ML-Based Edge Application for Detection of Forced Oscillations in Power Grids

Sergio A. Dorado-Rojas; Shunyao Xu; Luigi Vanfretti; M. Ilies I. Ayachi; Shehab Ahmed

doi:10.1109/PESGM48719.2022.9917070

ML-Based Edge Application for Detection of Forced Oscillations in Power Grids

Sergio A. Dorado-Rojas, Shunyao Xu, Luigi Vanfretti, M. Ilies I. Ayachi, Shehab Ahmed

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Scopus citations

Abstract

This paper presents a Machine Learning (ML) solution deployed in an Internet-of-Things (IoT) edge device for detecting forced oscillations in power grids. We base our proposal on a one-dimensional (1D) and two-dimensional (2D) Convolutional Neural Network (CNN) architecture, trained offline and deployed on an Nvidia Jetson TX2. Our work also shows the advantages of optimizing the CNNs models, after training, using TensorRT, a library for accelerating deep learning inference in real-time. Both real-world and synthetic measurement signals are employed to validate the applicability of the proposed approach.

Original language	English (US)
Title of host publication	2022 IEEE Power and Energy Society General Meeting, PESGM 2022
Publisher	IEEE Computer Society
ISBN (Electronic)	9781665408233
DOIs	https://doi.org/10.1109/PESGM48719.2022.9917070
State	Published - 2022
Event	2022 IEEE Power and Energy Society General Meeting, PESGM 2022 - Denver, United States Duration: Jul 17 2022 → Jul 21 2022

Publication series

Name	IEEE Power and Energy Society General Meeting
Volume	2022-July
ISSN (Print)	1944-9925
ISSN (Electronic)	1944-9933

Conference

Conference	2022 IEEE Power and Energy Society General Meeting, PESGM 2022
Country/Territory	United States
City	Denver
Period	07/17/22 → 07/21/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

Convolutional neural networks
forced oscillations
NVIDIA Jetson TX2
real-time detection
TensorRT

ASJC Scopus subject areas

Energy Engineering and Power Technology
Nuclear Energy and Engineering
Renewable Energy, Sustainability and the Environment
Electrical and Electronic Engineering

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/PESGM48719.2022.9917070

Cite this

@inproceedings{819d4475d9ed49ed928197f7c03fb6af,

title = "ML-Based Edge Application for Detection of Forced Oscillations in Power Grids",

abstract = "This paper presents a Machine Learning (ML) solution deployed in an Internet-of-Things (IoT) edge device for detecting forced oscillations in power grids. We base our proposal on a one-dimensional (1D) and two-dimensional (2D) Convolutional Neural Network (CNN) architecture, trained offline and deployed on an Nvidia Jetson TX2. Our work also shows the advantages of optimizing the CNNs models, after training, using TensorRT, a library for accelerating deep learning inference in real-time. Both real-world and synthetic measurement signals are employed to validate the applicability of the proposed approach.",

keywords = "Convolutional neural networks, forced oscillations, NVIDIA Jetson TX2, real-time detection, TensorRT",

author = "Dorado-Rojas, {Sergio A.} and Shunyao Xu and Luigi Vanfretti and Ayachi, {M. Ilies I.} and Shehab Ahmed",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE Power and Energy Society General Meeting, PESGM 2022 ; Conference date: 17-07-2022 Through 21-07-2022",

year = "2022",

doi = "10.1109/PESGM48719.2022.9917070",

language = "English (US)",

series = "IEEE Power and Energy Society General Meeting",

publisher = "IEEE Computer Society",

booktitle = "2022 IEEE Power and Energy Society General Meeting, PESGM 2022",

address = "United States",

}

Dorado-Rojas, SA, Xu, S, Vanfretti, L, Ayachi, MII & Ahmed, S 2022, ML-Based Edge Application for Detection of Forced Oscillations in Power Grids. in 2022 IEEE Power and Energy Society General Meeting, PESGM 2022. IEEE Power and Energy Society General Meeting, vol. 2022-July, IEEE Computer Society, 2022 IEEE Power and Energy Society General Meeting, PESGM 2022, Denver, United States, 07/17/22. https://doi.org/10.1109/PESGM48719.2022.9917070

ML-Based Edge Application for Detection of Forced Oscillations in Power Grids. / Dorado-Rojas, Sergio A.; Xu, Shunyao; Vanfretti, Luigi et al.
2022 IEEE Power and Energy Society General Meeting, PESGM 2022. IEEE Computer Society, 2022. (IEEE Power and Energy Society General Meeting; Vol. 2022-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - ML-Based Edge Application for Detection of Forced Oscillations in Power Grids

AU - Dorado-Rojas, Sergio A.

AU - Xu, Shunyao

AU - Vanfretti, Luigi

AU - Ayachi, M. Ilies I.

AU - Ahmed, Shehab

PY - 2022

Y1 - 2022

N2 - This paper presents a Machine Learning (ML) solution deployed in an Internet-of-Things (IoT) edge device for detecting forced oscillations in power grids. We base our proposal on a one-dimensional (1D) and two-dimensional (2D) Convolutional Neural Network (CNN) architecture, trained offline and deployed on an Nvidia Jetson TX2. Our work also shows the advantages of optimizing the CNNs models, after training, using TensorRT, a library for accelerating deep learning inference in real-time. Both real-world and synthetic measurement signals are employed to validate the applicability of the proposed approach.

AB - This paper presents a Machine Learning (ML) solution deployed in an Internet-of-Things (IoT) edge device for detecting forced oscillations in power grids. We base our proposal on a one-dimensional (1D) and two-dimensional (2D) Convolutional Neural Network (CNN) architecture, trained offline and deployed on an Nvidia Jetson TX2. Our work also shows the advantages of optimizing the CNNs models, after training, using TensorRT, a library for accelerating deep learning inference in real-time. Both real-world and synthetic measurement signals are employed to validate the applicability of the proposed approach.

KW - Convolutional neural networks

KW - forced oscillations

KW - NVIDIA Jetson TX2

KW - real-time detection

KW - TensorRT

UR - http://www.scopus.com/inward/record.url?scp=85130723765&partnerID=8YFLogxK

U2 - 10.1109/PESGM48719.2022.9917070

DO - 10.1109/PESGM48719.2022.9917070

M3 - Conference contribution

AN - SCOPUS:85130723765

T3 - IEEE Power and Energy Society General Meeting

BT - 2022 IEEE Power and Energy Society General Meeting, PESGM 2022

PB - IEEE Computer Society

T2 - 2022 IEEE Power and Energy Society General Meeting, PESGM 2022

Y2 - 17 July 2022 through 21 July 2022

ER -

ML-Based Edge Application for Detection of Forced Oscillations in Power Grids

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this