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Novel power flow problem solutions method’s based on genetic algorithm optimization for banks capacitor compensation using an fuzzy logic rule bases for critical nodal detections

机译:基于遗传算法优化的新颖潮流问题解决方案方法,用于模糊电容器规则库的电容器组补偿,用于临界节点检测

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摘要

The Reactive power flow’s is one of the most electrical distribution systems problem wich have great of interset of the electrical network researchers, it’s cause’s active power transmission reduction, power losses decreasing, and the drop voltage’s increase. In this research we described the efficiency of the FLC-GAO approach to solve the optimal power flow (OPF) combinatorial problem. The proposed approach employ tow algorithms, Fuzzy logic controller (FLC) algorithm for critical nodal detection and gentic algorithm optimization (GAO) algorithm for optimal seizing capacitor.GAO method is more efficient in combinatory problem solutions. The proposed approach has been examined and tested on the standard IEEE 57-bus the resulats show the power loss minimization denhancement, voltage profile, and stability improvement. The proposed approach results have been compared to those that reported in the literature recently. The results are promising and show the effectiveness and robustness of the proposed approach.
机译:无功功率流是电网研究人员之间相互影响最大的配电系统问题之一,它导致有功功率传输减少,功率损耗减少以及压降增加。在这项研究中,我们描述了FLC-GAO方法解决最佳功率流(OPF)组合问题的效率。该方法采用拖曳算法,用于临界节点检测的模糊逻辑控制器(FLC)算法和用于优化捕获电容器的遗传算法优化(GAO)算法。GAO方法在组合问题解决方案中效率更高。该提议的方法已经在标准IEEE 57总线上进行了测试和测试,结果表明功率损耗最小化,电压分布和稳定性得到了提高。拟议的方法结果已与最近文献报道的方法进行了比较。结果是有希望的,并表明了该方法的有效性和鲁棒性。

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