对比多种机器学习模型，提升电网电能质量事件分类准确率
Classification of power quality events in the transmission grid: comparative evaluation of different machine learning models
- 采用Cubic SVM与XGBoost模型进行电能质量事件根因分类
- 最佳模型在真实电网数据上达到高准确率，但对ABC/ABCG故障易混淆
- 成果将部署于土耳其输电网监控系统，助力智能运维
针对电力系统电能质量事件的根因自动分类对电网管理至关重要。本文基于大量实验，对比评估了多种机器学习模型在电能质量事件分类任务中的表现。实验使用不同机器学习库进行训练与验证，结果表明，Cubic SVM和XGBoost表现最优。误差分析显示，两类模型性能下降的主要原因在于将三相短路（ABC）故障误判为四相短路（ABCG）故障，或反之。最终，表现最佳的模型将被集成至土耳其国家输电网大规模电能质量与电网监测系统的事件分类模块中。
原文摘要 · Abstract (English)
Automatic classification of electric power quality events with respect to their root causes is critical for electrical grid management. In this paper, we present comparative evaluation results of an extensive set of machine learning models for the classification of power quality events, based on their root causes. After extensive experiments using different machine learning libraries, it is observed that the best performing learning models turn out to be Cubic SVM and XGBoost. During error analysis, it is observed that the main source of performance degradation for both models is the classification of ABC faults as ABCG faults, or vice versa. Ultimately, the models achieving the best results will be integrated into the event classification module of a large-scale power quality and grid monitoring system for the Turkish electricity transmission system.
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