Assessment of genetic algorithm selection, crossover and mutation techniques in power loss optimization for a hydrocarbon facility

2015 50th International Universities Power Engineering Conference (UPEC) Pub Date : 2015-12-03 DOI:10.1109/UPEC.2015.7339894

M. T. Al-Hajri, M. A. Abido, M. Darwish

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引用次数: 5

Abstract

In this paper, different selection, crossover including deferential evolution and mutation techniques are considered for optimizing the electrical power loss in real hydrocarbon industrial plant using genetic algorithm (GA). The subject plant electrical system consists of 275 buses, two gas turbine generators, two steam turbine generators, large synchronous motors, and other rotational and static loads. The minimization of power losses objective is used to guide the optimization process. Eight GA selection, crossover and mutation techniques combination cases are simulated for optimizing the system real power loss. The potential of power loss optimization for each case versus the base case will be discussed in the results. The results obtained demonstrate the potential and effectiveness of the proposed techniques combination cases in optimizing the power consumption. Also, in this paper a cost appraisal for the potential daily, monthly and annual cost saving associated with the power loss optimization for each case will be addressed.

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油气设施电力损耗优化中的遗传算法选择、交叉和突变技术评价

本文采用遗传算法对实际油气工业装置的电力损耗进行优化，考虑了不同选择、交叉、顺从进化和突变技术。课题厂电气系统由275辆母线、两台燃气轮机发电机、两台蒸汽轮机发电机、大型同步电动机以及其他旋转和静态负载组成。以功率损耗最小为目标，指导优化过程。模拟了8种遗传算法选择、交叉和变异技术组合的实例，以优化系统的实际功率损耗。将在结果中讨论每种情况相对于基本情况的功率损耗优化的潜力。结果表明，所提出的技术组合案例在优化功耗方面具有潜力和有效性。此外，本文还将对每种情况下与功率损耗优化相关的潜在每日、每月和年度成本节约进行成本评估。

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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2015 50th International Universities Power Engineering Conference (UPEC)

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