Moving From Preventive to Predictive Maintenance
Supported by a successful pilot carried out during 2017 in the Soverzene hydropower plant, ENEL strengthened its use of plant data to optimize its maintenance strategy by deploying a risk-based approach.
In 2018, ENEL launched the PreSAGHO project (Predictive System and Analytics for Global Hydro Operations) with a selected group of partners, including GE. The project is deploying a predictive diagnostic monitoring system for large-sized power plants (over 50 MW) -- 86 facilities in seven countries with a total combine installed capacity of 18 GW.
This system will identify potential faults in hydropower plants by analyzing data collected in ENEL’s data lake with ad hoc predictive models.
Overtime, ENEL will review its maintenance strategies and gradually switch from a preventive to a predictive approach.
GE Renewable Energy’s Hydro Solutions
APM Reliability On-Premises (APR analytics) is deployed in two sequential bulks accounting for 29 HPPs and 4 GW of installed capacity. The first bulk contains 11 HPPs representing 29 units (~1.5 GW). APR analytics uses GE’s SmartSignal Predictive Analytics & Diagnostics software. APM server is connected to the Enel data lake.
Site assessment: (1) Understand what information and data is available at each hydro plant, identify the tags with appropriate data modeling; and (2) identify missing data through a gap analysis between the available data and the tags required to perform predictive analytics.
GE’s Premier Acceleration Plan features General Technical Support at a global scale and Education & Training Services.
Remote M&D services is delivered by a team of monitoring engineers and subject matter experts. This team supports Enel in analyzing alerts, providing O&M advisories as well as deep expertise for the most difficult cases.