论文标题
风能场景使用图形卷积生成对抗网络生成
Wind Power Scenario Generation Using Graph Convolutional Generative Adversarial Network
论文作者
论文摘要
产生风能场景对于研究与电网相互关联的多个风电场的影响非常重要。我们通过利用GAN在不使用统计建模的情况下利用GAN生成大量现实场景来开发图形卷积生成对抗网络（GCGAN）方法。与现有的基于GAN的风能数据生成方法不同，我们设计了GAN的隐藏层以匹配基本的空间和时间特征。我们主张使用图滤波器嵌入多个风电场之间的空间相关性，以及一维（1D）卷积层代表时间特征过滤器。提出的图形和特征滤波器设计显着降低了GAN模型的复杂性，从而提高了训练效率和计算复杂性。使用来自澳大利亚的实际风能数据的数值结果表明，所提出的GCGAN产生的场景比其他基于GAN的输出更现实的空间和时间统计。
Generating wind power scenarios is very important for studying the impacts of multiple wind farms that are interconnected to the grid. We develop a graph convolutional generative adversarial network (GCGAN) approach by leveraging GAN's capability in generating large number of realistic scenarios without using statistical modeling. Unlike existing GAN-based wind power data generation approaches, we design GAN's hidden layers to match the underlying spatial and temporal characteristics. We advocate the use of graph filters to embed the spatial correlation among multiple wind farms, and a one-dimensional (1D) convolutional layer to represent the temporal feature filters. The proposed graph and feature filter design significantly reduce the GAN model complexity, leading to improvements in training efficiency and computation complexity. Numerical results using real wind power data from Australia demonstrate that the scenarios generated by the proposed GCGAN exhibit more realistic spatial and temporal statistics than other GAN-based outputs.
