气候变化领域集成服务门户(CCPortal): Maximizing solar power generation through conventional and digital MPPT techniques: a comparative analysis

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DOI	10.1038/s41598-024-59776-z
	Maximizing solar power generation through conventional and digital MPPT techniques: a comparative analysis
	Sarang, Shahjahan Alias; Raza, Muhammad Amir; Panhwar, Madeeha; Khan, Malhar; Abbas, Ghulam; Touti, Ezzeddine; Altamimi, Abdullah; Wijaya, Andika Aji
发表日期	2024
ISSN	2045-2322
起始页码	14
结束页码	1
卷号	14 期号:1
英文摘要	A substantial level of significance has been placed on renewable energy systems, especially photovoltaic (PV) systems, given the urgent global apprehensions regarding climate change and the need to cut carbon emissions. One of the main concerns in the field of PV is the ability to track power effectively over a range of factors. In the context of solar power extraction, this research paper performs a thorough comparative examination of ten controllers, including both conventional maximum power point tracking (MPPT) controllers and artificial intelligence (AI) controllers. Various factors, such as voltage, current, power, weather dependence, cost, complexity, response time, periodic tuning, stability, partial shading, and accuracy, are all intended to be evaluated by the study. It is aimed to provide insight into how well each controller performs in various circumstances by carefully examining these broad parameters. The main goal is to identify and recommend the best controller based on their performance. It is notified that, conventional techniques like INC, P&O, INC-PSO, P&O-PSO, achieved accuracies of 94.3, 97.6, 98.4, 99.6 respectively while AI based techniques Fuzzy-PSO, ANN, ANFIS, ANN-PSO, PSO, and FLC achieved accuracies of 98.6, 98, 98.6, 98.8, 98.2, 98 respectively. The results of this study add significantly to our knowledge of the applicability and effectiveness of both AI and traditional MPPT controllers, which will help the solar industry make well-informed choices when implementing solar energy systems.
英文关键词	Conventional MPPTs; Artificial intelligence MPPTs; Solar energy; Sustainability
语种	英语
WOS研究方向	Science & Technology - Other Topics
WOS类目	Multidisciplinary Sciences
WOS记录号	WOS:001207621600054
来源期刊	SCIENTIFIC REPORTS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/298261
作者单位	Mehran University Engineering & Technology; Southeast University - China; Northern Border University; Majmaah University; Majmaah University; University of Business & Technology
推荐引用方式 GB/T 7714	Sarang, Shahjahan Alias,Raza, Muhammad Amir,Panhwar, Madeeha,et al. Maximizing solar power generation through conventional and digital MPPT techniques: a comparative analysis[J],2024,14(1).
APA	Sarang, Shahjahan Alias.,Raza, Muhammad Amir.,Panhwar, Madeeha.,Khan, Malhar.,Abbas, Ghulam.,...&Wijaya, Andika Aji.(2024).Maximizing solar power generation through conventional and digital MPPT techniques: a comparative analysis.SCIENTIFIC REPORTS,14(1).
MLA	Sarang, Shahjahan Alias,et al."Maximizing solar power generation through conventional and digital MPPT techniques: a comparative analysis".SCIENTIFIC REPORTS 14.1(2024).