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

机译:基于粒子群算法和遗传算法的配电网功耗最小化:在阿尔及利亚孤立电网中的应用

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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:这项工作的目的是应用随机优化方法(例如粒子群优化(PSO)和遗传算法(GA))来确定分布式发电机(DG)的位置并最大程度地降低配电网络中的功率损耗,同时考虑到以下因素:说明分布式可再生能源的整合。因此,进行了与文献中提出的先前技术的比较研究。研究的第一步是将提议的方法应用于标准IEEE测试系统,例如:IEEE 12总线和IEEE 69总线,以验证计算程序。第二步,在来自阿尔及利亚网络的真实隔离配电系统上测试了所提出的方法。最大限度地减少有功损耗取决于可再生能源的渗透率,因此本文要采取一些约束措施。确实,该研究的目的不仅是使使用PSO和GA的有源损耗最小化,而且还通过改善电压曲线来增强电压稳定性。

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