |
| Gönderilme Tarihi | 21 Şubat 2019 |
| Yayımlandığı Sayı | Yıl 2019 Cilt: 7 Sayı: 2 |
