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K-means Algorithm And Mixture Distributions For Locating Faults In Power Systems

机译:电力系统故障定位的K-均值算法和混合分布

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Enhancement of power distribution system reliability requires of a considerable investment in studies and equipment, however, not all the utilities have the capability to spend time and money to assume it. Therefore, any strategy that allows the improvement of reliability should be reflected directly in the reduction of the duration and frequency interruption indexes (SAIFI and SAIDI).rnIn this paper, an alternative solution to the problem of power service continuity associated to fault location is presented. A methodology of statistical nature based on finite mixtures is proposed. A statistical model is obtained from the extraction of the magnitude of the voltage sag registered during a fault event, along with the network parameters and topology. The objective is to offer an economic alternative of easy implementation for the development of strategies oriented to improve the reliability from the reduction of the restoration times in power distribution systems.rnIn the application case for an application example in a power distribution system, the faulted zones were identified, having low error rates.
机译:要提高配电系统的可靠性,就需要在研究和设备上进行大量投资,但是,并非所有公用事业公司都有能力花费时间和金钱来承担这项工作。因此,任何能够提高可靠性的策略都应直接体现在减少持续时间和频率中断指标(SAIFI和SAIDI)上。rn本文提出了一种与故障定位相关的电力服务连续性问题的替代解决方案。提出了一种基于有限混合的统计性质方法。从故障事件期间记录的电压骤降幅度的提取以及网络参数和拓扑中获取统计模型。目的是为制定旨在简化配电系统以提高可靠性的策略提供经济可行的替代方案。在配电系统应用示例的应用案例中,故障区域被确定为具有低错误率。

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