PREDICTIVE MAINTENANCE

In this case study, ENGIE collaborates with Modelon to build a digital twin for predictive maintenance on solar power PV plant.  ENGIE, a leading global utility company, made a strategic shift several years ago – committing to accelerate the growth of renewable energy and the transition towards a carbon-neutral economy. With currently hundreds of solar installations around the world, ENGIE aims to double the capacity and production of solar power supply by investing in innovative solutions that can harness solar power – including concentrated solar power plants and organic photovoltaic, centralized, and decentralized production solutions, sometimes combined with energy storage.

To ensure ENGIE’s reliable, efficient, and sustainable electricity mix, ENGIE’s growing solar capacity needs to operate reliably and efficiently. Learn how ENGIE successfully collaborated with Modelon to develop a digital twin for predictive maintenance – monitor performance, identify and anticipate failures, and schedule maintenance.

“Modelon’s team of industry experts gave us the confidence to move forward. Their knowledge of renewable systems and infrastructure as well as their physics-based modeling was exactly what we needed. In a short amount of time, we were able to have a model in hand, integrate it into our own platform, and resolve an error that was preventing our solar power plant from running efficiently – saving time and money.”

Benedicte Piret, Project leader, and
Cristian Solís, IT integration specialist

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