Grid Maintenance Optimization for Kärnten Netz GmbH
The trend towards a more distributed energy ecosystem leads to higher complexity of operations for stakeholders. Distribution grid operators like Kärnten Netz GmbH in Austria strive to avoid outages by maintaining their extensive infrastructure in good health even with a growing amount of distributed energy sources. The involved maintenance efforts are substantial, consequently data-driven solutions predicting failure of critical assets can increase maintenance efficiency tremendously while reducing outage risk.
Our client is expected to advance the sustainable energy system, facing high maintenance efforts for a grid that has to cope with a growing amount of distributed energy sources and increasing overall grid complexity. In particular, outage risk caused by environmental factors, such as weather or soil conditions, and higher stress on assets through fluctuating sources pose challenges to the grid. Budget constraints and the desire to postpone infrastructure replacements where feasible characterize the client's general ecosystem challenges while the demand for optimized maintenance operations and a more sustainable grid grows.
By identifying, integrating, enriching and interpreting data, we co-created a decision support system to predict future outages.
- We identified influencing parameters and available data sources
- We integrated and enriched our client's own data with available external data sources, such as weather data
- Working hand-in-hand with our client and leveraging our domain expertise, we created an outage prediction application based on Artificial Intelligence
Our data-driven outage prediction model optimized our client's asset management, prioritizing maintenance on assets with highest failure probability.
The most crucial benefits for our clients are:
1. Prediction accuracy for future failures at named assets >90%
2. Optimized utilization of maintenance resources due to dynamic inspection schedules
3. Smart route optimization for workforce crew as spin-off for added value
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