Curtailment of one form or another is a frequent occurrence on most wind farms. A range of circumstances can dictate when a wind farm must reduce the amount of energy that it produces, resulting in a hit on the financial returns of the project.
Common reasons for curtailment include: an unstable electrical grid, strict grid requirements, low overnight demand, icing conditions, wildlife in the area, prevention of shadow flicker, noise limitations, and protective operation to prolong component life.
Protective operation is an interesting topic and one that is sometimes not as well understood as other curtailment sources. Worse, strategies for managing this kind of curtailment are sometimes hastily implemented, resulting in more energy loss than necessary. How is this so and how does i4SEE view this?
For example, consider a pair of 3 MW wind turbines, both affected by the same mechanical issue with their gearboxes (perhaps detected by i4SEE GearDrive™ ). Replacement gearboxes are ordered but unfortunately not available for two months. On top of it all, this is during the windiest season and the resulting lost energy production will be substantial. What should be done in this situation?
One option is to stop both turbines and wait for the gearboxes to arrive. Production loss would be 100%. Another option would be to monitor the turbines using SCADA, and allow them to run at reduced capacity, with power limited to e.g. 50% of the nameplate rating, to avoid catastrophic failure and keeping a margin of safety. That would be better than nothing. However, another option could be to run the turbines at 90% of rated power, knowing that while the gearbox is not in perfect condition it is unlikely to fail completely and help is on the way with a replacement date not too far into the future. The resulting production losses would be much lower, making this the most financially advantageous option. But where is the optimum level of curtailment, in this complex trade-off between performance and reliability?
Such optimisation strategies require expert knowledge and must be supported by intelligent data analytics with tools such as the i4SEE applications. The i4SEE Damage™ application explores the relationship between load and failure probability and can help to answer such questions. Note also the i4SEE Performance™ application is an automated tool for detecting and quantifying these energy losses, saving time for busy asset managers and data analysts.
Keep in mind that there are often perverse incentives within availability contracts, and the OEM or operator may choose to impose far greater limitations on turbine output than the owner may prefer, simply to protect their KPIs or to avoid the worst case scenario of a complete failure…even if that case is extremely unlikely! Often owners do not feel empowered and have to simply read a report and accept the chosen strategy. We at i4SEE believe that having good data and efficient analysis tools, which are fast, flexible and cost effective, will put the owner in a much better position to push for smarter operational choices which deliver the best overall results.