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  4. Detection of Bad PMU Data using Machine Learning Techniques
 
conference paper

Detection of Bad PMU Data using Machine Learning Techniques

Karpilow, Alexandra  
•
Cherkaoui, Rachid  
•
D'Arco, Salvatore
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January 1, 2020
2020 Ieee Power & Energy Society Innovative Smart Grid Technologies Conference (Isgt)
IEEE-Power-and-Energy-Society Innovative Smart Grid Technologies Conference (ISGT)

This project presents a pre-State Estimation method for the detection of Bad Data (BD) in Phasor Measurement Units (PMUs) using correlation analysis and a Neural Network Classifier. Presented in this paper is the algorithm design, the steps for generating training and testing data, and the metrics used for evaluation. It is shown that the algorithm is able to detect noisy BD in measurements with load variations, low-level natural noise and transients from bus faults. The benefits of the proposed algorithm include that it can be applied to several measurement subsets in parallel, it is data driven and therefore independent of network model errors, and it is computationally fast, making it a promising technique for online detection.

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Type
conference paper
DOI
10.1109/ISGT45199.2020.9087782
Web of Science ID

WOS:000578005500150

Author(s)
Karpilow, Alexandra  
Cherkaoui, Rachid  
D'Arco, Salvatore
Thuc Dinh Duong
Date Issued

2020-01-01

Publisher

IEEE

Publisher place

New York

Published in
2020 Ieee Power & Energy Society Innovative Smart Grid Technologies Conference (Isgt)
ISBN of the book

978-1-7281-3103-0

Series title/Series vol.

Innovative Smart Grid Technologies

Subjects

bad data

•

correlation analysis

•

neural network classifier

•

phasor measurement units (pmu)

•

power systems

•

anomaly detection

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
DESL  
Event nameEvent placeEvent date
IEEE-Power-and-Energy-Society Innovative Smart Grid Technologies Conference (ISGT)

Washington, DC

Feb 17-20, 2020

Available on Infoscience
October 29, 2020
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/172844
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