Analysis of maximum power tracking methods in photovoltaic panels

IF 1.3 4区材料科学 Q4 MATERIALS SCIENCE, MULTIDISCIPLINARY

Emerging Materials Research Pub Date : 2022-09-01 DOI:10.1680/jemmr.22.00061

Ali Akdemir, Abdulhakim Karakaya

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引用次数: 2

Abstract

In the last century, the demand for energy has soared with the rapid development of technology and the human population. The fossil fuels such as coal, natural gas, oil, etc. used today to obtain energy have a limited lifespan. Therefore, the interest in renewable energy sources is increasing. Solar energy, which is one of these renewable energy sources, comes to the fore more than other renewable energy sources because the energy emitted instantly by the sun is so much greater than the energy consumed by the world in a year. Therefore, much research has been done to produce a greater amount of energy from the sun. This study examines the Maximum Power Point Tracking (MPPT) methods that are widely used today. The methods under review include; Constant Voltage, Short Circuit Current, Perturb and Observe, Fuzzy Logic, Incremental Conductance and Artificial Neural Networks. Of these methods, Perturb and Observe, Fuzzy Logic and Artificial Neural Networks have been analyzed using the PVSOL software, which is for making designs and simulations for photovoltaic systems. The analysis includes a comparison of the depreciation periods for the Standard Systems and the facilities where these control algorithms are used.

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光伏板最大功率跟踪方法分析

在上个世纪，随着技术和人口的快速发展，对能源的需求激增。今天用于获取能源的化石燃料，如煤炭、天然气、石油等，使用寿命有限。因此，人们对可再生能源的兴趣正在增加。太阳能是这些可再生能源之一，比其他可再生能源更为突出，因为太阳瞬间释放的能量远大于世界一年消耗的能量。因此，人们已经做了大量的研究来从太阳中产生更多的能量。本研究考察了目前广泛使用的最大功率点跟踪（MPPT）方法。正在审查的方法包括：；恒压、短路电流、扰动与观测、模糊逻辑、增量电导和人工神经网络。在这些方法中，扰动与观测、模糊逻辑和人工神经网络已经使用PVSOL软件进行了分析，该软件用于光伏系统的设计和仿真。该分析包括对标准系统和使用这些控制算法的设施的折旧期的比较。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Emerging Materials Research MATERIALS SCIENCE, MULTIDISCIPLINARY-

CiteScore

4.50

自引率

9.10%

发文量

期刊介绍： Materials Research is constantly evolving and correlations between process, structure, properties and performance which are application specific require expert understanding at the macro-, micro- and nano-scale. The ability to intelligently manipulate material properties and tailor them for desired applications is of constant interest and challenge within universities, national labs and industry.