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Journals / Havacılık ve Uzay Teknolojileri Dergisi / 2021 / Cilt: 14 - Sayı: 2

Comparison of Metaheuristic Algorithm Performances for Optimization of Fractional Order PID Controllers Applied to Gas Turbine Power Plant

Gaz Türbin Santraline Uygulanmış Kesir Dereceli PID Kontrolör Optimizasyonu için Metasezgisel Algoritma Performanslarının Karşılaştırılması

Nevra BAYHAN
Kunter Sercan SEZER
Pages
209–219
Publication Date
2022-10-18
DOI
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AI Summary & Key Takeaways

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

The settling time, maximum overshoot percentage and rise time are used as comparison criteria, and then it is concluded that the grey wolf optimization and artificial bee colony algorithms provided superior results with respect to the other algorithms discussed.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Nowadays, the use of metaheuristic optimization algorithms is becoming widespread because of easy applying and be able to provide requirements in the solution of high level complex optimization problems.
📊
Key Findings & Data:The settling time, maximum overshoot percentage and rise time are used as comparison criteria, and then it is concluded that the grey wolf optimization and artificial bee colony algorithms provided superior results with respect to the other algorithms discussed.
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Conclusion & Impact:By using metaheuristic algorithms, the optimization of the fractional order PID controller applied to the gas turbine power plant model has been achieved.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

Nowadays, the use of metaheuristic optimization algorithms is becoming widespread because of easy applying and be able to provide requirements in the solution of high level complex optimization problems. In this study, four different metaheuristic optimization algorithms (particle swarm optimization, artificial bee colony, gray wolf optimization, moth-flame optimization) are used for the optimization of the fractional order proportional-integral-derivative (FOPID) controller applied to the gas turbine power plant model, which is considered as the sample model, and the transient responses of the optimized systems compared according to the system output signals. The settling time, maximum overshoot percentage and rise time are used as comparison criteria, and then it is concluded that the grey wolf optimization and artificial bee colony algorithms provided superior results with respect to the other algorithms discussed. By using metaheuristic algorithms, the optimization of the fractional order PID controller applied to the gas turbine power plant model has been achieved.

Özet

Günümüzde karmaşıklık seviyesi yüksek olan optimizasyon problemlerinin çözümünde uygulama kolaylığı ve gereklilikleri sağlayan sonuçlar vermesi sebebiyle metasezgisel optimizasyon algoritmalarının kullanımı giderek yaygınlaşmaktadır. Bu çalışmada örnek model olarak ele alınan gaz türbinli santral modeline uygulanmış kesir dereceli oransal-integral-türev (FOPID) kontrolörün optimizasyonu için dört farklı metasezgisel optimizasyon algoritması (parçacık sürüsü optimizasyonu, yapay arı kolonisi, gri kurt optimizasyonu, güve-ateş optimizasyonu) kullanılmış ve sistem çıkış sinyalleri incelenerek geçici hal yanıtları karşılaştırılmıştır. Yerleşme süresi, en büyük yüzde aşım değerleri ve yükselme süresi karşılaştırma kriteri olarak kullanılmış ve gri kurt optimizasyonu ve yapay arı kolonisi algoritmalarının ele alınan diğer algoritmalara göre daha üstün sonuçlar sağladığı anlaşılmıştır. Metasezgisel algoritmalar kullanılarak, gaz türbinli santral modeline uygulanan kesir dereceli PID kontrolörün optimizasyonu sağlanmıştır.

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Cite This Article

BAYHAN, N., SEZER, K. S. (2022). Comparison of Metaheuristic Algorithm Performances for Optimization of Fractional Order PID Controllers Applied to Gas Turbine Power Plant. Havacılık ve Uzay Teknolojileri Dergisi, Cilt: 14 - Sayı: 2, 209–219. https://ajindex.com/articles/535169

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References (10)

  1. K. S. Sezer, “Metasezgisel Algoritmalarla Optimize Edilmiş Kesir Dereceli PID Kontrolörler ile Gaz Türbin Elektrik Santralinin Kontrolü”, Yüksek Lisans Tezi, Lisansüstü Eğitim Enstitüsü, İstanbul Üniversitesi-Cerrahpaşa, 2021
  2. A. Tzanetos, G. Dounias, “Nature Inspired Optimization Algorithms or Simply Variations of Metaheuristics?”, Artifical Intelligence Rev. vol. 54, pp.1841–1862, 2021 [3] R. H. Bishop, The Mechatronics Hand Book, Texas: The University of Texas at Austin, CRC Press, 2002
  3. I. Podlubny, “Fractional Order Systems and Fractional Order Controllers”, Institute of Exp. Physics, Slovak Academy of Sciences, Kosice, Slovakia, Tech. Rep. UEF-03-94, 1994
  4. J. Kennedy ve R. Eberhart, “Particle Swarm Optimization” in IEEE Proceedings of ICNN'95, 27 November-1 December 1995, Perth, WA, Australia [Online]. Available: IEEE Xplore, http://www.ieee. org. [Accessed: 5 Jan. 2020] [6] D. Karaboğa, “An Idea Based on Honey Bee Swarm For Numerical Optimization”, Erciyes University, Engineering Faculty, Computer Engineering Department, Turkey, Technical Report-TR06, 2005
  5. S. Mirjalili, S. M. Mirjalili ve A. Lewis, “Grey Wolf Optimizer”, Adv. in Eng. Soft., Vol. 69, pp. 46- 61, March 2014
  6. S. Mirjalili, “Moth-Flame Optimization Algorithm: A Novel Nature Inspired Heuristic Paradigm”, Knowledge-Based Systems, vol. 89, pp. 228-249, November 2015
  7. M.T. Schobeiri, “Introduction, Gas Turbines, Applications, Types” in Gas Turbine Design, Components and System Design Integration, Springer, pp. 1-30, 2018, [Online]. Available: https://doi.org/ 10.1007/978-3-319-58378-5_1. [Accessed: 10 Jan. 2020] [10] Z. Liu, Iftekhar A. Karimi, “Gas turbine performance prediction via machine learning”, Energy, vol. 192, February 2020. [Online]. Available: https://doi.org/10.1016/j.energy.2019.116627 [Accessed: 2 Feb. 2020] [11] Y. Arıkuşu, N. Bayhan ve H. Tiryaki, “Comparison of Conventional and Modern Controllers for a Gas Turbine Power Plant”, Electrica, vol. 19, pp. 146-157, July 2019
  8. H. Tiryaki, “Modern Kontrol Yöntemlerinin Yük Dağıtım Sistemlerinde Uygulanması”, Doktora Tezi, Fen Bilimleri Enstitüsü, İstanbul Üniversitesi, 2013
  9. B. Hekimoğlu, “Robust Fractional Order PID Stabilizer Design for Multi-machine Power System Using Grasshoper Optimization Algorithm” Journal of the Faculty of Engin. and Architect. of Gazi Univ, vol. 35, pp.165-180, 2020
  10. M. Shehab, L. Abualigah, H. Al Hamad ve H. Alabool, “Moth–flame optimization algorithm: varian