## 1 Introduction
Table 1 highlights the key differences between this study and previous research on frequency control of stand-alone synchronous generators. Unlike traditional studies that primarily rely on simulations, this research integrates AI optimization with fuzzy logic and demonstrates its effectiveness in real-world industrial systems. This study is motivated by the need for practical control solutions in industrial applications, where ensuring real-time performance, robustness, and reliability is essential. Existing approaches often lack real-world validation, limiting their applicability in dynamic industrial environments. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Factors**} & \multicolumn{8}{c}{**References**} & \multirow{2}{*}{**This Study**} \\ \cline{2-2} \cline{4-9}  & [36] & [37] & [38] & [39] & [40] & [41] & [42] \\ \hline
**Using Industrial Tools** & No & No & No & Yes & No & No & Yes & Yes \\ \hline
**Classical Controller** & Yes & No & Yes & No & Yes & No & Yes & Yes \\ \hline
**Using AI** & Yes & No & Yes & No & Yes & No & No & Yes \\ \hline
**Real-Time Operation** & No & Yes & No & No & No & Yes & No & Yes \\ \hline
**Implemented in a Real System** & No & Yes & No & No & No & Yes & No & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of control methods and technologies in power system control studies. To bridge this gap, this study implements a PSO-Fuzzy PID controller on a real system, using industrial tools such as PLCs for real-time monitoring and control. The reasons for choosing this approach are as follows:  
1. Limitations of classical controllers: While classical controllers are widely used, they struggle with adaptability to varying operating conditions. AI-based methods, particularly PSO-Fuzzy PID, offer online tuning capabilities that enhance stability and performance. 2. Industrial applicability: Many studies do not consider the constraints of industrial environments where computational efficiency and real-time execution are essential. This study addresses this challenge by offloading complex calculations to external computers while ensuring real-time operation via PLCs. 3. Experimental design justification: The experiment involves controlling the frequency of a synchronous generator through an induction motor and a frequency converter. This setup mimics real industrial scenarios, making the findings directly applicable to power system automation and industrial control. This paper provides a comparative analysis of three control strategies for frequency regulation of a stand-alone synchronous generator: a classical PLC-PID controller, a Fuzzy PID controller, and a PSO-Fuzzy PID controller. The main contributions of this study are as follows:  
* Optimization-based dynamic control design: Implementing an optimization-driven control strategy for frequency regulation, ensuring robust performance under industrial operating conditions. * Effective use of metaheuristic algorithms in industrial applications: demonstrating that PSO combined with Fuzzy PID can be practically applied in automation, even with simple PLC-based control systems. * Reliable convergence with PSO-Fuzzy PID: Validating the performance improvement and reliable convergence with PSO-Fuzzy PID, the PSO-Fuzzy PID controller enables efficient parameter tuning, improves dynamic response, and ensures stable frequency control. * Expansion to large interconnected and multi-area power systems: Presenting a framework that extends to primary and secondary frequency control in interconnected power networks. * This approach can be applied to develop advanced real-time controllers for use in both academic and industrial applications. This study provides a practical implementation framework, ensuring its applicability in real-world industrial settings. This article is organized as follows. Detailed information about the system established as the experimental setup and its modeling are presented in Section 2. Section 3 describes the proposed controller designs.