基于梯度惩罚生成对抗网络的配电网缺失数据修复方法

吕朋蓬; 卜强生; 郭野; 罗飞

doi:10.19912/j.0254-0096.tynxb.2024-0160

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太阳能学报 ›› 2025, Vol. 46 ›› Issue (5) : 185-192. DOI: 10.19912/j.0254-0096.tynxb.2024-0160

基于梯度惩罚生成对抗网络的配电网缺失数据修复方法

吕朋蓬, 卜强生, 郭野, 罗飞

作者信息 +

METHOD FOR MISSING DATA IMPUTATION IN DISTRIBUTION NETWORK BASED ON GRADIENT PENALTY GENERATIVE ADVERSARIAL NETWORK

Lyu Pengpeng, Bu Qiangsheng, Guo Ye, Luo Fei

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文章历史 +

摘要

提出一种基于梯度惩罚生成对抗网络的配电网缺失数据修复方法。针对配电网数据特点设计生成器和判别器的结构及参数,并在生成对抗网络训练中引入梯度惩罚项以提高收敛性能。该方法仅以数据驱动,通过无监督学习理解数据中难以表征的高维、非线性特征,不需要配电网的具体拓扑结构建模,提高了数据修复方法的适用性。算例结果表明该方法与传统生成对抗网络相比具有更高的修复精度,在数据缺失比例为10%时,数据修复精度提高18.9%。

Abstract

With the rapid development of renewable energy such as wind power and photovoltaic, the measurement data of distribution networks present characteristics such as non-linearity, high-dimensionality, and time-variation, significantly increasing the difficulty of data imputation. This paper proposes a method for missing data imputation in distribution networks based on the gradient penalty generative adversarial network. The structure and parameters of the generator and discriminator are designed according to the characteristics of the distribution network data, and a gradient penalty term is introduced in the training of the generative adversarial network to improve convergence performance. This method is data-driven, understanding high-dimensional, non-linear features in the data that are difficult to characterize through unsupervised learning and does not require modeling of the specific topology of the distribution network, enhancing the applicability of the data restoration method. The results of the example show that this method has higher imputation accuracy compared to traditional generative adversarial networks. When the proportion of missing data is 10%, the accuracy of data repair is increased by 18.9%.

导出引用

吕朋蓬, 卜强生, 郭野, 罗飞. 基于梯度惩罚生成对抗网络的配电网缺失数据修复方法[J]. 太阳能学报. 2025, 46(5): 185-192 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0160

Lyu Pengpeng, Bu Qiangsheng, Guo Ye, Luo Fei. METHOD FOR MISSING DATA IMPUTATION IN DISTRIBUTION NETWORK BASED ON GRADIENT PENALTY GENERATIVE ADVERSARIAL NETWORK[J]. Acta Energiae Solaris Sinica. 2025, 46(5): 185-192 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0160

中图分类号： TM93

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