A Novel AVR System Utilizing Fuzzy PIDF Enriched by FOPD Controller Optimized via PSO and Sand Cat Swarm Optimization Algorithms

Power system stability is managed through various control loops, including the Automatic Voltage Regulator (AVR), which regulates the terminal voltage of synchronous generators. This study integrated Fuzzy Logic Control (FLC) and a Proportional–Integral–Derivative controller with Filtered derivative action (PIDF) to propose a hybrid Fuzzy PIDF controller enhanced by Fractional-Order Proportional-Derivative (FOPD) for AVR applications. For the first time, the newly introduced Sand Cat Swarm Optimization (SCSO) algorithm was applied to the AVR system to tune the parameters of the proposed fuzzy controller. The SCSO algorithm has been recognized as a powerful optimization tool and has demonstrated success across various engineering applications. The well-known Particle Swarm Optimization (PSO) algorithm was also utilized in this study to optimize the gains of the proposed controller. The Fuzzy PIDF plus FOPD is a novel configuration that is designed to be a robust control technique for AVR to achieve an excellent performance. In this research, the Fuzzy PIDF + FOPD controller was optimized using the PSO and SCSO algorithms by minimizing the Integral Time Absolute Error (ITAE) objective function to enhance the overall performance of AVR systems. A comparative analysis was conducted to evaluate the superiority of the proposed approach by benchmarking the results against those of other controllers reported in the literature. Furthermore, the robustness of the controller was assessed under parametric uncertainties and varying load disturbances. Also, its robustness was examined against disturbances in the control signal. The results demonstrate that the proposed Fuzzy PIDF + FOPD controller tuned by the PSO and SCSO algorithms delivers exceptional performance as an AVR controller, outperforming other controllers. Additionally, the findings confirm the robustness of the Fuzzy PIDF + FOPD controller against parametric uncertainties, establishing its potential for a successful implementation in real-time applications.