基于粒子群算法和人工蜂群算法的供电系统无功电源优化配置

S. Kokin, V. Manusov, P. Matrenin
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引用次数: 2

摘要

通过解决无功补偿问题来降低现代供电系统中输电线路的有功损耗是一项重要的任务。补偿的效率取决于补偿单元在网络节点上的功率选择和位置。所考虑的任务是一个多目标、多因素的优化问题,求解该问题所需的时间随着任务维数的增加呈指数增长。在这种情况下,建议采用进化优化和群优化方法。本文应用了两种群体智能算法:人工蜂群优化算法和粒子群优化算法。本文特别关注了算法在实际中的应用,即解决无功电源的最佳配置问题。研究表明,在优化算法和优化问题模型之间使用专门开发的接口可以使研究人员快速应用随机优化算法。此外,还探讨了粒子群和蜂群的启发式算法参数对其有效性的影响。研究表明,无功补偿可使供电系统的有功损耗降低20%,必要设备的安装回收期为2 ~ 4年。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimal placement of reactive power sources in power supply systems, using particle swarm optimization and artificial Bees colony optimization
The reduction of active power losses in transmission lines of modern power supply systems by solving the problem of reactive power compensation is an important task. The efficiency of the compensation depends on the choice of powers and placement of compensation units in nodes of a network. The task considered is the multi-objective and multi-factor optimization problem, and the time required to solve it increases exponentially with the increase of the dimension of this task. Under these circumstances, it is advisable to use evolutionary and swarm optimization methods. In this paper, two swarm intelligence algorithms have been applied: Artificial Bees Colony optimization and Particle Swarm optimization. Particular attention has been devoted to applying the algorithms in practice, in this case, to solving the problem of the optimal placement of reactive power sources. It has been shown that using a specially developed interface between an optimization algorithm and a model of the optimization problem allows researchers to quickly apply stochastic optimization algorithms. In addition, the influence of the heuristic algorithms parameters of Particle Swarm and Bee Swarm on their effectiveness has been explored. It has been shown that the reactive compensation can reduce the active power losses in the power supply system up to 20%, and the payback period of the installation of the necessary equipment is 2 to 4 years.
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