Brainstorm optimisation algorithm (BSOA): An efficient algorithm for finding optimal location and setting of FACTS devices in electric power systems
Brainstorm optimisation algorithm (BSOA): An efficient algorithm for finding optimal location and setting of FACTS devices in electric power systems
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DOI：
10.1016/j.ijepes.2014.12.083
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发表时间：
2015-07-01
影响因子：
5.2
通讯作者：
Jordehi, A. Rezaee
中科院分区：
文献类型：
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作者：
Jordehi, A. Rezaee
在电力系统中，灵活交流输电系统(FACTS)装置的最优布置是一个困难的优化问题。这是由于其离散性、多目标、多模式和约束性。在这样一个问题上找到近乎全球的解决方案是非常困难的。头脑风暴优化算法是受人类头脑风暴过程启发而产生的一种新的启发式优化算法。本文利用BSOA来寻找FAD设备的最优位置和设置。FACTS装置采用静止无功补偿器(SVC)和可控串补装置(TCSC)。FAD的分配问题被描述为一个多目标问题，其目标是改善电压分布、最小化过载和最小化损耗。将BSOA应用于IEEE 57节点系统的FAD分配问题，结果表明该方法在解决TCSC和SVC机组的FAD分配问题上具有较高的效率。与粒子群优化(PSO)、遗传算法(GA)、差分进化(DE)、模拟退火法(SA)、遗传算法与模式搜索(GA-PS)、回溯搜索算法(BSA)、重力搜索算法(GSA)和无性繁殖优化(ARO)相比，BSOA具有更好的电压分布和更低的损耗。这项研究的结果可供电力系统决策者使用，以便在发生事故时建立更好的电压分布和更低的电压偏差。(C)2015爱思唯尔有限公司。保留所有权利。
In electric power systems, finding optimal location and setting of flexible AC transmission system (FACTS) devices represents a difficult optimisation problem. This is due to its discrete, multi-objective, multi-modal and constrained nature. Finding near-global solutions in such a problem is very demanding. Brainstorm optimisation algorithm (BSOA) is a novel promising heuristic optimisation algorithm inspired by brainstorming process in human beings. In this paper, BSOA is employed to find optimal location and setting of FAD'S devices. Static var compensators (SVC's) and thyristor controlled series compensators (TCSC's) are used as FACTS devices. FAD'S allocation problem is formulated as a multi-objective problem whose objectives are voltage profile enhancement, overload minimisation and loss minimisation. The results of applying BSOA to FAD'S allocation problem in IEEE 57 bus system demonstrate its high efficacy in solving this problem both with TCSC and SVC units. BSOA leads to better voltage profile and lower losses than particle swarm optimisation (PSO), genetic algorithm (GA), differential evolution (DE), simulated annealing (SA), hybrid of genetic algorithm and pattern search (GA-PS), backtracking search algorithm (BSA), gravitational search algorithm (GSA) and asexual reproduction optimisation (ARO). The findings of this research can be used by power system decision makers in order to establish a better voltage profile and lower voltage deviations during contingencies. (C) 2015 Elsevier Ltd. All rights reserved.