一种独特的基于新颖的FLC方法,用于考虑天气条件的突然/逐渐变化,以增强太阳能系统的MPPT运行。

IF 2.9 4区 综合性期刊 Q2 MULTIDISCIPLINARY SCIENCES Science Progress Pub Date : 2025-01-01 Epub Date: 2025-03-17 DOI:10.1177/00368504251323732
Kareem M AboRas, Mohammed Hassan El-Banna, Ashraf Ibrahim Megahed
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引用次数: 0

摘要

太阳能是一种很有前途的可再生能源。光伏系统正变得越来越受欢迎。使用最大功率点跟踪技术对于最大限度地从太阳系获取能量至关重要。太阳系最高功率点的变化被认为是由外部元素与系统的相互作用引起的。鉴于上述情况,本研究的首要目标是找出如何使用一种创新的模糊逻辑控制器来跟踪基于升压变流器的光伏系统的峰值功率点。我们使用模糊逻辑控制器使系统对环境温度的变化、辐照度的快速和适度变化以及其他环境因素更加动态敏感。本研究的主要目的是改进模糊逻辑控制器的标度因子和隶属函数。这些特征与控制器的精度、稳定性和速度之间存在相关性。为了更好地执行优化概念,采用了前沿的元启发式方法,即北极海雀优化。北极海雀优化从大自然中汲取动力。与其他有效的优化算法(如粒子群优化算法和灰狼优化算法)的比较和分析表明,北极海雀优化算法在模糊逻辑控制器调谐方面优于其他优化程序。使用MATLAB/Simulink R2020a环境,我们测试了每种方法的跟踪精度,效率,响应时间,瞬态超调和稳态纹波。各种各样的天气条件被用来进行调查。仿真结果表明,基于北极海雀优化的模糊最大功率点跟踪控制器在所有工况下的跟踪效率均在99.8%以上。
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A unique novel-based FLC approach for enhancing MPPT operation of solar systems considering sudden/gradual variation in weather conditions.

Solar power is one renewable energy source that has great promise. Photovoltaic systems are becoming increasingly popular. Using maximum power point tracker technologies is essential for maximizing the amount of power that can be harvested from a solar system. The variability of the highest power point of a solar system is thought to be caused by the interaction of external elements with the system. In light of the foregoing, the study's overarching goal is to figure out how to use an innovative fuzzy logic controller to track a boost converter-based photovoltaic system's peak power point. We use the fuzzy logic controller to make the system more dynamically sensitive to changes in ambient temperature, quick and moderate variations in irradiance, and other environmental factors. The major objective of this research is to improve the fuzzy logic controller's scaling factors and membership functions. There is a correlation between these features and the controller's accuracy, stability, and speed. For the concept of optimization to be executed well, the cutting-edge metaheuristic method known as Arctic puffin optimization was employed. Arctic Puffin Optimization draws its motivation from nature. Comparisons and analyses with other effective optimization algorithms, such as particle swarm optimization and gray wolf optimizer, have shown that Arctic Puffin Optimization outperforms these other optimization procedures when it comes to fuzzy logic controller tuning. Using the MATLAB/Simulink R2020a environment, we test each method for tracking accuracy, efficiency, response time, transient overshoot, and steady-state ripple. A broad variety of weather conditions was used to conduct the investigation. The Arctic puffin optimization-based fuzzy maximum power point tracker controller's tracking efficacy was consistently over 99.8% in all the conditions investigated, according to the simulation findings.

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来源期刊
Science Progress
Science Progress Multidisciplinary-Multidisciplinary
CiteScore
3.80
自引率
0.00%
发文量
119
期刊介绍: Science Progress has for over 100 years been a highly regarded review publication in science, technology and medicine. Its objective is to excite the readers'' interest in areas with which they may not be fully familiar but which could facilitate their interest, or even activity, in a cognate field.
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