在早期设计阶段考虑系统级控制

Tianlei Zhang, R. Dougal, Yucheng Zhang
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引用次数: 1

摘要

船舶电力系统的设计者在早期机组设计中选择发电机时必须考虑许多权衡。这不仅仅是基于物理设计要求(如尺寸和重量限制)的“设备问题”。在评价发电机设计的优劣时,必须考虑发电机的运行策略。为此,本文开发了一种设计方法,对发电厂施加系统级控制以评估其最优性。定义了两个关键的设计指标-燃料消耗和服务质量(QOS)来结合这些控制变量,并共同优化以反映面向任务的系统性能。基于一种考虑有功、无功平衡和系统冗余要求的新型经济调度算法,计算最小油耗。QOS度量的定义是为了反映基于经典的平均故障间隔时间概念的系统级控制策略,它被定义为与产生的功率成反比。我们开发了一个多目标粒子群优化,以有效地定位系统性能的帕累托前沿在整个给定任务的所有设计方案。因此,可以快速选择一组最优设计方案来表示这两个指标的非支配最优折衷。以船舶发电机组设计为例,对该设计方法进行了论证,并与单目标设计方法进行了比较。这样就能找到更优的替代方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Accounting for system-level controls during early-stage design
Designers of shipboard power systems must consider many tradeoffs when selecting generators in the early-stage set-based design. This is not just an “equipment problem” based on physical design requirements such as limits of size and weight. The operating strategy for generators must be considered when evaluating the goodness of the design. To this end, this paper develops a design approach to impose system-level controls onto generating plants to evaluate their optimality. Two critical design metrics-fuel consumption and quality of service (QOS) are defined to incorporate these control variables, and co-optimized to reflect mission-oriented system performance. The minimum fuel consumption is computed based on a new concept of economic dispatch algorithm, which takes active and reactive power balance, and system redundancy requirements into account. QOS metric is defined to reflect system-level control strategies based on the classical concept of mean-time-between-failure, which is defined to depend inversely on the power produced. We develop a multi-objective particle swarm optimization to effectively locate the Pareto fronts of system performance for all design alternatives over a whole given mission. Thus a set of optimal design alternatives can be rapidly selected to represent the non-dominated optimal compromises of the two metrics. This design approach is demonstrated and compared with a single-objective design approach in a design example of shipboard generating plant design. And more optimal alternatives are able to be found.
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