使用集成分类器提高线路停电定位性能

中文摘要 AI 辅助

在许多情况下，系统中一条输电线路的停电可以通过监测另一条线路的潮流来定位，机器学习方法可用于区分不确定情况下的情况。本研究考察了各种集成分类器与单模型方法相比在线路停电定位性能上的提升。在案例研究中，基于线路停电分布因子（LODFs）和线路停电影响因子（LOIFs）这两个灵敏度因子，我们将使用贪婪最大覆盖问题（MCP）、高eta和随机选择这三种算法选择的观测输电线路（OTLs）处收集的测量数据的分类结果进行比较。我们发现，贪婪MCP算法选择的OTLs产生了最高的F1分数，并且集成分类器显著优于基础kNN分类器。在许多情况下，极端随机树装袋技术获得了最高的F1分数。所有结果在统计上都是显著的。

英文摘要

In many cases, the outage of one transmission line in a system can be localized by monitoring the power flow of another line, and machine learning methods can be used to distinguish the cases under uncertainty. In this study, we examine the improvements in line outage localization performance achieved by various ensemble classifiers compared to single-model methods. In the case studies, we compared the classification results with measurement data collected at observed transmission lines (OTLs) selected using three algorithms, i.e, greedy maximum coverage problem (MCP), high-eta, and random selection, based on two sensitivity factors, i.e., line outage distribution factors (LODFs) and line outage impact factors (LOIFs). We found that the OTLs selected by the greedy MCP algorithm yielded the highest F1 score and the ensemble classifiers significantly outperformed a base kNN classifier. The extra-trees bagging technique achieved the highest F1 score in many instances. All the findings were statistically significant.

发表机构

• Dept. of Electrical and Computer Engineering（电气与计算机工程系）
• University of Texas at El Paso（德克萨斯大学埃尔帕索分校）

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