电力系统保护与控制2026，Vol.54Issue(6)：11-21,11.DOI:10.19783/j.cnki.pspc.250608
基于时空卷积-注意力网络的分布式光伏缺失数据插补方法
Missing data imputation method for distributed photovoltaic systems based on spatial-attention temporal-convolution network
摘要
Abstract
Operational data of photovoltaic(PV)systems are often affected by equipment failure,communication interruption,and other objective factors,leading to widespread data missing issues and degraded data quality.Effective data restoration is an important foundation for achieving high-precision PV power forecasting and optimal power plant operation.Traditional data imputation methods struggle to effectively capture the complex spatiotemporal correlations in distributed PV data and exhibit limited adaptability to special scenarios such as missing meteorological data,resulting in unsatisfactory imputation accuracy.Therefore,a spatial-attention temporal-convolution network(SATCN)is proposed.A one-dimensional convolutional neural network is used to learn temporal dependencies,while a spatial attention network inspired by the self-attention mechanism of the Transformer is constructed as a spatial aggregator.By leveraging limited observable data,the proposed model fully exploits spatiotemporal dependencies to achieve high-quality restoration of data from multiple distributed PV clusters.In addition,a joint optimization training strategy is adopted,incorporating both observed value reconstruction and artificially masked data imputation tasks,thereby preventing the model from focusing solely on observed data while neglecting missing data imputation.Experimental results show that the proposed method has good imputation performance under various missing rates and achieves high-precision restoration of missing data without supervision.关键词
缺失数据插补/时空依赖/注意力机制/联合优化训练/分布式光伏Key words
missing data imputation/spatiotemporal dependence/attention mechanism/joint optimization training/distributed photovoltaic system引用本文复制引用
余思杪,许玉格,樊淼嘉,刘俊峰,曾君..基于时空卷积-注意力网络的分布式光伏缺失数据插补方法[J].电力系统保护与控制,2026,54(6):11-21,11.基金项目
This work is supported by the National Natural Science Foundation of China(No.62173148 and No.52377186). 国家自然科学基金项目资助(62173148,52377186) （No.62173148 and No.52377186）
广东省自然科学基金项目资助(2024A1515012428) （2024A1515012428）
