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PresAGHO is a new predictive maintenance model for hydroelectric power plants

4 min.

PresAGHO is a new predictive maintenance model for hydroelectric power plants

Enel Green Power is the world’s first utility to roll out - in partnership with major equipment manufacturers - a wide-ranging predictive maintenance model for its hydroelectric power plants.

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Prevention is better than cure. This goes far beyond a mere Italian advertising headline from the 80s. Enel Green Power is seriously determined to prove how hydro - the oldest energy source - can stay in step with the times or even go one step ahead in terms of safety.

EGP has given the green light to the PresAGHO project (Predictive System and Analytics for Global Hydro Operation), tasked with elaborating a predictive maintenance model to tackle potential faults in hydroelectric power plants.

This project showcases innovative and cutting-edge technological solutions with Enel Green Power at its forefront. EGP is the first company of Enel Group and in the world, to spearhead such a large predictive maintenance program through a public tender for manufacturers. The program will focus on a whopping 260 hydro power plants, the largest and most powerful in its portfolio, totalling up to 18 GW of installed capacity.

From preventive to predictive maintenance

PresAGHO will set forth a game-changing review of maintenance strategies, fostering a gradual switch from a preventive to a predictive type of approach.

While preventive maintenance is about constantly monitoring the status of a power plant and the proper operation of its components - and this requires adequate time and resources to perform - predictive maintenance represents instead a significant change of pace thanks to its data-based management strategy.

Listening to a power plant’s “voice” throughout its life is achieved through a myriad of sensors that gather an enormous set of data, allowing maintenance teams to schedule works before a potential fault could unravel, thus optimizing costs and man-hours. The first test from EGP was carried out during 2017 in the Soverzene hydroelectric power plant, in Belluno Province, Veneto, Italy.

This choice will allow Enel Green Power to further advance on the overall quality of monitoring and controls. As a result, EGP will benefit from an optimization of performances and the bolstering of its approach on risk management, focusing on the electromechanical parts of its power plants.

Such a painstaking work is leading up to 2022, during which the ordinary preventive maintenance strategy will follow a transition roadmap towards the setting up of a predictive strategy, based on an innovative approach to failure mode and a new optimization of maintenance plans.

Maintenance strategies are to be diversified based on the requirements and sizes of each individual hydro plant. First of all, the largest types of equipment will benefit from the upgrade of existing sensors with new technologies, while the smaller types of equipment - currently lacking the most advanced sensors - will see the setup of low-cost sensors engineered and manufactured by Enel Green Power. The strategy put in place for civil engineering works is to improve monitoring through new GIS (Geographic Information System) technologies and advanced algorithms, compatible with drone inspection campaigns.

These new technologies, coupled with the proven experience our colleagues on managing power plants, will allow the start of the optimization process, thus shifting the selected hydro plants towards a more advanced data-driven management model.

What does PresAGHO entail?

First of all, PresAGHO will play out through the integration of power plants with installed sensors. A real change of pace will be rapidly achieved via a three-year service contract to be ratified with three of the world’s major manufacturers of hydraulic and electric equipment.

This strategy will feature a two-fold approach, based on reference clusters. For large-sized power plants (86 facilities in 7 countries with an installed capacity of over 50 MW), accounting for 78% of total energy production, a fully predictive approach will be put in place.

Since the largest hydroelectric power plants all feature SCADA (Supervisory Control And Data Acquisition) systems, this will allow to gather data directly on-site, transmit them to the local Control Room and subsequently render them available on a cloud storage platform to the benefit of colleagues working at the plants, all in all while featuring ancillary information to make them “tell the story” of a power plant’s condition.

For small-sized power plants (209 facilities in 13 countries with an installed capacity between 10 and 50 MW-  accounting for 17% of total production) as well as micro-sized power plants (486 facilities in 13 countries with an installed capacity under 10 MW -  accounting for 5% of total production), it’s all about overcoming the large number of units and their scattered locations through an edge computing strategy, coupled with the newly-placed Teneuro system: a low-cost, cutting-edge, scalable solution that foresees to increase its readings by 200,000 new units. These devices will record even the smallest “vibration” from the power plant, by gathering health status reports on the rotating equipment (mostly turbines and generators) by signaling remotely any anomalous readings to our colleagues. Even in the context of an energy market that requires increased levels of flexibility, this is an innovation that allows to boost performances while safeguarding sustainability.

With SCADA systems at the core of our larger sized power plants, we’ll be able to realize the full potential of our new predictive approach pillars: the sheer size of gathered data and their complexity will generate four new benchmark KPIs (Key Performance Indicators), focusing on the risk management of a fault. Here they are:

  • KDIkey diagnostic indicator, the health status of a component
  • KTIkey trend indicator, the remaining time until a critical threshold is reached
  • FPPfault presence probability, the probability for the occurrence of a fault
  • FSIfault severity index, the severity of the possible fault

Innovation and safety

A fundamental step in defining the new predictive strategy will be a detailed realignment in the classification of potential faults or failure mode, in order to improve our data-driven approach and to measure its performance. Moreover, Enel Green Power will partner with other utility companies to pitch for a common ground where feedback on critical situations can be shared, in order to bolster awareness on the safety of hydraulic works for electromechanical activities as well.

PresAGHO will create the basis for the elaboration of predictive models addressing faults in hydroelectric power plants, while optimizing costs and standardizing maintenance procedures in various countries. PresAGHO is a good omen for a greener and safer future.

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