电气传动2026，Vol.56Issue(9)：56-63,8.DOI:10.19457/j.1001-2095.dqcd26623
基于CWGAN-GP的新型配网源荷场景生成方法
Source and Load Scenario Generation for Novel Distribution Networks Based on CWGAN-GP
摘要
Abstract
A method for generating source and load scenarios of novel distribution networks based on conditional Wasserstein generative adversarial network with gradient penalty(CWGAN-GP)was proposed to address the issues of training instability,mode collapse,and insufficient controllability in traditional generative adversarial networks(GANs)in source and load scenario generation for novel power system.The theoretical foundation of CWGAN-GP was first discussed,including the advantages of Wasserstein distance and the gradient penalty mechanism,as well as the conditional control capabilities of conditional generative adversarial networks.By optimizing the network structure and training strategy,a CWGAN-GP model for source and load scenario generation was constructed.To comprehensively evaluate the quality of the generated scenarios,a multi-dimensional evaluation system was developed,which includes the cumulative distribution function(CDF),Fréchet inception distance(FID),and mean absolute percentage error(MAPE).The results of the case study demonstrate that the proposed method significantly outperforms traditional methods in terms of FID and MAPE.The generated load and photovoltaic output time-series curves are highly consistent with real data in terms of periodicity and fluctuation characteristics,validating the effectiveness and robustness of the model.关键词
源荷场景生成/条件Wasserstein生成对抗网络-梯度惩罚/Wasserstein距离/梯度惩罚/条件GAN/数据驱动Key words
source and load scenario generation/conditional Wasserstein generative adversarial network with gradient penalty(CWGAN-GP)/Wasserstein distance/gradient penalty(GP)/conditional generative adversarial network(GAN)/data-driven分类
信息技术与安全科学引用本文复制引用
吴霜,刘汇川,翟晓萌,仓敏,王静怡,章兴睿..基于CWGAN-GP的新型配网源荷场景生成方法[J].电气传动,2026,56(9):56-63,8.基金项目
国家电网有限公司科技项目(J2024010) （J2024010）
