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A Novel Method Based on PPSO for Optimal Placement and Sizing of Distributed Generation

机译:一种基于PPSO的新方法,用于最佳放置和大小分布式生成

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Energy loss minimization, voltage profile improvement, and increasing reliability of the power system are the prominent advantages of distributed generation (DG) unit's integration in distribution systems. Therefore, optimal placement and sizing of DG become a critical issue. This article proposes a recently developed adaptive particle swarm optimization (PSO) algorithm known as phasor particle swarm optimization (PPSO) to solve the problem of optimal placement and sizing of DG units in radial distribution networks. The PPSO algorithm is based on modeling the particle control parameters with a phase angle ( θ ) transforming standard PSO into a self‐adaptive and parametric independent metaheuristic optimization algorithm. The main objective is to minimize the energy loss in the network considering various technical constraints and load variations. In particular, the proposed approach is implemented on a practical radial distribution feeder located at the Haripur area in Pakistan taking into account the hourly actual load values. Comparison of the devised PPSO algorithm with other techniques in the literature was done via an IEEE 33 bus test system. The results obtained by the proposed PPSO algorithm show that it enables a significant reduction in power loss for all the analyzed cases, and outperforms many of other solution techniques applied for the same problem. ? 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
机译:能量损失最小化,电压曲线的改善和功率系统的可靠性提高是分布式生成(DG)单元在分配系统中的集成的显着优势。因此,DG的最佳放置和大小成为关键问题。本文提出了一种最近开发的自适应粒子群优化(PSO)算法,称为相粒子群优化(PPSO),以解决径向分布网络中DG单位的最佳位置和尺寸的问题。 PPSO算法是基于对粒子控制参数建模的相位角度(θ),将标准PSO转换为自适应和参数独立的元磁性优化算法。主要目的是考虑各种技术限制和负载变化的网络中的能源损失。特别是,考虑到每小时实际的负载值,该方法是在位于巴基斯坦哈里浦尔地区的实用径向分配馈线上实施的。通过IEEE 33总线测试系统完成了设计的PPSO算法与文献中其他技术的比较。提出的PPSO算法获得的结果表明,它可以显着降低所有分析情况的功率损失,并且胜过用于同一问题的许多其他解决方案技术。 ? 2019年日本电气工程师研究所。由John Wiley&amp出版Sons,Inc。

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