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首页> 外文期刊>Electric power systems research >Fault diagnosis in low voltage smart distribution grids using gradient boosting trees
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Fault diagnosis in low voltage smart distribution grids using gradient boosting trees

机译：使用渐变升压树的低压智能配电网格的故障诊断

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摘要

In this paper, a gradient boosting tree model is proposed to detect, identify and localize single-phase-to-ground and three-phase faults in low voltage (LV) smart distribution grids. The proposed method is based on gradient boosting trees and considers branch-independent input features to be generalizable and applicable to different grid topologies. Particularly, as it is shown, the method can be estimated in a specific grid topology and be employed in a different one. To test the algorithm, the method is evaluated in a simulated real LV distribution grid of Portugal. In this case study, different fault resistances, fault locations and hours of the day are considered. In detail, the algorithm is evaluated at eighteen fault resistance values between 0.1 and 1000 fl; similarly, nine fault locations are considered within each one of the 32 sectors of the grid and the faults are simulated across different hours of a day. The developed algorithm showed promising results in both out-of-sample branch and fault resistance data especially for fault detection, demonstrating a maximum fault detection error of 0.72%.
机译：在本文中，提出了一种梯度升压树模型来检测，识别和定位低电压（LV）智能分配网格中的单相对地和三相故障。该方法基于梯度升压树，并考虑分支独立的输入功能，以概括并适用于不同的网格拓扑。特别地，如图所示，该方法可以估计在特定的网格拓扑中，并在不同的网格拓扑中估计。为了测试算法，该方法在葡萄牙的模拟实际LV分布网中进行评估。在这种情况下，考虑不同的故障电阻，故障位置和时间的时间。详细地，算法在0.1和1000F1之间的十八个故障电阻值下进行评估;类似地，在网格的32个扇区中的每个扇区中的每个一个中考虑九个故障位置，并且在不同时间的时间内模拟故障。开发算法表现出有希望的导致采样外分支和故障电阻数据，特别是对于故障检测，展示了0.72％的最大故障检测误差。

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