面向电力系统机器学习的故障与事件仿真数据集
A simulation based dataset of faults and events for machine learning in power systems
AI总结：
针对电力系统缺乏开放故障事件仿真数据集的问题，提出EvEMTBench电磁暂态仿真数据集，用于机器学习模型的训练、微调与基准测试，支持多种电力系统相关检测任务。
AI中文摘要：
基于逆变器的可再生能源接入电网对传统电力系统保护提出挑战，基于机器学习的解决方案可利用现代智能电网的可用数据应对这些挑战。然而，缺乏开放数据集阻碍了不同方法及其结果的可复现性与公平比较，进而阻碍了进一步进展。因此，本文提出EvEMTBench，这是一个通过电磁暂态仿真生成的故障与事件合成数据集。电力系统仿真的物理合理性通过将仿真参数与已建立的文献进行验证，并提供仿真过程的全面文档来确保。该数据集设计用于训练、微调及基准测试机器学习模型，它提供了9600Hz同步波头电压与电流测量值，涵盖多种拓扑结构和电压等级。包含的各类故障与运行事件使EvEMTBench可用于不同任务，如初期故障检测、故障定位或事件检测。
英文摘要：
The integration of inverter-based renewable energy sources into electric grids challenges conventional power system protection. Machine learning-based solutions can address these challenges by utilizing available data in modern smart grids. However, the lack of open datasets prevents reproducibility and fair comparisons between different approaches and their results, which hinders further progress. Therefore, this paper presents EvEMTBench, a synthetic dataset of faults and events generated using electromagnetic transient simulations. The physical plausibility of the power system simulation is ensured by validating the simulation parameters against established literature and providing comprehensive documentation of the simulation procedure. The dataset is designed for training, fine-tuning, and benchmarking machine learning models by providing synchronized point-on-wave voltage and current measurements at 9600 Hz across a diverse set of topologies and voltage levels. The inclusion of a wide range of fault and operating events allows the utilization of EvEMTBench for different tasks like incipient fault detection, fault localization, or event detection.
