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Voltage sag state estimation in power systems by applying genetic algorithms

机译:应用遗传算法的电力系统电压暂降状态估计

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This study presents an application of genetic algorithms to the solution of the voltage sag state estimation problem. Here, voltage sag state estimation is interpreted as the estimation of the number of voltage sags occurring at non-monitored buses by using the number of sags recorded at a limited number of monitored buses. This problem is formulated as an underdetermined system of equations. In this study, in order to streamline the solution process of this estimation problem, the Genetic Algorithm Matlab Toolbox® was used. The performance of the proposed methodology is assessed by means of case studies applied in the IEEE 24-bus reliability test system and in the IEEE 57-bus test system and is compared with the performance of integer linear programming methods.
机译:这项研究提出了遗传算法在解决电压暂降状态估计问题中的应用。在此,电压暂降状态估计被解释为通过使用在有限数量的受监控总线处记录的骤降的数量来估计在非受监控总线上发生的电压暂降的数量。这个问题被公式化为一个欠定的方程组。在本研究中,为了简化此估计问题的求解过程,使用了遗传算法MatlabToolbox®。通过在IEEE 24总线可靠性测试系统和IEEE 57总线测试系统中应用的案例研究评估了所提出方法的性能,并与整数线性编程方法的性能进行了比较。

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