Summary
General awareness about the smart grid technologies has improved in the last decade due to various energy liberalization actions taken by the European Union. However, the lack of well-developed technologies, has been main cause of slow acceptance of smart grids. This calls for the identification of unexplored research areas in smart grids. Positive outcomes of the research can help in laying down new and well-defined standards for the smart grids and associated intelligent technologies. A convenient and easily integrable product can also help in encouraging various distribution system operators to accept the new technologies. Massive amount of data is already being collected from the distribution networks using smart meters. Rapid advancements in machine learning research have opened up new avenues for data utilization in smart grid.
Forerunners like DEPsys (a smart grid technology company based in Switzerland), have now simplified the distribution system data for further analysis and research. A critical concern raised by DEPsys customers, is their inability to trace the source of power quality issues in the distribution network, which in-turn leads to both energy and economic losses over time. This project builds up on existing infrastructure of DEPsys and aims to be an AMROUR (by improving robustness) for distribution networks against power quality events. The main objectives are: (i) leveraging machine learning for condition monitoring and tracing power quality events, and (ii) to develop a smart grid technology which assists the distribution system operators in prevention and diagnosis of power quality events.
Forerunners like DEPsys (a smart grid technology company based in Switzerland), have now simplified the distribution system data for further analysis and research. A critical concern raised by DEPsys customers, is their inability to trace the source of power quality issues in the distribution network, which in-turn leads to both energy and economic losses over time. This project builds up on existing infrastructure of DEPsys and aims to be an AMROUR (by improving robustness) for distribution networks against power quality events. The main objectives are: (i) leveraging machine learning for condition monitoring and tracing power quality events, and (ii) to develop a smart grid technology which assists the distribution system operators in prevention and diagnosis of power quality events.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/890844 |
| Start date: | 15-10-2020 |
| End date: | 14-10-2022 |
| Total budget - Public funding: | 191 149,44 Euro - 191 149,00 Euro |
Cordis data
Original description
General awareness about the smart grid technologies has improved in the last decade due to various energy liberalization actions taken by the European Union. However, the lack of well-developed technologies, has been main cause of slow acceptance of smart grids. This calls for the identification of unexplored research areas in smart grids. Positive outcomes of the research can help in laying down new and well-defined standards for the smart grids and associated intelligent technologies. A convenient and easily integrable product can also help in encouraging various distribution system operators to accept the new technologies. Massive amount of data is already being collected from the distribution networks using smart meters. Rapid advancements in machine learning research have opened up new avenues for data utilization in smart grid.Forerunners like DEPsys (a smart grid technology company based in Switzerland), have now simplified the distribution system data for further analysis and research. A critical concern raised by DEPsys customers, is their inability to trace the source of power quality issues in the distribution network, which in-turn leads to both energy and economic losses over time. This project builds up on existing infrastructure of DEPsys and aims to be an AMROUR (by improving robustness) for distribution networks against power quality events. The main objectives are: (i) leveraging machine learning for condition monitoring and tracing power quality events, and (ii) to develop a smart grid technology which assists the distribution system operators in prevention and diagnosis of power quality events.
Status
CLOSEDCall topic
MSCA-IF-2019Update Date
28-04-2024
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