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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics International Journal of Robotics and Control Systems
Audomsi, Sitthisak
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Optimal turning of a 2-DOF proportional-integral-derivative controller based on a chess algorithm for load frequency control Buranaaudsawakul, Techatat; Ardhan, Kittipong; Audomsi, Sitthisak; Sa-ngiamvibool, Worawat; Dulyala, Rattapon
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp146-155

Abstract

Load frequency control is necessary for power system management. The power system must maintain a frequency range to ensure power supply stability. System faults and demand fluctuations may cause frequencies to change quickly. System stability and integrity suffer. We are optimizing the two-degree-of-freedom (2-DOF) proportional-integral-derivative (PID) controllers chess algorithm. This article addresses electrical load frequency regulation. We employ classical control theory and current adjustment. It aims for electrical system efficiency and dependability. It checks for errors using integral absolute error (IAE), integral squared error (ISE), integral of time multiply absolute error (ITAE), and integral time squared error (ITSE). Particle swarm algorithm (PSO) compares performance. The IAE of 0.03364, nearly identical to it, shows that chess trumps other algorithms in many scenarios. The chess algorithm's ISE was 0.00035, like PSO's 0.03363. The ISE was 0.00036, indicating PSO's error-reduction capabilities. For the chess algorithm, PSO is 0.07929, and ITAE is 0.07647. This indicates the PSO responds faster to system breakdowns and load changes. Finally, the chess algorithm's ITSE is 0.00072, below the PSO 0.00076. The chess algorithm is better at managing long-term load frequency.
Development of a 2 degree of freedom-proportional integral derivative controller using the hippopotamus algorithm Dulyala, Rattapon; Sa-ngiamvibool, Worawat; Audomsi, Sitthisak; Ardhah, Kittipong; Buranaaudsawakul, Techatat
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp780-787

Abstract

This research project investigates the regulation of autonomous power generation in two interconnected regions using two hydroelectric power plants. It specifically addresses the challenges posed by significant electrical system issues. The hippopotamus optimization algorithm (HOA) has demonstrated enhanced gain value in research and designs of 2 degree of freedom (2DOF)-proportional integral derivative (PID) controllers. The objective is to provide efficient and uninterrupted functioning of the electrical network in both areas. Contemporary technology and methods enable the electrical system to efficiently and accurately fulfill user requirements, resolving any problems related to system balance and stability. This experiment evaluates the efficacy of several algorithms in accurately selecting optimal values. We evaluate performance using the integral of absolute error (IAE) and integral of time-weighted absolute error (ITAE) functions. This experiment evaluates and contrasts different algorithms. Summarizing the analysis using verifiable evidence. Optimization when evaluated using the ITAE measurement, the HOA earned the lowest result of 0.08744 for ITAE. Empirical research has demonstrated that this strategy is the most effective in reducing the ITAE. The sine-cosine algorithm (SCA) and whale optimization algorithm (WOA) have similar ITAE values, with SCA having an error of 0.08967 and WOA having an error of 0.08967. The numerical number is 0.08970.
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.
Chess Optimizer for Load Frequency Control of Three-Area Multi-Source Renewable Energy Based on PID Plus Second Order Derivative Controller Areeyat, Chatmongkol; Audomsi, Sitthisak; Obma, Jagraphon; Yang, Xiaoqing; Sa-Ngiamvibool, Worawat
International Journal of Robotics and Control Systems Vol 5, No 3 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i3.2052

Abstract

Renewable energy sources such as solar and wind are increasingly integrated into multi-area power systems. However, their fluctuating and unpredictable characteristics pose challenges for sustaining system stability. Therefore, automatic generation control (AGC) is essential for the continual regulation of power and frequency in the system. This article presents the use of a Proportional–Integral–Derivative plus second-order derivative (PID+DD) controller for load frequency control in a three-area multi-source power system, which includes a thermal reheat power plant with a generation rate constraint (GRC) representing the maximum permissible change rate of generation output of 5% per min , a hydroelectric power plant with a  GRC of 370% per min, and a wind power plant where wind speeds vary across areas. The power generation ratio of the three areas is 1:2:4. The controller parameters were tuned using a Chess Optimizer (CO), a metaheuristic inspired by chess move complexity and planning, with specific weights assigned to each type of chess piece. Two load change scenarios were studied: a 10% step load perturbation (10% SLP) and a random load pattern (RLP).  Furthermore, experimental results based on the Integral of Time-weighted Absolute Error (ITAE) indicate that the PID+DD controller tuned by the Chess Optimizer achieved the lowest steady-state error in both scenarios (10% SLP and RLP). In Case 1 (SLP), it achieved an ITAE of 25.5072, representing a 9.70% reduction compared to the PID controller and a 1.96% reduction compared to the PI controller. In Case 2 (RLP), it achieved an ITAE of 88.0654, representing a 1.14% reduction compared to the PID controller and a 2.03% reduction compared to the PI controller. These improvements contribute to enhanced oscillation damping, reduced overshoot and undershoot, and improved frequency stability, demonstrating the practical applicability of the proposed approach in future smart grids with high renewable energy penetration.
Load frequency control of multi-source power system using PID+DD controller based on chess algorithm Areeyat, Chatmongkol; Audomsi, Sitthisak; Obma, Jagraphon; Yang, Xiaoqing; Sa-ngiamvibool, Worawat
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.10425

Abstract

This article presents load frequency control for a nonlinear multi-source power system divided into three areas, consisting of thermal reheat power plants, hydropower, and wind generation, while considering generation rate constraints (GRC). A proportional–integral–derivative (PID) plus second-order derivative (PID+DD) controller optimized using the chess algorithm (CA) is proposed. The effectiveness of CA is validated against hippopotamus optimization (HO), grey wolf optimizer (GWO), and ant lion optimizer (ALO) under two scenarios: a 10% step load perturbation (SLP) and a random load pattern (RLP). Simulation results indicate that the proposed CA significantly improves dynamic performance. In scenario 1 (10% SLP), CA achieves a reduction of approximately 30.5% in integral weight time absolute error (ITSE) compared to GWO and 43.7% compared to HO, while also reducing frequency undershoot in Area 2 by 15.2% compared to HO. In scenario 2 RLP, CA maintains robustness, limiting tie-line power deviations to ±8 MW, whereas HO exhibits deviations exceeding ±12 MW. Overall, the CA-tuned PID+DD controller demonstrates superior damping, reduced overshoot and undershoot, and enhanced stability across multi-area interconnected renewable systems, making it a promising approach for future real-time load frequency control (LFC) applications with higher renewable penetration.
Co-Authors Ardhah, Kittipong Ardhan, Kittipong Areeyat, Chatmongkol Buranaaudsawakul, Techatat Chansom, Natpapha Dulyala, Rattapon Obma, Jagraphon Sa-ngiamvibool, Worawat Yang, Xiaoqing