A distributed data‐driven modelling framework for power flow estimation in power distribution systems
Abstract The power distribution system has increasing importance and complexity as a result of the exponential growth in the adoption of smart grid technologies. The ability to model the power distribution system is critical to ensure a smooth transition to a sustainable power system. This study pre...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-09-01
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| Series: | IET Energy Systems Integration |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/esi2.12035 |
| _version_ | 1811180268951699456 |
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| author | Hasala Dharmawardena Ganesh K. Venayagamoorthy |
| author_facet | Hasala Dharmawardena Ganesh K. Venayagamoorthy |
| author_sort | Hasala Dharmawardena |
| collection | DOAJ |
| description | Abstract The power distribution system has increasing importance and complexity as a result of the exponential growth in the adoption of smart grid technologies. The ability to model the power distribution system is critical to ensure a smooth transition to a sustainable power system. This study presents a distributed data‐driven framework based on Cellular Computational Networks (CCN) for power distribution system modelling where the CCN framework facilitates for system decomposition. The learning in CCN is distributed and asynchronous, thus adaptive models can be developed. The computational engine of the CCN cells is based on data‐driven, physics‐driven, or a hybrid approach. The CCN‐based distribution system modelling secures the privacy and security of the sensitive utility information, thus allowing third‐party application providers access to system models and behaviours. The application of a CCN‐based power flow model is illustrated on a modified IEEE 34 test system. Typical results show the suitability of the new approach in modelling the sample distribution system, as well as its enhanced performance when compared with the centralised modelling approach. |
| first_indexed | 2024-04-11T06:47:30Z |
| format | Article |
| id | doaj.art-e9c7e0fa077043b99d8f8d647605bca8 |
| institution | Directory Open Access Journal |
| issn | 2516-8401 |
| language | English |
| last_indexed | 2024-04-11T06:47:30Z |
| publishDate | 2021-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Energy Systems Integration |
| spelling | doaj.art-e9c7e0fa077043b99d8f8d647605bca82022-12-22T04:39:19ZengWileyIET Energy Systems Integration2516-84012021-09-013336737910.1049/esi2.12035A distributed data‐driven modelling framework for power flow estimation in power distribution systemsHasala Dharmawardena0Ganesh K. Venayagamoorthy1Real‐Time Power and Intelligent Systems Laboratory Department of Electrical and Computer Engineering Clemson University Clemson South Carolina USAReal‐Time Power and Intelligent Systems Laboratory Department of Electrical and Computer Engineering Clemson University Clemson South Carolina USAAbstract The power distribution system has increasing importance and complexity as a result of the exponential growth in the adoption of smart grid technologies. The ability to model the power distribution system is critical to ensure a smooth transition to a sustainable power system. This study presents a distributed data‐driven framework based on Cellular Computational Networks (CCN) for power distribution system modelling where the CCN framework facilitates for system decomposition. The learning in CCN is distributed and asynchronous, thus adaptive models can be developed. The computational engine of the CCN cells is based on data‐driven, physics‐driven, or a hybrid approach. The CCN‐based distribution system modelling secures the privacy and security of the sensitive utility information, thus allowing third‐party application providers access to system models and behaviours. The application of a CCN‐based power flow model is illustrated on a modified IEEE 34 test system. Typical results show the suitability of the new approach in modelling the sample distribution system, as well as its enhanced performance when compared with the centralised modelling approach.https://doi.org/10.1049/esi2.12035distributed power generationdistribution networksload flowpower system simulationpower distribution controlsmart power grids |
| spellingShingle | Hasala Dharmawardena Ganesh K. Venayagamoorthy A distributed data‐driven modelling framework for power flow estimation in power distribution systems IET Energy Systems Integration distributed power generation distribution networks load flow power system simulation power distribution control smart power grids |
| title | A distributed data‐driven modelling framework for power flow estimation in power distribution systems |
| title_full | A distributed data‐driven modelling framework for power flow estimation in power distribution systems |
| title_fullStr | A distributed data‐driven modelling framework for power flow estimation in power distribution systems |
| title_full_unstemmed | A distributed data‐driven modelling framework for power flow estimation in power distribution systems |
| title_short | A distributed data‐driven modelling framework for power flow estimation in power distribution systems |
| title_sort | distributed data driven modelling framework for power flow estimation in power distribution systems |
| topic | distributed power generation distribution networks load flow power system simulation power distribution control smart power grids |
| url | https://doi.org/10.1049/esi2.12035 |
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