一个大型的合成数据集用于电力传输网中的机器学习应用
Marc Gillioz1, Guillaume Dubuis2, Philippe Jacquod2,3
1School of Engineering, University of Applied Sciences and Arts of Western Switzerland HES-SO, 1951, Sion, Switzerland. marc.gillioz@hevs.ch.
Scientific data
|January 28, 2025
概括
研究人员创建了一个合成数据集用于电网分析,解决了缺乏现实数据的问题. 这使得对不断变化的能源网进行更快,实时的安全和稳定性评估成为可能.
科学领域:
- 电气工程 电气工程
- 计算科学 计算科学
- 数据科学数据科学数据科学
背景情况:
- 由于能源转型,电网面临着越来越多的运营挑战,在不稳定的条件下运行更接近极限.
- 实时评估电网安全性,稳定性和可靠性至关重要,但由于对历史运营数据的有限访问受到阻碍.
- 机器学习方法需要大量的数据,这对于现实世界的电力系统来说很难获得.
研究的目的:
- 介绍一种新的方法,用于生成电力传输网电力注入的大型合成数据集.
- 开发一种能够为网格分析创建任意大时间序列数据的算法.
- 提供统计验证的合成数据集,模仿现实世界电网运营数据.
主要方法:
- 使用电网拓 (线路接入) 和发电机信息 (位置,类型,容量) 来生成合成电力注入数据.
- 整合聚合的电力消耗数据,例如来自ENTSO-E.的国家负载数据.
- 将生成的合成数据集与实际现实数据进行统计验证.
主要成果:
- 为欧洲大陆电力传输电网模型生成了对电力注入的综合数据集.
- 开发的算法成功地创建了大规模的,统计学上具有代表性的时间序列数据.
- 合成数据表现出与现实世界电网运行数据可比的统计特性.
结论:
- 提出的方法为克服电网运营分析中的数据稀缺性提供了可行的解决方案.
- 合成数据集有助于开发和测试快速的实时计算方法来评估电网稳定性.
- 这项工作支持在正在进行的能源转型的背景下推进电网可靠性和安全性评估.
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