## 5 Conclusions and Future Work  
This study successfully implemented an experimental framework to evaluate the frequency control of a stand-alone synchronous generator using a PSO-Fuzzy PID controller. Unlike previous studies that relied on purely simulation-based validation, this research incorporated real industrial hardware, utilizing a Siemens S7-1200 PLC and TIA Portal V17 software for real-time monitoring and control. Communication between the PLC and MATLAB was successfully established using the KEPServerEX interface and OPC UA protocol, ensuring precise data exchange and operational efficiency.  
The proposed PSO-based metaheuristic algorithm proved to be highly effective in optimizing controller parameters and balancing computational efficiency with optimization accuracy. The selection of the ITAE function as the fitness criterion enabled the systematic determination of the best control parameters, significantly improving system stability and performance. Comparative analysis with conventional PLC-PID and Fuzzy PID controllers highlighted the superiority of the PSO-Fuzzy PID method, demonstrating the following:  
* A 30-50% reduction in settling time compared to PLC-PID and Fuzzy PID.
* A 10-56% reduction in maximum undershoot under dynamic load conditions.
* A 20-43% reduction in maximum overshoot during unloading experiments.  
Considerable reductions in the overshoot, undershoot, and settling time under dynamic load variations are observed. By integrating an advanced yet computationally efficient optimization technique with an industrially viable control system, this research bridges the gap between theoretical development and practical application. The findings emphasize the potential of PSO-driven controller optimization for enhancing control precision in real-world industrial environments, paving the way for further research into adaptive and intelligent control techniques.  
These improvements indicate the ability of the PSO-Fuzzy PID method to enhance control precision, robustness, and versatility under varying load conditions. While this study focused primarily on comparisons with conventional PID-based controllers, future research will investigate comprehensive and detailed comparative performance with other optimized control approaches, such as GA-PID and ANFIS-PID, for a broader perspective of optimization techniques.  
Future research will also focus on further improving the controller's performance under more challenging conditions and for higher-power SGs. The integration of predefined-time and event-triggered control strategies will be explored, to achieve maximum responsiveness of the system. Additionally, hybrid optimization approaches involving other metaheuristic algorithms, machine learning, and deep learning will be investigated to further enhance system performance and robustness for real-world industrial applications.  
Conceptualization, M.E. and H.A.; methodology, M.E. and H.A.; software, M.E. and H.A.; validation, M.E. and H.A.; formal analysis, M.E. and H.A.; investigation, M.E. and H.A.; resources, M.E. and H.A.; data curation, M.E.; writing--original draft preparation, M.E.; writing--review and editing, H.A.; visualization, H.A.; supervision, H.A.; project administration, H.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors.  
The authors declare no conflicts of interest.