Frequency Control in a Hydroelectric Power Plant with Adaptive Neuro-Fuzzy Inference System-Based Modern Controllers

Abstract

In power systems, the constant frequency, constant voltage, and the power output are desired and determine the quality of the generated electrical energy. Therefore, frequency control is crucial in power systems. The parameters of conventional controllers used in power generation plants are determined according to the system's characteristics at the stage of installation, they cannot adapt to the changing system dynamics as the lifespan of power plants increases. Thus, studies on the automatic adaptation of controller parameters to the continuously changing system dynamics are needed. In this study, conventional PI and PID controllers applied to the power system for frequency control of a hydroelectric power plant were examined comparatively with Fuzzy Gain Scheduled PI (FGPI) controller and Adaptive Neuro-Fuzzy Inference System-based PI (ANFIS-PI) and PID (ANFIS-PID) controllers in the simulation environment. The obtained results demonstrated that Adaptive Neuro-Fuzzy Inference System-based controllers were quite successful compared to the others.

Keywords

• Power Systems,
• Frequency Control,
• Hydroelectric Power Plant,
• FGPI,
• ANFIS-PI,
• ANFIS-PID,

Uyarlanabilir Nöro-Bulanık Çıkarım Sistemi Tabanlı Modern Kontrolörlerle Bir Hidroelektrik Santralinde Frekans Kontrolü

Abstract

Güç sistemlerinde, tüketicinin beklentisi olan sabit frekans, sabit gerilim ve istenen değerdeki güç üretilen elektrik enerjisinin kalitesini belirler. Bu sebeple güç sistemlerinde frekans kontrolü oldukça önemlidir. Enerji üretim santrallerinde kullanılan klasik kontrolörlerin parametreleri kurulum aşamasındaki sistem özelliklerine göre belirlendiği için santral ömürleri arttıkça değişen sistem dinamiklerine uyum gösterememektedir. Bu istenmeyen durumu önleyebilmek için kontrolör parametrelerinin sürekli değişen sistem dinamiklerine kendiliğinden uyum gösterebilecek şekilde çalışmalara ihtiyaç duyulmaktadır. Bu noktadan hareketle enerji santrallerinde kullanılan kontrolörlerin ve parametrelerinin belirlenmesi konusunda yapılan bu çalışmada, bir hidroelektrik santralinin frekans kontrolü için güç sistemine uygulanan klasik PI ile PID kontrolörler, Fuzzy Gain Scheduled PI (FGPI) kontrolör ve Adaptive Neuro-Fuzzy Inference System tabanlı PI (ANFIS-PI) ile PID (ANFIS-PID) kontrolörler simülasyon ortamında karşılaştırmalı olarak incelenmiştir. Elde edilen sonuçlar Adaptive Neuro-Fuzzy Inference System tabanlı kontrolörlerin diğerlerine göre oldukça başarılı olduğunu göstermiştir.

Keywords

• güç sistemleri
• frekans kontrolü
• hidroelektrik güç santrali
• FGPI
• ANFIS-PI
• ANFIS-PID

References

1. Abraham A. Adaptation of fuzzy inference system using neural learning. In: Fuzzy systems engineering. Berlin: Springer, 2005, pp. 53-83.
2. Alhanafy TE, Zaghlool F and Moustafa ASED. Neuro fuzzy modeling scheme for the prediction of air pollution. Journal of American Science 2010; 6.12: 605-616.
3. Aurelien YT, Hervé SA and Martial NG. Synthesis of a digital corrector for frequency control in hydroelectric power plants.Control Science and Engineering 2019; 2.1: 36-49.
4. De Jaeger E, Janssens N, Malfliet B, et al. Hydro turbine model for system dynamic studies. IEEE Transactions on Power Systems 1994; 9.4: 1709-1715.
5. Eke İ. Modeling and simulation of hydroelectric power plants. Msc Thesis, Kırıkkale University, Turkey, 2004. (In Turkish) Gheisarnejad M and Khooban MH. Design an optimal fuzzy fractional proportional integral derivative controller with derivative filter for load frequency control in power systems. Transactions of the Institute of Measurement and Control 2019; 41.9: 2563-2581.
6. Jang JS. ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man and Cybernetics 1993; 23.3: 665-685.
7. Kocaarslan İ and Çam E. Load-frequency control of two area interconnected power plants. In: Automatic Control National Meeting (TOK), Ankara, Turkey, 9-11 September 2002, pp. 631-637, Ankara: TOK. (In Turkish) Kundur P, Balu NJ and Lauby MG. Power system stability and control. New York: McGraw-hill, 1994.
8. Lutfy OF, Noor MS, Marhaban MH, et al. A genetically trained adaptive neuro-fuzzy inference system network utilized as a proportional-integral-derivative-like feedback controller for non-linear systems. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 2009; 223.3: 309-321.

9. Lutfy OF, Noor MS, Marhaban MH, et al. Non-linear modelling and control of a conveyor-belt grain dryer utilizing neuro-fuzzy systems. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 2011; 225.5: 611-622.
10. MATLAB R2020a, Reference Manual, Licence Number: 40827100, 2020.
11. Milosavljevic A, Stoimenov L and Rancic D. An algorithm for automatic generation of fuzzy neural network based on perception frames. In: 9th WSEAS International Conference on Neural Networks, Sofia, Bulgaria, 2-4 May 2008, pp. 215-220. Sofia: WSEAS.
12. Naghizadeh RA, Jazebi S and Vahidi B. Modeling hydro power plants and tuning hydro governors as an educational guideline. International Review on Modelling and Simulations 2012; 5.4: 1780-1790.
13. Nazmy T, El-Messiry H, Al-Bokhity B, et al. Adaptive neuro-fuzzy inference system for classification of ECG signals. Journal of Theoretical and Applied Information Technology 2010; 12.2: 71-76.
14. Qian D and Jianqiang Y. A new control system design for a small hydro-power plant based on particle swarm optimization-fuzzy sliding mode controller with Kalman estimator: a comment. Transactions of the Institute of Measurement and Control 2013; 35.8: 1152.
15. Rinaldi G, Cucuzzella M and Ferrara A. Sliding mode observers for a network of thermal and hydroelectric power plants. Automatica 2018; 98: 51-57.
16. Sevilgen SH and Erdem HH. Generation planning methodology based on load factor for hydroelectric power plants. Advances in Mechanical Engineering 2014; 6: 282513.
17. Shahgholian G. Power system stabilizer application for load frequency control in hydroelectric power plant. International Journal of theoretical and Applied Mathematics 2017; 3.4: 148.
18. Simani S, Alvisi S and Venturini M. Fuzzy control techniques applied to wind turbine systems and hydroelectric plants. In: 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), New Orleans, Louisiana, USA, 23-26 June 2019, pp. 1-6. New Orleans: IEEE.
19. Tabakh R. Application of modern control methods in power plants. Msc Thesis, Istanbul University - Cerrahpaşa, Turkey, 2020. (In Turkish).
20. Tiryaki H and Gün A. Frequency control in a hydroelectric power plant with modern optimization methods. International Journal of Engineering Research and Development 2019; 11.1: 266-274.
21. Vournas CD and Zaharakis A. Hydro turbine transfer functions with hydraulic coupling. IEEE Transactions on Energy Conversation 1993; 8.3: 527-532 (1993).
22. Yüksel İ. Automatic control system dynamics and control systems, Ankara: Nobel Press, 2009. (In Turkish) Zayoud A. Circulating fluidized bed combustor towards third generation of oxy-fuel combustion, PhD Thesis, Indian Institute of Technology Guwahati, India, 2016.