The power system is equipped with a number of sensors that constantly register the power and current flowing through the system. Many of these sensors have very high resolution; taking thousands of measurements per second. At this resolution, the sensors may register small disturbances in the system, caused for instance by equipment that is damaged or weakened, but has yet to fail completely.
It will be of great value if weaknesses can be detected at this point, so actions may be taken before failure occurs.
At the same time, such sensors generate enormous amounts of data. Manual interpretation is cumbersome, and impossible to do constantly or in real time. EarlyWarn will therefore apply techniques from Big Data, artificial intelligence (AI) and machine learning to automatically and constantly monitor the sensor data, in order to alert system operators of instabilities or disturbances that would otherwise have gone unnoticed.
- Lyse Elnett
- Nettalliansen / Hallingdal Kraftnett
- SINTEF Digital
- Hydro Energi AS
- Haugaland Kraft AS Nett
This is a KPN-project (Competence building project) financed by the Research Council of Norway - ENERGIX programme.
ENERGIX is a large-scale programme for energy research in the Research Council of Norway. The ENERGIX-programme provides funding for research on renewable energy, efficient use of energy, energy systems and energy policy. The programme is a key instrument in the implementation of Norway's national RD&D strategy, Energi21, as well as for achieving other energy policy objectives.