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.

翻译：生成风电场景对于研究多个风电场互联接入电网的影响至关重要。我们开发了一种图卷积生成对抗网络（GCGAN）方法，利用GAN在不使用统计建模的情况下生成大量真实场景的能力。与现有基于GAN的风电数据生成方法不同，我们设计GAN的隐藏层以匹配潜在的时空特征。我们主张使用图滤波器来嵌入多个风电场之间的空间相关性，并使用一维（1D）卷积层来表征时间特征滤波器。所提出的图和特征滤波器设计显著降低了GAN模型的复杂度，从而提高了训练效率和计算复杂度。使用来自澳大利亚的真实风电数据进行数值实验的结果表明，与其他基于GAN的输出相比，所提出的GCGAN生成的场景展现出更真实的时空统计特性。