Generation of synthetic multi‐resolution time series load data
Generation of synthetic multi‐resolution time series load data
复制标题
生成合成多分辨率时间序列负载数据
DOI：
10.1049/stg2.12116
复制
发表时间：
2023
期刊：
影响因子：
2.3
通讯作者：
Kosut, Oliver
中科院分区：
文献类型：
--
作者：
Pinceti, Andrea;Sankar, Lalitha;Kosut, Oliver
大型数据集的可用性对于开发新的电力系统应用和工具至关重要；不幸的是，很少有人可以公开免费获得。作者设计了一个端到端生成框架，用于为传输网络创建合成总线级时间序列负载数据。该模型在跨越多年的超过 70 TB 同步相量测量的真实数据集上进行训练。利用主成分分析和条件生成对抗网络模型的组合，开发的方案允许以不同的采样率（每秒最多 30 个样本）生成数据，长度范围从几秒到几年不等。生成模型经过广泛测试，以验证它们是否正确捕获了实际负载的各种特征。最后，开发了一个名为 LoadGAN 的开源工具，研究人员可以通过图形界面访问经过充分训练的生成模型。
The availability of large datasets is crucial for the development of new power system applications and tools; unfortunately, very few are publicly and freely available. The authors designed an end‐to‐end generative framework for the creation of synthetic bus‐level time‐series load data for transmission networks. The model is trained on a real dataset of over 70 Terabytes of synchrophasor measurements spanning multiple years. Leveraging a combination of principal component analysis and conditional generative adversarial network models, the developed scheme allows for the generation of data at varying sampling rates (up to a maximum of 30 samples per second) and ranging in length from seconds to years. The generative models are tested extensively to verify that they correctly capture the diverse characteristics of real loads. Finally, an opensource tool called LoadGAN is developed which gives researchers access to the fully trained generative models via a graphical interface.