Online PID Parameter Optimization Using Genetic Algorithm for a Wind Power Generation System
DOI:
https://doi.org/10.4108/eetsmre.11140Keywords:
Genetic Algorithm, PID controller, online optimization, wind power generation, anti-windup controlAbstract
INTRODUCTION: In wind power generation systems, the unstable variability of wind energy significantly affects control quality and power stability. Conventional PID controllers often show limitations in nonlinear systems or systems with time-varying parameters, especially when integral windup and degraded transient performance occur.
OBJECTIVES: This paper proposes an online optimization method for PID parameters based on a Genetic Algorithm (GA), applied to a simplified dynamic model of a wind power generation system, in order to improve the system response quality.
METHODS: The studied system is modeled by a second-order transfer function representing the system’s inertia and friction characteristics. The GA is implemented in a real-time optimization manner, using an objective function based on the ITAE criterion to evaluate and select the optimal PID parameter set.
RESULTS: Simulation results show that the proposed online GA–PID approach improves settling time, reduces overshoot, and eliminates steady-state error more effectively than fixed PID and conventional anti-windup PID controllers.
CONCLUSION: The proposed online GA–PID method is suitable for energy systems with high variability and adaptive control requirements, especially in wind power generation applications.
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References
[1] C.-L. Lee and C.-C. Peng, “Analytic Time Domain Specifications PID Controller Design for a Class of 2nd Order Linear Systems: A Genetic Algorithm Method,” IEEE Access, vol. 9, pp. 99266–99280, 2021.
[2] H. Wang, S. Xu, and H. Hu, “PID Controller for PMSM Speed Control Based on Improved Quantum Genetic Algorithm Optimization,” IEEE Access, vol. 11, pp. 61091–61102, 2023.
[3] Z. Qu, W. Younis, X. Liu, A. K. Junejo, S. Z. Almutairi, and P. Wang, “Optimized PID Controller for Load Frequency Control in Multi-Source and Dual-Area Power Systems Using PSO and GA Algorithms,” IEEE Access, vol. 12, pp. 186658–186670, 2024.
[4] H. Liu, et al., “Renewable Energy Power System Frequency Control by Using PID Controller and Genetic Algorithm,” IEEE Access, vol. 8, pp. 133721–133732, 2020.
[5] C. Rajinikanth and S. Latha, “Parameter Optimization of PID Controller Using Genetic Algorithm,” Journal of Electrical Engineering, vol. 17, no. 4, pp. 45–52, 2020
[6] A. Z. D’Antuono et al., “Optimization of UAV Robust Control Using Genetic Algorithm,” IEEE Access, vol. 11, pp. 122252–122270, 2023.
[7] X. Li et al., “Optimal Control of AVR System With Tree Seed Algorithm-Based PID Controller,” IEEE Access, vol. 9, pp. 167222–167233, 2021.
[8] H. Benbouhenni et al., “Experimental Analysis of Genetic Algorithm-Enhanced PI Controller for Power Optimization in Multi-Rotor Variable-Speed Wind Turbine Systems,” Scientific Reports, vol. 15, Art. no. 1407, 2025.
[9] R. Devaraj et al., “Robust Queen Bee Assisted Genetic Algorithm Optimized Fractional Order PID Controller,” IEEE Transactions on Power Electronics, vol. 37, no. 5, pp. 5412–5423, 2021.
[10] A.-W. Ibrahim et al., “Intelligent Nonlinear PID Controller Combined With Optimization Algorithm for Effective Global Maximum Power Point Tracking of PV Systems,” IEEE Access, vol. 12, pp. 185265–185280, 2024.
[11] R. Singh and M. Khan, “Design and Performance Analysis of a Nonlinear Magnetic Levitation System Using PID Controller Optimized With COOT Algorithm,” IEEE Access, vol. 10, pp. 151230–151242, 2022.
[12] A. Rodríguez-Abreo et al., “Self-Tuning Neural Network PID With Dynamic Response Control,” IEEE Access, vol. 9, pp. 78000–78010, 2021.
[13] S. Xiao et al., “Research on Control Strategy of Battery Roller Press Deflection Device by Introducing Genetic Algorithm to Optimize Integral Separation PID,” IEEE Access, vol. 11, pp. 132000–132012, 2023.
[14] V. D’Antuono et al., “Optimization of UAV Robust Control Using Genetic Algorithm,” IEEE Access, vol. 11, pp. 122252–122270, 2023.
[15] L. Zhang et al., “Application Research of Fuzzy PID Control Optimized by Genetic Algorithm in Medium and Low-Speed Maglev Train Charger,” IEEE Access, vol. 9, pp. 152131–152145, 2021.
[16] J. Li and W. Li, “On-Line PID Parameters Optimization Control for Wind Power Generation System Based on Genetic Algorithm,” IEEE Access, vol. 8, pp. 137094–137100, 2020.
[17] Z. Ortatepe, “Genetic Algorithm based PID Tuning Software Design and Implementation for a DC Motor Control System,” Gazi University Journal of Science Part A: Engineering and Innovation, vol. 10, no. 3, pp. 286–300, 2023.
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