Missing Value Replacement for PMU Data via Deep Learning Model With Magnitude Trend Decoupling

Missing Value Replacement for PMU Data via Deep Learning Model With Magnitude Trend Decoupling

This paper develops a forecasting-based missing value replacement model for Phasor Measurement Unit (PMU) data during power system events. The proposed forecasting model leverages a sequence-to-sequence (Seq2Seq) long short-term memory (LSTM) network with an attention mechanism, which is trained wit...

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Bibliographic Details
Main Authors: Yuanbin Cheng, Brandon Foggo, Koji Yamashita, Nanpeng Yu
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Access
Subjects:
Power systems
machine learning
deep learning
missing value replacement
phasor measurement unit
grid disturbance
Online Access:https://ieeexplore.ieee.org/document/10063976/

Internet

https://ieeexplore.ieee.org/document/10063976/

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