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

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

• Author's copy

• URL

bib

@inproceedings{dorado-rojas2022b,
  title = {{{ML-Based Edge Application}} for {{Detection}} of {{Forced Oscillations}} in {{Power Grids}}},
  booktitle = {2022 {{IEEE Power}} \& {{Energy Society General Meeting}} ({{PESGM}})},
  author = {{Dorado-Rojas}, Sergio A. and Xu, Shunyao and Vanfretti, Luigi and Ayachi, M. Ilies I. and Ahmed, Shehab},
  year = 2022,
  month = jul,
  issn = {1944-9933},
  doi = {10.1109/PESGM48719.2022.9917070},
}

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.

Back