Low-rank Tensor Completion for PMU Data Recovery

Ghasemkhani, Amir; Liu, Yunchuan; Yang, Lei

doi:10.1109/isgt49243.2021.9372250

Title: Low-rank Tensor Completion for PMU Data Recovery

Abstract

This paper proposes a tensor completion method for the recovery of missing phasor measurement unit (PMU) measurements. Tensor completion as the general case of matrix completion has attracted increasing attention in recent years. The imputation accuracy for the existing matrix completion methods may be significantly reduced when there are consecutive data losses across multiple data channels. To tackle this issue, we explore the multi-way characteristics of PMU measurements by using a tensor model. We leverage the low-rank property of the PMU measurements and formulate the missing PMU data recovery problem as a low-rank tensor completion problem. An efficient algorithm based on alternating direction method of multipliers (ADMM) is developed to solve the tensor completion problem. The experiments using the real PMU dataset show that the proposed method exhibits better imputation accuracy compared with the conventional data recovery methods.

Authors:: Ghasemkhani, Amir; Liu, Yunchuan; Yang, Lei

Publication Date:: 2021-02-16

Research Org.:: Univ. of Nevada, Reno, NV (United States)

Sponsoring Org.:: USDOE

OSTI Identifier:: 1958830

DOE Contract Number:: OE0000911

Resource Type:: Conference

Journal Name:: 2021 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT)

Additional Journal Information:: Conference: 2021 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT),Washington, DC, USA,16-18 February 2021

Country of Publication:: United States

Language:: English

Citation Formats


                    Ghasemkhani, Amir, Liu, Yunchuan, and Yang, Lei. Low-rank Tensor Completion for PMU Data Recovery.  United States: N. p., 2021. 
        Web.  doi:10.1109/isgt49243.2021.9372250.

Copy to clipboard


                    Ghasemkhani, Amir, Liu, Yunchuan, & Yang, Lei. Low-rank Tensor Completion for PMU Data Recovery.  United States.  https://doi.org/10.1109/isgt49243.2021.9372250

Copy to clipboard


                    Ghasemkhani, Amir, Liu, Yunchuan, and Yang, Lei. 2021.  
        "Low-rank Tensor Completion for PMU Data Recovery".  United States.  https://doi.org/10.1109/isgt49243.2021.9372250.  https://www.osti.gov/servlets/purl/1958830.

Copy to clipboard


                    
@article{osti_1958830,

  title        = {Low-rank Tensor Completion for PMU Data Recovery},

  author       = {Ghasemkhani, Amir and Liu, Yunchuan and Yang, Lei},

  abstractNote = {This paper proposes a tensor completion method for the recovery of missing phasor measurement unit (PMU) measurements. Tensor completion as the general case of matrix completion has attracted increasing attention in recent years. The imputation accuracy for the existing matrix completion methods may be significantly reduced when there are consecutive data losses across multiple data channels. To tackle this issue, we explore the multi-way characteristics of PMU measurements by using a tensor model. We leverage the low-rank property of the PMU measurements and formulate the missing PMU data recovery problem as a low-rank tensor completion problem. An efficient algorithm based on alternating direction method of multipliers (ADMM) is developed to solve the tensor completion problem. The experiments using the real PMU dataset show that the proposed method exhibits better imputation accuracy compared with the conventional data recovery methods.},

  doi          = {10.1109/isgt49243.2021.9372250},

  url          = {https://www.osti.gov/biblio/1958830},
  journal      = {2021 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT)},
number       = ,

  volume       = ,

  place        = {United States},

  year         = {2021},

  month        = {2}

}

Copy to clipboard

Conference:

View Conference (0.16 MB)

https://doi.org/10.1109/isgt49243.2021.9372250

Other availability

Please see Document Availability for additional information on obtaining the full-text document. Library patrons may search WorldCat to identify libraries that hold this conference proceeding.

Save / Share:

Export Metadata

Save to My Library

Works referenced in this record:

Missing Data Recovery by Exploiting Low-Dimensionality in Power System Synchrophasor Measurements
journal, March 2016

Gao, Pengzhi; Wang, Meng; Ghiocel, Scott G.
IEEE Transactions on Power Systems, Vol. 31, Issue 2
https://doi.org/10.1109/TPWRS.2015.2413935

A Low-Rank Matrix Approach for the Analysis of Large Amounts of Power System Synchrophasor Data
conference, January 2015

Wang, Meng; Chow, Joe H.; Gao, Pengzhi
2015 48th Hawaii International Conference on System Sciences
https://doi.org/10.1109/HICSS.2015.318

Modeless reconstruction of missing synchrophasor measurements
conference, July 2014

Gao, Pengzhi; Wang, Meng; Ghiocel, Scott G.
2014 IEEE PES General Meeting: Conference & Exposition, 2014 IEEE PES General Meeting | Conference & Exposition
https://doi.org/10.1109/PESGM.2014.6938973

Estimate the Lost Phasor Measurement Unit Data Using Alternating Direction Multipliers Method
conference, April 2018

Liao, Mang; Shi, Di; Yu, Zhe
2018 IEEE/PES Transmission and Distribution Conference and Exposition (T&D)
https://doi.org/10.1109/TDC.2018.8440469

A Singular Value Thresholding Algorithm for Matrix Completion
journal, January 2010

Cai, Jian-Feng; Candès, Emmanuel J.; Shen, Zuowei
SIAM Journal on Optimization, Vol. 20, Issue 4
https://doi.org/10.1137/080738970

Tensor Versus Matrix Completion: A Comparison With Application to Spectral Data
journal, July 2011

Signoretto, Marco; Van de Plas, Raf; De Moor, Bart
IEEE Signal Processing Letters, Vol. 18, Issue 7
https://doi.org/10.1109/LSP.2011.2151856

Trace Norm Regularized CANDECOMP/PARAFAC Decomposition With Missing Data
journal, November 2015

Liu, Yuanyuan; Shang, Fanhua; Jiao, Licheng
IEEE Transactions on Cybernetics, Vol. 45, Issue 11
https://doi.org/10.1109/TCYB.2014.2374695

Tensor Decompositions and Applications
journal, August 2009

Kolda, Tamara G.; Bader, Brett W.
SIAM Review, Vol. 51, Issue 3
https://doi.org/10.1137/07070111X

Wavelet-Based Event Detection Method Using PMU Data
journal, May 2017

Kim, Do-In; Chun, Tae Yoon; Yoon, Sung-Hwa
IEEE Transactions on Smart Grid, Vol. 8, Issue 3
https://doi.org/10.1109/TSG.2015.2478421

Generating Unit Model Validation and Calibration Through Synchrophasor Measurements
journal, January 2015

Hajnoroozi, Ali A.; Aminifar, Farrokh; Ayoubzadeh, Hossein
IEEE Transactions on Smart Grid, Vol. 6, Issue 1
https://doi.org/10.1109/TSG.2014.2322821

Design, Testing, and Implementation of a Linear State Estimator in a Real Power System
journal, July 2017

Zhang, Lin; Bose, Anjan; Jampala, Anil
IEEE Transactions on Smart Grid, Vol. 8, Issue 4
https://doi.org/10.1109/TSG.2015.2508283

Anomaly Detection in IoT-Based PIR Occupancy Sensors to Improve Building Energy Efficiency
conference, February 2020

Samani, Ehsan; Khaledian, Parviz; Aligholian, Armin
2020 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT)
https://doi.org/10.1109/ISGT45199.2020.9087681

Design Requirements of Wide-Area Damping Systems—Using Empirical Data From a Utility IP Network
journal, March 2014

Zhu, Kun; Chenine, Moustafa; Nordstrom, Lars
IEEE Transactions on Smart Grid, Vol. 5, Issue 2
https://doi.org/10.1109/TSG.2013.2290493

Real Time Prediction and Control of Transient Stability Using Transient Energy Function
journal, January 2016

Bhui, Pratyasa; Senroy, Nilanjan
IEEE Transactions on Power Systems
https://doi.org/10.1109/TPWRS.2016.2564444

Optimal Design of a Wide Area Measurement System for Improvement of Power Network Monitoring Using a Dynamic Multiobjective Shortest Path Algorithm
journal, December 2017

Ghasemkhani, Amir; Monsef, Hassan; Rahimi-Kian, Ashkan
IEEE Systems Journal, Vol. 11, Issue 4
https://doi.org/10.1109/JSYST.2015.2469742

Modelless Data Quality Improvement of Streaming Synchrophasor Measurements by Exploiting the Low-Rank Hankel Structure
journal, November 2018

Hao, Yingshuai; Wang, Meng; Chow, Joe H.
IEEE Transactions on Power Systems, Vol. 33, Issue 6
https://doi.org/10.1109/TPWRS.2018.2850708

PMU Missing Data Recovery Using Tensor Decomposition
journal, November 2020

Osipov, Denis; Chow, Joe H.
IEEE Transactions on Power Systems, Vol. 35, Issue 6
https://doi.org/10.1109/TPWRS.2020.2991886

Factor Matrix Trace Norm Minimization for Low-Rank Tensor Completion
conference, April 2014

Liu, Yuanyuan; Shang, Fanhua; Cheng, Hong
Proceedings of the 2014 SIAM International Conference on Data Mining
https://doi.org/10.1137/1.9781611973440.99

Tensor rank is NP-complete
journal, December 1990

Håstad, Johan
Journal of Algorithms, Vol. 11, Issue 4
https://doi.org/10.1016/0196-6774(90)90014-6

Similar records in OSTI.GOV collections:

A Regularized Tensor Completion Approach for PMU Data Recovery

Journal Article Ghasemkhani, Amir; Niazazari, Iman; Liu, Yunchuan; ... - IEEE Transactions on Smart Grid

This article presents a novel data recovery framework for missing synchrophasor measurements. The imputation accuracy for the existing data recovery methods is significantly reduced when there are consecutive data losses across multiple data streams. Besides, the recovered data do not necessarily meet the physical constraints of the power grid. To tackle these issues, a regularized low rank tensor completion (LRTC) method is proposed to incorporate the domain knowledge (e.g., Kirchhoff's voltage and current laws, and three phase circuit relationships) as the regularization terms in order to efficiently exploit the data inter-dependencies for a better recovery. We leverage the tensor decompositionmore »« less
https://doi.org/10.1109/tsg.2020.3030566

Full Text Available
Real-time Event Detection Using Rank Signatures of Real-world PMU Data

Conference Ghasemkhani, Amir; Liu, Yunchuan; Yang, Lei - 2022 IEEE Power & Energy Society General Meeting (PESGM)

Timely detection of power system events is a crucial task, which can facilitate the implementation of remedial actions to improve reliability, resiliency, and security of the system. Meanwhile, the widespread deployment of phasor measurement units (PMUs) makes it possible to develop data-driven event detection techniques. However, relying purely on data without incorporating domain knowledge for the event detection task in power systems poses substantial security and stability risks due to issues associated with data misinterpretation and model accuracy. In this regard, we propose a real-time event detection method using real-world PMU data by incorporating domain knowledge to adequately capture themore »« less
https://doi.org/10.1109/pesgm48719.2022.9917156

Full Text Available
Robust Event Classification Using Imperfect Real-world PMU Data

Journal Article Liu, Yunchuan; Yang, Lei; Ghasemkhani, Amir; ... - IEEE Internet of Things Journal (Online)

Here, this paper studies robust event classification using imperfect real-world phasor measurement unit (PMU) data. By analyzing the real-world PMU data, we find it is challenging to directly use this dataset for event classifiers due to the low data quality observed in PMU measurements and event logs. To address these challenges, we develop a novel machine learning framework for training robust event classifiers, which consists of three main steps: data preprocessing, fine-grained event data extraction, and feature engineering. Specifically, the data preprocessing step addresses the data quality issues of PMU measurements (e.g., bad data and missing data); in the fine-grainedmore »« less
https://doi.org/10.1109/jiot.2022.3177686
Mitigating Smart Meter Asynchrony Error Via Multi-Objective Low Rank Matrix Recovery

Journal Article Yuan, Yuxuan; Dehghanpour, Kaveh; Wang, Zhaoyu - IEEE Transactions on Smart Grid

Smart meters (SMs) are being widely deployed by distribution utilities across the U.S. Despite their benefits in real-time monitoring. SMs suffer from certain data quality issues; specifically, unlike phasor measurement units (PMUs) that use GPS for data synchronization, SMs are not perfectly synchronized. The asynchrony error can degrade the monitoring accuracy in distribution networks. To address this challenge, we propose a principal component pursuit (PCP)-based data recovery strategy. Since asynchrony results in a loss of temporal correlation among SMs, the key idea in our solution is to leverage a PCP-based low rank matrix recovery technique to maximize the temporal correlationmore »« less
https://doi.org/10.1109/tsg.2021.3088835

Full Text Available
Decentralized Low-Rank State Estimation for Power Distribution Systems

Journal Article Sagan, April; Liu, Yajing; Bernstein, Andrey - IEEE Transactions on Smart Grid

This article considers the low-observability state estimation problem in power distribution networks and develops a decentralized state estimation algorithm leveraging the matrix completion methodology. Matrix completion has been shown to be an effective technique in state estimation that exploits the low dimensionality of the power system measurements to recover missing information. This technique can utilize an approximate (linear) load flow model, or it can be used with no physical models in a network where no information about the topology or line admittance is available. The direct application of matrix completion algorithms requires solving a semi-definite programming (SDP) problem, which becomesmore »« less
https://doi.org/10.1109/TSG.2021.3058609

Similar Records

Title: Low-rank Tensor Completion for PMU Data Recovery

Abstract

Citation Formats

Missing Data Recovery by Exploiting Low-Dimensionality in Power System Synchrophasor Measurements journal, March 2016

A Low-Rank Matrix Approach for the Analysis of Large Amounts of Power System Synchrophasor Data conference, January 2015

Modeless reconstruction of missing synchrophasor measurements conference, July 2014

Estimate the Lost Phasor Measurement Unit Data Using Alternating Direction Multipliers Method conference, April 2018

A Singular Value Thresholding Algorithm for Matrix Completion journal, January 2010

Tensor Versus Matrix Completion: A Comparison With Application to Spectral Data journal, July 2011

Trace Norm Regularized CANDECOMP/PARAFAC Decomposition With Missing Data journal, November 2015

Tensor Decompositions and Applications journal, August 2009

Wavelet-Based Event Detection Method Using PMU Data journal, May 2017

Generating Unit Model Validation and Calibration Through Synchrophasor Measurements journal, January 2015

Design, Testing, and Implementation of a Linear State Estimator in a Real Power System journal, July 2017

Anomaly Detection in IoT-Based PIR Occupancy Sensors to Improve Building Energy Efficiency conference, February 2020

Design Requirements of Wide-Area Damping Systems—Using Empirical Data From a Utility IP Network journal, March 2014

Real Time Prediction and Control of Transient Stability Using Transient Energy Function journal, January 2016

Optimal Design of a Wide Area Measurement System for Improvement of Power Network Monitoring Using a Dynamic Multiobjective Shortest Path Algorithm journal, December 2017

Modelless Data Quality Improvement of Streaming Synchrophasor Measurements by Exploiting the Low-Rank Hankel Structure journal, November 2018

PMU Missing Data Recovery Using Tensor Decomposition journal, November 2020

Factor Matrix Trace Norm Minimization for Low-Rank Tensor Completion conference, April 2014

Tensor rank is NP-complete journal, December 1990

Missing Data Recovery by Exploiting Low-Dimensionality in Power System Synchrophasor Measurements
journal, March 2016

A Low-Rank Matrix Approach for the Analysis of Large Amounts of Power System Synchrophasor Data
conference, January 2015

Modeless reconstruction of missing synchrophasor measurements
conference, July 2014

Estimate the Lost Phasor Measurement Unit Data Using Alternating Direction Multipliers Method
conference, April 2018

A Singular Value Thresholding Algorithm for Matrix Completion
journal, January 2010

Tensor Versus Matrix Completion: A Comparison With Application to Spectral Data
journal, July 2011

Trace Norm Regularized CANDECOMP/PARAFAC Decomposition With Missing Data
journal, November 2015

Tensor Decompositions and Applications
journal, August 2009

Wavelet-Based Event Detection Method Using PMU Data
journal, May 2017

Generating Unit Model Validation and Calibration Through Synchrophasor Measurements
journal, January 2015

Design, Testing, and Implementation of a Linear State Estimator in a Real Power System
journal, July 2017

Anomaly Detection in IoT-Based PIR Occupancy Sensors to Improve Building Energy Efficiency
conference, February 2020

Design Requirements of Wide-Area Damping Systems—Using Empirical Data From a Utility IP Network
journal, March 2014

Real Time Prediction and Control of Transient Stability Using Transient Energy Function
journal, January 2016

Optimal Design of a Wide Area Measurement System for Improvement of Power Network Monitoring Using a Dynamic Multiobjective Shortest Path Algorithm
journal, December 2017

Modelless Data Quality Improvement of Streaming Synchrophasor Measurements by Exploiting the Low-Rank Hankel Structure
journal, November 2018

PMU Missing Data Recovery Using Tensor Decomposition
journal, November 2020

Factor Matrix Trace Norm Minimization for Low-Rank Tensor Completion
conference, April 2014

Tensor rank is NP-complete
journal, December 1990