电力电网故障检测与线路识别的机器学习方法系统评估
A Systematic Evaluation of Machine Learning Methods for Fault Detection and Line Identification in Electrical Power Grids
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中文总结 AI 辅助
本研究首次在10毫秒关键时间窗内系统评估多种机器学习模型用于电网故障检测与故障线路定位，最优模型F1分数达0.991±0.018，处理时间0.342毫秒。
中文摘要 AI 辅助
可再生能源并入电网给故障检测和电网恢复机制的协调带来了复杂挑战。基于静态规则和预定义阈值运行的传统继电保护系统不足以应对这些挑战，尤其是在检测和隔离短路等故障方面。因此，应用于电网保护的传统方法在故障检测中常常无法达到最优性能，特别是在遵守安全标准和选择性限制损害方面。近期研究表明，基于机器学习（ML）的方法可以有效解决这些问题；然而，电网配置和分析窗口的差异阻碍了一致的比较评估。在本研究中，我们首次在10毫秒测量间隔内——这是实时运行可行性的关键时间框架——评估了多种ML模型在检测电气故障和定位故障输电线路方面的有效性。最有效的模型达到了0.991±0.018的F1分数，并展示了0.342毫秒±0.509毫秒的处理时间。
英文摘要
The integration of renewable energy sources into the electrical grid introduces complex challenges in fault detection and coordination of grid recovery mechanisms. Traditional relay protection systems, which operate based on static rules and predefined thresholds, are inadequate for addressing these challenges, particularly in detecting and isolating faults such as short circuits. Consequently, the conventional methodologies applied to electrical network protection frequently fail to achieve optimal performance in fault detection, especially in terms of adherence to safety standards and the selective limitation of damage. Recent research indicates that machine learning (ML)-based approaches can effectively tackle these issues; however, variations in grid configurations and analysis windows have impeded consistent comparative assessments. In this study, we assess the efficacy of various ML models in detecting electrical faults and pinpointing defective transmission lines within a 10 ms measurement interval - a critical time-frame for real-time operational viability, for the first time. The most effective model attained an F1 score of 0.991 +/- 0.018 and demonstrated a processing time of 0.342ms +/- 0.509ms.
发表机构
- Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡大学）
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