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A multi-objective genetic algorithm approach to optimal allocation of multi-type FACTS devices for power systems security

机译:一种多目标遗传算法对电力系统安全多型事实装置的最佳分配方法

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A multi-objective programming procedure is used for solving the problem of optimal allocation of flexible AC transmission systems (FACTS) devices in a power system. The evolutionary approach consists of a multi-objective genetic algorithm (MOGA), which is used to characterize the Pareto optimal frontier (non-dominated solutions) and to provide to decision makers and engineers insightful information about the trade-offs to be made. In this paper, two technical and economical objective functions are considered: maximization of system security and minimization of investment cost for FACTS devices. The optimization process is focused on three parameters: the location of FACTS in the network, their types and their sizes. For these proposals, we employed a hybrid software developed in Matlab/spl trade/ which uses the EUROSTAG/spl trade/ software for load flow calculations. The proposed procedures are successfully tested on an IEEE 14-bus power system for several numbers of FACTS devices.
机译:多目标编程程序用于解决电力系统中灵活AC传输系统(事实)设备的最佳分配问题。进化方法包括多目标遗传算法(MOGA),用于表征Pareto最佳边界(非主导解决方案),并为决策者和工程师提供有关待售权衡的富有洞察力的信息。在本文中,考虑了两个技术和经济的客观函数:系统安全的最大化和最小化事实设备的投资成本。优化过程专注于三个参数:网络中事实的位置,它们的类型及其大小。对于这些提案,我们雇用了在Matlab / SPL贸易中开发的混合动力软件/使用欧洲欧盟欧洲欧洲扶盟/拼接贸易/软件进行负载流计算。拟议的程序在IEEE 14总线电力系统上成功测试了几个事实设备。

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