Abstract
The aim of the present study is to perform a comparative analysis of two actuator disc methods (ACD) and two analytical wake models for wind farm power production assessment. To do so, wind turbine power production data from the Lillgrund offshore wind farm in Sweden is used. The measured power production for individual wind turbines is compared with results from simulations, done in the WindSim software, using two ACD methods (ACD (2008) and ACD (2016)) and two analytical wake models widely used within the wind industry (Jensen and Larsen wake models). It was found that the ACD (2016) method and the Larsen model outperform the other method and model in most cases. Furthermore, results from the ACD (2016) method show a clear improvement in the estimated power production in comparison to the ACD (2008) method. The Jensen method seems to overestimate the power deficit for all cases. The ACD (2016) method, despite its simplicity, can capture the power production within the given error margin although it tends to underestimate the power deficit.
Highlights
As a wind turbine extracts energy from the wind, it creates a region downstream where the wind velocity is decreased, and the turbulence intensity is increased
The goal is not to investigate the influence of the effect of using different turbulence closer models, rather we are interested in a comparative analysis of two actuator disc methods (ACD) methods and two analytical wake models for a wind farm configuration
This paper compared the power production results of four different methods/models against measurements for the offshore wind farm of Lillgrund, which is in Sweden
Summary
As a wind turbine extracts energy from the wind, it creates a region downstream where the wind velocity is decreased, and the turbulence intensity is increased. Turbines are currently placed in close configurations for several economic, environmental, and technical reasons [1] The results of these configurations are that turbines often operate in the wake of other turbines which results in reduced production. It is apparent that the ability to accurately predict wind turbine wakes has a significant impact on increasing wind farm profitability. This insight can provide value in at least two stages of a wind farm’s project lifespan, i.e., the pre-construction phase and the operational phase
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