“Sometimes, the particular type of fault can only be detected when damage has already been done, but if it has not progressed very far, it is possible to carry out a minor repair to prevent further damage, which greatly reduces the cost of the repair.”
This model, which we at Endesa developed, now holds information from 1400 turbines; 600 are in Endesa’s renewable energy generation fleet in Spain; the rest are in Enel Green Power’s installations in Mexico, Chile, Italy, Greece and Romania, amongst other countries. The project began in 2014, when the experts began studying the potential of such systems, but it was not until 2016 that predictive analysis of wind turbines began being applied effectively.
Money saved on repairs, and less downtime
This analysis system, which can be extrapolated to any model of turbine, is capable of delivering savings ranging between 15% and 95% on the cost of repairs of the main components throughout the turbine’s lifetime. The saving that can be made by detecting just one fault may be over €100,000 per turbine.
“Before predictive vibration analysis and maintenance came into use, the repairs required were of greater consequence, and the turbines had to be put out of service for longer periods. Oversight of similar quality to that which is possible with the vibration analysis system would have cost eight times as much with the previous system.”
The experts managing this system monitor the status of the machines and carry out expert diagnostics by analysing the vibration data. What is more, they are continuously seeking out new opportunities to improve the system’s detection capabilities and find new solutions that can be applied, in the wind power industry or indeed in other settings.
The new technologies we are discovering and incorporating represent an opportunity for growth in our digitisation process. Knowledge and innovation must go hand in hand so that the techniques related to machine learning and automation can also be useful in other areas. It is key to study these techniques for future applications to continue improving our services.