Optimally Tuned PID Controller Design for an AVR System: A Comparison Study

Öz

Voltage control is performed to reduce network losses in power systems. Automatic Voltage Regulator (AVR) system is commonly used in power systems to keep output voltage on a constant value defined in a specified range. In order to improve dynamic response of an AVR system and minimize obtained steady state error, researchers focus on developing control schemes and designing controllers for the AVR system. In controller design process, meta-heuristic algorithms are generally preferred to optimally tune the parameters of the controller. In this comparison study, parameters of traditional Proportional-IntegralDerivative (PID) controller, utilized for the voltage control of an AVR system, are tuned using Particle Swarm Optimization (PSO) and Symbiotic Organism Search (SOS) algorithms. Integral of Time-multiplied Absolute Error (ITAE) function which is a widely preferred error-based objective function, is used during the optimization processes. The performances of the designed PID controllers are compared both visually and numerically. Integral of Time-multiplied Square Error (ITSE), Integral of Absolute Value of Error (IAE), and ITAE performance metrics are utilized in addition to maximum overshoot, settling time, rise time and steady-state error values in numerical comparison. It is concluded that ITAE objective function provides better result than both ITSE and IAE metrics in AVR system. In addition, it is seen that the transient response characteristics obtained by SOS algorithm are superior than those obtained by PSO algorithm.

Anahtar Kelimeler

• Automatic Voltage Regulator (AVR),Proportional-Integral-Derivative (PID) Control,Particle Swarm Optimization (PSO),Symbiotic Organism Search (SOS)

Kaynakça

1. H. Gozde, M. C. Taplamacioglu, S, Comparative performance analysis of artificial bee colony algorithm for automatic voltage regulator (AVR) system, Journal of the Franklin Institute, 348 (2011) 1927-1946.
2. L. S. Coelho, Tuning of PID controller for an automatic Voltage regulator system using chaotic optimization approach, Chaos, Solitons and Fractals 39 (2009) 1504-1514.
3. S. Priyambada, P. K. Mohanty, B. K. Sahu, Automatic Voltage Regulator using TLBO algorithm optimized PID controller, India.
4. S. Panda, B. K. Sahu, P. K. Mohanty, Designd and performance analysis of PID controller for an automatic voltage regulator system using simplified particle swarm optimization, Journal of the Franklin Institute, 349 (2012) 2609-2625.
5. S. Ekinci, B. Hekimoğlu, Improved Kidney-Inspired Algorithm Approach for Tuning of PID Controller in AVR System, IEEE Access, March 2019.
6. S. Kansit, W. Assawinchaichote, Optimization of PID controller based on PSOGSA for an automatic voltage regulator system, Procedia Computer Science 86 (2016) 87 – 90.
7. M. Zamani, M. K. Ghartemani, N. Sadati, M. Parniani, Design of fractional order PID controller for an AVR using particle swarm optimization, Control Engineering Practice 17 (2009) 1380–1387.
8. Z. Bingul, O. Karahan, A novel performance criterion approach to optimum design of PID controller using Cuckoo search algorithm for AVR system, Journal of the Franklin Institute, 355 (2018) 5534-5559.

9. Ayas, M. S. (2019). Design of an optimized fractional high-order differential feedback controller for an AVR system. Electrical Engineering, 1-13.
10. B. D. Martin and E. Schwab, "Symbiosis: 'Living Together' in Chaos", Studies in The History of Biology. 2012. Volume 4. No. 4., 7-25.
11. M. Y. Cheng, ve D. Prayogo, Symbiotic organism search: a new metaheuristic optimization algorithm, Computers and Structures, 139 (2014) 98-112.
12. Kennedy, J. and Eberhart, R., Particle Swarm Optimization, Proceedings of the IEEE International Conference on Neural Networks, Vol. 4 (1995), 1942-1948.