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In this paper, a supervisory control concept for wind farms is proposed based on the neighboring wind turbines control functions in localized areas for power optimization considering wake effects. The flow control in wind farms to maximize power production is a challenging problem due to its time-varying nonlinear wake dynamics. Hence, we develop a method that authorizes coordination in a wind farm for a squarely payoff-based scenario where the turbines have access only to measurements from their neighbors via repeated interactions. Therefore, in order to maximize output power in a wind farm, an Adaptive Learning Game Theory (ALGT) method is introduced. This control scheme provides an interaction framework that constructs a series of common control functions. Here, in every iteration, each turbine chooses an independent decision according to a localized control law. The control objective of wind turbine i determines how each turbine adjusts a decision at each iteration by processing available information. [ABSTRACT FROM AUTHOR]

This paper proposes a control strategy to achieve minimum wake-induced power losses in a wind farm. At first, the axial-induction-based wake model is developed to consider the aerodynamic wake interactions among wind turbines. To optimize the generated power of the whole wind farm, the axial induction factor of each wind turbine is calculated by the genetic algorithm. As a supervisory controller, each wind turbine's optimal axial induction factor calculated by the genetic algorithm is implemented as a setpoint of each wind turbine's internal controller. In the internal control loop, a comprehensive controller is designed to track the commanded axial induction factor. In the partial load region, the commanded axial induction factor was attained by tuning the generator torque. In the transient and full load regions, the blade pitch angle is tuned to keep the generator speed and torque at the rated values. The performance of the proposed control strategy is investigated through case studies, including three different wind speeds and a time-varying wind speed case in a 3 × 3 wind-farm layout. The simulation results show the satisfactory performance of the proposed approach. [ABSTRACT FROM AUTHOR]

Wake models play a fundamental role in predicting loads and power generated by wind farms. Using them, it is possible to assess how wakes develop and interact with each other and what are the effects generated in the context of a real operating farm. In this paper, a Gaussian Wake Model implemented in OpenFAST is validated against experimental data gathered in wind tunnel GVPM @ Politecnico di Milano in the context of the European CL-Windcon project. The software used to reproduce the mechanical dynamics of the G1 wind turbines is OpenFAST. It is coupled with FLORIS, a NREL's software based on the Gaussian Wake Model, that allows to simulate the wake and partial wake conditions. The rotor aerodynamics is calculated using the BEMT on the actual rotor flow field. However, to properly use OpenFAST and the coupled GWM, eight parameters have to be estimated. Thus, a Least Squares Minimization procedure is performed using the available experimental data. [ABSTRACT FROM AUTHOR]

Analyzing or optimizing wind farm layouts often requires reduced-order wake models to estimate turbine wake interactions and wind velocity. We propose a wake model for vertical-axis wind turbines in streamwise and crosswind directions. Using vorticity data from computational fluid dynamic simulations and cross-validated Gaussian distribution fitting, we produced a wake model that can estimate normalized wake velocity deficits of an isolated vertical-axis wind turbine using normalized downstream and lateral positions, tip-speed ratio, and solidity. Compared with computational fluid dynamics, taking over a day to run one simulation, our wake model predicts a velocity deficit in under a second with an appropriate accuracy and computational cost necessary for wind farm optimization. The model agreed with two experimental studies producing percent differences of the maximum wake deficit of 6.3% and 14.6%. The wake model includes multiple wake interactions and blade aerodynamics to calculate power, allowing its use in wind farm layout analysis and optimization. [ABSTRACT FROM AUTHOR]

Accurate wake model in wind farm layout optimization can help extracting maximum power generation, minimizing cost of energy and prolonging wind turbines' lifetime as well. With the development of different wake models, the wind farm layout optimization results based on the models should be updated. This paper investigates the performances of four wake models in wind farm layout optimization using multi-population genetic algorithm (MPGA) with the wind farm power generation, COST/AEP and wind farm efficiency been reported. Comparison of results between typical wake models' performance shows that Jensen's wake model reported a higher wind farm power generation and efficiency because it underestimates the velocity deficit in the wake, and to the contrary, in the Frandsen wake model, the velocity in the wake is underestimated, resulting in a deceased power generation. The expression of 2D_k model shall be out of work in complicated wind condition. The 2D Jensen–Gaussian wake model performed better in the wind farm layout optimization using the MPGA program which can be promoted in real-world wind farm micrositing. [ABSTRACT FROM AUTHOR]

A prescribed-wake vortex model for evaluating the aerodynamic loads on offshore floating turbines has been developed. As an extension to the existing UMass analysis tool, WInDS, the developed model uses prescribed empirical wake node velocity functions to model aerodynamic loading. This model is applicable to both dynamic flow conditions and dynamic rotational and translational platform motions of floating offshore turbines. With this model, motion-induced wake perturbations can be considered, and their effect on induction can be modeled, which is useful for floating offshore wind turbine design. The prescribed-wake WInDS model is shown to increase computational efficiency drastically in all presented cases and maintain comparable accuracy to the free wake model. Results of prescribed-wake model simulations are presented and compared to results obtained from the free wake model to confirm model validity. [ABSTRACT FROM AUTHOR]

The FLORIS (FLOw Redirection and Induction in Steady-state) model, a parametric wind turbine wake model that predicts steady-state wake characteristics based on wind turbine position and yaw angle, was developed for optimization of control settings and turbine locations. This article provides details on changes made to the FLORIS model to make the model more suitable for gradient-based optimization. Changes to the FLORIS model were made to remove discontinuities and add curvature to regions of non-physical zero gradient. Exact gradients for the FLORIS model were obtained using algorithmic differentiation. A set of three case studies demonstrate that using exact gradients with gradient-based optimization reduces the number of function calls by several orders of magnitude. The case studies also show that adding curvature improves convergence behavior, allowing gradient-based optimization algorithms used with the FLORIS model to more reliably find better solutions to wind farm optimization problems. [ABSTRACT FROM AUTHOR]

This paper presents an investigation on improving individual pitch control (IPC) for wind turbine load reduction for Region 3 operation with the wake interaction included under the context of wind farm operation. The Larsen wake model is applied for composing the rotor wind profile for downstream turbines under wake interaction, and the simple Jensen model is also described for comparison. The switched controllers were then designed based on the composite wind profile. The wind profile of the turbine wake was generated by modifying the NREL's TurbSim codes. The state-space models of wind turbine were obtained via linearization of wind turbine model available in the NREL's FAST. In particular, in order to obtain more accurate state-space models for IPC design, equivalent circular wind profile was generated so as to better determine the local pitch reference. Based on such models, IPC controllers were designed following the disturbance accommodating control (DAC) and periodic control framework for load reduction when wake interaction was considered. The simulation results showed that the turbine loads can be further reduced using the switching control scheme based on wake modeling, as compared with the generic DAC without wake consideration. [ABSTRACT FROM AUTHOR]

This paper describes a fast, accurate, fully unsteady aerodynamic prediction scheme for the calculation of horizontal axis wind turbine performance in yawed flow. The method couples a prescribed wake vortex model with a semi-empirical unsteady aerofoil model to provide the unsteady aerodynamic response of the blades owing to the cyclic variation of incidence. Predictions from the model are shown to compare well with experiment, both in terms of gross performance prediction and detailed blade loads. Finally, the potential for future improvements to the predictive capability of the model is discussed. [ABSTRACT FROM AUTHOR]