An unsupervised adversarial autoencoder for cyber attack detection in power distribution grids

Detection of cyber attacks in smart power distribution grids with unbalanced configurations poses challenges due to the inherent nonlinear nature of these uncertain and stochastic systems. It originates from the intermittent characteristics of the distributed energy resources (DERs) generation and l...

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Vydané v:	Electric power systems research Ročník 232; s. 110407
Hlavní autori:	Zideh, Mehdi Jabbari, Khalghani, Mohammad Reza, Solanki, Sarika Khushalani
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier B.V 01.07.2024
Predmet:	Adversarial autoencoder Cyber attack detection False data injection attacks Generative adversarial networks Power distribution grids Unsupervised data-driven method Generative adversarial networks False data injection attacks Power distribution grids Cyber attack detection Unsupervised data-driven method Adversarial autoencoder
ISSN:	0378-7796, 1873-2046
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Popis
Shrnutí:	Detection of cyber attacks in smart power distribution grids with unbalanced configurations poses challenges due to the inherent nonlinear nature of these uncertain and stochastic systems. It originates from the intermittent characteristics of the distributed energy resources (DERs) generation and load variations. Moreover, the unknown behavior of cyber attacks, especially false data injection attacks (FDIAs) in the distribution grids with complex temporal correlations and the limited amount of labeled data increases the vulnerability of the grids and imposes a high risk in the secure and reliable operation of the grids. To address these challenges, this paper proposes an unsupervised adversarial autoencoder (AAE) model to detect FDIAs in unbalanced power distribution grids integrated with DERs, i.e., PV systems and wind generation. The proposed method utilizes long short-term memory (LSTM) in the structure of the autoencoder to capture the temporal dependencies in the time-series measurements and leverages the power of generative adversarial networks (GANs) for better reconstruction of the input data. The advantage of the proposed data-driven model is that it can detect anomalous points for the system operation without reliance on abstract models or mathematical representations and data labels. To evaluate the efficacy of the approach, it is tested on IEEE 13-bus and 123-bus systems with historical meteorological data (wind speed, ambient temperature, and solar irradiance) as well as historical real-world load data under three types of data falsification functions. The comparison of the detection results of the proposed model with other data-driven methods verifies its superior performance in detecting cyber attacks in unbalanced power distribution grids. •An unsupervised neural network model combines the GANs and autoencoder for FDI attacks.•The proposed model utilizes the outputs of autoencoder and critic score for attack detection.•The proposed model is tested on unbalanced power distribution grids integrated with DERs.•Simulation results verify the model’s superior performance compared to other data-driven models.
ISSN:	0378-7796 1873-2046
DOI:	10.1016/j.epsr.2024.110407