Concept & Challenges

Di-Hydro’s innovative concept involves introducing a Decision-Making Platform and Digital Twin technology to the hydropower sector. Our primary goal is to address the digital divide among hydropower plants and clusters while enhancing their operational performance and environmental sustainability.

Digital Twin for Clusters

Di-Hydro will create a Digital Twin platform for all hydropower entities within a country, forming clusters for collaborative operations and maintenance.

Advanced Technology

Our approach utilises federated architecture, decentralised storage, AI technologies, and Reinforcement Learning for resilience and robustness.

Data Integration
We analyse historical data, implement advanced sensors, and integrate market forecasting to optimise operations and participation in wholesale markets.

Our solution reduces maintenance costs, increasing competitiveness and sustainability, aligning with the European Green Deal’s goals.


Di-Hydro’s approach centres on the following key areas:

Structural Health Monitoring

Development of a real-time, low-cost monitoring system for assessing the structural health of hydropower structures. It provides Damage Tolerance assessments and supports automatic decision-making.

Condition Monitoring

The solution proposed will use wireless sensors for vibration, pressure, temperature, and oil conditions. It issues intelligent reports and early maintenance warnings, enabling continuous updates in condition monitoring of critical HP machinery.

Biofouling Prevention

Di-Hydro will introduce a novel solution using ultrasonic technology to prevent and clean biofouling in hydropower heat exchangers, reducing economic impact and downtime.

Unmanned Underwater Inspection

Advanced unmanned underwater vehicles visually inspect water reservoirs and pipelines, enhancing maintenance and safety.

Environmental and biodiversity monitoring, modelling and forecasting

Di-Hydro will develop water quality sensors for monitoring environmental and biodiversity parameters for upstream and downstream locations of the HPP catchment. Parameters such as pH, dissolved oxygen, oxidation reduction potential (ORP), total dissolved solids (TDS), salinity, turbidity, electrical conductivity (EC), ammonium, nitrate, residual chlorine, temperature, concentration of including pathogens and toxins. The monitoring data will be combined with historical data to monitor provide biodiversity and environmental forecasting.

Water and flow monitoring / Forecasting models

Weather and water flow monitoring will be combined with historical and satellite data to develop AI prediction models for short, medium and long forecasting to assist HPPs in decision-making.

Expected results

Innovative sensors for hydropower machinery operation

Hydropower structural health monitoring and prediction

Hydropower inspection and automatic defect detection using underwater unmanned vehicle

Monitoring and predictive models for biodiversity and environmental effects of hydropower operations and maintenance

Forecasting models for weather and water flow of hydropower plants

Digital twin of hydropower plants and cluster connectivity

AI-based decision support platform for hydropower plants and clusters

Advanced encryption algorithms for hydropower
data collection, exchange and storage

Sister projects

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Sister projects

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Sister projects

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