PSO Tabanlı PID Denetimci kullanarak Zaman Gecikmeli AVR Sisteminin Analizi

Abstract

Bu çalışmada, zaman gecikmesi ve değişken yükler karşısında Otomatik Voltaj Regülatörü (AVR) sistemi terminal referans voltaj gerilimi takip problemi için bir parçacık sürüsü optimizasyonu (PSO) algoritması tabanlı Oransal-İntegral-Türev (PID) kontrolörü önerilmiştir. AVR, jeneratör çıkış terminal voltajını belirli bir referansta zaman gecikmeleri ve değişken yük altında tutmak için yaygın olarak kullanılan bir sistemdir, bundan dolayı zor bir elektriksel problemi ortaya çıkarır. Zaman gecikmeleri, iletim ve aktarmadaki gecikmelerden dolayı gerçek dünyadaki birçok sistemde bulunur, genel olarak kararlılık ve kontrol tasarımı üzerinde olumsuz bir etkiye sahiptirler. Bu araştırmada, zaman gecikmesi, asgari olmayan faz sistemine yol açan Pade yaklaşımı ile yaklaşık olarak tahmin edilmektedir. Karmaşık faz sistemi, s-düzleminin sağ tarafında bulunan sıfırları nedeniyle kontrol güçlüğüne neden olur. Bu amaçla, AVR için gerçek zamanlı sistemlerde yaygın olarak kullanılan PID kontrolör tercih edilmiştir. Optimal kontrolörün kazançları Kp, Ki ve Kd, yaygın olarak kullanılan bir hata minimizasyon objektif fonksiyonuna dayanarak PSO algoritması ile optimize edilmiştir. PSO tabanlı en uygun katsayılı PID denetleyicisinin performansı; kök yer eğrisi, bode analizi, sağlamlık ve bozucu karşısındaki dayanımı gibi çeşitli yöntemlerle analiz edilmiştir. Önerilen PID denetleyicisinin AVR çıkış referans terminal gerilim izleme performansını iyileştirdiği görülmüştür. Elde edilen sonuçlara göre, önerilen PSO tabanlı PID kontrolörünün zaman gecikmesi ve yük değişimi altında izleme özelliklerini geliştirdiği, böylece senkron jeneratör otomatik voltaj regülatörü (AVR) sistemi terminal voltaj kararlılığı için etkili bir şekilde kullanılabileceği ortaya çıkmıştır.

Keywords

• Otomatik voltaj regülatörü,Zaman gecikmeli sistemler,Parçacık sürüsü optimizasyonu,PID denetimcisi,kararlılık

Time-delay AVR System Analysis Using PSO-based PID Controller

Abstract

In this study, a particle swarm optimization (PSO) algorithm-based Proportional-Integral-Derivative (PID) controller is proposed for the Automatic Voltage Regulator (AVR) system terminal tracking problem in the existence of time-delay and varying loads. AVR is a commonly used electronic device for maintaining generator output terminal voltage at a given reference under time-delays and varying load thus introduces a challenging electrical system problem. Time-delays exist in many real-world systems due to the lags in transmission and transport, in general, they have a negative effect on the stability and control design. In this research, the time delay is approximated by Pade approximation leading to the so-called non-minimum phase system. A nonminimum phase system represents the difficulty of controlling due to its zeroes in the complex right half side of the s-plane. To this aim, we utilize a PID controller, its design and application widely studied in real-time systems, thus it is a suitable selection for the AVR system. The optimal controller gains Kp, Ki, and Kd are optimized with the proposed PSO algorithm based on a commonly used error minimization objective function. The PSO-based optimal PID controller’s performance is analyzed with several methods including root locus, bode analysis, robustness, and disturbance rejection. It is demonstrated that the proposed PID controller improves the reference terminal voltage tracking performance of the AVR system. According to the obtained results, it has been revealed that the proposed PSO-based PID controller improves tracking properties under time-delay and load change thus it can be effectively used for synchronous generator automatic voltage regulator (AVR) system terminal voltage stability.

Keywords

• Automatic voltage regulator,Time-delay systems,Particle swarm optimization PID controller

References

1. Bhati, S., & Nitnawwre, D. (2012). Genetic optimization tuning of an automatic voltage regulator system. International Journal of Scientific Engineering and Technology, 1(3), 120-124.
2. Sahib, M. A. (2015). A novel optimal PID plus second order derivative controller for AVR system. Engineering Science and Technology, an International Journal, 18(2), 194-206.
3. 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.
4. 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.
5. Ekinci, S., Hekimoğlu, B., Yurtlu, Ö. F., & Uzer, F. (2018). Whale optimization algorithm for optimal controller design in AVR system. Proc. IENSC, 1890-1900.
6. Ekinci, S., Hekimoğlu, B., & Eker, E. (2019, October). Optimum Design of PID Controller in AVR System Using Harris Hawks Optimization. In 2019 3rd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) (pp. 1-6). IEEE.
7. dos Santos Coelho, L. (2009). Tuning of PID controller for an automatic regulator voltage system using chaotic optimization approach. Chaos, Solitons & Fractals, 39(4), 1504-1514.
8. Razmjooy, N., Khalilpour, M., & Ramezani, M. (2016). A new meta-heuristic optimization algorithm inspired by FIFA world cup competitions: theory and its application in PID designing for AVR system. Journal of Control, Automation and Electrical Systems, 27(4), 419-440.

9. Ekinci, S., & Hekimoğlu, B. (2019). Improved kidney-inspired algorithm approach for tuning of PID controller in AVR system. IEEE Access, 7, 39935-39947.
10. Ribeiro, R. L. A., Neto, C. M. S., Costa, F. B., Rocha, T. O. A., & Barreto, R. L. (2015). A sliding-mode voltage regulator for salient pole synchronous generator. Electric Power Systems Research, 129, 178-184.
11. Elsisi, M. (2019). Design of neural network predictive controller based on imperialist competitive algorithm for automatic voltage regulator. Neural Computing and Applications, 31(9), 5017-5027.
12. Abegaz, B., & Kueber, J. ( 2019). Smart Control of Automatic Voltage Regulators using K-means Clustering. IEEE 14th Annual Conference System of Systems Engineering (SoSE), Anchorage, AK, USA, 2019.
13. Bhutto, A. A., Chachar, F. A., Hussain, M., Bhutto, D. K., & Bakhsh, S. E. (2019, January). Implementation of Probabilistic Neural Network (PNN) Based Automatic Voltage Regulator (AVR) for Excitation Control System in MATLAB. In 2019 2nd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET) (pp. 1-5). IEEE.
14. Ortiz-Quisbert, M. E., Duarte-Mermoud, M. A., Milla, F., Castro-Linares, R., & Lefranc, G. (2018). Optimal fractional order adaptive controllers for AVR applications. Electrical Engineering, 100(1), 267-283.
15. J. Kennedy and R. C. Eberhart, “Particle swarm optimization,” in Proc. IEEE Int. Conf. Neural Networks (ICNN’95), Perth, Australia, 1995, vol. IV, pp. 1942–1948.
16. Doctor, S., Venayagamoorthy, G. K., & Gudise, V. G. (2004, June). Optimal PSO for collective robotic search applications. In Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No. 04TH8753) (Vol. 2, pp. 1390-1395). IEEE.
17. Jeong, Y. W., Park, J. B., Jang, S. H., & Lee, K. Y. (2010). A new quantum-inspired binary PSO: application to unit commitment problems for power systems. IEEE Transactions on Power Systems, 25(3), 1486-1495.
18. Zhou, C., Yin, K., Cao, Y., & Ahmed, B. (2016). Application of time series analysis and PSO–SVM model in predicting the Bazimen landslide in the Three Gorges Reservoir, China. Engineering geology, 204, 108-120.
19. Godio, A., & Santilano, A. (2018). On the optimization of electromagnetic geophysical data: Application of the PSO algorithm. Journal of Applied Geophysics, 148, 163-174.
20. Madoliat, R., Khanmirza, E., & Pourfard, A. (2017). Application of PSO and cultural algorithms for transient analysis of natural gas pipeline. Journal of Petroleum Science and Engineering, 149, 504-514.
21. Malmir, P., Suleymani, M., & Bemani, A. (2018). Application of ANFIS-PSO as a novel method to estimate effect of inhibitors on Asphaltene precipitation. Petroleum Science and Technology, 36(8), 597-603.
22. Birs, I., Muresan, C., Nascu, I., Folea, S., & Ionescu, C. (2018, November). Experimental results of fractional order PI controller designed for second order plus dead time (SOPDT) processes. In 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV) (pp. 1143-1147). IEEE.
23. Narang, A., Shah, S. L., & Chen, T. (2010, June). Tuning of fractional PI controllers for fractional order system models with and without time delays. In Proceedings of the 2010 American Control Conference (pp. 6674-6679). IEEE.
24. Zhang, Y., Wang, S., & Ji, G. (2015). A comprehensive survey on particle swarm optimization algorithm and its applications. Mathematical Problems in Engineering, 2015.
25. J. Kennedy and R. C. Eberhart, Swarm Intelligence. San Francisco, CA: Morgan Kaufmann, 2001.
26. Mühürcü, G., Kose, E., Muhurcu, A., & Kuyumcu, A. (2017, September). Parameter optimization of PI controller by PSO for optimal controlling of a buck converter's output. In 2017 International Artificial Intelligence and Data Processing Symposium (IDAP) (pp. 1-6). IEEE.
27. Wang, B., Wu, Z. S., Zhao, Z., & Wang, H. G. (2009). Retrieving evaporation duct heights from radar sea clutter using particle swarm optimization (PSO) algorithm. Progress In Electromagnetics Research, 9, 79-91.