Local turbulence parameterization improves the Jensen wake model and its implementation for power optimization of an operating wind farm

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&thinsp;% to 20&thinsp;% to approximately 5&thinsp;%. 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&thinsp;% for a two-wind-turbine case with close spacing and 1&thinsp;% to 1.5&thinsp;% 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&thinsp;%, 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.</p

Wind Farm Coordinated Control and Optimisation

This thesis develops and implements computationally efficient and accurate wind farm coordinated control strategies increasing energy per area by mitigating wake losses. Simulations with data from the Brazos, Le Sole de Moulin Vieux (SMV) and Lillgrund wind farms show an increase of up to 8% in farm production and up to 6% in efficiency. A live field implementation of coordinated control strategies show that curtailing upstream turbine by up to 17% in full or near-full wake conditions can increase downstream turbine’s production by up to 11%. To the best knowledge of the author, this is the first practical implementation of Light Detection And Ranging (LiDAR) based coordinated control strategies in an operating wind farm. With coordinated control, upstream turbines are curtailed using coefficient of power or yaw offsets in such a way that the decrease in upstream turbines’ production is less than the increase in downstream turbines’ production resulting in net gain. This optimum curtailment is achieved with on-line coordinated control which requires an accurate and fast processing wind deficit model and an optimiser which achieves the desired results with high processing speed using minimum overheads. Performance evaluation of carefully selected optimisers was undertaken using an objective function developed for increasing farm production based on coordinated control. This evaluation concluded that Particle Swarm Optimisation (PSO) is the most suitable optimiser for on-line coordinated control due to its high processing speed, computational efficiency and solution quality. The standard Jensen model was used as a starting point for developing a fast processing and accurate wind deficit model referred to as the Turbulence Intensity based Jensen Model (TI-JM), taking wake added turbulence intensity and deep array effect into consideration. The TI-JM uses free-stream and wake-added turbulence intensities for predicting effective values of wake decay coefficients deep inside the farm. This model is validated using WindPRO and data from three wind farms case studies as benchmarks. A methodology for assessing the impact of wakes on farm production is developed. This methodology visualises wake effects (in 360°) by calculating power production using data from the wind farms (case-studies). The wake affected wind conditions are further analysed by calculating relative efficiency. The innovative coordinated control strategies are evaluated using data from the wind farms case studies and WindPRO as benchmarks. A live field implementation of coordinated control strategies demonstrated that the production of downstream turbines can be increased by curtailing upstream turbines. This field setup consisted of two operating wind turbines equipped with modern LiDAR. Analyses of the high frequency real time data were performed comparing field results with simulations. It was found that simulations are in good agreement (within a range of 1.5%) with field results