随着智能电表的广泛应用，电网公司积累了大量原始用电数据，然而复杂工作环境下用电信息采集设备仍存在数据丢失的现象。本文在充分考虑高斯噪声影响的情况下，对存在缺失的的原始用电数据进行填补。首先，对独立用户数据序列重排得到原始用电数据矩阵，将其中的理想用电数据矩阵进行非负矩阵分解替代；其次，分别选择F范数和核范数对高斯噪声和具有低秩特性的理想用电数据进行正则化约束以构建优化模型；最后，基于块坐标最小算法框架使用EM算法和直接法交替更新非负矩阵分解得到的矩阵因子，从而有效实现数据的准确插补。仿真分析和实验结果验证了算法的有效性和准确性。

With the wide application of smart meter, the power grid has accumulated a large number of original power consumption data. However, data loss still exists in power consumption information acquisition equipment under complex working environment. This article fills in the missing original electricity consumption data while fully considering the influence of Gaussian noise. Firstly, the original electricity consumption data matrix is obtained by rearranging the independent user data sequence, and the ideal electricity consumption data matrix is replaced by nonnegative matrix factorization; Secondly, F norm and kernel norm are selected to regularization Gaussian noise and ideal power consumption data with low rank characteristics to build optimization models; Finally, based on the block coordinate minimum algorithm framework, the matrix factors obtained from non negative matrix factorization are alternately updated using EM algorithm and direct method, effectively achieving accurate interpolation of data. Simulation analysis and experimental results have verified the effectiveness and accuracy of the algorithm.

U469.72

云南电网科技项目