Hybrid PV-Reheat Thermal Power System Automatic Generation Control  Using PIDD Controller Based on Hippopotamus Algorithm

Jirawat Riyawong; Sitthisak Audomsi; Worarat Phakditha; Chatmongkol Areeyat; Kunakorn Pakdeesuwan; Supakorn Ukumphan; Palapol Sawatphol; Worawat Sa-ngiamvibool; Supannika Wattana

doi:10.55674/ias.v14i3.263235

Authors

Jirawat Riyawong Faculty of Engineering, Mahasarakham University, Maha Sarakham, 44150, Thailand
Sitthisak Audomsi Faculty of Engineering, Mahasarakham University, Maha Sarakham, 44150, Thailand
Worarat Phakditha Faculty of Engineering, Mahasarakham University, Maha Sarakham, 44150, Thailand
Chatmongkol Areeyat Faculty of Engineering, Mahasarakham University, Maha Sarakham, 44150, Thailand
Kunakorn Pakdeesuwan Faculty of Engineering, Mahasarakham University, Maha Sarakham, 44150, Thailand
Supakorn Ukumphan Faculty of Engineering, Mahasarakham University, Maha Sarakham, 44150, Thailand
Palapol Sawatphol Faculty of Engineering, Mahasarakham University, Maha Sarakham, 44150, Thailand
Worawat Sa-ngiamvibool Faculty of Engineering, Mahasarakham University, Maha Sarakham, 44150, Thailand
Supannika Wattana Faculty of Engineering, Mahasarakham University, Maha Sarakham, 44150, Thailand

DOI:

https://doi.org/10.55674/ias.v14i3.263235

Keywords:

Hybrid PV-reheat thermal system, Hippopotamus algorithm, Load frequency control, PIDD

Abstract

This paper examines load frequency control, an essential element of the power system that ensures frequency stability and enhances reliability, particularly in contemporary power systems incorporating renewable energy sources like photovoltaic power plants (PV) in two-area configurations and reheat thermal power plants. This research examines and contrasts metaheuristic algorithms for optimizing the settings of two sets of proportional-integral-double derivative (PIDD) controllers in regulating a two-area power plant to enhance system response. The comparative analysis of the results employed Hippopotamus algorithm (HO), Particle swarm optimization (PSO), Water cycle algorithm (WCA), and Grey wolf optimizer (GWO) to evaluate performance based on the ITAE objective function, which encompasses Overshoot, Undershoot, and Settling Time. The experimental results indicate that HO provides the lowest ITAE objective function values compared to other algorithms, exhibiting exceptional responsiveness, stability, and less settling time. This suggests that HO is an appropriate technique to be used in load frequency control for a two-area PV-reheat thermal power system.

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