BIG DATA FOR NEW GENERATION ENERGY – BD4NRG GATHER IN RIGA
BD4NRG Project ("Big Data for New Generation Energy") had its plenary meeting this July, hosted in Riga, Latvia.
R&D Nester and REN integrate the project BD4NRG - "Big Data for New Generation Energy" under the EU funded H2020 program. This project lasts for 3 years (January 2021 to December 2023).
The main goal of BD4NRG is to address big data management and decision-making challenges in the energy sector using state-of-the art data science and Artificial Intelligence techniques. This project will therefore unlock new solutions for this sector which have never been explored in the past, giving energy stakeholders the opportunity to improve their operations and economic returns.
From a technical standpoint, the BD4NRG ecosystem will make available a set of distributed data services, including big data processing and analytics tools as well as Machine Learning and other AI based models.
The
technical solutions developed in the project will be validated through 12 large
scale pilots across 10 countries.
R&D Nester, along with REN, is contributing with one of such pilots, focusing on the application of AI solutions to condition based monitoring and predictive maintenance for two classes of transmission system assets: overhead lines and circuit breakers. Currently,
R&D
Nester and REN are
working in the development of the required algorithms to execute these data
related pilots.
The primary objective of the 5th Plenary Meeting in Riga was to ensure that each LSP (large-scale pilot) was progressing according to the pilot project schedule, and that follow-up action was taken in the case of any deviation from the defined objectives. Pilot project partners were invited to present the progress of each LSP. The current status of the LSP projects was discussed, as well as the steps necessary to reach the final evaluation cycle and expected results. Partners were encouraged to work closely together to overcome potential obstacles and thereby ensuring the success of the pilot project as a whole.
The R&D Nester presentation focused on two use cases related to LSP1 (Integrating off-grid Data with Condition based Monitoring for Enhanced Predictive Asset Maintenance).
- The first use case concerns the development of a condition-based monitoring approach for circuit breakers using historical data from incident reports. The purpose is to assess the operational conditions of circuit breakers and identify potential correlations between faults and external causes.
- The second use case involves generating a semi-automatic maintenance plan for overhead lines using historical LiDAR data obtained from right-of-way corridors and transmission towers.
The
development status of these two use cases was thoroughly discussed. The
successful integration of the services into the projects was highlighted, and
the availability of simulated data for validation was appreciated.
The presentation demonstrated R&D Nester 's commitment to data-driven solutions for the predictive maintenance of critical energy assets, and highlighted the importance of these use cases for the advancement of their technological capabilities.
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