基于PSO的直流动力系统结构海上支援船柴油发电机组优化调度提高燃油效率

P. Chauhan, K. S. Rao, S. K. Panda, Gary Wilson, Xiong Liu, A. Gupta
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引用次数: 15

摘要

本文针对典型的采用4台相同柴油发电机组的直流动力系统结构的海上支援船,提出了以燃油消耗最小化为目标的最优发电计划。利用粒子群算法(Particle Swarm Optimization, PSO)分析了DC架构下dg之间的负载均衡,DC架构下dg之间的负载均衡,以及DC架构下dg之间的最优负载均衡。针对柴油机比FC (SFC)曲线存在非线性的特点,采用粒子群算法求解。采用分段三次埃尔米特插值多项式(PCHIP)插值方法对柴油机制动SFC (BSFC)图中SFC-vs-载荷关系进行了表征。通过比较三种情况下目标船的年燃料消耗量,得到了问题的解决方案。该方法可应用于任何具有直流电源系统结构的多发电机组的船舶。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fuel efficiency improvement by optimal scheduling of diesel generators using PSO in offshore support vessel with DC power system architecture
This paper presents optimal generation scheduling for minimization of fuel consumption (FC) in a typical offshore support vessel with DC power system architecture employing four identical diesel generator (DG) sets. FC is analyzed for (i) equal load sharing among DGs in AC architecture, (ii) equal load sharing among DGs in DC architecture and (iii) optimal load sharing among DGs in DC architecture using Particle Swarm Optimization (PSO). Due to the presence of non-linearity in Specific FC (SFC) curves of the diesel engines, the PSO algorithm is used. The SFC-vs-load relationship obtained from Brake SFC (BSFC) map of the diesel engines is characterized using a Piecewise Cubic Hermite Interpolating Polynomial (PCHIP) interpolation. A solution to problem has been attained by comparing yearly fuel consumption in the three cases for the load profile of target vessel. This method can be applied to any vessel equipped with multiple generation units having DC power system architecture.
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