PMU Missing Data Recovery Using Tensor Decomposition

被引:35
|
作者
Osipov, Denis [1 ]
Chow, Joe H. [1 ]
机构
[1] Rensselaer Polytech Inst, Dept Elect Comp & Syst Engn, Troy, NY 12180 USA
关键词
Tensors; Phasor measurement units; Data structures; Voltage measurement; Current measurement; Matrix decomposition; Power systems; Missing data recovery; phasor measurement unit; polyadic decomposition; tensor decomposition; Tucker decomposition; SECANT VARIETIES; OPTIMIZATION; SIGNALS;
D O I
10.1109/TPWRS.2020.2991886
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
The paper proposes a new approach for the recovery of missing data from phasor measurement units (PMUs). The approach is based on the application of tensor decomposition to PMU data organized as three-dimensional tensors with respect to time, location and type of variables. The organization of the PMU data is in the form of a bus-oriented data structure and a branch-oriented data structure. Two versions of the approach are introduced. The first version uses polyadic tensor decomposition and the second version uses Tucker tensor decomposition. Several case studies are conducted validating the proposed approaches including measured PMU data in the New York transmission system. Comparison with existing approaches is performed to demonstrate the improvement in missing data recovery accuracy achieved by the new approach.
引用
收藏
页码:4554 / 4563
页数:10
相关论文
共 50 条
  • [1] A Regularized Tensor Completion Approach for PMU Data Recovery
    Ghasemkhani, Amir
    Niazazari, Iman
    Liu, Yunchuan
    Livani, Hanif
    Centeno, Virgilio A.
    Yang, Lei
    IEEE TRANSACTIONS ON SMART GRID, 2021, 12 (02) : 1519 - 1528
  • [2] An adaptive PMU missing data recovery method
    Yang, Zhiwei
    Liu, Hao
    Bi, Tianshu
    Li, Zikang
    Yang, Qixun
    INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2020, 116
  • [3] PMU Data Compression in Power Systems Using Adaptive Rank-Based Tensor Ring
    Sun, Bo
    Xu, Yijun
    Gu, Wei
    Huang, Xinghua
    Mili, Lamine
    Fan, Yuanliang
    Lu, Shuai
    Wu, Zhi
    Korkali, Mert
    IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2025,
  • [4] Total Variation Regularized Weighted Tensor Ring Decomposition for Missing Data Recovery in High-Dimensional Optical Remote Sensing Images
    Wang, Minghua
    Wang, Qiang
    Chanussot, Jocelyn
    Hong, Danfeng
    IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2022, 19
  • [5] Completion of High Order Tensor Data with Missing Entries via Tensor-Train Decomposition
    Yuan, Longhao
    Zhao, Qibin
    Cao, Jianting
    NEURAL INFORMATION PROCESSING, ICONIP 2017, PT I, 2017, 10634 : 222 - 229
  • [6] Block tensor train decomposition for missing data estimation
    Lee, Namgil
    Kim, Jong-Min
    STATISTICAL PAPERS, 2018, 59 (04) : 1283 - 1305
  • [7] Additive Tensor Decomposition Considering Structural Data Information
    Mou, Shancong
    Wang, Andi
    Zhang, Chuck
    Shi, Jianjun
    IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, 2022, 19 (04) : 2904 - 2917
  • [8] Missing Value Replacement for PMU Data via Deep Learning Model With Magnitude Trend Decoupling
    Cheng, Yuanbin
    Foggo, Brandon
    Yamashita, Koji
    Yu, Nanpeng
    IEEE ACCESS, 2023, 11 : 27450 - 27461
  • [9] Privacy-Preserving Vertical Federated Learning With Tensor Decomposition for Data Missing Features
    Liao, Tianchi
    Fu, Lele
    Zhang, Lei
    Yang, Lei
    Chen, Chuan
    Ng, Michael K.
    Huang, Huawei
    Zheng, Zibin
    IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, 2025, 20 : 3445 - 3460
  • [10] Crime Prediction With Missing Data Via Spatiotemporal Regularized Tensor Decomposition
    Liang, Weichao
    Cao, Jie
    Chen, Lei
    Wang, Youquan
    Wu, Jia
    Beheshti, Amin
    Tang, Jiangnan
    IEEE TRANSACTIONS ON BIG DATA, 2023, 9 (05) : 1392 - 1407