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Optimal allocation and sizing of multiple distributed generators in distribution networks using a novel hybrid particle swarm optimization algorithm

机译:一种使用新型混合粒子群优化算法在配电网中多分布式发电机的优化分配和大小

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Optimal Integration of Distributed Generation (DG) in distribution grid is one of the important and effective options. Optimal allocation with a suitable sizing of DG units play efficient role in reducing operating cost and power losses in additional to improving voltage stability which are considered in optimization objective function. In this paper, a new effective and powerful optimization algorithm is produced. A novel hybrid particle swarm (HPSO) with Quasi-Newton (QN) algorithm is proposed to solve the problem of DG location and sizing distribution systems satisfying the operation constraints. In this paper, two stages are introduced. First, the loss sensitivity analysis is employed to select the most appropriate candidate DG placement. Then, a novel hybrid particle swarm optimization (HPSO) algorithm is implemented to find optimal sizing of DGs and their settings from the selected buses. The proposed algorithm has been tested on 33-bus, and 69-bus IEEE standard radial distribution systems under multi distributed generator types. In order to validate the proposed approach, the obtained results have been compared with other algorithms such as Genetic Algorithm (GA), Particle Swarm Algorithm (PSO), Novel combined Genetic Algorithm and Particle Swarm Optimization (GA/PSO), Simulation Annealing Algorithm (SA), and Bacterial Foraging Optimization Algorithm (BFOA). The numerical results have been proved the capability with good performance of the proposed approach to find the optimal solutions. Numerical results have been obtained by MATLAB package.
机译:分布式网格中分布式生成(DG)的最佳集成是重要且有效的选项之一。利用DG单位的合适尺寸的最佳分配在额外降低运营成本和功率损耗方面发挥有效作用,以提高在优化目标函数中考虑的电压稳定性。在本文中,产生了一种新的有效和强大的优化算法。提出了一种新的混合粒子群(HPSO)与准牛顿(QN)算法,以解决满足操作约束的DG位置和尺寸分布系统的问题。在本文中,介绍了两个阶段。首先,采用损耗敏感性分析来选择最合适的候选DG放置。然后,实现了一种新型混合粒子群优化(HPSO)算法,以找到从所选总线的DGS和其设置的最佳尺寸。在多分布式发电机类型下,所提出的算法已经在33柱和69母线IEEE标准径向分布系统上进行了测试。为了验证所提出的方法,已获得的结果与其他算法(GA),粒子群算法(PSO),新颖组合遗传算法和粒子群优化(GA / PSO),仿真退火算法( SA)和细菌觅食优化算法(BFOA)。数值结果已被证明具有良好性能的能力,即确定最佳解决方案的方法。 Matlab包已经获得了数值结果。

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