@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",
}
