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All Journal Bulletin of Electrical Engineering and Informatics International Journal of Robotics and Control Systems
Chansom, Natpapha
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Control & Systems Engineering Electrical & Electronics Engineering

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Strategic Chess Algorithm-Based PI Controller Optimization for Load Frequency Control in Two-Area Hybrid Photovoltaic–Thermal Power Systems Obma, Jagraphon; Audomsi, Sitthisak; Ardhan, Kittipong; Sa-Ngiamvibool, Worawat; Chansom, Natpapha
International Journal of Robotics and Control Systems Vol 5, No 2 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i2.1844

Abstract

Maintaining frequency stability in hybrid renewable-integrated power systems remains a critical challenge due to the inherent variability and uncertainty of photovoltaic–thermal (PV–T) energy sources. Traditional proportional–integral (PI) controllers, optimized using conventional metaheuristic algorithms such as the Whale Optimization Algorithm (WOA), Firefly Algorithm (FA), and Salp Swarm Algorithm (SSA), often suffer from limitations including slow convergence, premature convergence to local optima, and reduced robustness under severe load disturbances. The research contribution is the development and systematic evaluation of a chess algorithm (CA)-based PI controller tuning approach for enhancing load frequency control (LFC) in hybrid PV–T systems. Unlike population-based methods, the CA employs chess-inspired strategic decision-making processes, which improve the search efficiency and the ability to escape local optima in high-dimensional optimization problems. In this study, the proposed CA-based optimization method is applied to a two-area hybrid PV–T power system, where the system is subject to various operating conditions, including solar radiation fluctuations and step load perturbations. The tuning of PI controller parameters is performed using the integral of time-weighted absolute error (ITAE) as the objective function. Simulation results demonstrate that the CA-optimized PI controller achieves superior performance in minimizing overshoot, undershoot, and settling time when compared with controllers optimized by WOA, FA, and SSA. Specifically, the CA approach achieves faster stabilization and lower frequency deviations, highlighting its potential for real-time implementation and enhanced grid reliability. Future work will explore the scalability of the proposed method to multi-area power systems and evaluate its computational efficiency through hardware-in-the-loop validation.
Improved load frequency control with chess algorithm-driven optimization of 3DOF-PID controller Ardhan, Kittipong; Chansom, Natpapha; Audomsi, Sitthisak; Sa-Ngiamvibool, Worawat; Obma, Jagraphon
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9871

Abstract

In contemporary hybrid power systems, persistent load fluctuations disrupt the delicate balance between electrical output and mechanical torque, thereby compromising frequency stability. Load frequency control (LFC) mechanisms are indispensable in maintaining this equilibrium, particularly in systems integrating renewable and thermal energy sources. This study introduces a three-degree-of-freedom proportional-integral-derivative (3DOF-PID) controller optimized via the novel chess optimization algorithm (COA) and evaluates its efficacy against the ant lion optimizer (ALO) and Harris Hawks optimization (HHO). Extensive MATLAB/Simulink simulations were conducted on a hydrothermal system, with performance assessed through objective functions—integral of absolute error (IAE) and integral of time-weighted absolute error (ITAE). The COA consistently yielded the lowest cumulative error values (IAE=0.1548 and ITAE=0.2965), demonstrating its superiority in steady-state performance. However, COA exhibited substantial dynamic deviations, including an overshoot of 387.79% and undershoot of 4513.8% in ∆ftie. Conversely, HHO offered a significantly enhanced transient response, achieving 0% undershoot in ∆ftie with minimal oscillatory behavior. ALO displayed moderate performance but struggled with higher undershoots and prolonged settling time. The findings underscore the criticality of algorithm selection in controller design. While COA excels in minimizing long-term errors, HHO is preferable for applications requiring heightened dynamic stability and responsiveness.
Co-Authors Ardhan, Kittipong Audomsi, Sitthisak Obma, Jagraphon Sa-ngiamvibool, Worawat