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arXiv 2410.03365eess.SYcs.SY
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用于输电网机器学习应用的大型合成数据集

A large synthetic dataset for machine learning applications in power transmission grids

Marc Gillioz, Guillaume Dubuis, Philippe Jacquod

AI总结:

针对电网机器学习缺乏真实训练数据的问题,本文提出了一个欧洲大陆输电网的大型合成数据集,并开发了基于电网参数和聚合负荷数据生成任意大时间序列的算法,且数据集已通过真实数据统计验证。

摘要:

随着能源转型的推进,电网正快速演变。它们在日益波动的条件下越来越频繁地在接近其技术极限的状态下运行。因此,评估其运行安全性、稳定性和可靠性的快速、近乎实时的计算方法变得极为迫切。机器学习方法被提倡用于解决这一挑战,但它们是训练和测试数据的消耗大户,而真实电网的历史运行数据却难以获取,甚至根本无法访问。本文提出了一个欧洲大陆输电网模型中电力注入的大型合成数据集,并描述了为生成该数据集而开发的算法。该方法允许从电网知识——线路导纳以及发电机的位置、类型和容量——和聚合电力消耗数据(如 ENTSO-E 提供的国家负荷数据)中生成任意大的时间序列。所获得的数据集已通过真实数据进行了统计验证。

英文摘要:

With the ongoing energy transition, power grids are evolving fast. They operate more and more often close to their technical limit, under more and more volatile conditions. Fast, essentially real-time computational approaches to evaluate their operational safety, stability and reliability are therefore highly desirable. Machine Learning methods have been advocated to solve this challenge, however they are heavy consumers of training and testing data, while historical operational data for real-world power grids are hard if not impossible to access. This manuscript presents a large synthetic dataset of power injections in an electric transmission grid model of continental Europe, and describes the algorithm developed for its generation. The method allows one to generate arbitrarily large time series from the knowledge of the grid -- the admittance of its lines as well as the location, type and capacity of its power generators -- and aggregated power consumption data, such as the national load data given by ENTSO-E. The obtained datasets are statistically validated against real-world data.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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