AI for enhanced network assets predictive maintenance, integrating off-grid data with condition-based monitoring
AI services to be used to foster predictive maintenance strategies for system operators.
What is the challenge that is being addressed?
Over the last years, the amount of data streams and data sources have been increasing, showing the need for improved data analytics tools in order to derive predictive models for assets maintenance, which are expected to extend assets lifetime and/or reduce O&M assets costs, and hence enable in a later stage the integration of asset management with grid improved network operation.
What is the AI solution the project plans to implement?
This pilot aims to assess the condition of assets such as, Circuit Breakers (CB), in order to provide answers to two key challenges in asset management: predictive maintenance and implementing maintenance plans. This pilot will provide new AI services that can be used to foster predictive maintenance strategies for system operators. An AI energy analytics application will be developed that allows creating a condition-based monitoring strategy in order to evaluate the operation of CBs and determine the probability of failure taking into account different operational conditions. Main expected benefits will be consisting in optimising maintenance costs and hence improve reliability of the grid operations.
Who will help implement this solution?
This pilot is implemented within the framework of the “I-NΕRGΥ: Artificial Intelligence for Next Generation Energy” Project. The I-NERGY Project has received funding from the European Union's Horizon 2020 Research and Innovation programme under grant agreement No. 101016508.
The responsible partner for this Use Case is R&D Nester (https://www.rdnester.com/en-GB).