基于相关性分析和生成对抗网络的电网缺失数据填补方法

A power system missing data filling method based on correlation analysis and generative adversarial network

城市电网新型电力系统中多元资源增多,数据采集难度加大,导致数据随机缺失率升高,难以满足精细化分析决策需求。为解决新型电力系统中配网量测数据在采集与传输过程中频发的缺失问题,文中提出一种基于波动互相关分析(fluctuation cross-correlation analysis,FCCA)算法和生成对抗网络(generative adversarial network,GAN)的电网缺失数据填补方法。首先,融合FCCA算法提出强相关性电网数据多维特征提取方法;其次,基于核主成分分析(kernel principal component analysis,KPCA)对多维特征数据集进行降维处理;最后,设计改进型GAN结构,融合电网数据多维特征对低维向量进行重构,实现缺失数据填补。算例采用真实电网数据进行算法验证,并在某城市电网试运行。结果表明,所提方法比传统数据填补方法具有更高填补精度。因此,在新型电力系统中量测数据连续缺失和缺失量较大的情况下,融合强相关性特征进行数据填补,对提升量测数据的完整性和可用性有明显优势。

In the novel power system of urban grid,the multiple resources increase and the data collection becomes more difficult,which lead to a higher random missing data rate.It is difficult to meet the demand for refined analysis and decision making.For the frequent missing data problem in the distribution network,a new missing data filling method for power systems based on fluctuation cross-correlation analysis(FCCA)and generative adversarial network(GAN)is proposed in this paper.Firstly,a multi-dimensional feature extraction method for strongly correlated grid data is proposed by fusing FCCA.Secondly,based on kernel principal component analysis(KPCA),the multi-dimensional feature dataset is dimensionally reduced.Finally,an improved GAN structure is designed,which integrates multi-dimensional features of power grid equipment data to reconstruct low dimensional vectors.The missing data is accurately filled in,and the integrity and availability of the new power system measurement data is improved.The algorithm is validated using real grid data,and the proposed method is also tested in a city grid.The results show that the proposed method has higher filling accuracy than the traditional data filling methods.Therefore,it is conformed that in the case of continuous and significant data environment,integrating strong correlation features for data filling has significant advantages in improving the integrity and availability of measurement data.