We develop a cloud-based monitoring and diagnostics centre that will provide hydropower plant operators with remote, real-time insights into overall plant operations, sources of unplanned downtime and process inefficiencies. Our cloud platform will be the backbone for the integration of all D-HYDROFLEX tools and will support their setup at demonstration sites, assuring the replicability and scalability potential of the proposed solutions.

Tools of Hydropower toolkit 

Hydro unit digital twin

This tool allows developing digital twin of the hydro-unit for mirroring the turbine set and visualizing the sensor data and monitoring condition in digital model to facilitate the monitoring and maintenance process. 

Dam digital twin

This tool defines and implements methods and solutions to integrate geometric, non-geometric and management information into the asset management platform, including geometrics and semantics of dam components and the relationships among them. 

Digital twin geometric model generation framework

This is an AI-based framework that will automatically generate digital twin model of a dam. Tool development will include capturing spatial and visual data from the dam on-site, as well as importing, registering, and integrating data.

Fault detection and predictive maintenance system

This system utilizes various data sources, including vibration measurements and SCADA data, to detect abnormalities. The system considers data such as raw acceleration, speed, displacement measures, and frequency-related data. ISO 7919-5:2005 norms are used to compare vibration levels with hydropower standards. Advanced techniques like clustering and Normal Behaviour Modelling are employed for effective analysis and fault detection, utilizing neural networks and linear approaches for model implementation.

Decision support module for hybrid HPPs

It is a planning tool for hydro power plants featuring a hydrolyser and H2 storage. The tool provides recommendations about H2 production planning based on forecast of the energy produced by the HPP. This kind of hybridisation can be managed using well established optimal control or model-based predictive control strategies that lead to optimal operation planning.

Forecasting tool

The tool integrates a weather forecast downscaling model using artificial intelligence, specifically Bayesian Belief Networks, to refine ECMWF weather forecasts. Coupled with a daily hydrological rainfall-runoff model, it predicts inflow to hydroelectric power plant water intakes. The model incorporates snow and soil moisture dynamics, producing power production predictions based on hydro power plant characteristic curves and recorded data.

Biodiversity index tool

This tool utilizes acoustic cameras for efficient fish monitoring. An automated method, employing computer vision and image processing, will be developed to identify fish species based on their global shape and swimming patterns. This approach will be tested on various acoustic camera models, enhancing fish monitoring capabilities. Additionally, environmental indicators will be observed to assess the biotope condition.


It is a federated cybersecurity solution for energy systems, detecting diverse cyber threats using deep intrusion/anomaly detection models. It employs a centralised federated learning approach, combining local deep learning-based models, federated clients, and a federated server to enhance cybersecurity by orchestrating and distributing models personalized for each use case.

Join our newsletter

Stay up to date with information about our project