A Deep Convolutional Neural Network-Based Approach to Detect False Data Injection Attacks on PV-Integrated Distribution Systems
<p dir="ltr">The integration of photovoltaic (PV) panels has allowed power distribution systems (PDSs) to regulate their voltage through the injection/absorption of reactive power. The deployment of information and communication technologies (ICTs), which is required for this scheme,...
محفوظ في:
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| مؤلفون آخرون: | , , , |
| منشور في: |
2024
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| _version_ | 1864513545571401728 |
|---|---|
| author | Masoud Ahmadzadeh (21633053) |
| author2 | Ahmadreza Abazari (21633056) Mohsen Ghafouri (21633059) Amir Ameli (12512083) S. M. Muyeen (14778337) |
| author2_role | author author author author |
| author_facet | Masoud Ahmadzadeh (21633053) Ahmadreza Abazari (21633056) Mohsen Ghafouri (21633059) Amir Ameli (12512083) S. M. Muyeen (14778337) |
| author_role | author |
| dc.creator.none.fl_str_mv | Masoud Ahmadzadeh (21633053) Ahmadreza Abazari (21633056) Mohsen Ghafouri (21633059) Amir Ameli (12512083) S. M. Muyeen (14778337) |
| dc.date.none.fl_str_mv | 2024-01-09T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2023.3348549 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/A_Deep_Convolutional_Neural_Network-Based_Approach_to_Detect_False_Data_Injection_Attacks_on_PV-Integrated_Distribution_Systems/29445587 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Electrical engineering Information and computing sciences Artificial intelligence Machine learning Distribution systems false data injection cyberattacks convolutional neural network photovoltaic voltage regulation Voltage control Voltage measurement Cyberattack Convolutional neural networks Uncertainty Reactive power Photovoltaic systems |
| dc.title.none.fl_str_mv | A Deep Convolutional Neural Network-Based Approach to Detect False Data Injection Attacks on PV-Integrated Distribution Systems |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">The integration of photovoltaic (PV) panels has allowed power distribution systems (PDSs) to regulate their voltage through the injection/absorption of reactive power. The deployment of information and communication technologies (ICTs), which is required for this scheme, has made the PDS prone to various cyber threats, e.g., false data injection (FDI) attacks. To counter these attacks, this paper proposes a data-driven framework to detect FDI attacks against voltage regulation of PV-integrated PDS. Initially, an attack-free system is modeled along with its voltage regulation scheme, where the grid measurements are sent to a centralized controller and the control signals are transmitted back to PVs to be used by their local controllers. Then, a convolutional neural network (CNN) framework is proposed to detect FDI attacks. To train this framework—which should be able to distinguish between normal grid behaviors and attacks—a complete and realistic dataset is formed to cover all normal conditions and unpredictable changes of a PDS during a year. Since normal variations and fluctuations in power consumption lead to changes in the voltage profile, this dataset is enriched using features such as season, weekdays, weekends, load conditions, and PV generation power. The performance of the trained framework has been compared with other supervised Machine Learning-based and deep-learning techniques for FDI attacks against modified IEEE 33- and 141-bus PDSs. Simulation results demonstrate the superior performance of the proposed framework in detecting FDI attacks.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2023.3348549" target="_blank">https://dx.doi.org/10.1109/access.2023.3348549</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_18ad3d2477bf07847273d082d69913dd |
| identifier_str_mv | 10.1109/access.2023.3348549 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/29445587 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | A Deep Convolutional Neural Network-Based Approach to Detect False Data Injection Attacks on PV-Integrated Distribution SystemsMasoud Ahmadzadeh (21633053)Ahmadreza Abazari (21633056)Mohsen Ghafouri (21633059)Amir Ameli (12512083)S. M. Muyeen (14778337)EngineeringElectrical engineeringInformation and computing sciencesArtificial intelligenceMachine learningDistribution systemsfalse data injectioncyberattacksconvolutional neural networkphotovoltaicvoltage regulationVoltage controlVoltage measurementCyberattackConvolutional neural networksUncertaintyReactive powerPhotovoltaic systems<p dir="ltr">The integration of photovoltaic (PV) panels has allowed power distribution systems (PDSs) to regulate their voltage through the injection/absorption of reactive power. The deployment of information and communication technologies (ICTs), which is required for this scheme, has made the PDS prone to various cyber threats, e.g., false data injection (FDI) attacks. To counter these attacks, this paper proposes a data-driven framework to detect FDI attacks against voltage regulation of PV-integrated PDS. Initially, an attack-free system is modeled along with its voltage regulation scheme, where the grid measurements are sent to a centralized controller and the control signals are transmitted back to PVs to be used by their local controllers. Then, a convolutional neural network (CNN) framework is proposed to detect FDI attacks. To train this framework—which should be able to distinguish between normal grid behaviors and attacks—a complete and realistic dataset is formed to cover all normal conditions and unpredictable changes of a PDS during a year. Since normal variations and fluctuations in power consumption lead to changes in the voltage profile, this dataset is enriched using features such as season, weekdays, weekends, load conditions, and PV generation power. The performance of the trained framework has been compared with other supervised Machine Learning-based and deep-learning techniques for FDI attacks against modified IEEE 33- and 141-bus PDSs. Simulation results demonstrate the superior performance of the proposed framework in detecting FDI attacks.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2023.3348549" target="_blank">https://dx.doi.org/10.1109/access.2023.3348549</a></p>2024-01-09T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2023.3348549https://figshare.com/articles/journal_contribution/A_Deep_Convolutional_Neural_Network-Based_Approach_to_Detect_False_Data_Injection_Attacks_on_PV-Integrated_Distribution_Systems/29445587CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/294455872024-01-09T03:00:00Z |
| spellingShingle | A Deep Convolutional Neural Network-Based Approach to Detect False Data Injection Attacks on PV-Integrated Distribution Systems Masoud Ahmadzadeh (21633053) Engineering Electrical engineering Information and computing sciences Artificial intelligence Machine learning Distribution systems false data injection cyberattacks convolutional neural network photovoltaic voltage regulation Voltage control Voltage measurement Cyberattack Convolutional neural networks Uncertainty Reactive power Photovoltaic systems |
| status_str | publishedVersion |
| title | A Deep Convolutional Neural Network-Based Approach to Detect False Data Injection Attacks on PV-Integrated Distribution Systems |
| title_full | A Deep Convolutional Neural Network-Based Approach to Detect False Data Injection Attacks on PV-Integrated Distribution Systems |
| title_fullStr | A Deep Convolutional Neural Network-Based Approach to Detect False Data Injection Attacks on PV-Integrated Distribution Systems |
| title_full_unstemmed | A Deep Convolutional Neural Network-Based Approach to Detect False Data Injection Attacks on PV-Integrated Distribution Systems |
| title_short | A Deep Convolutional Neural Network-Based Approach to Detect False Data Injection Attacks on PV-Integrated Distribution Systems |
| title_sort | A Deep Convolutional Neural Network-Based Approach to Detect False Data Injection Attacks on PV-Integrated Distribution Systems |
| topic | Engineering Electrical engineering Information and computing sciences Artificial intelligence Machine learning Distribution systems false data injection cyberattacks convolutional neural network photovoltaic voltage regulation Voltage control Voltage measurement Cyberattack Convolutional neural networks Uncertainty Reactive power Photovoltaic systems |