• Rana, Soumya Prakash
• Dey, Maitreyee

Abstract

The invention provides methods and apparatus for processing of measurement data related to an electrical power grid or other electrical apparatus by using machine learning techniques and providing anomalous event detection from the electrical measurement data.Stopping climate change motivates implementation of renewable energy sources such as wind and solar with much smaller carbon footprints than non-renewable sources. However, the behaviour of renewable sources may be irregular and can bring challenges for consistent operation in power distribution systems. Utility-scale (>1 MW) solar farm owners may suffer from significant plant failure rates, reduces equipment life, unplanned outages, and replacement overheads. These problems can be countered through better condition monitoring data collection and knowledge discovery to automatically understand issues and predict problems before they occur.Monitoring tools can provide more granular and higher accuracy data capture together with precise timing information. However, there can be problems in the capacity to process the data and detect anomalous behaviour, faults and failure modes.

Topics

• Carbon
• machine learning