Data-Driven Detection of Electricity Theft Cyberattacks in PV Generation
Most of the existing research focuses on electricity theft cyber-attacks in the consumption domain. On the contrary, a high penetration level of distributed generators (DGs) may result in increased electricity theft cyber-attacks in the distributed generation domain, which is the focus of this paper...
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2021
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| Online Access: | http://hdl.handle.net/11073/21630 |
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| _version_ | 1864513440699121664 |
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| author | Shaaban, Mostafa |
| author2 | Tariq, Usman Ismail, Muhammad Almadani, Nouf Ahmad Mokhtar, Mohamed |
| author2_role | author author author author |
| author_facet | Shaaban, Mostafa Tariq, Usman Ismail, Muhammad Almadani, Nouf Ahmad Mokhtar, Mohamed |
| author_role | author |
| dc.creator.none.fl_str_mv | Shaaban, Mostafa Tariq, Usman Ismail, Muhammad Almadani, Nouf Ahmad Mokhtar, Mohamed |
| dc.date.none.fl_str_mv | 2021-09 2022-02-08T08:54:31Z 2022-02-08T08:54:31Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | M. Shaaban, U. Tariq, M. Ismail, N. A. Almadani and M. Mokhtar, "Data-Driven Detection of Electricity Theft Cyberattacks in PV Generation," in IEEE Systems Journal, doi: 10.1109/JSYST.2021.3103272. 1937-9234 http://hdl.handle.net/11073/21630 10.1109/JSYST.2021.3103272 |
| dc.language.none.fl_str_mv | en_US |
| dc.publisher.none.fl_str_mv | IEEE |
| dc.relation.none.fl_str_mv | https://doi.org/10.1109/JSYST.2021.3103272 |
| dc.subject.none.fl_str_mv | Cyber-attacks Electricity theft Machine Learning Photo-voltaic Smart Grid |
| dc.title.none.fl_str_mv | Data-Driven Detection of Electricity Theft Cyberattacks in PV Generation |
| dc.type.none.fl_str_mv | Peer-Reviewed Postprint info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | Most of the existing research focuses on electricity theft cyber-attacks in the consumption domain. On the contrary, a high penetration level of distributed generators (DGs) may result in increased electricity theft cyber-attacks in the distributed generation domain, which is the focus of this paper. In these attacks, malicious customers can hack into the smart meters monitoring their DG units, which are usually photovoltaic (PV), and manipulate their readings to report higher injected energy to the grid and claim more profit under feed-in tariff programs. This paper proposes a data-driven approach based on machine learning to detect such thefts. We adopt an anomaly detection approach where a theft detection unit (TDU) based on a regression tree model is designed to detect suspicious data. Historical records of solar irradiance, temperature, and smart meter readings are utilized in the training stage of the detector. The probability density function of the error between the actual readings from DG meters and the predicted generation by the regression model is utilized as a metric to detect suspicious data. Several theft scenarios are used to assess the performance of the TDU. Furthermore, a comparison study with other detectors is presented to demonstrate the superiority of the proposed TDU. |
| format | article |
| id | aus_0aab085b2e6304d237a542f0c9d15e50 |
| identifier_str_mv | M. Shaaban, U. Tariq, M. Ismail, N. A. Almadani and M. Mokhtar, "Data-Driven Detection of Electricity Theft Cyberattacks in PV Generation," in IEEE Systems Journal, doi: 10.1109/JSYST.2021.3103272. 1937-9234 10.1109/JSYST.2021.3103272 |
| language_invalid_str_mv | en_US |
| network_acronym_str | aus |
| network_name_str | aus |
| oai_identifier_str | oai:repository.aus.edu:11073/21630 |
| publishDate | 2021 |
| publisher.none.fl_str_mv | IEEE |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Data-Driven Detection of Electricity Theft Cyberattacks in PV GenerationShaaban, MostafaTariq, UsmanIsmail, MuhammadAlmadani, Nouf AhmadMokhtar, MohamedCyber-attacksElectricity theftMachine LearningPhoto-voltaicSmart GridMost of the existing research focuses on electricity theft cyber-attacks in the consumption domain. On the contrary, a high penetration level of distributed generators (DGs) may result in increased electricity theft cyber-attacks in the distributed generation domain, which is the focus of this paper. In these attacks, malicious customers can hack into the smart meters monitoring their DG units, which are usually photovoltaic (PV), and manipulate their readings to report higher injected energy to the grid and claim more profit under feed-in tariff programs. This paper proposes a data-driven approach based on machine learning to detect such thefts. We adopt an anomaly detection approach where a theft detection unit (TDU) based on a regression tree model is designed to detect suspicious data. Historical records of solar irradiance, temperature, and smart meter readings are utilized in the training stage of the detector. The probability density function of the error between the actual readings from DG meters and the predicted generation by the regression model is utilized as a metric to detect suspicious data. Several theft scenarios are used to assess the performance of the TDU. Furthermore, a comparison study with other detectors is presented to demonstrate the superiority of the proposed TDU.American University of SharjahDubai Electricity and Water AuthorityIEEE2022-02-08T08:54:31Z2022-02-08T08:54:31Z2021-09Peer-ReviewedPostprintinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfM. Shaaban, U. Tariq, M. Ismail, N. A. Almadani and M. Mokhtar, "Data-Driven Detection of Electricity Theft Cyberattacks in PV Generation," in IEEE Systems Journal, doi: 10.1109/JSYST.2021.3103272.1937-9234http://hdl.handle.net/11073/2163010.1109/JSYST.2021.3103272en_UShttps://doi.org/10.1109/JSYST.2021.3103272oai:repository.aus.edu:11073/216302024-08-22T12:08:16Z |
| spellingShingle | Data-Driven Detection of Electricity Theft Cyberattacks in PV Generation Shaaban, Mostafa Cyber-attacks Electricity theft Machine Learning Photo-voltaic Smart Grid |
| status_str | publishedVersion |
| title | Data-Driven Detection of Electricity Theft Cyberattacks in PV Generation |
| title_full | Data-Driven Detection of Electricity Theft Cyberattacks in PV Generation |
| title_fullStr | Data-Driven Detection of Electricity Theft Cyberattacks in PV Generation |
| title_full_unstemmed | Data-Driven Detection of Electricity Theft Cyberattacks in PV Generation |
| title_short | Data-Driven Detection of Electricity Theft Cyberattacks in PV Generation |
| title_sort | Data-Driven Detection of Electricity Theft Cyberattacks in PV Generation |
| topic | Cyber-attacks Electricity theft Machine Learning Photo-voltaic Smart Grid |
| url | http://hdl.handle.net/11073/21630 |