A. B. Hassan, Kazi Firoz Ahmed
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{"title":"Design and analysis of an off-grid PV plant for higher utilization efficiency in the field of pharmaceutical industry considering global pandemic state .","authors":"A. B. Hassan, Kazi Firoz Ahmed","doi":"10.53799/ajse.v20i1.144","DOIUrl":null,"url":null,"abstract":"According to the concern of WHO the less association of people in an office may restrict the likelihood of spreading this COVID-19 infection. On the other hand, the pharmaceutical companies are working hard to maintain uninterrupted production of vaccine and medicines. This paper focuses on the main layer which is the power system management and its utilization through automation and controlling remotely. In the design process the FDA (Food and Drug Administration) proposed structure and green energy solution is maintained. Solar energy utilization efficiency is increased using the data logging system and machine learning algorithms from archived data. A SCADA operated Off-Grid Solar PV Automation System has been proposed to increase the utilization efficiency. To predict solar power availability over time and perform efficient energy trafficking, the automation system will analyze previous data and perform situational awareness operations for uninterrupted solar power generation. A comprehensive analysis of the proposed automation system for pharmaceuticals industry applications has also been presented in this paper. The continuous monitoring system for this Off-Grid Solar PV power generating unit preserves multiple data entries, which increases with time and subjected to energy trafficking. And this energy trafficking based on machine learning increases the overall solar energy utilization efficiency from 64% to 99.92%. © 2021 AIUB Office of Research and Publication. All rights reserved.","PeriodicalId":36368,"journal":{"name":"AIUB Journal of Science and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"AIUB Journal of Science and Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.53799/ajse.v20i1.144","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Mathematics","Score":null,"Total":0}
引用次数: 1
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考虑全球大流行疫情的医药行业离网光伏电站设计与分析。
世卫组织担心,办公室人员交往较少可能会限制COVID-19感染传播的可能性。另一方面,制药公司正在努力维持疫苗和药品的不间断生产。本文重点研究了电力系统管理和远程自动化控制的主要内容。在设计过程中保持了FDA(食品和药物管理局)提出的结构和绿色能源解决方案。利用数据记录系统和机器学习算法从存档数据中提高太阳能利用效率。提出了一种SCADA操作的离网太阳能光伏自动化系统,以提高利用效率。为了预测一段时间内太阳能的可用性并执行有效的能源运输,自动化系统将分析以前的数据并执行态势感知操作,以实现不间断的太阳能发电。本文还对提出的用于制药工业的自动化系统进行了全面的分析。该离网太阳能光伏发电机组的连续监测系统保留了多个数据条目,这些数据条目随着时间的推移而增加,并受到能量贩运的影响。这种基于机器学习的能量传输将太阳能的整体利用率从64%提高到99.92%。©2021 AIUB研究与出版办公室。版权所有。
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