Bayesian High-Rank Hankel Matrix Completion for Nonlinear Synchrophasor Data Recovery
Author:
Affiliation:
1. Department of Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA
2. Energy System Division, Argonne National Laboratory, Lemont, IL, USA
3. Eaton Corporation, Golden, CO, USA
Funder
National Science Foundation
Air Force Office of Scientific Research
Electric Power Research Institute
U.S. Department of Energy
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Energy Engineering and Power Technology
Link
https://ieeexplore.ieee.org/ielam/59/10375285/10066187-aam.pdf
Reference40 articles.
1. Real-Time Event Identification Through Low-Dimensional Subspace Characterization of High-Dimensional Synchrophasor Data
2. Identifying Overlapping Successive Events Using a Shallow Convolutional Neural Network
3. Power System Real-Time Monitoring by Using PMU-Based Robust State Estimation Method
4. Power System Dynamic State Estimation With Synchronized Phasor Measurements
5. Robust Hybrid Linear State Estimator Utilizing SCADA and PMU Measurements
Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Synchrophasor Technology Applications and Optimal Placement of Micro-Phasor Measurement Unit (μPMU): Part II;2024 28th International Electrical Power Distribution Conference (EPDC);2024-04-23
2. Realtime Detection of PMU Bad Data and Sequential Bad Data Classifications in Cyber-Physical Testbed;IEEE Access;2023
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