Smart Power Systems Transformation: Advanced Fault Detection With Edge Computing and Signal Processing in LV Networks With EV Charging Stations
<p dir="ltr">This research presents a novel framework for improving fault detection and grid resilience in modern power systems by leveraging edge computing, optimized infrastructure placement, and advanced signal processing. At the core of the approach is an innovative time-frequenc...
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| مؤلفون آخرون: | , , , , , , |
| منشور في: |
2025
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| _version_ | 1864513523550257152 |
|---|---|
| author | Partha Chakraborty (12423883) |
| author2 | Pampa Sinha (19864778) Subhra Debdas (22828307) Kaushik Paul (19864781) Chidurala Saiprakash (14254520) Vasupalli Manoj (22828310) Taha Selim Ustun (22393126) Ahmet Onen (20838293) |
| author2_role | author author author author author author author |
| author_facet | Partha Chakraborty (12423883) Pampa Sinha (19864778) Subhra Debdas (22828307) Kaushik Paul (19864781) Chidurala Saiprakash (14254520) Vasupalli Manoj (22828310) Taha Selim Ustun (22393126) Ahmet Onen (20838293) |
| author_role | author |
| dc.creator.none.fl_str_mv | Partha Chakraborty (12423883) Pampa Sinha (19864778) Subhra Debdas (22828307) Kaushik Paul (19864781) Chidurala Saiprakash (14254520) Vasupalli Manoj (22828310) Taha Selim Ustun (22393126) Ahmet Onen (20838293) |
| dc.date.none.fl_str_mv | 2025-07-25T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2025.3586062 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Smart_Power_Systems_Transformation_Advanced_Fault_Detection_With_Edge_Computing_and_Signal_Processing_in_LV_Networks_With_EV_Charging_Stations/31289194 |
| 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 Distributed computing and systems software Machine learning Power system protection Hill climbing search SBCT Short circuit fault Edge computing Smart cities LV networks |
| dc.title.none.fl_str_mv | Smart Power Systems Transformation: Advanced Fault Detection With Edge Computing and Signal Processing in LV Networks With EV Charging Stations |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">This research presents a novel framework for improving fault detection and grid resilience in modern power systems by leveraging edge computing, optimized infrastructure placement, and advanced signal processing. At the core of the approach is an innovative time-frequency analysis method that enhances fault discrimination, even in noisy environments. By strategically positioning smart meters and Electric Vehicle (EV) charging stations, the framework improves fault detection efficiency and overall system reliability. The Adaptive SBCT index dynamically fine-tunes fault identification, ensuring a more responsive power grid. Additionally, Kernel Principal Component Analysis (KPCA) streamlines data processing without compromising critical information, enhancing real-time performance. Extensive simulations and case studies validate the framework’s effectiveness across diverse low-voltage networks, demonstrating its potential to minimize power outages, reduce maintenance costs, and strengthen grid reliability. Future directions include large-scale real-world deployment and integration with renewable energy sources to further enhance system sustainability and scalability.</p><h2 dir="ltr">Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" rel="noreferrer noopener" 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.2025.3586062" target="_blank">https://dx.doi.org/10.1109/access.2025.3586062</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_49a5eec5885e2eec86d3e407fee1a5e1 |
| identifier_str_mv | 10.1109/access.2025.3586062 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/31289194 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Smart Power Systems Transformation: Advanced Fault Detection With Edge Computing and Signal Processing in LV Networks With EV Charging StationsPartha Chakraborty (12423883)Pampa Sinha (19864778)Subhra Debdas (22828307)Kaushik Paul (19864781)Chidurala Saiprakash (14254520)Vasupalli Manoj (22828310)Taha Selim Ustun (22393126)Ahmet Onen (20838293)EngineeringElectrical engineeringInformation and computing sciencesArtificial intelligenceDistributed computing and systems softwareMachine learningPower system protectionHill climbing searchSBCTShort circuit faultEdge computingSmart citiesLV networks<p dir="ltr">This research presents a novel framework for improving fault detection and grid resilience in modern power systems by leveraging edge computing, optimized infrastructure placement, and advanced signal processing. At the core of the approach is an innovative time-frequency analysis method that enhances fault discrimination, even in noisy environments. By strategically positioning smart meters and Electric Vehicle (EV) charging stations, the framework improves fault detection efficiency and overall system reliability. The Adaptive SBCT index dynamically fine-tunes fault identification, ensuring a more responsive power grid. Additionally, Kernel Principal Component Analysis (KPCA) streamlines data processing without compromising critical information, enhancing real-time performance. Extensive simulations and case studies validate the framework’s effectiveness across diverse low-voltage networks, demonstrating its potential to minimize power outages, reduce maintenance costs, and strengthen grid reliability. Future directions include large-scale real-world deployment and integration with renewable energy sources to further enhance system sustainability and scalability.</p><h2 dir="ltr">Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" rel="noreferrer noopener" 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.2025.3586062" target="_blank">https://dx.doi.org/10.1109/access.2025.3586062</a></p>2025-07-25T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2025.3586062https://figshare.com/articles/journal_contribution/Smart_Power_Systems_Transformation_Advanced_Fault_Detection_With_Edge_Computing_and_Signal_Processing_in_LV_Networks_With_EV_Charging_Stations/31289194CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/312891942025-07-25T03:00:00Z |
| spellingShingle | Smart Power Systems Transformation: Advanced Fault Detection With Edge Computing and Signal Processing in LV Networks With EV Charging Stations Partha Chakraborty (12423883) Engineering Electrical engineering Information and computing sciences Artificial intelligence Distributed computing and systems software Machine learning Power system protection Hill climbing search SBCT Short circuit fault Edge computing Smart cities LV networks |
| status_str | publishedVersion |
| title | Smart Power Systems Transformation: Advanced Fault Detection With Edge Computing and Signal Processing in LV Networks With EV Charging Stations |
| title_full | Smart Power Systems Transformation: Advanced Fault Detection With Edge Computing and Signal Processing in LV Networks With EV Charging Stations |
| title_fullStr | Smart Power Systems Transformation: Advanced Fault Detection With Edge Computing and Signal Processing in LV Networks With EV Charging Stations |
| title_full_unstemmed | Smart Power Systems Transformation: Advanced Fault Detection With Edge Computing and Signal Processing in LV Networks With EV Charging Stations |
| title_short | Smart Power Systems Transformation: Advanced Fault Detection With Edge Computing and Signal Processing in LV Networks With EV Charging Stations |
| title_sort | Smart Power Systems Transformation: Advanced Fault Detection With Edge Computing and Signal Processing in LV Networks With EV Charging Stations |
| topic | Engineering Electrical engineering Information and computing sciences Artificial intelligence Distributed computing and systems software Machine learning Power system protection Hill climbing search SBCT Short circuit fault Edge computing Smart cities LV networks |