Mathematical modeling of an integrated photovoltaic-assisted PEM water electrolyzer system for hydrogen production

International Journal of Hydrogen Energy, Journal Year: 2024, Volume and Issue: 79, P. 594 - 608

Published: July 9, 2024

Language: Английский

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Evaluating and comparing machine learning approaches for effective decision making in renewable microgrid systems

Results in Engineering, Journal Year: 2024, Volume and Issue: 21, P. 101888 - 101888

Published: Feb. 10, 2024

This study addresses the critical issue of energy management in micro-grid (MG) systems incorporating renewable sources and hydrogen storage. The research introduces an innovative approach by conducting a comparative analysis two machine learning methods, namely k-Nearest Neighbors (k-NN) Random Forest (RF), to optimize decision-making. investigation reveals consistent superiority Forest, particularly precision F1-scores, across key components such as fuel cell relay, battery super-capacitor grid system relay. results demonstrate that RF method consistently achieves high macro average factors (90%, 86%, 84%, 82%) impressive F1-scores 87%, 88%, 85%), surpassing performance k-NN, which yields notably lower (30%, 15%, 14%, 28%) (41%, 23%, 26%, 34%). superior positions robust for decision-making, specifically realm storage sources. novelty this work lies establishing reliable tool capable handling intricacies thereby enhancing sustainable processes. Additionally, resilience imbalanced data adds its effectiveness diverse operational scenarios. sheds light on potential contribute significantly advancement solutions systems.

Language: Английский

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Techno-economic analysis and optimization of agrivoltaic systems for green hydrogen production in diverse climates

International Journal of Hydrogen Energy, Journal Year: 2025, Volume and Issue: 123, P. 247 - 264

Published: April 1, 2025

Language: Английский

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Transformer Core Fault Diagnosis via Current Signal Analysis with Pearson Correlation Feature Selection

Electronics, Journal Year: 2024, Volume and Issue: 13(5), P. 926 - 926

Published: Feb. 29, 2024

The role of transformers in power distribution is crucial, as their reliable operation essential for maintaining the electrical grid’s stability. Single-phase are highly versatile, making them suitable various applications requiring precise voltage control and isolation. In this study, we investigated fault diagnosis a 1 kVA single-phase transformer core subjected to induced faults. Our diagnostic approach involved using combination advanced signal processing techniques, such fast Fourier transform (FFT) Hilbert (HT), analyze current signals. analysis aimed differentiate characterize unique signatures associated with each type, utilizing statistical feature selection based on Pearson correlation machine learning classifier. results showed significant improvements all metrics classifier models, particularly k-nearest neighbor (KNN) algorithm, 83.89% accuracy computational cost 0.2963 s. For future studies, our focus will be deep models improve effectiveness proposed method.

Language: Английский

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An improved population segmentation-based multi-mutation differential evolution algorithm for parameter extraction of photovoltaic models

Energy Conversion and Management, Journal Year: 2025, Volume and Issue: 327, P. 119553 - 119553

Published: Jan. 28, 2025

Language: Английский

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Optimizing Solar Panel Classification with Yolov11: Bridging Physics Principles and Artificial Intelligence for Enhanced Performance

African Journal of Biomedical Research, Journal Year: 2025, Volume and Issue: unknown, P. 80 - 91

Published: Jan. 6, 2025

This paper presents a novel framework for solar panel classification, leveraging physics-informed enhancements integrated into the YOLOv11 architecture. By incorporating domain-specific augmentations such as tilt-induced irradiance adjustments, shading simulations, and temperature effects, model demonstrates significant improvements in performance robustness. A comprehensive dataset of over 10,000 high-resolution images was created, encompassing diverse environmental conditions, tilt angles, levels to replicate real-world scenarios. Physics-informed resulted 7.3% increase mean average precision (mAP) 12% improvement accuracy under challenging extreme occlusions, compared traditional methods. The optimized achieved top-1 91%, an mAP 89.7%, inference speed 25 FPS. study highlights integration physics-based insights deep learning pipelines transformative approach analysis, paving way more reliable scalable renewable energy monitoring systems.

Language: Английский

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Hardware-in-the-Loop Emulation of a SEPIC Multiplier Converter in a Photovoltaic System

Electricity, Journal Year: 2024, Volume and Issue: 5(3), P. 426 - 448

Published: July 5, 2024

This article presents the development and execution of a Single-Ended Primary-Inductor Converter (SEPIC) multiplier within Hardware-in-the-Loop (HIL) emulation environment tailored for photovoltaic (PV) applications. Utilizing advanced capabilities dSPACE 1104 platform, this work establishes dynamic data exchange mechanism between variable voltage power supply SEPIC converter, enhancing efficiency solar energy harnessing. The proposed model was crafted to simulate real-world capture, facilitating evaluation control strategies under laboratory conditions. By emulating realistic operational scenarios, approach significantly accelerates innovation cycle PV system technologies, enabling faster validation refinement emerging solutions. converter is new topology based on traditional with capability producing larger output in scalable manner. initiative sets benchmark conducting research, offering blend precision flexibility testing supervisory strategies, thereby streamlining path toward technological advancements utilization.

Language: Английский

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Comprehensive modeling and simulation of photovoltaic system performance by using matlab/simulink: integrating dynamic meteorological parameters for enhanced accuracy

Journal of Umm Al-Qura University for Applied Sciences, Journal Year: 2024, Volume and Issue: unknown

Published: July 5, 2024

Abstract Studying the operation of photovoltaic panels in presence varying meteorological parameters is a complex undertaking that requires development models to understand physical phenomena associated with different factors. The main aim this study examine impact factors, such as illuminance, temperature, and wind speed, on performance modules. Our goal develop precise illustrate how these factors affect output system at specific location. To achieve this, we utilized rigorously validated mathematical model, previously tested simulation software PVsyst, enabling accurate prediction installation output. We compared results our simulations, conducted chosen those obtained from PVsyst software. Subsequently, accuracy proposed model using real operating conditions simulated by PVsyst. Additionally, incorporated additional curves, not available database, accounting for speed parameter.

Language: Английский

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Maximizing solar photovoltaic energy efficiency: MPPT techniques investigation based on shading effects

Solar Energy, Journal Year: 2024, Volume and Issue: 285, P. 113082 - 113082

Published: Nov. 21, 2024

Language: Английский

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Photovoltaic to electrolysis off-grid green hydrogen production with DC-DC conversion

Renewable Energy, Journal Year: 2024, Volume and Issue: 237, P. 121687 - 121687

Published: Nov. 1, 2024

Language: Английский

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