用集成模型提升电网线路故障定位准确率
Increasing Line Outage Localization Performance with Ensemble Classifiers
- 采用集成分类器融合多模型预测结果
- 贪心最大覆盖算法选线使F1分数最高
- 适合电力系统故障诊断研究人员
在多数情况下，可通过监测另一条输电线路的潮流来定位某条线路的故障，机器学习方法可用于处理不确定性。本研究比较了多种集成分类器与单模型方法在输电线路故障定位中的表现。案例分析基于三种线路选择算法（贪心最大覆盖问题、高η值、随机选择）选取可观测输电线路（OTLs），并结合线路故障分布因子（LODF）和线路故障影响因子（LOIF）两个敏感度指标进行评估。结果显示，使用贪心最大覆盖算法选取的OTLs取得最高F1分数，且集成分类器显著优于基础kNN分类器。在多个实例中，额外树袋装技术（Extra-Trees Bagging）表现最佳。所有结果均具统计显著性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。