The park efficiency of a wind farm is a crucial factor in determining the energy efficiency and profitability of the wind farm. This measure indicates the proportion of usable energy generated out of the maximum possible energy, which could theoretically be produced from the kinetic energy contained in the wind speed.
Park efficiency arises from the combination of individual plant efficiencies and the interaction between wind turbines. In planning wind farms, the locations of individual wind turbines must be carefully chosen to minimize negative interactions such as the so-called wake effect. When a wind turbine is directly in the wind stream of another turbine, the wake effect reduces the wind speed, negatively impacting the plant efficiency.
Furthermore, factors such as the type of turbine, the diameter of the rotor blades, the distance between the turbines, the height of the turbines above the ground, and the characteristic wind speed of the location have a significant impact on the park efficiency.
Park efficiency can be predicted through numerical models such as the Jensen model, the Ainslie model, or the parallelepiped model. These models take into account a variety of factors, including wind direction and speed, topography, surface roughness, turbine type and arrangement, to provide an estimate of energy production and thus park efficiency.
It's important to note that park efficiency continuously varies in operation and depends on numerous factors, including maintenance status, operating conditions, and atmospheric conditions. Therefore, it is essential to continuously collect and analyze data on the performance of the wind farm to optimize operation and maximize park efficiency.
Turbit has found a way to effectively calculate park efficiencies over 10-minute SCADA averages. Data from turbines at the edges of the wind farm are selected, and depending on the wind direction, a virtual comparison turbine is created. This virtual wind turbine then shows a real power output that a turbine could theoretically produce if it were freely exposed at this location.
Of course, it must be taken into account that the wind turbines being compared are of the same type and the location is not hilly so that the hub height above sea level of all turbines is comparably the same.
The performance of the virtual plant is then taken as a reference to compare with the other turbines in the park. This methodology is very effective in obtaining a practical result that can be compared with the wind farm report before the park's commissioning.
Very often, we find that a park is performing poorly (compared to the wind farm report and thus the expectations), and we are asked if we can analyze why this is the case.
Frequently, the first thought is of nacelle misalignments (yaw error) or pitch misalignments, but the obvious is not checked beforehand. We are happy to perform this check at a low cost to exclude further causes of errors in the subsequent analysis.
In summary, from our experience, we can say that almost always, the park configuration and an inaccurate and overly optimistic wind farm report are the reasons for poor park performance (excluding shutdowns or throttlings).
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