SavedAdd your journal

Journals / Gazi Üniversitesi Fen Bilimleri Dergisi / 2004 / Cilt: 17 - Sayı: 3

Elektrik güç sistemlerinde dinamik bulanık ağ tabanlı bir yük frekans denetleyici tasarımı

Dynamic fuzzy networks based load frequency controller design in electrical power systems

Yusuf OYSAL
Ahmet Serdar YILMAZ
Etem KÖKLÜKAYA
Pages
101–114
Publication Date
2022-10-18
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The cost gradients with respect to the network parameters are calculated by adjoint sensitivity analysis.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this paper, a new controller based on intelligent control technologies for load frequency control (LFC) is proposed.
📊
Key Findings & Data:This controller overcome some drawbacks of neural network and fuzzy logic technologies and may also allow for the incorporation of both heuristics and deep knowledge to exploit the best characteristics of each.
💡
Conclusion & Impact:The cost gradients with respect to the network parameters are calculated by adjoint sensitivity analysis.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

In this paper, a new controller based on intelligent control technologies for load frequency control (LFC) is proposed. This controller overcome some drawbacks of neural network and fuzzy logic technologies and may also allow for the incorporation of both heuristics and deep knowledge to exploit the best characteristics of each. A "Dynamical Fuzzy Network (DFN)" that contains dynamical elements such as delayers or integrators in their processing units is used in the controller design for LFC. This design is based on adjusting parameters of DFN. This is done by minimizing the cost functional of load frequency errors. The cost gradients with respect to the network parameters are calculated by adjoint sensitivity analysis.

Özet

Bu makalede, akıllı denetim esasına dayanan bir yük frekans denetleyicisi önerilmiştir. Önerilen yeni kontrolör sinir ağları ve bulanık mantık teknolojilerinin sakıncalı taraflarının üstesinden gelmekte ve her birinin üstün taraflarını kullanmaktadır. Kendi işlem birimlerinde geciktiriciler ve integratörler gibi dinamik elemanları içeren bir dinamik bulanık ağı (DBA), bir yük frekans denetimi tasarımında kullanılmıştır. Tasarım, DBA parametrelerinin hesaplanmasına dayanır. Bu da yük frekans hata masraflarını içeren bir ölçütün en aza indirilmesi ile sağlanır. Gerekli olan ölçütün ağ parametrelerine göre gradyanları, adjoint duyarlılık analizi ile hesaplanmıştır.

🔖

Cite This Article

OYSAL, Y., YILMAZ, A. S., KÖKLÜKAYA, E. (2022). Elektrik güç sistemlerinde dinamik bulanık ağ tabanlı bir yük frekans denetleyici tasarımı. Gazi Üniversitesi Fen Bilimleri Dergisi, Cilt: 17 - Sayı: 3, 101–114. https://ajindex.com/articles/521718

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (9)

  1. Chang C.S., and Weihui, F., "Area Load Frequency Control Using Fuzzy Gain Scheduling of PI Controllers",Electric Power System Research, 42 (2): 145-152 (1997)
  2. Chang C.S., Weihui F., and Fushuan W., "Load Frequency Control Using Genetic Algorithm Based Fuzzy Gain Scheduling of PI Controllers", Electric Machines and Power Systems, Vol. 26, No 1: 39-52 (1998)
  3. El-Sherbiny M.K., El-Saady G., and Yousef A.M.,"Efficient Fuzzy Logic Load Frequency Controller", Energy Conversion and Management, Vol. 43, No. 14: 1853-1863 (2002)
  4. Chaturvedi D.K., Satsangi P.S., and Kalra P.K., "Load Frequency Control: A Generalized Neural Network Approach",Int. Journal of Electrical Power Systems, Vol. 21, No.6: 405-415 (1999)
  5. Oysal, Y., Yılmaz, A.S., and Koklukaya, E., Fuzzy PID Controller Design for Load Frequency Control using Gain Scaling Technique, Powertech Conference, Budapest, Hungary (1999)
  6. Oysal Y., Feuro Modeling and Optimal Fuzzy Control, Ph.D. Thesis, Sakarya University, Institute of Science and Technology, Sakarya/Turkey,15-25 (2002)
  7. Oysal Y., Becerikli Y., Konar A.F, and Samad T, Dynamic Fuzzy Networks for Real Time Application, submitted to Neural Network (2003)
  8. Kundur, P., Power System Stability and Control,, Mc Graw Hill, 621-636 (1994)
  9. Wood, A.J., Wollenberg, B.F., Power Generation, Operation and Control, Second Edition,, John Wiley and Sons,328-362(1996). 10.Hopfield, J.J., Neural Networks and Physical Systems with Emergent Collective Computational Abilities, Proc. of the National Academy of Sciences, Vol. 79, 2554-2558 (1989). 11.Hopfield, J.J., Neurons with Graded Response Have Collective Computational Properties Like Those of Two-State Neurons, Proc. of the National Academy of Sciences, Vol. 81, 3088-3092 (1984). 12.Passino, K.M., and Yurkovich, S., Fuzy Control, Addison-Wesley, Menlopark, Cal., 423-424 (1998). 13.Becerikli, Y., Oysal, Y., and Konar, F., "On a Dynamic Wavelet Network and Its Modeling Applications", submitted to Neuro Processing Letters (2003). 14.Becerikli, Y., Oysal, Y., and Konar, F., Modeling of Nonlinear Systems with Dynamic Wavelet Networks, Proc.Turkish Symposium on Automatic Control, Vol.1, Ankara, METU, 71-79. 15.Konar, A.F., Becerikli, Y. and Samad, T., "Trajectory Tracking with Dynamic Neural Networks", Proc. of the 1997 IEEE Int. Sym. on Intelligent Control (ISIC'97), İstanbul/Turkey (1997). 16.Becerikli, Y., Konar, A.F., and Samad, T., Intelligent Optimal Control with Dynamic Neural Networks, Neu