时序复合扩散网络驱动的电网数据恢复方法

严彦东; 李晨熙; 李士杰; 杨漾; 葛宇昊; 黄雨

doi:10.11999/JEIT250435

时序复合扩散网络驱动的电网数据恢复方法

doi: 10.11999/JEIT250435 cstr: 32379.14.JEIT250435

严彦东^1,,
李晨熙²,
李士杰³,
杨漾⁴,
葛宇昊⁴,
黄雨^5, ,

1.
北京大学计算机学院北京 100871
2.
电子科技大学信息与软件工程学院成都 610054
3.
中国南方电网有限责任公司广州 510000
4.
南方电网数字平台科技有限公司广州 510000
5.
北京大学软件工程国家工程研究中心北京 100871

基金项目: 中国南方电网有限责任公司科技项目(ZBKJXM20240174)

详细信息

作者简介:
严彦东：男，博士研究生在读，研究方向为表型数据、时序数据、多模态数据的生成与增强

李晨熙：男，本科在读，研究方向为扩散式生成模型、数据增强、数据恢复

李士杰：男，高级工程师，研究方向为高电压与绝缘技术

杨漾：女，高级工程师，研究方向为软件工程与体系结构

葛宇昊：男，中级工程师，研究方向为数据分析与人工智能

黄雨：男，研究员，研究方向为大数据管理、多模态数据分析、电网大数据、医学信息交叉

通讯作者:
黄雨　hy@pku.edu.cn

中图分类号: TN911.7; TP39
计量
- 文章访问数: 30
- HTML全文浏览量: 9
- PDF下载量: 1
- 被引次数: 0
出版历程
- 收稿日期: 2025-05-20
- 修回日期: 2025-10-11
- 录用日期: 2025-11-03
- 网络出版日期: 2025-11-12

Power Grid Data Recovery Method Driven by Temporal Composite Diffusion Networks

YAN Yandong^1
,,
LI Chenxi²,
LI Shijie³,
YANG Yang⁴,
GE Yuhao⁴,
HUANG Yu^{5
, ,}

1.
School of Computer Science, Peking University, Beijing 100871, China
2.
School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
3.
China Southern Power Grid (csg), Guangzhou 510000, China
4.
China Southern Power Grid Digital Platform Technology, Guangzhou 510000, China
5.
National Engineering Research Center for Software Engineering, Peking University, Beijing 100871, China

Funds: China Southern Power Grid (csg) Science and Technology Item (ZBKJXM20240174)

摘要

摘要: 电网作为连接电力传输与终端用户的重要枢纽，其数据的管理与分析在保障电网稳定性和提升供电质量方面扮演着举足轻重的角色。电网相关数据涵盖范围广泛，内容复杂，包括用户用电模式、气象条件、设备信息及营销数据等多个层面。这些多源异构数据在采集和传输过程中，常受到噪声信号等冗余信息的影响，容易出现数据缺失现象。数据不完整不仅使运行状态监测变得更加困难，也严重制约了故障诊断、健康评估及运维决策等关键工作的效率与准确性。为了提高电网数据的效用性，更好地利用其来保障电网稳定运行，该文提出一种基于扩散模型的电网数据恢复方法，通过独特设计的双层扩散流，能将时序序列嵌入为条件信息，大幅优化了扩散网络在电网场景下的表现。模型将输入的高斯噪声映射到缺失数据的目标分布空间，从而按照其原始分布规律恢复出缺失数据，增强了数据的可用性和价值。实验表明，与以往的方法相比，该方法能够达到领先的恢复效果。
- 电力工业 /
- 电网故障诊断 /
- 电力电网运维 /
- 数据恢复 /
- 扩散模型
Abstract: Objective Smart grid construction drives modern power systems, and distribution networks serve as the key interface between the main grid and end users. Their stability, power quality, and efficiency depend on accurate data management and analysis. Distribution networks generate large volumes of multi-source heterogeneous data that contain user consumption records, real-time meteorology, equipment status, and marketing information. These data streams often become incomplete during collection or transmission due to noise, sensor failures, equipment aging, or adverse weather. Missing data reduces the reliability of real-time monitoring and affects essential tasks such as load forecasting, fault diagnosis, health assessment, and operational decision making. Conventional approaches such as mean or regression imputation lack the capacity to maintain temporal dependencies. Generative models such as Generative Adversarial Networks (GANs) and Variational AutoEncoders (VAEs) do not represent the complex statistical characteristics of grid data with sufficient accuracy. This study proposes a diffusion model based data recovery method for distribution networks. The method is designed to reconstruct missing data, preserve semantic and statistical integrity, and enhance data utility to support smart grid stability and efficiency. Methods This paper proposes a power grid data augmentation method based on diffusion models. The core of the method is that input Gaussian noise is mapped to the target distribution space of the missing data so that the recovered data follows its original distribution characteristics. To reduce semantic discrepancy between the reconstructed data and the actual data, the method uses time series sequence embeddings as conditional information. This conditional input guides and improves the diffusion generation process so that the imputation remains consistent with the surrounding temporal context. Results and Discussions Experimental results show that the proposed diffusion model based data augmentation method achieves higher accuracy in recovering missing power grid data than conventional approaches. The performance demonstrates that the method improves the completeness and reliability of datasets that support analytical tasks and operational decision making in smart grids. Conclusions This study proposes and validates a diffusion model based data augmentation method designed to address data missingness in power distribution networks. Traditional restoration methods and generative models have difficulty capturing the temporal dependencies and complex distribution characteristics of grid data. The method presented here uses temporal sequence information as conditional guidance, which enables accurate imputation of missing values and preserves the semantic integrity and statistical consistency of the original data. By improving the accuracy of distribution network data recovery, the method provides a reliable approach for strengthening data quality and supports the stability and efficiency of smart grid operations.
- Power industry /
- Power grid fault diagnosis /
- Power grid operation and maintenance /
- Data recovery /
- Diffusion model

HTML全文

图 1 本方法的整体架构

下载: 全尺寸图片幻灯片

图 2 时序复合扩散网络细节

下载: 全尺寸图片幻灯片

图 3 典型样本可视化图例

下载: 全尺寸图片幻灯片

表 1 不同模型在各数据集上不同序列长度输入的MSE(越低越好)对比，加粗代表最佳

数据集	序列长度	NLinear	CNN	LSTM	GAN	VAE	DDPM	LDM	本文方法
ETTh1	96	0.32	0.29	0.27	0.24	0.21	0.21	0.18	0.16
ETTh1	192	0.36	0.32	0.29	0.27	0.25	0.23	0.21	0.19
ETTh2	96	0.27	0.25	0.23	0.21	0.19	0.18	0.16	0.15
ETTh2	192	0.31	0.28	0.26	0.24	0.22	0.2	0.18	0.17
ETTm1	96	0.295	0.27	0.25	0.23	0.21	0.19	0.18	0.16
ETTm1	192	0.31	0.28	0.26	0.24	0.22	0.20	0.19	0.17
ETTm2	96	0.26	0.24	0.22	0.21	0.19	0.18	0.16	0.15
ETTm2	192	0.28	0.26	0.24	0.22	0.20	0.19	0.17	0.16
Electricity	96	0.19	0.18	0.16	0.15	0.13	0.12	0.11	0.09
Electricity	192	0.21	0.19	0.18	0.16	0.15	0.13	0.12	0.11

下载: 导出CSV

表 2 不同模型在各数据集上不同序列长度输入的MAE(越低越好)对比

数据集	序列长度	NLinear	CNN	LSTM	GAN	VAE	DDPM	LDM	本文方法
ETTh1	96	0.43	0.41	0.39	0.37	0.35	0.34	0.32	0.31
ETTh1	192	0.46	0.43	0.41	0.39	0.37	0.36	0.34	0.32
ETTh2	96	0.38	0.36	0.34	0.33	0.32	0.30	0.29	0.28
ETTh2	192	0.42	0.39	0.37	0.36	0.34	0.33	0.31	0.28
ETTm1	96	0.39	0.37	0.35	0.33	0.32	0.30	0.29	0.27
ETTm1	192	0.41	0.38	0.36	0.35	0.33	0.31	0.30	0.28
ETTm2	96	0.35	0.33	0.32	0.31	0.29	0.28	0.26	0.25
ETTm2	192	0.37	0.35	0.34	0.32	0.31	0.29	0.28	0.26
Electricity	96	0.27	0.25	0.24	0.23	0.215	0.20	0.19	0.17
Electricity	192	0.28	0.27	0.25	0.24	0.22	0.21	0.19	0.18

下载: 导出CSV

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