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首页> 外文期刊>International journal of electrical power and energy systems >Optimal allocation of capacitor banks in radial distribution systems for minimization of real power loss and maximization of network savings using bio-inspired optimization algorithms
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Optimal allocation of capacitor banks in radial distribution systems for minimization of real power loss and maximization of network savings using bio-inspired optimization algorithms

机译:使用生物启发式优化算法,在径向配电系统中优化电容器组的分配,以最大程度地减少实际功率损耗并最大程度地节省网络

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摘要

In this paper, two new algorithms are implemented to solve optimal placement of capacitors in radial distribution systems in two ways that is, optimal placement of fixed size of capacitor banks (Variable Locations Fixed Capacitor banks-VLFQ) and optimal sizing and placement of capacitors (Variable Locations Variable sizing of Capacitors-VLVQ) for real power loss minimization and network savings maximization. The two bio-inspired algorithms Bat Algorithm (BA) and Cuckoo Search (CS): search for all possible locations in the system along with the different sizes of capacitors, in which the optimal sizes of capacitor are chosen to be standard sizes that are available in the market. To check the feasibility, the proposed algorithms are applied on standard 34 and 85 bus radial distribution systems. And the results are compared with results of other methods like Particle Swarm Optimization (PSO), Harmonic Search (HS), Genetic Algorithm (GA), Artificial Bee Colony (ABC), Teaching Learning Based Optimization (TLBO) and Plant Growth Simulation Algorithm (PGSA), as available in the literature. The proposed approaches are capable of producing high-quality solutions with good performance of convergence. The entire simulation has been developed in MATLAB R2010a software. (C) 2015 Elsevier Ltd. All rights reserved.
机译:本文采用两种新算法以两种方式解决径向分布系统中电容器的最佳放置问题,即电容器组固定尺寸的最佳放置(Variable Locations Fixed Capacitor bank-VLFQ)和电容器的最佳尺寸和放置(可变位置可变大小的电容器(VLVQ),可将实际功耗降至最低,并最大限度地节省网络。两种受生物启发的算法,即蝙蝠算法(BA)和布谷鸟搜索(CS):搜索系统中所有可能的位置以及不同尺寸的电容器,其中将电容器的最佳尺寸选择为可用的标准尺寸在市场上。为了检验可行性,将所提出的算法应用于标准的34和85母线径向分配系统。并将结果与​​其他方法的结果进行比较,例如粒子群优化(PSO),谐波搜索(HS),遗传算法(GA),人工蜂群(ABC),基于教学学习的优化(TLBO)和植物生长模拟算法( PGSA),如文献中所述。所提出的方法能够产生具有良好收敛性能的高质量解决方案。整个仿真已在MATLAB R2010a软件中开发。 (C)2015 Elsevier Ltd.保留所有权利。

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