Hybrid Brown-Bear and Hippopotamus Algorithms with Fractional Order Chaos Maps for Precise Solar PV Model Parameter Estimation DOI Open Access

Lakhdar Chaib,

Mohammed Tadj,

Abdelghani Choucha

и другие.

Processes, Год журнала: 2024, Номер 12(12), С. 2718 - 2718

Опубликована: Дек. 2, 2024

The rise in photovoltaic (PV) energy utilization has led to increased research on its functioning, as accurate modeling is crucial for system simulations. However, capturing nonlinear current–voltage traits challenging due limited data from cells’ datasheets. This paper presents a novel enhanced version of the Brown-Bear Optimization Algorithm (EBOA) determining ideal parameters circuit model. presented EBOA incorporates several modifications aimed at improving searching capabilities. It combines Fractional-order Chaos maps (FC maps), which support BOA settings be adjusted an adaptive manner. Additionally, it integrates key mechanisms Hippopotamus (HO) strengthen algorithm’s exploitation potential by leveraging surrounding knowledge more effective position updates while also balance between global and local search processes. was subjected extensive mathematical validation through application benchmark functions rigorously assess performance. Also, PV parameter estimation achieved combining with Newton–Raphson approach. Numerous module cell varieties, including RTC France, STP6-120/36, Photowatt-PWP201, were assessed using double-diode single-diode models. higher performance shown statistical comparison many well-known metaheuristic techniques. To illustrate this, root mean-squared error values our scheme (SDM, DDM) PWP201 are follows: (8.183847 × 10−4, 7.478488 10−4), (1.430320 10−2, 1.427010 10−2), (2.220075 10−3, 2.061273 10−3), respectively. experimental results show that works better than alternative techniques terms accuracy, consistency, convergence.

Язык: Английский

Technical and Optimization Insights into PV Penetration in Power Distribution Systems-based Wild Horse Algorithm: Real Cases on Egyptian Networks DOI Creative Commons
Asmaa Nasef, Mohammed H. Alqahtani,

Abdullah M. Shaheen

и другие.

Results in Engineering, Год журнала: 2025, Номер unknown, С. 104603 - 104603

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

1
Enhanced Solar Power Prediction Models With Integrating Meteorological Data Toward Sustainable Energy Forecasting DOI Creative Commons

Mohammed A. Atiea,

Abdullah M. Shaheen, Abdullah Alassaf

и другие.

International Journal of Energy Research, Год журнала: 2024, Номер 2024(1)

Опубликована: Янв. 1, 2024

Sustainable energy management hinges on precise forecasting of renewable sources, with a specific focus solar power. To enhance resource allocation and grid integration, this study introduces an innovative hybrid approach that integrates meteorological data into prediction models for photovoltaic (PV) power generation. A thorough analysis is performed utilizing the Desert Knowledge Australia Solar Centre (DKASC) Hanwha dataset encompassing PV output variables from sensors. The aim to develop distinctive predictive model framework by integrating feature selection techniques various regression algorithms. This model, referred as generation (PVPGPM), utilizes DKASC. In study, are implemented, including Pearson correlation (PC), variance inflation factor (VIF), mutual information (MI), step forward (SFS), backward elimination (BE), recursive (RFE), embedded method (EM), identify most influential factors prediction. Furthermore, multiple algorithms introduced, linear regression, ridge Least Absolute Shrinkage Selection Operator (LASSO) Elastic Net, Extra Trees Regressor, random forest regressor, gradient boosting (GB) eXtreme Gradient Boosting (XGBoost) thereof. Extensive experimentation evaluation showcase effectiveness proposed in achieving high accuracy. Results demonstrate comprising XGBoost GB regressor surpasses other algorithms, yielding minimal root mean square error (RMSE) 0.108735 highest R ‐squared ( 2 ) value 0.996228. findings underscore importance insights sustainable planning management.

Язык: Английский

Процитировано

6
Chemical-Inspired Material Generation Algorithm (MGA) of Single- and Double-Diode Model Parameter Determination for Multi-Crystalline Silicon Solar Cells DOI Creative Commons
Wafaa Alsaggaf, Mona Gamal, Shahenda Sarhan

и другие.

Applied Sciences, Год журнала: 2024, Номер 14(18), С. 8549 - 8549

Опубликована: Сен. 23, 2024

The optimization of solar photovoltaic (PV) cells and modules is crucial for enhancing energy conversion efficiency, a significant barrier to the widespread adoption energy. Accurate modeling estimation PV parameters are essential optimal design, control, simulation systems. Traditional methods often suffer from limitations such as entrapment in local optima when addressing this complex problem. This study introduces Material Generation Algorithm (MGA), inspired by principles material chemistry, estimate effectively. MGA simulates creation stabilization chemical compounds explore optimize parameter space. algorithm mimics formation ionic covalent bonds generate new candidate solutions assesses their stability ensure convergence parameters. applied two different modules, RTC France Kyocera KC200GT, considering manufacturing technologies cell models. nature comparison other algorithms further demonstrated experimental statistical findings. A comparative analysis results indicates that outperforms strategies previous researchers have examined systems terms both effectiveness robustness. Moreover, demonstrate enhances electrical properties accurately identifying under varying operating conditions temperature irradiance. In reported methods, KC200GT module, consistently performs better decreasing RMSE across variety weather situations; SD DD models, percentage improvements vary 8.07% 90.29%.

Язык: Английский

Процитировано

5
Performance of pelican optimizer for energy losses minimization via optimal photovoltaic systems in distribution feeders DOI Creative Commons
Zuhair Alaas, Ghareeb Moustafa, Hany S. E. Mansour

и другие.

PLoS ONE, Год журнала: 2025, Номер 20(3), С. e0319298 - e0319298

Опубликована: Март 12, 2025

In distribution grids, excessive energy losses not only increase operational costs but also contribute to a larger environmental footprint due inefficient resource utilization. Ensuring optimal placement of photovoltaic (PV) systems is crucial for achieving maximum efficiency and reliability in power networks. This research introduces the Pelican Optimizer (PO) algorithm optimally integrate solar PV radial electrical grids. The PO novel bio-inspired optimization that draws inspiration from pelicans’ intelligence behavior which incorporates unique methods exploration exploitation, improving its effectiveness various challenges. It hyper-heuristic phase change, allowing dynamically adjust strategy based on problem’s characteristics. suggested aims reduce possible minimum value. developed version tested Ajinde 62-bus network, practical Nigerian system, typical IEEE grid with 69 nodes. simulation findings demonstrate enhanced version’s efficacy, showing significant decrease energy. With 62-node grid, obtains substantial 30.81% total loss expenses contrast initial scenario. Similarly, 69-node achieves 34.96%. Additionally, model’s indicate proposed performs comparably Differential Evolution (DE), Particle Swarm Optimization (PSO), Satin bowerbird optimizer (SBO) algorithms.

Язык: Английский

Процитировано

0
Simultaneous Allocation of PV Systems and Shunt Capacitors in Medium Voltage Feeders Using Quadratic Interpolation Optimization‐Based Gaussian Mutation Operator DOI Creative Commons
Mona Gamal, Shahenda Sarhan, Abdullah M. Shaheen

и другие.

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

Опубликована: Янв. 1, 2025

This study introduces an enhanced version of quadratic interpolation optimization (QIO) merged with Gaussian mutation (GM) operator for optimizing photovoltaic (PV) units and capacitors within distribution systems, addressing practical considerations discrete nature capacitors. In this regard, the variations in power loading productions from PV sources are taken into consideration. The QIO is inspired by generalized (GQI) method mathematics GM that randomness solution to explore search space avoid premature convergence. proposed QIO‐GM tested on Egyptian standard IEEE demonstrating its effectiveness minimizing energy losses. Comparative studies against QIO, northern goshawk (NGO), optical microscope algorithm (OMA), as well other reported algorithms, validate QIO‐GM’s superior performance. Numerically, first system, designed achieves 2.5% improvement over a 4.4% NGO, 9.2% OMA, leading substantial reduction carbon dioxide (Co 2 ) emissions 110,823.886 79,402.82 kg, reflecting commendable 28.35% decrease. Similarly, second demonstrates significant Co 72,283.328 54,627.65 28.3% These results underscore not only losses but also contributing environmental benefits through reduced emissions.

Язык: Английский

Процитировано

0
Adaptive operational allocation of D-SVCs in distribution feeders using modified artificial rabbits algorithm DOI
Ali S. Aljumah, Mohammed H. Alqahtani, Abdullah M. Shaheen

и другие.

Electric Power Systems Research, Год журнала: 2025, Номер 245, С. 111588 - 111588

Опубликована: Март 7, 2025

Язык: Английский

Процитировано

0
Hybrid Brown-Bear and Hippopotamus Algorithms with Fractional Order Chaos Maps for Precise Solar PV Model Parameter Estimation DOI Open Access

Lakhdar Chaib,

Mohammed Tadj,

Abdelghani Choucha

и другие.

Processes, Год журнала: 2024, Номер 12(12), С. 2718 - 2718

Опубликована: Дек. 2, 2024

The rise in photovoltaic (PV) energy utilization has led to increased research on its functioning, as accurate modeling is crucial for system simulations. However, capturing nonlinear current–voltage traits challenging due limited data from cells’ datasheets. This paper presents a novel enhanced version of the Brown-Bear Optimization Algorithm (EBOA) determining ideal parameters circuit model. presented EBOA incorporates several modifications aimed at improving searching capabilities. It combines Fractional-order Chaos maps (FC maps), which support BOA settings be adjusted an adaptive manner. Additionally, it integrates key mechanisms Hippopotamus (HO) strengthen algorithm’s exploitation potential by leveraging surrounding knowledge more effective position updates while also balance between global and local search processes. was subjected extensive mathematical validation through application benchmark functions rigorously assess performance. Also, PV parameter estimation achieved combining with Newton–Raphson approach. Numerous module cell varieties, including RTC France, STP6-120/36, Photowatt-PWP201, were assessed using double-diode single-diode models. higher performance shown statistical comparison many well-known metaheuristic techniques. To illustrate this, root mean-squared error values our scheme (SDM, DDM) PWP201 are follows: (8.183847 × 10−4, 7.478488 10−4), (1.430320 10−2, 1.427010 10−2), (2.220075 10−3, 2.061273 10−3), respectively. experimental results show that works better than alternative techniques terms accuracy, consistency, convergence.

Язык: Английский

Процитировано

2