标题:Energy Consumption Analysis and Optimization of the Deep-Sea Self-Sustaining Profile Buoy
作者:Liu, Mingcong; Yang, Shaobo; Li, Hongyu; Xu, Jiayi; Li, Xingfei
作者机构:[Liu, Mingcong; Yang, Shaobo; Xu, Jiayi; Li, Xingfei] Tianjin Univ, State Key Lab Precis Measuring Technol & Instrume, Tianjin 300072, Peoples R China 更多
通讯作者:Li, XF
通讯作者地址:[Li, XF]Tianjin Univ, State Key Lab Precis Measuring Technol & Instrume, Tianjin 300072, Peoples R China.
来源:ENERGIES
出版年:2019
卷:12
期:12
DOI:10.3390/en12122316
关键词:energy consumption optimization; deep-sea self-sustaining profile buoy; (DSPB); multi-objective optimization; non-dominated sorted genetic; algorithm-II (NSGA-II)
摘要:In order to reduce the energy consumption of deep-sea self-sustaining profile buoy (DSPB) and extend its running time, a stage quantitative oil draining control mode has been proposed in this paper. System parameters have been investigated including oil discharge resolution (ODR), judgment threshold of the floating speed and frequency of oil draining on the energy consumption of the system. The single-objective optimization model with the total energy consumption of DSPB's ascent stage as the objective function has been established by combining the DSPB's floating kinematic model. At the same time, as the static working current of the DSPB can be further optimized, a multi-objective energy consumption optimization model with the floating time and the energy consumption of the oil pump motor as objective functions has been established. The non-dominated sorted genetic algorithm-II (NSGA-II) has been employed to optimized the energy consumption model in the ascent stage of the DSPB. The results showed that the NSGA-II method has a good performance in the energy consumption optimization of the DSPB, and can reduce the dynamic energy consumption in the floating process by 28.9% within 2 h considering the increase in static energy consumption.
收录类别:SCOPUS;SCIE
WOS核心被引频次:1
Scopus被引频次:1
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068386078&doi=10.3390%2fen12122316&partnerID=40&md5=7368fee5b296ecf2331030bc16738a45
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