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Comparison of sGA and SEGA Methods to Solve the Problem of Power Generation and Power Losses on Distributed Generating Systems

机译:SGA和SEGA方法对分布式发电系统发电和功率损耗问题的比较

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This paper introduces a new algorithm to solve the problem of power flow optimization. This algorithm is the Spontaneous Evolutionary Genetic Algorithm (SEGA). SEGA is a combination of Neural Network (NN) and Standard Genetic Algorithm (sGA). SEGA conducts individual selection in a population to get the best results. This individual is a representation of each power station in an electrical network system, because the generator is the object to be optimized. SEGA is different from the previous generation of SGA Standard Genetic Algorithm. SEGA generates new populations (excluding the main population), to get more optimal results. Crossover, mutation and recombination are the selection algorithms used in SEGA, to obtain individual or more optimal results. In this simulation is used IEEE 57 bus plant, as well as compared the quality of the solution between the proposed algorithms with sGA. With this algorithm proven that, SEGA able to improve result (Fitness) from sGA. By using SEGA, the cost of generation on an IEEE 57 bus system is cheaper at 110 $ / hr compared to using sGA.
机译:本文介绍了一种解决电流优化问题的新算法。该算法是自发进化遗传算法(SEGA)。 SEGA是神经网络(NN)和标准遗传算法(SGA)的组合。 SEGA在人口中进行个人选择以获得最佳结果。该个人是电网系统中每个电站的表示,因为发电机是要优化的对象。 SEGA与前一代SGA标准遗传算法不同。 SEGA产生新的人群(不包括主要人口),以获得更优化的结果。交叉,突变和重组是SEGA中使用的选择算法,以获得个体或更高的最佳结果。在该模拟中,使用IEEE 57总线工厂,以及与SGA的提出算法之间的解决方案的质量进行了比较。通过该算法证明,SEGA能够从SGA改善结果(健身)。通过使用SEGA,与使用SGA相比,IEEE 57总线系统上的发电成本与110美元/小时更便宜。

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