Wind farms are very complicate systems. A farm consists of many turbines which individual behavior is dependent on the weather and the other turbines in the farm. The wear of tear on different turbines in a farm can vary allot. Some turbines can for example be worn out after 20 years, while others have many years of remaining useful lifetime.
Simulating wind farms is difficult for practical industrial use. You could use a very advanced wind farm simulator that does a good job at predicting the behavior of each turbine. This can however require so much computational resources and time that it is unfeasible for practical use. Another way is to use much simpler simulation tools that are fast enough for practical use, but they are however less realistic.
We have a novel approach in the our NEXTFARM project that we believe can solve the dilemma. We attack the problem from the perspective of the individual turbine. It only cares about the wind that hit it, and not how it came about. If we can describe all the wind conditions any turbines in a farm can experience, then we can feed this to wind turbine simulator and realistically predict its behavior for any condition. Many thousand wind turbine simulations are needed to cover all eventualities, but its only done once. We can then use a fast wind farm model and replace the highly simplified wind turbine behavior such simulators use with our pre-calculated simulation results. Such a wind farm simulator could simulate the whole lifetime of a wind farm in minutes. It will enable engineers to design cheaper new wind farm. It will enable wind farm operators with an existing farm to run it longer and more efficiently. Both will contribute to reducing the cost of renewable and environmentally friendly electricity from wind farms on land or offshore.
The project will be divided in three phases, In the first part a simplified and parameter-based version of a wake model will be developed, in order to achieve engineering friendly computational speeds for our turbine inflow model, a reduced version of the Dynamic Meandering Wake model (DWM) will be developed. In particular, the problem will be treated from the point of view of the single turbine, rather than of the whole wind park. The main assumption is that, deep within the interacting wind park, a turbine will experience only the wakes of the closest upstream turbines as concentrated, meandering wakes. This means concentrated wakes will be shed by turbines from maximum one or two rows upstream, positioned in a certain angular sector, aligned with the wind direction. The wakes of the turbines further upstream will be experienced only as a diffused wakes, generating a deep wind park velocity deficit.
In the second part of the project, direct correlations between fatigue and local inflow for each turbine will be evaluated, studied, developed. This will be done either by simulating a large number of load cases and evaluating the resultant matrix. Advanced algorhitms for complex systems and matrix reduction will be studied in order to reduce the resultant matrix and generate a look up table that can be used to directly estimate fatigue from local inflow condition. The resultant model will then be successively verified and validated against other similar models and against full scale available data.
In the third phase of the project, the developed tools and models will be applied to cost models and their impact on the wind park LCOE of a farm will be studied