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Distribution power loss minimization using Particle Swarm Optimization and genetic algorithms: Application on Algerian isolated grid

机译:使用粒子群优化和遗传算法的分配功率损耗最小化:在阿尔及利亚孤立网格上的应用

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The objective of this work is the application of stochastic methods of optimization as Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) in order to locate the distributed generators (DG) placement and minimize the power loss in the electrical distribution network, taking into account the integration of distributed renewable generation sources. Thus, a comparative study with previous techniques proposed in literature is performed. The first step of the study is to apply the proposed methodology on standard IEEE test systems such as: IEEE 12-bus and IEEE 69-bus to validate the calculation program. In a second step, the proposed approach is tested on a real isolated distribution system from the Algerian network. Minimizing the active losses is subject to several constraints which are undertaken in this paper, depending on the penetration rate of the renewable source. Indeed, the aim of the study is not only minimizing active loss using PSO and GA, but also enhancing voltage stability by improving the voltage profile.
机译:这项工作的目的是在粒子群优化(PSO)和遗传算法(GA)中,以定位分布式发电机(DG)放置并最大限度地减少电气分配网络中的功率损耗,以应用随机优化的应用算帐分布式可再生生成源的集成。因此,进行了在文献中提出的先前技术的对比研究。该研究的第一步是在标准IEEE测试系统上应用所提出的方法,例如:IEEE 12-总线和IEEE 69-BUS,以验证计算程序。在第二步中,在来自阿尔及利亚网络的真实隔离分配系统上测试了所提出的方法。最小化主动损失受到本文中进行的若干约束的影响,这取决于可再生源的渗透率。实际上,该研究的目的不仅最小化了使用PSO和GA的主动损失,而且通过改善电压曲线来提高电压稳定性。

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