AHMAD, TANVIR (2017) Wind Farm Coordinated Control and Optimisation. Doctoral thesis, Durham University.
|PDF - Accepted Version|
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.
|Item Type:||Thesis (Doctoral)|
|Award:||Doctor of Philosophy|
|Keywords:||Coordinated control of wind farms; wind farm power production maximisatio; wind farm control and optimisation; wake mitigation; LiDAR based wind farm control; Cp-based wind farm production optimisation; yaw-based wind farm production optimisation; Jensen model; particle swarm optimisation; heuristic optimisation techniques; wake and wind deficit modelling; Turbulence Intensity based Jensen Model (TI-JM)|
|Faculty and Department:||Faculty of Science > Engineering and Computing Science, School of (2008-2017)|
|Copyright:||Copyright of this thesis is held by the author|
|Deposited On:||09 Nov 2017 14:44|