Modeling of comprehensive power load of fishery energy internet considering fishery meteorology

IF 7.7 Q1 AGRICULTURE, MULTIDISCIPLINARY Information Processing in Agriculture Pub Date : 2023-02-21 DOI:10.1016/j.inpa.2023.02.008
Xueqian Fu , Tong Gou
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引用次数: 1

Abstract

Accurate calculation for comprehensive power load of fishery energy internet plays a significant role in reasonable using of energy and reducing environmental pollution. However, as fishery power load is of greatly unique meteorology sensitivity, it continues to be a difficult problem. Therefore, the research of fishery meteorology is an important part of the rational development of fishery resources, the protection of production safety, and the pursuit of high and stable yield. This paper makes a deep study on the power load of the fishery energy internet under the influence of fishery meteorology and takes onshore fish pond as the research object. First of all, the power load is divided into three parts: oxygen enrichment power load, feeding power load, and water replenishment and drainage power load. The impact mechanism of fishery meteorology (including temperature, surface wind speed, precipitation, relative humidity, etc.) on it is described, and then the overall power load is obtained through modeling and integration. Finally, taking the Yuguang Complementary Project in Zhouquan Town, Tongxiang, Zhejiang Province, China as an example, using the meteorological data of its typical spring day and using the MATLAB tool to solve, the hourly comparison of the three types of power loads, the comprehensive power load demand, the full-day electricity charge forecast and the total annual power consumption are calculated. The annual power consumption per hectare and per kilogram of output calculated by simulation are basically consistent with the order of magnitude of the survey data, which proves the validity of the model proposed. The model established in this paper is an original work, and the exploration of fishery energy internet can draw lessons from it.

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考虑渔业气象的渔业能源互联网综合电力负荷建模
准确计算渔业能源网综合电力负荷,对合理利用能源、减少环境污染具有重要意义。然而,由于渔业电力负荷具有非常独特的气象敏感性,它仍然是一个难题。因此,渔业气象学的研究是合理开发渔业资源、保障生产安全、追求高产稳产的重要组成部分。本文以陆上鱼塘为研究对象,对渔业气象影响下的渔业能源互联网电力负荷进行了深入研究。首先,电力负荷分为三部分:富氧电力负荷、进料电力负荷和补水排水电力负荷。首先描述了渔业气象(包括温度、地面风速、降水、相对湿度等)对其的影响机理,然后通过建模和集成得到总体电力负荷。最后,以中国浙江省桐乡市舟泉镇玉光互补工程为例,利用其典型春日气象数据,利用MATLAB工具求解,计算出三种电力负荷的逐时比较、综合电力负荷需求、全天电费预测和全年总用电量。通过仿真计算得到的每公顷年耗电量和每公斤产量年耗电量与实测数据的数量级基本一致,证明了所提模型的有效性。本文建立的模型具有独创性,渔业能源互联网的探索可以借鉴。
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
Information Processing in Agriculture
Information Processing in Agriculture Agricultural and Biological Sciences-Animal Science and Zoology
CiteScore
21.10
自引率
0.00%
发文量
80
期刊介绍: Information Processing in Agriculture (IPA) was established in 2013 and it encourages the development towards a science and technology of information processing in agriculture, through the following aims: • Promote the use of knowledge and methods from the information processing technologies in the agriculture; • Illustrate the experiences and publications of the institutes, universities and government, and also the profitable technologies on agriculture; • Provide opportunities and platform for exchanging knowledge, strategies and experiences among the researchers in information processing worldwide; • Promote and encourage interactions among agriculture Scientists, Meteorologists, Biologists (Pathologists/Entomologists) with IT Professionals and other stakeholders to develop and implement methods, techniques, tools, and issues related to information processing technology in agriculture; • Create and promote expert groups for development of agro-meteorological databases, crop and livestock modelling and applications for development of crop performance based decision support system. Topics of interest include, but are not limited to: • Smart Sensor and Wireless Sensor Network • Remote Sensing • Simulation, Optimization, Modeling and Automatic Control • Decision Support Systems, Intelligent Systems and Artificial Intelligence • Computer Vision and Image Processing • Inspection and Traceability for Food Quality • Precision Agriculture and Intelligent Instrument • The Internet of Things and Cloud Computing • Big Data and Data Mining
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