An Approach Based on Golden Eagle Optimization Algorithm for Maximum Power Point Tracking of PV Panel Under Partial Shading Conditions

Journal of Polytechnic, Год журнала: 2025, Номер unknown, С. 1 - 1

Опубликована: Янв. 16, 2025

This study introduces an innovative Maximum Power Point Tracking (MPPT) technique utilizing the Golden Eagle Optimization (GEO) method, specifically designed to enhance efficiency of photovoltaic (PV) systems under partial shading conditions. Unlike traditional MPPT approaches that struggle with local peaks in power-voltage curves caused by shading, GEO method leverages hunting behavior-inspired algorithm accurately locate global maximum power point (GMPP). The effectiveness is demonstrated through extensive simulations across three diverse case scenarios, each representing different patterns. In all outperforms conventional techniques, showcasing its adaptability and superior performance challenging successful implementation leads substantial improvements PV panel energy extraction efficiency, even when faced complexities shading. research contributes significantly advancement solar systems, enhancing their reliability real-world environments. By mitigating impact this work promotes wider adoption as a viable sustainable solution.

Язык: Английский

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Artificial neural networks for the prediction of the soil-water characteristic curve: An overview

Soil and Tillage Research, Год журнала: 2025, Номер 248, С. 106466 - 106466

Опубликована: Янв. 26, 2025

Язык: Английский

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Rock Slope Stability Prediction: A Review of Machine Learning Techniques

Geotechnical and Geological Engineering, Год журнала: 2025, Номер 43(3)

Опубликована: Фев. 18, 2025

Язык: Английский

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Estimation of Working Error of Electricity Meter Using Artificial Neural Network (ANN)

Energies, Год журнала: 2025, Номер 18(5), С. 1265 - 1265

Опубликована: Март 5, 2025

Together with the rapidly growing world population and increasing usage of electrical equipment, demand for energy has continuously increased energy. For this reason, especially considering inflation rates around world, using an electricity meter, which works least operating error, great economic importance. In study, artificial neural network (ANN)-based prediction methodology is presented to estimate active meter’s combined maximum error rate by variable factors such as current, voltage, temperature, power factor that affect permissible error. The estimation results obtained developed ANN model are evaluated statistically, then suitability accuracy approach tested. At end research, it understood can be used high working meter help a suitable based on internal factors.

Язык: Английский

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Optimizing Solar Water-Pumping Systems Using PID-Jellyfish Controller with ANN Integration

Electronics, Год журнала: 2025, Номер 14(6), С. 1172 - 1172

Опубликована: Март 17, 2025

This study presents a novel approach to improving the efficiency and reliability of solar water pumping systems by integrating proportional–integral–derivative (PID) controller with Jellyfish Algorithm (PID-JC) artificial neural networks (ANN). Solar water-pumping are gaining attention due their sustainable eco-friendly nature; however, performance is often limited fluctuating irradiance varying demand. To address these challenges, Monte Carlo simulations were employed account for system uncertainties. Traditional PID controllers, although widely used, struggle adapt effectively dynamic environmental conditions. The proposed utilizes an ANN predict demand patterns based on historical data, enabling real-time adjustments pump operations through PID-JC. inspired adaptive behavior jellyfish in environments. PID-JC adjusts parameters dynamically predictions, optimizing performance. Simulation experimental results conducted Mrada City, Northeastern Libya, demonstrated significant improvements delivery, energy consumption, compared conventional controllers. PID-JC’s ability diverse conditions ensures robust across various geographical locations seasonal changes. Additionally, comparisons other optimization algorithms, such as Firefly Golden Eagle Optimization, show that outperforms them 6.30% improvement cost function 28.13% reduction processing time Firefly, 26.81% 20.69% Optimization.

Язык: Английский

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Estimation of Soil Deformation Modulus from Cone Penetration Test Data Using Machine-Learning Methods

Soil Mechanics and Foundation Engineering, Год журнала: 2025, Номер unknown

Опубликована: Апрель 26, 2025

Язык: Английский

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Artificial Intelligence (AI) approach for the quantification of C-phycocyanin in Spirulina platensis: Hybrid stacking-ensemble model based on machine learning and deep learning

Ecological Informatics, Год журнала: 2025, Номер unknown, С. 103186 - 103186

Опубликована: Май 1, 2025

Язык: Английский

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Experimental Study and ANN Development for Modeling Tensile and Surface Quality of Fiber-Reinforced Nylon Composites

Polymers, Год журнала: 2025, Номер 17(11), С. 1528 - 1528

Опубликована: Май 30, 2025

Additive manufacturing (AM) is gaining widespread adoption in the industry due to its capability fabricate intricate and high-performance components. In parallel, increasing emphasis on functional materials AM has highlighted critical need for accurate prediction of mechanical behavior composite systems. This study experimentally investigates tensile strength surface quality carbon fiber-reinforced nylon composites (PA12-CF) fabricated via fused deposition modeling (FDM) models their using artificial neural networks (ANNs). A Taguchi L27 orthogonal array was employed design experiments involving five printing parameters: layer thickness (100, 200, 300 µm), infill pattern (gyroid, honeycomb, triangles), nozzle temperature (250, 270, 290 °C), speed (50, 100, 150 mm/s), density (30, 60, 90%). An analysis variance (ANOVA) revealed that had most significant influence resulting strength, contributing 53.47% variation, with substantially at higher densities. contrast, dominant factor determining roughness, accounting 53.84% thinner layers yielding smoother surfaces. Mechanistically, a enhances internal structural integrity parts, leading an improved load-bearing capacity, while improve interlayer adhesion finish. The highest achieved 69.65 MPa, lowest roughness recorded 9.18 µm. ANN model developed predict both based input parameters. Its performance compared three other machine learning (ML) algorithms: support vector regression (SVR), random forest (RFR), XGBoost. exhibited superior predictive accuracy, coefficient determination (R2 > 0.9912) mean validation error below 0.41% outputs. These findings demonstrate effectiveness ANNs complex relationships between FDM parameters properties highlight potential integrating ML statistical optimize composites.

Язык: Английский

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Seismic performance comparison of symmetrical and asymmetrical low-, mid-, and high-rise Rc structures: a height-based evaluation of structural behavior and material efficiency

Asian Journal of Civil Engineering, Год журнала: 2025, Номер unknown

Опубликована: Июнь 3, 2025

Язык: Английский

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Detection of optic disc in human retinal images utilizing the Bitterling Fish Optimization (BFO) algorithm

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Окт. 28, 2024

Early detection and correct identification of the optic disc (OD) on scanned retinal images are significant for diagnosing treating several ophthalmic conditions, including glaucoma diabetic retinopathy. Conventional methods detecting OD often struggle with processing due to noise, changes in illumination, complex overlapping images. This study presents development effective accurate fixation using Bitterling Fish Optimization (BFO) algorithm, which enhances processes imaging speed precision. The proposed method begins image enhancement noise suppression preprocessing, followed by applying BFO algorithm locate delineate region. performance evaluation was conducted within public domain images, DRIVE, STARE, ORIGA, DRISHTI-GS, DiaRetDB0, DiaRetDB1 datasets about some internal metrics: sensitivity (SE), specificity (SP), accuracy (ACC), DICE overlap coefficient, time respectively. technique based provided better results, 99.33%, 99.94%, 98.22% achieved DiaRetDB 1, approach also demonstrated high overlaps good a coefficient 0.9501 DRISHTI-GS database. On average, per less than 2.5 s, proving approach's efficiency computations. has its effectiveness scalability discs an automated manner. It showed impressive levels terms computation variation resistant irrespective quality pathology present it. holds potential clinical use, providing meaningful way managing ocular disease at early stage.

Язык: Английский

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