Whooshing opportunities – RAM analysis for wind farms
We have recently published a whitepaper to discuss the challenges in the Wind Turbine market in regards to maintenance and operations!
Please download from here: Wind turbines!
If you want to give a try to Maros Lite for RAM analysis on wind farms, please do not hesitate to get in touch!
Recently, I attended ESREL where we’ve presented a paper on balancing safety and performance through QRA and RAM. The conference was very informative – one of the discussion topics was Energy production and distribution– with wind farms being a popular theme. A number of papers discussed operations and maintenance challenges using the Monte Carlo method to predict the performance of wind turbines, i.e. RAM analysis for wind farms.
To give us some context, in March 2007, EU leaders set the 2020 targets, committing to address the (always) increasing energy production from hydrocarbon sources. The main goal is to become a highly energy-efficient and low carbon economy.
In this programme, they mention the 2020, 20-20-20 targets. The programme describes an integrated approach to climate and energy policy that aims to combat climate change. One of the “20” refers to increasing the share of EU energy consumption produced from renewable resources to 20%.
To achieve this target, wind turbines must play an essential role. Good news, one might say – a solution to the global issue. The bad news however, is how do we support a big “fan” sitting afar? In the same way we support a big metal structure far way – with some additional challenges!
Akin to an oil and gas production platform, the operation is 24/7. The wind turbines are unmanned, imposing challenges to maintenance campaigns. Space is also limited and only small spare parts can be stored in the turbine.
RAM analysis for wind turbines involve a number of extra variables that must be taken into account such as:
- Power curve and wind speed profiles
- Statistical definition for wind speed
- Shutdown when the Wind is too strong
Some of this required information is available publicly and can be found in local governmental websites. As a customary challenge, reliability data might be scarce but some references in public domain are obtainable.
Maintenance strategy is one of many topics that can be explored in details. For instance, when empowered with a RAM model, the analyst can explore a number of variables that will impact directly, not only the uptime of the system but also operational expenditure. RAM analysis enables different strategies to be evaluated including planned inspections, condition based maintenance, spares management (offshore and onshore resources), crew management, and supporting vessels.
DNV GL incorporates a vast expertise in this area from legacy DNV, Kema, Garrad Hassan and GL Renewables certification. RAM analysis for wind turbines is one of the supported analyses.