Abstract In the present work, Binary Particle Swarm Optimization (BPSO) based optimal re-configuration for balanced and unbalanced radial distribution networks using Affine Arithmetic (AA), with uncertainty in generation and load, is proposed to minimize the system losses. An expression for three phase real affine power loss is derived with partial deviations of real power loss in lines with respect to power injections in other buses and also with respect to power injections in other phases in case of unbalanced distribution systems. The major contribution of the present work is the application of AA based optimal network reconfiguration, to both balanced and unbalanced radial distribution networks with uncertainty. The proposed method is tested on IEEE 16, 33, 85 and 119 bus balanced distribution systems and an unbalanced 123 bus system with Distributed Generation (DG) connected at some buses. The optimal loss intervals obtained by the proposed method are compared with that obtained by Interval Arithmetic (IA) and Monte Carlo (MC) simulations based methods. The simulation results show that proposed AA based analysis gives an optimal reconfiguration, for both balanced and unbalanced radial distribution systems with uncertainty as compared to existing IA based method.

中文翻译：

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一种新的基于仿射算术的优化网络重构，以使用二元粒子群优化将配电系统中的损失降至最低，同时考虑不确定性

摘要 在目前的工作中，提出了基于二元粒子群优化 (BPSO) 的平衡和不平衡径向配电网络的最优重构，使用仿射算术 (AA)，在发电和负载方面具有不确定性，以最大限度地减少系统损失。三相实际仿射功率损耗的表达式是通过线路实际功率损耗的部分偏差推导出来的，这些偏差相对于其他母线中的功率注入以及不平衡配电系统情况下其他相中的功率注入。目前工作的主要贡献是将基于 AA 的最优网络重构应用于具有不确定性的平衡和不平衡径向配电网络。所提出的方法在 IEEE 16、33、85 和 119 母线平衡配电系统和非平衡 123 母线系统，在某些母线上连接了分布式发电 (DG)。将所提出的方法获得的最佳损失区间与基于区间算术 (IA) 和蒙特卡罗 (MC) 模拟的方法获得的损失区间进行比较。仿真结果表明，与现有的基于 IA 的方法相比，所提出的基于 AA 的分析为具有不确定性的平衡和不平衡径向分布系统提供了最佳重构。