ABB’s digital technology facilitates predictive maintenance for Enel Green Power

The solution improves efficiency and reliability while optimizing asset performance and lowering costs for Enel’s hydropower plants across Italy.

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In 2018, Enel Green Power and ABB joined forces to develop predictive and condition-based maintenance solutions through the ABB Ability™ Collaborative Operations Center for power generation and water in Genoa, Italy. With these cutting-edge technologies, the company is adopting an advanced data-driven management model that will help drive smart, safe and sustainable operations.

The 36-month-long operations and maintenance stage of the PreSAGHO (Predictive System and Analytics for Global Hydro Operation) project will leverage an asset performance management solution through ABB Ability™ Genix APM up to 33 Enel Green Power’s hydroelectric plants, totaling around 100 power generating units. As part of a global reorganization of the maintenance processes at Enel Green Power (EGP), the project aims to improve sustainability, lower maintenance costs, and optimize the performance, reliability and efficiency of its hydroelectric fleet.  Activities to include: site assessment, gap analysis, hydro data modeling, software platform implementation, specialized monthly support as well as operations and maintenance.

Until today, the ABB's solution was able to detect 8 failure predictions. These predictions helped Enel to implement monitoring and predictive maintenance actions, further improving overall hydro plants performance.

“With the PreSAGHO project, we are able to understand each hydropower plant at its core and to hear their voices. We are able to use crucial information to improve the performance of each asset, thus increasing overall availability and productivity for the entire hydropower fleet,” said Alessandro Benanti, Head of Maintenance and Plant Risk Analysis, Hydro Technology Line Enel Green Power.

Actionable insights for improved operations and maintenance

In early 2020, ABB successfully completed the first phase of the project by identifying the parameters required to achieve the basic level of efficiency for Enel Green Power and indicating what can be added to improve these results further. Following the gap analysis, ABB has provided information on the hydropower plants, based on the data gathered, to help the utility perform predictive analysis to improve its automation and digitalization processes.“ABB’s expertise in predictive diagnostics and performance calculations as well as their strong competencies in digital, automation and data analysis were some of the major reasons that we chose ABB for this undertaking,” continues Alessandro Benanti, Head of Maintenance and Plant Risk Analysis, Hydro Technology Line Enel Green Power.

Collaboration and innovation for sustainable operations

In August 2020, the project entered the go-live phase. ABB is currently performing specialized analysis of the platform's outputs by supporting Enel Green Power with technical assistance, and building on Enel’s enhanced data quality to enable more effective decision-making. ABB is also fine-tuning the models and algorithms that are currently implemented in the platform.

Periodic reports are shared among the project teams on a weekly basis to assess and review the overall asset health status. The reports are part of the new global maintenance process of the hydro fleet, which work based on EGP’s own ticketing system that interfaces directly with ABB Ability™ Genix platform.

“Through the PreSAGHO project, Enel will be gaining access to a centralized view of data by asset, enabling them to make assessments and build development strategies after mapping, analyzing and rationalizing this information,” said Sergio Durando, manager of ABB’s Energy Industries business in Italy, Italy, Greece, Turkey and Algeria.

Once the tailored ABB Ability™ Genix APM solution will be fully commissioned, Enel Green Power will be able to digitalize their assets, minimize unplanned failures and conduct more efficient planned maintenance.

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