Small hydropower plants play a central role in decentralised, sustainable energy generation in our Swiss Alps. However, the harsh environment, strong temperature fluctuations and inaccessible locations place high demands on the operation and maintenance of the plants. This is exactly where predictive maintenance plays a role. The aim is to avoid breakdowns, reduce maintenance costs and ensure long-term system availability.
Predictive maintenance uses sensor technology, data analysis and machine learning to continuously monitor the condition of system components. Instead of carrying out maintenance at fixed intervals or reacting to faults (reactive maintenance), predictive maintenance enables targeted maintenance at the right time.
For example, a small run-of-river power plant in the canton of Graubünden uses predictive maintenance to monitor bearing and shaft vibrations in the turbine, temperature curves in the generator and vibration patterns in the control hydraulics. The installed sensors send the recorded data in real time to a cloud platform, where algorithms recognise deviations from defined normal states. This enabled an anomaly in the bearing raceway to be detected at an early stage and rectified during a planned shutdown without any loss of production.
The advantages for small Alpine hydropower plants are obvious:
- Avoidance of unplanned downtimes
- Targeted maintenance work in difficult-to-access locations
- Extending the service life of critical components
- Improved planning of spare parts procurement and personnel resources
- Data-driven investment decisions
Especially in remote alpine regions with limited maintenance personnel and high logistics costs, predictive maintenance is an essential key to increasing the efficiency of small hydropower plants. The technology makes it possible to introduce a smart, resource-saving maintenance strategy and ensures the continuous supply of renewable energy to the grid. Even where traditional maintenance approaches reach their limits. In the area of predictive maintenance as well as continuous data acquisition and forecasting, we collaborate with Mechmine GmbH.
