ARMOUR · smARt Monitoring Of distribUtion netwoRks for robust power quality
Horizon 2020 — Marie Skłodowska-Curie Actions
- Duration
- 2020-10-15 → 2022-10-14
- EU contribution
- €191,149
- Participants
- 1
- Scheme
- MSCA-IF
Lines connect the coordinator with its partners.
Results in brief
smARt Monitoring Of distribUtion netwoRks for robust power quality
The project ARMOUR aimed to develop methodologies for root-cause analysis of power quality (PQ) issues by combining time series correlations and statistical data analysis and information derived from a knowledge highlighting the interlinkages between different PQ issues and external contributing factors. The purpose was to assist the distribution system operators (DSOs) in mitigation/ reduction of PQ emissions through automated recommendations based on the root-cause analysis. Solutions for power quality (PQ) issue source-tracing and alleviation lead to lesser outages and ditribution losses towards customer end. This implies customer satisfaction and economic distribution network. Energy conserved is energy produced therefore the project ultimately contributes to both green and digital intiatives of the European society by utilizing digital tools to build tools for a more sustainable ecosystem. The specific goal of the project was to provide maximum possible insights on PQ situation with minimal monitoring, without complete knowledge of network topology while respecting data privacy. The main objectives are: leveraging machine learning for condition monitoring and tracing power quality events, and to develop a smart grid technology which assists the distribution system operators in prevention and diagnosis of power quality events.
Data: CORDIS, © European Union
Project objective
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.
Original text from CORDIS.
Participants
- DEPSYS SA · PUIDOUXCoordinatorSwitzerland
Links
Data: CORDIS, © European Union