- International Journal of Multidisciplinary Studies and Innovative Technologies
- Vol: 3 Issue: 2
- Optimally Tuned PID Controller Design for an AVR System: A Comparison Study
Optimally Tuned PID Controller Design for an AVR System: A Comparison Study
Authors : Büşra Özgenç, Mustafa Şinasi Ayas, Ismail Altaş
Pages : 157-161
View : 20 | Download : 8
Publication Date : 2019-12-23
Article Type : Other
Abstract :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.Keywords : Automatic Voltage Regulator (AVR), Proportional-Integral-Derivative (PID) Control, Particle Swarm Optimization (PSO), Symbiotic Organism Search (SOS)