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New Multi Objective Approach for Optimal Network Reconfiguration in Electrical Distribution Systems Using Modified Ant Colony Algorithm

机译:改进蚁群算法的配电系统最优网络重构的多目标新方法

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The losses in networks of Beninese Electrical Energy Company (SBEE) are very high and therefore constitute a concern for the operators. This work consisted in finding an optimal topology of a 41 nodes real network of SBEE by Modified Ant Colony Algorithms (MACA) in order to reduce the losses and ensure a continuous power supply to the customers in case of occurrence disturbances on any branch of this network. With technological breakthrough of Automation and Supervision Systems (SCADA), the operation of distribution networks can be ensured remotely in real time with the aim of minimizing losses, eliminating equipment overload and improving reliability. The criteria of technical performance improvement formulated under operating constraints are solved by Modified Ant Colony Algorithm (MACA) which is association of ant system and fuzzy logic on the Matlab platform. The best results obtained show the effectiveness and efficiency of this method. The SBEE's HVA networks can then be reconfigured automatically to significantly improve their continuity of supply and reliability. The improved results obtained after tests on a standard 33-nodes and a real 41 nodes networks show the robustness and accuracy of this MACA algorithm.
机译:贝宁电力公司(SBEE)的网络损失非常高,因此引起运营商的关注。这项工作包括通过改进的蚁群算法(MACA)找到SBEE的41个节点的真实网络的最佳拓扑,以减少损耗并确保在该网络的任何分支出现干扰时为客户提供连续的电源。随着自动化和监控系统(SCADA)的技术突破,可以实时远程确保配电网络的运行,以最大程度地减少损耗,消除设备过载并提高可靠性。通过改进的蚁群算法(MACA)解决了在运行约束条件下制定的技术性能改进标准,该算法是Matlab平台上的蚂蚁系统与模糊逻辑的关联。获得的最佳结果表明了该方法的有效性和效率。然后可以自动重新配置SBEE的HVA网络,以显着提高其供电的连续性和可靠性。经过在标准的33节点和实际的41节点网络上进行测试后获得的改进结果显示了该MACA算法的鲁棒性和准确性。

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