Scenario

In the context of an innovation project carried out in collaboration with Eurac Research, the IT company SAIDEA Srl has appointed us to develop a predictive maintenance model to optimize O&M activities on photovoltaic plants.

Problem

Maintenance activities in a photovoltaic plant involve high costs and require time: the higher the number of failures or breakdowns, the more challenging become maintenance routine operations. Being able to prevent such breakdowns allows prompt intervention, which in turn results in a reduction of both technical downtimes and associated costs.
Given the high level of competitiveness in the sector, the opportunity of relying on predictive maintenance tools and services makes the difference between growing on the market and fighting for survival.

Solution

Following SAIDEA's needs and specifications, our team developed a model for predictive maintenance that a) can autonomously detect atypical behaviours and suggest intervention before a major breakdown occurs, and b) can be integrated into the existing plant's management platform.

This solution has been developed in the context of the project “EU FESR1128 PV 4.0 - Use of Industry 4.0 and Internet of Things logic in the photovoltaic sector”, which has received funding from the Fondo Europeo di Sviluppo Regionale (FESR) under grant agreement Asse 1 “Ricerca e Innovazione”.

Clients involved