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All Journal Journal of Renewable Energy, Electrical, and Computer Engineering
Feroni Suci Maharani
Universitas Negeri Semarang
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Performance Comparison of Genetic Algorithm and Particle Swarm Optimization for PID Governor Tuning in Micro Hydro Power Plants Eko Prasetyo; Feroni Suci Maharani; Bisena Aryo Wibowo; Aditya Dwi Laksana; Yusuf Nofiansah
Journal of Renewable Energy, Electrical, and Computer Engineering Vol. 6 No. 2 (2026): September 2026
Publisher : Institute for Research and Community Service (LPPM), Universitas Malikussaleh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jreece.v6i1.25677

Abstract

Micro hydro power plant performance strongly depends on the capability of the governor system to regulate rotor speed under varying load conditions. This paper presents a comparative analysis of a proportional–integral–derivative (PID) governor optimized using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). A simulation-based framework is developed by implementing the micro hydro power plant model and the PID-based governor in MATLAB/Simulink, where the optimization problem is formulated by minimizing the Integral Time Absolute Error (ITAE) criterion. The dynamic performance of the optimized controllers is examined under nominal operating conditions and load disturbance scenarios. System behavior is evaluated using time-domain performance indices, including rise time, settling time, overshoot, and ITAE. Simulation results demonstrate that both GA- and PSO-tuned PID controllers significantly enhance governor performance compared to the conventional PID approach. The GA-based PID controller achieves lower overshoot, faster rise time, and shorter settling time, indicating superior transient response characteristics. Meanwhile, the PSO-based PID controller produces a slightly lower ITAE value, demonstrating competitive error minimization capability. These findings indicate that both optimization techniques effectively improve governor performance, with GA providing the best overall dynamic response and PSO offering comparable performance in terms of long-term error reduction.
Co-Authors Aditya Dwi Laksana Bisena Aryo Wibowo Eko Prasetyo Yusuf Nofiansah