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Distribution Systems Reconfiguration Using Ant Colony Optimization and Harmony Search Algorithms

机译:使用蚁群优化和和谐搜索算法的配电系统重构

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One objective of the feeder reconfiguration problem in distribution systems is to minimize the distribution network total power loss for a specific load. For this problem, mathematical modeling is a non-linear mixed integer problem that is generally hard to solve. This article proposes two heuristic algorithms inspired from natural phenomena to solve the network reconfiguration problem: (1) "real ant-behavior-inspired" ant colony optimization implemented in the hyper cube framework and (2) the "musician behavior-inspired" harmony search algorithm. The optimization problem is formulated taking into account the operational constraints of distribution systems. A 32-bus system and a 118-bus distribution were selected for optimizing the configuration to minimize the losses. The results of reconfiguration using the proposed algorithms show that both of them yield the optimum configuration with minimum power loss for each case study: however, the harmony search required shorter simulation time but more practice of the iterative process than the hyper cube-ant colony optimization. Implementing the ant colony optimization in the hyper cube framework resulted in a more robust and easier handling of pheromone trails than the standard ant colony optimization.
机译:配电系统中馈线重新配置问题的一个目标是最大程度地降低特定负载下配电网络的总功率损耗。对于此问题,数学建模是通常很难解决的非线性混合整数问题。本文提出了两种受自然现象启发的启发式算法来解决网络重新配置问题:(1)在超立方体框架中实现的“真实蚂蚁行为启发”蚁群优化,以及(2)“音乐家行为启发”和谐搜索算法。考虑分配系统的运行约束条件来制定优化问题。选择32总线系统和118总线配电来优化配置,以最大程度地减少损失。使用提出的算法进行重新配置的结果表明,对于每种案例研究,它们都可以在最小功率损耗的情况下产生最优配置:但是,和声搜索比超级立方体蚁群优化需要更短的仿真时间,但是需要更多的迭代过程实践。 。与标准的蚁群优化相比,在超立方体框架中实施蚁群优化可以使信息素踪迹更强大,更容易处理。

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