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Adversarial Attacks on Deep Neural Network-based Power System Event Classification Models

Conference · · 2022 IEEE PES Innovative Smart Grid Technologies - Asia (ISGT Asia)
 [1];  [2];  [2]
  1. University of California, Riverside,Department of Electrical and Computer Engineering,Riverside,California,USA,92507; University of California, Riverside
  2. University of California, Riverside,Department of Electrical and Computer Engineering,Riverside,California,USA,92507
+ Show Author Affiliations
Online event classification is essential to strengthening the reliability of the power transmission system. Recently, deep learning based methods have achieved great success in numerous domains such as computer vision and natural language processing. Researchers began to adopt deep learning based methods to solve the power system event identification problem and achieved effective results. However, these previous works do not consider that deep learning models are vulnerable to adversarial attacks, potentially influencing real-world applications' reliability. In this paper, we adopt several adversarial attack mechanisms by adding tailored noise signal to the input Phasor Measurement Units (PMU) time series and make the deep learning model misclassify the power system event. This numerical study discloses that current state-of-the-art deep learning based power system event classifiers are extremely vulnerable to adversarial attacks, which may jeopardize the reliability of the power transmission system.
Research Organization:
University of California, Riverside
Sponsoring Organization:
U.S. Department of Energy
DOE Contract Number:
OE0000916
OSTI ID:
1958411
Report Number(s):
DOE-UCR-ISGT-Asia-2022
Conference Information:
Journal Name: 2022 IEEE PES Innovative Smart Grid Technologies - Asia (ISGT Asia)
Country of Publication:
United States
Language:
English

References (8)

Power System Event Identification Based on Deep Neural Network With Information Loading journal November 2021
Information Losses in Neural Classifiers From Sampling journal October 2020
Deep Residual Learning for Image Recognition conference June 2016
Hierarchical Convolutional Neural Networks for Event Classification on PMU Measurements journal January 2021
Robust Physical-World Attacks on Deep Learning Visual Classification conference June 2018
DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks conference June 2016
Online Power System Event Detection via Bidirectional Generative Adversarial Networks journal January 2022
Learning-Based Real-Time Event Identification Using Rich Real PMU Data journal November 2021

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Related Subjects

24 POWER TRANSMISSION AND DISTRIBUTION
Adversarial attacks
Deep learning
Event identification
Phasor measurement unit