最大化光伏发电的先进技术:系统的文献综述

IF 3.3 Q2 MULTIDISCIPLINARY SCIENCES
Amal Satif , Mohcin Mekhfioui , Rachid Elgouri
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引用次数: 0

摘要

由于环境变化、热损失和次优能源提取策略,光伏(PV)系统的能量产量最大化仍然是一个关键的工程挑战。本文提出了一个全面的系统文献综述(SLR),独特地集成了三个主要的光伏功率增强领域:MPPT,太阳能跟踪和热管理。与之前孤立评估这些领域的综述相反,这项工作提供了一个统一和比较的视角,强调人工智能(AI)和元启发式优化算法在所有三个领域中日益重要的作用。通过严格的纳入和排除标准选择了过去十年的同行评议研究。技术分为传统的、基于人工智能的和混合的三类。通过特定于实现的指标(例如,复杂性,鲁棒性,收敛时间)和基于仿真的性能指标(例如,跟踪效率,能量增益)来评估MPPT方法。同样,太阳能跟踪系统在跟踪精度和能源改进方面进行了分析,而冷却策略,包括被动,主动和混合解决方案,对其对热调节和电气效率的影响进行了回顾。还结合了实际部署示例来评估实际的适用性。该综述强调了持续存在的挑战,如智能和混合方法的高计算和硬件要求,对参数调整的敏感性,某些算法的振荡或缓慢收敛,以及高级跟踪和冷却解决方案的成本和机械复杂性增加,此外,光伏冷却应用的智能优化仍未得到充分探索。该研究总结了主要的研究差距,并概述了开发集成、智能和气候适应型光伏系统的未来方向。这项工作为旨在设计高性能太阳能解决方案的研究人员和实践者提供了有价值的参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Advanced techniques for maximizing photovoltaic power: A systematic literature review
Maximizing energy yield in photovoltaic (PV) systems remains a critical engineering challenge due to environmental variability, thermal losses, and suboptimal energy extraction strategies. This paper presents a comprehensive systematic literature review (SLR) that uniquely integrates three major PV power enhancement domains: MPPT, solar tracking, and thermal management. In contrast to prior reviews that assess these areas in isolation, this work offers a unified and comparative perspective, emphasizing the growing role of artificial intelligence (AI) and metaheuristic optimization algorithms across all three domains. Peer-reviewed studies from the last decade were selected using strict inclusion and exclusion criteria. Techniques are classified into conventional, AI-based, and hybrid categories. MPPT methods are evaluated through both implementation-specific metrics (e.g., complexity, robustness, convergence time) and simulation-based performance indicators (e.g., tracking efficiency, energy gain). Similarly, solar tracking systems are analyzed with respect to tracking accuracy and energy improvements, while cooling strategies, spanning passive, active, and hybrid solutions, are reviewed for their impact on thermal regulation and electrical efficiency. Real-world deployment examples are also incorporated to assess practical applicability. The review highlights persistent challenges such as high computational and hardware requirements for intelligent and hybrid methods, sensitivity to parameter tuning, oscillations or slow convergence in certain algorithms, and increased cost and mechanical complexity in advanced tracking and cooling solutions, moreover, intelligent optimization remains underexplored in PV cooling applications. The study concludes with key research gaps and outlines future directions for developing integrated, intelligent, and climate-resilient PV systems. This work serves as a valuable reference for researchers and practitioners aiming to design high-performance solar energy solutions.
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来源期刊
Scientific African
Scientific African Multidisciplinary-Multidisciplinary
CiteScore
5.60
自引率
3.40%
发文量
332
审稿时长
10 weeks
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