Bonobo Optimizer Inspired PI‐(1+DD) Controller for Robust Load Frequency Management in Renewable Wind Energy Systems DOI Creative Commons
Sulaiman Z. Almutairi, Ghareeb Moustafa,

Sultan Hassan Hakmi

и другие.

International Journal of Energy Research, Год журнала: 2025, Номер 2025(1)

Опубликована: Янв. 1, 2025

With the growing presence of renewable energy sources (RESs), necessity for adaptive and robust control strategies becomes more pronounced. This article proposes a self‐adaptive bonobo optimizer (SABO)‐based proportional integral one plus double derivative (PI‐(1+DD)) controller that offers novel solution to load frequency (LFC). It draws inspiration from reproductive bonobos, employing unique mating behaviors enhance optimization processes. innovative approach introduces memory capabilities, repulsion‐based learning, diverse‐mating strategies. is developed tune PI‐(1+DD) handling LFC in two‐area power system involving thermal plant RESs wind farm. The proposed SABO algorithm applied comparative manner standard (BOA), Coot algorithm, particle swarm (PSO), Pelican (POA). Also, SABO‐based contrasted PI PIDn controllers. simulation findings distinguish as versatile offering resilient efficient tackle complexities introduced by evolving landscape. demonstrates its potential significantly improve dynamic response systems, particularly face step changes random fluctuations. shows significant enhancement compared BOA, Coot, POA, PSO with 38.81%, 46.27%, 16.79%, 37.40%, respectively. it an impressive percentage improvement 97.1% 74.88% over considering consecutive fluctuations system.

Язык: Английский

Newton Raphson Based Optimizer for Optimal Integration of FAS and RIS in Wireless Systems DOI Creative Commons
Ahmed S. Alwakeel, Ali M. El‐Rifaie, Ghareeb Moustafa

и другие.

Results in Engineering, Год журнала: 2025, Номер unknown, С. 103822 - 103822

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

2
Fractional Order PID Controller Based‐Neural Network Algorithm for LFC in Multi‐Area Power Systems DOI Creative Commons
Ali M. El‐Rifaie,

Slim Abid,

Ahmed R. Ginidi

и другие.

Engineering Reports, Год журнала: 2025, Номер 7(2)

Опубликована: Фев. 1, 2025

ABSTRACT Modern power systems are increasingly challenged by frequency stability issues due to dynamic load variations and the growing complexity of interconnected networks. Traditional PID controllers, while widely utilized, struggle address rapid fluctuations uncertainties inherent in contemporary multi‐area (MAIPS). This paper introduces an innovative approach Load Frequency Control (LFC) using a Fractional‐Order (FOPID) controller, optimized Neural Network Algorithm (NNA). The proposed NNA‐FOPID framework leverages biological principles neural networks dynamically tune controller parameters, significantly enhancing system performance. solution is tested under various scenarios involving step changes across systems. method demonstrates marked improvements over traditional controllers advanced optimization techniques such as Differential Evolution (DE) Artificial Rabbits (ARA). comparisons show that FOPID controller's NNA‐based design effectively successfully handles LFC MAIPSs for ITAE minimizations, statistical evaluation supports its superiority.

Язык: Английский

Процитировано

1
An Improved Kepler optimization algorithm for Module Parameter Identification supporting PV Power Estimation DOI Creative Commons
Ghareeb Moustafa,

Hashim Alnami,

Ahmed R. Ginidi

и другие.

Heliyon, Год журнала: 2024, Номер 10(21), С. e39902 - e39902

Опубликована: Окт. 31, 2024

Язык: Английский

Процитировано

5
Bonobo Optimizer Inspired PI‐(1+DD) Controller for Robust Load Frequency Management in Renewable Wind Energy Systems DOI Creative Commons
Sulaiman Z. Almutairi, Ghareeb Moustafa,

Sultan Hassan Hakmi

и другие.

International Journal of Energy Research, Год журнала: 2025, Номер 2025(1)

Опубликована: Янв. 1, 2025

With the growing presence of renewable energy sources (RESs), necessity for adaptive and robust control strategies becomes more pronounced. This article proposes a self‐adaptive bonobo optimizer (SABO)‐based proportional integral one plus double derivative (PI‐(1+DD)) controller that offers novel solution to load frequency (LFC). It draws inspiration from reproductive bonobos, employing unique mating behaviors enhance optimization processes. innovative approach introduces memory capabilities, repulsion‐based learning, diverse‐mating strategies. is developed tune PI‐(1+DD) handling LFC in two‐area power system involving thermal plant RESs wind farm. The proposed SABO algorithm applied comparative manner standard (BOA), Coot algorithm, particle swarm (PSO), Pelican (POA). Also, SABO‐based contrasted PI PIDn controllers. simulation findings distinguish as versatile offering resilient efficient tackle complexities introduced by evolving landscape. demonstrates its potential significantly improve dynamic response systems, particularly face step changes random fluctuations. shows significant enhancement compared BOA, Coot, POA, PSO with 38.81%, 46.27%, 16.79%, 37.40%, respectively. it an impressive percentage improvement 97.1% 74.88% over considering consecutive fluctuations system.

Язык: Английский

Процитировано

0