Citation

Aprilianto, Rizky Ajie and Sutrisno, Deyndrawan and Nugroho, Dwi Bagas and Arrosyid, Wildan Hazballah and Maulana, Alfan and Rachmat, Siva Khaaifina and Bourezg, Abdrabbi and Tiang, Jun Jiat and Azzouz, Abdelbasset (2026) Load Frequency Control Optimization of Micro Hydro Power Plant Using Genetic Algorithm Variant. Energies, 19 (9). p. 2025. ISSN 1996-1073

Text energies-19-02025-v2.pdf - Published Version Restricted to Repository staff only Download (4MB)

Abstract

The aim of this work is to explore a load frequency control (LFC) strategy in micro hydro power plants (MHPPs). Using MATLAB/Simulink, we examined several variants of genetic algorithms (GAs), including Roulette, Tournament, and Uniform, which are utilized to optimize tuning proportional integral derivative (PID) parameters by addressing the problem of instability caused by load variations. The performances are compared with conventional PID methods and other advanced techniques like particle swarm optimization (PSO), adaptive neuro-fuzzy inference system (ANFIS), and artificial neural networks (ANN) algorithms for both single and dual-area MHPP systems. The results show that the GA-optimized PID controller with the roulette wheel achieves the fastest settling time of 0.3 s and the smallest undershoot of 0.015 pu in the single area. Also, optimizing GA demonstrates superior performance in the dual area, with the fastest settling times of 2.5 s for both Roulette and Uniform. In contrast, PSO is slower than GA, and conventional PID requires a much longer settling time of 19.8 s, a similar result occurring in the dual area. These findings confirm the effectiveness of the GA-optimized PID controller, especially the Roulette variant, as a reliable and fast solution for maintaining frequency stability in MHPPs.

Item Type:	Article
Uncontrolled Keywords:	Load frequency control (LFC); genetic algorithm
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK1001-1841 Production of electric energy or power. Powerplants. Central stations
Divisions:	Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	08 Jun 2026 00:50
Last Modified:	08 Jun 2026 00:50
URII:	http://shdl.mmu.edu.my/id/eprint/16095

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