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Pareto Dominance-Based Multiobjective Optimization Method for Distribution Network Reconfiguration

机译:基于帕累托优势的配电网重构多目标优化方法

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摘要

With ever increasing deployment of automation and communication systems in smart grids, distribution network reconfiguration is becoming a viable solution for improving the operation of power grids. A novel hybrid optimization algorithm is proposed in this paper that determines Pareto frontiers, as the candidate solutions, for multiobjective distribution network reconfiguration problem. The proposed hybrid optimization algorithm combines the concept of fuzzy Pareto dominance with shuffled frog leaping algorithm (SFLA) to recognize optimal nondominated solutions identified by SFLA. The local search step of SFLA is also customized for power systems application so that it automatically creates and analyzes only the feasible and radial configurations in its optimization procedure, which significantly increases the convergence speed of the algorithm. Moreover, an adaptive reliability-based frog encoding is introduced that supervises the algorithm to concentrate on more reliable network topologies. The performance of the proposed method is demonstrated on a 136-bus electricity distribution network.
机译:随着智能电网中自动化和通信系统部署的不断增加,配电网络的重新配置正成为改善电网运行的可行解决方案。提出了一种新颖的混合优化算法,该算法将多目标配电网重构问题的帕累托边界确定为候选解。提出的混合优化算法将模糊帕累托优势与混洗蛙跳算法(SFLA)相结合,以识别由SFLA识别的最优非支配解。 SFLA的本地搜索步骤也针对电力系统应用进行了定制,因此它可以在其优化过程中自动创建并仅分析可行和径向的配置,从而大大提高了算法的收敛速度。此外,引入了基于自适应可靠性的青蛙编码,该青蛙监督该算法以集中于更可靠的网络拓扑。在136总线的配电网络上证明了该方法的性能。

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