Super-efficient detector and defense method for adversarial attacks in power quality classification

Liangheng Zhang, Congmei Jiang, Aiping Pang and Yu He

Applied Energy, 2024, vol. 361, issue C, No S0306261924002551

Abstract: The correct classification of power quality (PQ) is the key step to ensure the normal operation of smart grid. Deep neural networks have been widely used for PQ classification, but they face serious threats from adversarial attacks. At present, there are few studies on defense algorithms for adversarial attacks in PQ classification. Furthermore, detection algorithms have not yet been studied. In order to solve the above problems, we first use Convolutional Neural Network-Long Short-Term Memory Network (CNN-LSTM) to classify PQ signals. Four representative adversarial attack algorithms (i.e., FGSM, FGSM-variant, PGD and SSA) in PQ classification are compared and summarized from three aspects: average running time of attack, robustness of attack and classification accuracy of attacked model. Second, we propose a Multi-Source Feature Detector (MSFD) to detect adversarial attacks. The model architecture of MSFD is a binary classification model based on CNN. The detection function can be implemented simply by placing MSFD in front of the classification model. Third, we propose a Multi-Source Feature Adversarial Training (MSFAT) to defend against adversarial attacks. The experimental results demonstrate the effectiveness and superiority of the proposed methods. MSFD can detect the above four representative adversarial attacks with an extremely high recognition rate, and MSFAT can significantly improve the classification accuracy of the attacked model under these attacks. MSFD and MSFAT can effectively deal with adversarial attacks in different environments without any adjustment. Compared with the most advanced adversarial training defense method in PQ classification, the proposed MSFAT can overcome the drawback that adversarial training may reduce the classification accuracy of the attacked model and is significantly effective for multiple adversarial attacks in PQ classification.

Keywords: Smart grid; Power quality; Deep neural network; Adversarial attack; Multi-source feature detector; Multi-source feature adversarial training (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261924002551
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:361:y:2024:i:c:s0306261924002551

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2024.122872

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().