Modern Güç Sistemlerinde AVR Kontrolörleri için Yeni Bir Enerji Verimli Optimizasyon Çerçevesi

Öz

Enerji verimliliği, sürdürülebilirlik gereksinimleri ve yenilenebilir enerji kaynaklarının artan entegrasyonu nedeniyle modern güç sistemi kontrolünde kritik bir tasarım hedefi haline gelmiştir. Bu çalışmada, güvenilir gerilim regülasyonu sağlarken kontrol enerjisini en aza indirmeyi amaçlayan, Otomatik Gerilim Regülatörü sistemleri için yeni bir enerji verimli optimizasyon çerçevesi önerilmektedir. Önerilen çerçeve, gerilim izleme doğruluğu ile kontrol sinyali enerjisini tek bir amaç fonksiyonunda birleştiren yeni tanımlanmış EEPI üzerine kuruludur. Hızlı geçici rejimi önceliklendiren geleneksel performans indekslerinin aksine, EEPI dinamik performans ile enerji tüketimi arasında sistematik bir denge kurarak Pareto-dengeli bir çalışma noktası seçmektedir. Yöntem, PSO algoritması ile optimize edilen PID ve FOPID kontrolörlerine uygulanmıştır. Pratik uygulanabilirlik, TMS320F28035 platformunda PIL gerçekleştirmesi ile doğrulanmıştır. IAE, ISE, ITAE, ITSE, ZLG ve TR indeksleri ile yapılan karşılaştırmalar, EEPI’nin kabul edilebilir geçici rejim performansını korurken kontrol enerjisini azalttığını göstermektedir. Bu sonuçlar, sürdürülebilir AVR kontrolör tasarımı için ölçeklenebilir bir çözüm sunmaktadır.

Anahtar Kelimeler

• Otomatik Gerilim Regülatörü
• Kontrolör Optimizasyonu
• Enerji Verimli Amaç Fonksiyonu
• Parçacık Sürü Optimizasyonu
• Sürdürülebilir Güç Sistemleri

A Novel Energy-Efficient Optimization Framework for AVR Controllers in Modern Power Systems

Öz

Energy efficiency has become a critical objective in modern power system control due to sustainability requirements and the increasing penetration of renewable energy sources. This paper proposes a novel energy-efficient optimization framework for AVR systems to ensure reliable voltage regulation while minimizing control energy. The framework is based on a newly defined EEPI, integrating voltage tracking accuracy and control-signal energy into a unified objective function. Unlike conventional indices prioritizing fast transient response, the EEPI enables a systematic trade-off between dynamic performance and energy consumption, selecting a Pareto-balanced operating point. The approach is applied to PID and FOPID controllers optimized using the PSO algorithm. Practical feasibility is verified through Processor-in-the-Loop implementation on a TMS320F28035 platform. Comparative results with IAE, ISE, ITAE, ITSE, ZLG, and TR indices show that EEPI reduces control energy while preserving acceptable transient performance, providing a scalable solution for sustainable AVR controller design.

Anahtar Kelimeler

• Automatic voltage regulator
• controller optimization
• energy-efficient objective function
• particle swarm optimization
• sustainable power systems.

Kaynakça

1. 1. Gopi, P., Reddy, S.V., Bajaj, M., Zaitsev, I. & Prokop, L. (2024). Performance and robustness analysis of V-Tiger PID controller for automatic voltage regulator. Scientific Reports, 14(1), 7867.
2. 2. Teke, A., Sarıbulut, L. & Tümay, M. (2011). Power quality disturbances and custom power devices. Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, 26(1), 57-65.
3. 3. Tekin, P. (2022). Çukurova bölgesi için kısa vadeli yapay zeka tabanlı rüzgar güç tahmini. Çukurova Üniversitesi Mühendislik Fakültesi Dergisi, 37(4), 1143-1154.
4. 4. Micev, M., Ćalasan, M. & Radulović, M. (2023). Optimal tuning of the novel voltage regulation controller considering the real model of the automatic voltage regulation system. Heliyon, 9(8), e18707.
5. 5. Chatterjee, S. & Mukherjee, V. (2016). PID controller for automatic voltage regulator using teaching–learning based optimization technique. International Journal of Electrical Power & Energy Systems, 77, 418-429.
6. 6. Pan, I. & Das, S. (2012). Chaotic multi-objective optimization based design of fractional order PIλDμ controller in AVR system. International Journal of Electrical Power & Energy Systems, 43(1), 393-407.
7. 7. Veinović, S., Stojić, D. & Joksimović, D. (2022). Optimized four-parameter PID controller for AVR systems with respect to robustness. International Journal of Electrical Power & Energy Systems, 135, 107529.
8. 8. Gaing, Z.-L. (2004). A particle swarm optimization approach for optimum design of PID controller in AVR system. IEEE Transactions on Energy Conversion, 19(2), 384-391.

9. 9. Dogruer, T. & Can, M.S. (2022). Design and robustness analysis of fuzzy PID controller for automatic voltage regulator system using genetic algorithm. Transactions of the Institute of Measurement and Control, 44(9), 1862-1873.
10. 10. Gozde, H. & Taplamacioglu, M.C. (2011). Comparative performance analysis of artificial bee colony algorithm for automatic voltage regulator (AVR) system. Journal of the Franklin Institute, 348(8), 1927-1946.
11. 11. Kar, M.K., Kumar, S., Singh, A.K., Panigrahi, S. & Cherukuri, M. (2022). Design and analysis of FOPID-based damping controllers using a modified Grey Wolf Optimization algorithm. International Transactions on Electrical Energy Systems, 2022, 1-31.
12. 12. Bingul, Z. & Karahan, O. (2018). A novel performance criterion approach to optimum design of PID controller using cuckoo search algorithm for AVR system. Journal of the Franklin Institute, 355(13), 5534-5559.
13. 13. Sahin, A.K., Cavdar, B. & Ayas, M.S. (2024). An adaptive fractional controller design for automatic voltage regulator system: sigmoid-based fractional-order PID controller. Neural Computing and Applications, 36(23), 14409-14431.
14. 14. Veinović, S., Stojić, D. & Ivanović, L. (2023). Optimized PIDD2 controller for AVR systems regarding robustness. International Journal of Electrical Power & Energy Systems, 145, 108646.
15. 15. Panda, S., Sahu, B.K. & Mohanty, P.K. (2012). Design and performance analysis of PID controller for an automatic voltage regulator system using simplified particle swarm optimization. Journal of the Franklin Institute, 349(8), 2609-2625.
16. 16. Mohanty, P.K., Sahu, B.K. & Panda, S. (2014). Tuning and assessment of proportional–integral–derivative controller for an automatic voltage regulator system employing local unimodal sampling algorithm. Electric Power Components and Systems, 42(9), 959-969.
17. 17. Duman, S., Yörükeren, N. & Altaş, İ.H. (2016). Gravitational search algorithm for determining controller parameters in an automatic voltage regulator system. Turkish Journal of Electrical Engineering & Computer Sciences, 24, 2387-2400.
18. 18. Shouran, M. & Alenezi, M. (2025). A novel fuzzy PIDF enhancing PIDF controller tuned in two stages by TLBO and PSO algorithms for reliable AVR performance. IEEE Access, 13, 93505-93522.
19. 19. Sekyere, Y.O.M., Ajiboye, P.O., Effah, F.B. & Opoku, B.T. (2025). Optimizing PID control for automatic voltage regulators using ADIWACO PSO. Scientific African, 27, e02562.
20. 20. Eltag, K., Abdullahi, S., Weining, L. & Xiaohu, C. (2025). Design TLBO fuzzy fractional order PID filter controller for AVR excitation system. IEEE Access, 13, 216466-216495.
21. 21. Izci, D., Abualigah, L., Can, Ö., Andiç, C. & Ekinci, S. (2025). A novel cascaded RPIDD²-PI controller tuned by enhanced cooperation search algorithm for automatic voltage regulator systems. IET Control Theory & Applications, 19(1).
22. 22. Chetty, N.D., Sharma, G., Gandhi, R. & Çelik, E. (2024). A novel salp swarm optimization oriented 3-DOF-PIDA controller design for automatic voltage regulator system. IEEE Access, 12, 20181-20196.
23. 23. Dai, F. & Gao, S. (2024). Optimal design of a PIDD² controller for an AVR system using hybrid whale optimization algorithm. IEEE Access, 12, 128525-128540.
24. 24. Türksoy, Ö. & Türksoy, A. (2024). A fast and robust sliding mode controller for automatic voltage regulators in electrical power systems. Engineering Science and Technology, an International Journal, 53, 101697.
25. 25. Izci, D., Ekinci, S. & Zeynelgil, H.L. (2024). Controlling an automatic voltage regulator using a novel Harris hawks and simulated annealing optimization technique. Advanced Control for Applications, 6(2), 1-26.
26. 26. Izci, D., Abualigah, L., Can, Ö., Andiç, C. & Ekinci, S. (2024). Achieving improved stability for automatic voltage regulation with fractional-order PID plus double-derivative controller and mountain gazelle optimizer. International Journal of Dynamics and Control, 12(7), 2550-2565.
27. 27. Ahmadnia, M., Hajipour, A. & Tavakoli, H. (2024). Robust variable-order fractional PID-LP fuzzy controller for automatic voltage regulator systems. Applied Soft Computing, 167, 112268.
28. 28. Saka, M. (2024). Novel hVsaGwo algorithm for non-linear dynamic weighted state feedback with 1DOF-PID based controllers in AVR. Engineering Science and Technology, an International Journal, 59, 101857.
29. 29. Furat, M. & Cucu, G.G. (2022). Design, implementation, and optimization of sliding mode controller for automatic voltage regulator system. IEEE Access, 10, 55650-55674.
30. 30. Micev, M., Ćalasan, M., Ali, Z.M., Hasanien, H.M. & Abdel Aleem, S.H.E. (2021). Optimal design of automatic voltage regulation controller using hybrid simulated annealing–manta ray foraging optimization algorithm. Ain Shams Engineering Journal, 12(1), 641-657.
31. 31. Micev, M., Ćalasan, M. & Oliva, D. (2021). Design and robustness analysis of an automatic voltage regulator system controller by using equilibrium optimizer algorithm. Computers & Electrical Engineering, 89, 106930.
32. 32. Eke, I., Saka, M., Gozde, H., Arya, Y. & Taplamacioglu, M.C. (2021). Heuristic optimization based dynamic weighted state feedback approach for 2DOF PI-controller in automatic voltage regulator. Engineering Science and Technology, an International Journal, 24(4), 899-910.
33. 33. Kose, E. (2020). Optimal control of AVR system with tree seed algorithm-based PID controller. IEEE Access, 8, 89457-89467.
34. 34. Jumani, T.A., Mustafa, M.W., Rasid, M.M., Saeed, M.S. Khan, İ. & Nisar, K.S. (2020). Jaya optimization algorithm for transient response and stability enhancement of a fractional-order PID based automatic voltage regulator system. Alexandria Engineering Journal, 59(4), 2429-2440.
35. 35. Ekinci, S. & Hekimoglu, B. (2019). Improved kidney-inspired algorithm approach for tuning of PID controller in AVR system. IEEE Access, 7, 39935-39947.
36. 36. Zhou, Y., Zhang, J., Yang, X. & Ling, Y. (2019). Optimization of PID controller based on water wave optimization for an automatic voltage regulator system. Information Technology and Control, 48(1).
37. 37. Çelik, E. (2018). Incorporation of stochastic fractal search algorithm into efficient design of PID controller for an automatic voltage regulator system. Neural Computing and Applications, 30(6), 1991-2002.
38. 38. Furat, M. (2023). Chattering attenuation analysis in variable structure control for automatic voltage regulator with input constraints. Engineering Science and Technology, an International Journal, 45, 101499.
39. 39. IEEE (2006). IEEE recommended practice for excitation system models for power system stability studies. IEEE Power Engineering Society, 95.