Abstract. In this paper, a new calculation procedure to improve the accuracy of the
Jensen wake model for operating wind farms is proposed. In this procedure, the
wake decay constant is updated locally at each wind turbine based on the
turbulence intensity measurement provided by the nacelle anemometer. This
procedure was tested against experimental data at the Sole
du Moulin Vieux (SMV) onshore wind farm in France and the Horns Rev-I offshore wind farm in
Denmark. Results indicate that the wake deficit at each wind turbine is
described more accurately than when using the original model, reducing the
error from 15 % to 20 % to approximately 5 %. Furthermore, this
new model properly calibrated for the SMV wind farm is then used for
coordinated control purposes. Assuming an axial induction control strategy,
and following a model predictive approach, new power settings leading to an
increased overall power production of the farm are derived. Power gains found
are on the order of 2.5 % for a two-wind-turbine case with close spacing
and 1 % to 1.5 % for a row of five wind turbines with a larger
spacing. Finally, the uncertainty of the updated Jensen model is quantified
considering the model inputs. When checked against the predicted power gain,
the uncertainty of the model estimations is seen to be excessive, reaching
approximately 4 %, which indicates the difficulty of field observations
for such a gain. Nevertheless, the optimized settings are to be implemented
during a field test campaign at SMV wind farm in the scope of the national
project SMARTEOLE.
This paper presents, with a live field experiment, the potential of increasing wind farm power generation by optimally yawing upstream wind turbine for reducing wake effects as a part of the SmartEOLE project. Two 2MW turbines from the Le Sole de Moulin Vieux (SMV) wind farm are used for this purpose. The upstream turbine (SMV6) is operated with a yaw offset ( α ) in a range of − 12 ° to 8° for analysing the impact on the downstream turbine (SMV5). Simulations are performed with intelligent control strategies for estimating optimum α settings. Simulations show that optimal α can increase net production of the two turbines by more than 5%. The impact of α on SMV6 is quantified using the data obtained during the experiment. A comparison of the data obtained during the experiment is carried out with data obtained during normal operations in similar wind conditions. This comparison show that an optimum or near-optimum α increases net production by more than 5% in wake affected wind conditions, which is in confirmation with the simulated results.
A practical wind farm controller for production maximisation based on coordinated control is presented. The farm controller emphasises computational efficiency without compromising accuracy. The controller combines Particle Swarm Optimisation (PSO) with a turbulence intensity based Jensen wake model (TI-JM) for exploiting the benefits of either curtailing upstream turbines using coefficient of power ($C_P$) or deflecting wakes by applying yaw-offsets for maximising net farm production. First, TI-JM is evaluated using convention control benchmarking WindPRO and real time SCADA data from three operating wind farms. Then the optimized strategies are evaluated using simulations based on TI-JM and PSO. The innovative control strategies can optimise a medium size wind farm, Lillgrund consisting of 48 wind turbines, requiring less than 50 seconds for a single simulation, increasing farm efficiency up to a maximum of 6% in full wake conditions.
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