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Article

Detecting System Fault/Cyberattack within a Photovoltaic System Connected to the Grid: A Neural Network-Based Solution

Department of Electrical, Electronic and Telecommunications Engineering, and Naval Architecture-DITEN, University of Genoa, Via Opera Pia 11A, 16145 Genoa, Italy
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J. Sens. Actuator Netw. 2020, 9(2), 20; https://doi.org/10.3390/jsan9020020
Received: 28 February 2020 / Revised: 4 April 2020 / Accepted: 9 April 2020 / Published: 20 April 2020
(This article belongs to the Special Issue Advanced Technologies for Smart Cities)
The large spread of Distributed Energy Resources (DERs) and the related cyber-security issues introduce the need for monitoring. The proposed work focuses on an anomaly detection strategy based on the physical behavior of the industrial process. The algorithm extracts some measures of the physical parameters of the system and processes them with a neural network architecture called autoencoder in order to build a classifier making decisions about the behavior of the system and detecting possible cyber-attacks or faults. The results are quite promising for a practical application in real systems. View Full-Text
Keywords: distributed energy resources; photovoltaic systems; cyber-security; anomaly detection; neural networks; autoencoder distributed energy resources; photovoltaic systems; cyber-security; anomaly detection; neural networks; autoencoder
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MDPI and ACS Style

Gaggero, G.B.; Rossi, M.; Girdinio, P.; Marchese, M. Detecting System Fault/Cyberattack within a Photovoltaic System Connected to the Grid: A Neural Network-Based Solution. J. Sens. Actuator Netw. 2020, 9, 20. https://doi.org/10.3390/jsan9020020

AMA Style

Gaggero GB, Rossi M, Girdinio P, Marchese M. Detecting System Fault/Cyberattack within a Photovoltaic System Connected to the Grid: A Neural Network-Based Solution. Journal of Sensor and Actuator Networks. 2020; 9(2):20. https://doi.org/10.3390/jsan9020020

Chicago/Turabian Style

Gaggero, Giovanni B., Mansueto Rossi, Paola Girdinio, and Mario Marchese. 2020. "Detecting System Fault/Cyberattack within a Photovoltaic System Connected to the Grid: A Neural Network-Based Solution" Journal of Sensor and Actuator Networks 9, no. 2: 20. https://doi.org/10.3390/jsan9020020

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