Systems and methods of predicting performance of one or more wind turbines are provided. Exemplary methods include entering data inputs and analyzing and estimating the data inputs. The data inputs include nacelle position information and/or wind condition information. A wake model is determined based on the analysis and estimating of the data inputs. Wind turbine behavior predictions are generated including predicted power outputs of the wind turbines and predicted effects of waking on the predicted power outputs. The data inputs can be adjusted to improve the wind turbine behavior predictions, and the wake model can be corrected by machine learning. By focusing on an observable quantitypowerbased on other observable quantities like wind speed, yaw error, nacelle position, disclosed embodiments enable optimization to start immediately after the hardware and software are installed, and as the controller operates more in the field, additional training and validation data are collected.

A method, system, and apparatus for improving wind harvest efficiency comprises: a wind turbine, a processor, and a computer-usable medium embodying computer code, said computer-usable medium being coupled to said processor, said computer code comprising non-transitory instruction media executable by said processor configured for: setting a desired location range of a wind wake with a controller, determining an error signal indicative of a difference between a currently measured cross-stream thrust component of wind and an optimal cross-stream thrust component of wind, adjusting a speed of a rotor associated with the wind turbine according to the error signal, and confining a real-time position of the wind wake to the desired location range of the wind wake.

A method of controlling wake in a floating wind park is provided. The method includes monitoring a wind condition at at least one of the plurality of floating wind turbines to generate at least a first monitored wind condition, monitoring one or more parameters indicative of a position and/or orientation of at least one of the plurality of floating wind turbines to generate at least a first monitored floating motion state and generating a control parameter based on a parameter set comprising at least the first monitored wind condition and the first monitored floating motion state. The control parameter is derived so as to reduce the wake influence on the downstream wind turbine. The method further includes controlling based on the control parameter, an operation of at least one of the plurality of floating wind turbines.

A flat-packable wind turbine assembly kit, includes a flat-packed bendable airfoil having an upper edge and a lower edge. The flat-packed bendable airfoil is capable of assuming a predefined curvature upon assembly. The flat-packed bendable airfoil includes upper connecting elements distributed along the upper edge and lower connecting elements distributed along the lower edge of the flat-packed bendable airfoil. The flat-packed bendable airfoil also includes a flat-packed upper plate including an upper mating orifices distributed in a contour corresponding to the predefined curvature. Further, the flat-packed bendable airfoil includes a flat-packed lower plate including lower mating orifices distributed in the contour corresponding to the predefined curvature. Thus, upon assembly when the upper connecting elements are connected to the upper mating orifices and when the lower connecting elements are connected to the lower mating orifices, the flat-packed bendable airfoil assumes the predefined curvature.

A method and system for operating a wind farm by reconciling performance and operational constraints is disclosed. A wind farm may be subject to wakes, thereby reducing performance In order to lessen the effects of wakes, wake steering of the wind turbines may be performed. Specifically, both an operationally-independent analysis (such as by using a computational fluid dynamic model) and an operationally-dependent analysis may be performed, and the outputs of each analysis may be reconciled in order to determine whether (and how much) to wake steer.

The present disclosure relates to a wind farm layout and yaw control method, and an electronic device. The wind farm layout and yaw control method comprises the following steps: acquiring wind farm data, importing the wind farm data into a WFSim model for simulation, obtaining original power data of the wind farm, counting and recording modifiable wind farm layout and yaw parameters, adjusting variable parameters to be changed, using the WFSim model for simulation to obtain an optimal parameter range of different variables and combining the optimal parameters to obtain an optimal working condition, and using the WFSim model for simulation to obtain optimal power data. The wind farm layout and yaw control method according to the present disclosure is based on optimizing the power output of the wind farm, so that it is very convenient to acquired information. Meanwhile, a WFSim two-dimensional fidelity model is used to simulate and solve the wind farm, so that the simulation time can be reduced while ensuring data accuracy.

System, methods, and computer readable medium are disclosed for altering orientation of fluid turbines within a cluster. Altering orientation of fluid turbines within a cluster includes a first turbine assuming a first orientation relative to a direction of fluid flow; a second turbine in proximity to the first turbine, and assuming a second orientation relative to the first orientation, wherein the first and/or second orientations are adjustable to mitigate interference with downstream turbine operation; a processor for receiving an indication that the first turbine imposes interference on the second turbine; based on the indication, determine a third orientation enabling the first and second turbines to produce greater aggregate electrical energy than would be produced with the first turbine in the first orientation and the second turbine in the second orientation; and transmit a signal for changing one of the first and second orientations to the third orientation.

A reduction in wake effects in large wind farms through wake-aware control can improve farm efficiency. The wake of a wind turbine presents complications for nearby turbines, depending on the atmospheric conditions, turbine characteristics, and turbine siting. The nascent field of wind farm flow control seeks to reduce the deleterious effects of the wake momentum deficit by leveraging the turbine as a flow actuator though the intelligent scheduling of either the blade pitch, rotor speed, or nacelle yaw.

A control arrangement for a variable-speed wind turbine includes a loading analysis module configured to analyse a number of environment values to establish whether the momentary wind turbine loading is lower than a loading threshold when the rotational speed of the aerodynamic rotor has reached its rated value; and a speed boost module configured to determine a speed increment for the rotational speed of the aerodynamic rotor if the wind turbine loading is lower than the loading threshold.

A wind turbine support system configured to support an off-shore wind turbine, an offshore wind turbine farm and a method for controlling a floating offshore wind park with such turbine support system are described. The wind turbine support system includes: a floating body configured to hold a lower end of a tower of the wind turbine; and a single point mooring system. The single point mooring system includes a seabed anchor; and a mooring line configured to be connected to the seabed anchor at a first end thereof. The floating body has a bow and a stern, and the bow is configured to be connected to a second end of the mooring line.