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DG allocation and reconfiguration in distribution systems by metaheuristic optimisation algorithms: a comparative analysis

机译:基于启发式优化算法的配电系统DG分配与重新配置:比较分析

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Using distributed generation (DG) units is a viable strategy for improving the characteristics of electric distribution systems, in terms of power loss, voltage profile and power congestion. Finding optimal location and setting of DG's is referred to as DG allocation problem and is typically formulated as an optimisation problem which is solved by metaheuristic optimisation algorithms. In this paper, the performance of four metaheuristic optimisation algorithms, including particle swarm optimisation (PSO), grey wolf optimisation (GWO), backtracking search algorithm (BSA) and whale optimisation algorithm (WOA) in solving DG allocation problem and also in solving simultaneous reconfiguration and DG allocation problem have been compared. The simulations have been done for six different scenarios. The results indicate the outperformance of GWO in most of the cases.
机译:使用分布式发电(DG)单元是一种改善配电系统特性(在功率损耗,电压曲线和功率拥挤方面)的可行策略。找到DG的最佳位置和设置被称为DG分配问题,通常被表述为通过亚启发式优化算法解决的优化问题。在本文中,包括粒子群优化(PSO),灰太狼优化(GWO),回溯搜索算法(BSA)和鲸鱼优化算法(WOA)在内的四种元启发式优化算法在解决DG分配问题以及同时求解问题上的性能比较了重新配置和DG分配问题。针对六种不同情况进行了仿真。结果表明,在大多数情况下,GWO的性能均优于后者。

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