Detecting System Fault/Cyberattack within a Photovoltaic System Connected to the Grid: A Neural Network-Based Solution
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 p...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-04-01
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| Series: | Journal of Sensor and Actuator Networks |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2224-2708/9/2/20 |
| _version_ | 1797570184528003072 |
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| author | Giovanni Battista Gaggero Mansueto Rossi Paola Girdinio Mario Marchese |
| author_facet | Giovanni Battista Gaggero Mansueto Rossi Paola Girdinio Mario Marchese |
| author_sort | Giovanni Battista Gaggero |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T20:21:19Z |
| format | Article |
| id | doaj.art-71112d288bdd456fae0edb886ccddd0a |
| institution | Directory Open Access Journal |
| issn | 2224-2708 |
| language | English |
| last_indexed | 2024-03-10T20:21:19Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Sensor and Actuator Networks |
| spelling | doaj.art-71112d288bdd456fae0edb886ccddd0a2023-11-19T22:10:04ZengMDPI AGJournal of Sensor and Actuator Networks2224-27082020-04-01922010.3390/jsan9020020Detecting System Fault/Cyberattack within a Photovoltaic System Connected to the Grid: A Neural Network-Based SolutionGiovanni Battista Gaggero0Mansueto Rossi1Paola Girdinio2Mario Marchese3Department of Electrical, Electronic and Telecommunications Engineering, and Naval Architecture-DITEN, University of Genoa, Via Opera Pia 11A, 16145 Genoa, ItalyDepartment of Electrical, Electronic and Telecommunications Engineering, and Naval Architecture-DITEN, University of Genoa, Via Opera Pia 11A, 16145 Genoa, ItalyDepartment of Electrical, Electronic and Telecommunications Engineering, and Naval Architecture-DITEN, University of Genoa, Via Opera Pia 11A, 16145 Genoa, ItalyDepartment of Electrical, Electronic and Telecommunications Engineering, and Naval Architecture-DITEN, University of Genoa, Via Opera Pia 11A, 16145 Genoa, ItalyThe 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.https://www.mdpi.com/2224-2708/9/2/20distributed energy resourcesphotovoltaic systemscyber-securityanomaly detectionneural networksautoencoder |
| spellingShingle | Giovanni Battista Gaggero Mansueto Rossi Paola Girdinio Mario Marchese Detecting System Fault/Cyberattack within a Photovoltaic System Connected to the Grid: A Neural Network-Based Solution Journal of Sensor and Actuator Networks distributed energy resources photovoltaic systems cyber-security anomaly detection neural networks autoencoder |
| title | Detecting System Fault/Cyberattack within a Photovoltaic System Connected to the Grid: A Neural Network-Based Solution |
| title_full | Detecting System Fault/Cyberattack within a Photovoltaic System Connected to the Grid: A Neural Network-Based Solution |
| title_fullStr | Detecting System Fault/Cyberattack within a Photovoltaic System Connected to the Grid: A Neural Network-Based Solution |
| title_full_unstemmed | Detecting System Fault/Cyberattack within a Photovoltaic System Connected to the Grid: A Neural Network-Based Solution |
| title_short | Detecting System Fault/Cyberattack within a Photovoltaic System Connected to the Grid: A Neural Network-Based Solution |
| title_sort | detecting system fault cyberattack within a photovoltaic system connected to the grid a neural network based solution |
| topic | distributed energy resources photovoltaic systems cyber-security anomaly detection neural networks autoencoder |
| url | https://www.mdpi.com/2224-2708/9/2/20 |
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