数据缺失情况下配电网时间序列数据分类算法  

Time series data classification algorithm for distribution networks with missing data

作　　者：萧展辉 张世良 邓丽娟 徐菡 XIAO Zhanhui;ZHANG Shiliang;DENG Lijuan;XU Han(School of Computer Science&Engineering,South China University of Technology,Guangzhou 510614,Guangdong,China;Data Platform Product Department,China Southern Power Grid Digital Grid Research Institute Co.,Ltd.,Guangzhou 510663,Guangdong,China)

机构地区：[1]华南理工大学计算机科学与工程学院,广东广州510614 [2]南方电网数字电网研究院有限公司数据平台产品部,广东广州510663

出　　处：《沈阳工业大学学报》2025年第1期29-36,共8页Journal of Shenyang University of Technology

基　　金：国家自然科学基金项目(61308394);南网数研院平台安全分公司数据中心管理体系研究项目(0002200000091292)。

摘　　要：【目的】在智能电网快速发展的背景下,配电网作为电力传输与分配的关键环节,其数据的有效管理和分析对于保障电网稳定运行、提升供电质量至关重要。然而,配电网数据种类繁多且复杂,涵盖了用户用电行为、天气情况、设备基础信息及营销数据等多个维度。不同类型的数据在采集和传输过程中,会因磁场信号、噪声信号、冗余数据等干扰出现缺失,不仅增加了配电网运行监控的难度,还为故障分析、状态评估及优化决策等工作带来极大挑战。【方法】为提高数据处理的准确性和效率,提出一种数据缺失情况下的配电网时间序列数据分类算法。根据时间序列数据在配电网中的分布状态,利用平滑算法去除数据噪声,从而显著提升数据的准确性和可靠性,优化因冗余数据干扰而产生的问题。对缺失数据进行增量填补,依据时间序列数据的内在规律和相邻数据点的相关性,对缺失数据进行合理推测和填补,保持了数据的完整性,同时确保了时间序列的连续性和一致性。计算不同时间序列的数据缺失情况,将高维和低维数据状态空间与单元、多元时间序列相结合,凭借维度映射得到数据维度因子,实现簇内分类。【结果】设计方法填补后数据均在原始数据附近,无冗余问题,且分类耗时点均匀分布,呈现出线性趋势,充分展示了其高效稳定的数据处理能力。设计方法分类配电网时间序列数据后,同种类配电网数据聚集且互不干扰,噪声数据大幅减少,相对差异值(RDV)始终保持在0.05以下,特异度在数据缺失率5%~35%的范围内均维持在95.0%以上,显著高于对比方法的91.5%和92.0%。【结论】设计方法通过平滑去噪、增量填补和维度映射等技术手段,有效应对数据缺失带来的挑战,提高了数据处理的准确性和效率。同时,验证了设计方法在保持高分类精度和快速收敛速度方面的优�[Objective]In the context of the rapid development of smart grids,the effective management and analysis of data in the distribution network,as a key link in power transmission and distribution,is crucial for ensuring the stable operation of the power grid and improving the quality of power supply.However,the distribution network data are diverse and complex,covering multiple dimensions such as users′electricity consumption behavior,weather conditions,basic information of equipment,and marketing data.In the process of collecting and transmitting different types of data,data missing occurs due to interference such as magnetic field signals,noise signals,and redundant data,which not only increases the difficulty of monitoring the operation of the distribution networks but also brings great challenges to fault analysis,state assessment,and optimization decision-making.[Methods]To improve the accuracy and efficiency of data processing,this paper proposed a time series data classification algorithm for distribution networks with missing data.According to the distribution status of time series data in the distribution networks,a smoothing algorithm was used to remove data noise,significantly improving the accuracy and reliability of the data and optimizing the problems caused by redundant data interference.Incremental filling was carried out for missing data,and based on the inherent rules of time series data and the correlation between adjacent data points,reasonable speculation and filling were made for the missing data,maintaining the integrity of the data while ensuring the continuity and consistency of the time series.Calculations were conducted on the missing data of different time series,and the high-dimensional and low-dimensional data state spaces were combined with univariate and multivariate time series.By using dimension mapping,the dimensional factors of data were obtained,achieving intra-cluster classification.[Results]The experimental results show that the designed method filled in the data near the ori

关 键 词：数据缺失 配电网 维度映射 平滑算法 多元序列 数据分类 噪声干扰 维度因子 

分 类 号：TP26[自动化与计算机技术—检测技术与自动化装置]