Open AccessA Smoothness Regularized Low-Rank Completion Method for Power Grid Middle Platform Data
Author(s) -
Jijun Wang,
Mingsheng Xu,
Situ Zhou
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1815/1/012036
Subject(s) - smoothness , missing data , computer science , rank (graph theory) , smart grid , matrix completion , grid , power (physics) , data mining , matrix (chemical analysis) , algorithm , mathematics , engineering , machine learning , mathematical analysis , physics , geometry , combinatorics , quantum mechanics , electrical engineering , gaussian , materials science , composite material
Currently, the construction of power middle platform plays a vitally important role in the evolution of the smart grid. However, due to sensor failures or network delays, the sampled power middle platform data is often inevitably missing. To address this challenge, in this paper, we propose a smoothness regularized low-rank completion method for power middle platform missing data. Technically, the acquired middle platform data are formed to a time-series data matrix. Then, the low-rank matrix recovery model is applied to complete the missing data. Since the middle platform data is time-continuous, we adopt a total variation term to use this piece-wise smoothness. Finally, the proposed model is efficiently solved by the distributed alternating direction method of multipliers. Experimental evaluations on a real middle platform dataset validate the performance of the proposed method.