1.重庆大学电气工程学院;2.云南电网有限责任公司计量中心
U469.72
国家电网公司总部科技项目“基于震电联合检测技术的变电站接地网状态'可视化’评估方法研究与工程应用”(5500-202427168A-1-1-ZN)资助
1.School of Electrical Engineering,Chongqing University;2.Measurement Center of Yunnan Power Grid Co.
This work wassupported by the State GridCorporation of China(5500-202427168A-1-1-ZN)
针对实际采集、传输和储存海量用电数据过程中存在丢失的数据质量问题，本文利用大型台区用户用电数据的时序和空间相关性，提出一种基于CP分解与核范数正则化联合约束的用电数据张量重构方法。首先，根据大型台区用户用电行为的部分潜在相似特征构建台区用电数据张量；其次，分别选择F范数和核范数对高斯噪声和理想完整用电数据进行正则化约束建立张量恢复模型；最后采用逐列更新的直接方式交替更新CP分解得到的矩阵因子直至收敛，从而实现缺失数据的高精度补全。通过仿真分析和对比实验验证了算法的有效性和准确性。
In response to the issue of data quality loss in the actual collection, transmission, and storage of massive electricity data, this paper proposes a method for reconstructing electricity data based on CP decomposition and kernel norm regularization joint constraints, utilizing the temporal and spatial correlations of large-scale substation user electricity data. Firstly, a tensor of electricity consumption data for the substation area is constructed based on some potential similar characteristics of the electricity consumption behavior of large-scale substation users; Secondly, the F-norm and kernel norm are selected to regularize Gaussian noise and ideal complete electricity data, respectively, to establish tensor recovery models; Finally, the matrix factors obtained from CP decomposition are alternately updated column by column until convergence, thereby achieving high-precision completion of missing data. The effectiveness and accuracy of the algorithm were verified through simulation analysis and comparative experiments.
