Abstract: 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 quantity—power—based 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.

Abstract: Systems and methods of autonomous farm-level control and optimization of wind turbines are provided. Exemplary embodiments comprise a site controller running on a site server. The site controller collects and analyzes yaw control data of a plurality of wind turbines and wind direction data relating to the plurality of wind turbines. The site server determines collective wind direction across an area occupied by the plurality of wind turbines and sends yaw control signals including desired nacelle yaw position instructions to the plurality of wind turbines. The site controller performs wake modeling analysis and determines desired nacelle positions of one or more of the plurality of wind turbines. The desired nacelle yaw position instructions systematically correct static yaw misalignment for all of the plurality of wind turbines. Embodiments of the disclosure provide means to perform whole site or partial site level controls of the yaw controllers of a utility scale wind turbine farm.

Abstract: Embodiments of the present disclosure include a retrofit auxiliary nacelle yaw position control system that enables advanced nacelle yaw position control of a wind turbine by comparing a desired nacelle yaw position signal with the actual nacelle yaw position and generating a virtual relative wind direction signal that is provided to the existing turbine control unit. This method and system enable implementation of wake steering, collective yaw optimization and dynamic yaw optimization of a collection of wind turbines referred to as a wind plant. Modification of the existing turbine control unit is not required, greatly simplifying the implementation process of advanced yaw control strategies on existing wind plants.

Abstract: Systems and methods of autonomous farm-level control and optimization of wind turbines are provided. Exemplary embodiments comprise a site controller running on a site server. The site controller collects and analyzes yaw control data of a plurality of wind turbines and wind direction data relating to the plurality of wind turbines. The site server determines collective wind direction across an area occupied by the plurality of wind turbines and sends yaw control signals including desired nacelle yaw position instructions to the plurality of wind turbines. The site controller performs wake modeling analysis and determines desired nacelle positions of one or more of the plurality of wind turbines. The desired nacelle yaw position instructions systematically correct static yaw misalignment for all of the plurality of wind turbines. Embodiments of the disclosure provide means to perform whole site or partial site level controls of the yaw controllers of a utility scale wind turbine farm.

Abstract: Systems and methods of autonomous farm-level control and optimization of wind turbines are provided. Exemplary embodiments comprise a site controller running on a site server. The site controller collects and analyzes yaw control data of a plurality of wind turbines and wind direction data relating to the plurality of wind turbines. The site server determines collective wind direction across an area occupied by the plurality of wind turbines and sends yaw control signals including desired nacelle yaw position instructions to the plurality of wind turbines. The site controller performs wake modeling analysis and determines desired nacelle positions of one or more of the plurality of wind turbines. The desired nacelle yaw position instructions systematically correct static yaw misalignment for all of the plurality of wind turbines. Embodiments of the disclosure provide means to perform whole site or partial site level controls of the yaw controllers of a utility scale wind turbine farm.

Abstract: Embodiments of the present disclosure include a retrofit auxiliary nacelle yaw position control system that enables advanced nacelle yaw position control of a wind turbine by comparing a desired nacelle yaw position signal with the actual nacelle yaw position and generating a virtual relative wind direction signal that is provided to the existing turbine control unit. This method and system enable implementation of wake steering, collective yaw optimization and dynamic yaw optimization of a collection of wind turbines referred to as a wind plant. Modification of the existing turbine control unit is not required, greatly simplifying the implementation process of advanced yaw control strategies on existing wind plants.

Abstract: Embodiments of the present disclosure include a retrofit auxiliary nacelle yaw position control system that enables advanced nacelle yaw position control of a wind turbine by comparing a desired nacelle yaw position signal with the actual nacelle yaw position and generating a virtual relative wind direction signal that is provided to the existing turbine control unit. This method and system enable implementation of wake steering, collective yaw optimization and dynamic yaw optimization of a collection of wind turbines referred to as a wind plant. Modification of the existing turbine control unit is not required, greatly simplifying the implementation process of advanced yaw control strategies on existing wind plants.

Abstract: Embodiments of the present disclosure include a data processing and control augmentation system capable of identifying overloading of one or more wind turbine assemblies and providing information to a wind farm controller to reduce a power output of each overloaded turbine. The augmentation system thus reduces the power output of each overloaded turbine and, in turn, reduces loads applied to the wind turbine assembly, such as for a period of time until conditions favorably change. A described analysis of the present disclosure is able to utilize several incoming data streams from sensors so arranged to measure wind effects on blades to calculate and compare cyclic loads to threshold limits to o keep the loads within design limits. The control strategy reduces premature failure of components within the wind turbine assembly, and can be applied across an entire wind farm, even with only a subset of wind turbine assemblies being retrofitted.

Abstract: Embodiments of the present disclosure include a retrofit auxiliary nacelle yaw position control system that enables advanced nacelle yaw position control of a wind turbine by comparing a desired nacelle yaw position signal with the actual nacelle yaw position and generating a virtual relative wind direction signal that is provided to the existing turbine control unit. This method and system enable implementation of wake steering, collective yaw optimization and dynamic yaw optimization of a collection of wind turbines referred to as a wind plant. Modification of the existing turbine control unit is not required, greatly simplifying the implementation process of advanced yaw control strategies on existing wind plants.

Abstract: Embodiments of the present disclosure include a data processing and control augmentation system capable of identifying overloading of one or more wind turbine assemblies and providing information to a wind farm controller to reduce a power output of each overloaded turbine. The augmentation system thus reduces the power output of each overloaded turbine and, in turn, reduces loads applied to the wind turbine assembly, such as for a period of time until conditions favorably change. A described analysis of the present disclosure is able to utilize several incoming data streams from sensors so arranged to measure wind effects on blades to calculate and compare cyclic loads to threshold limits to o keep the loads within design limits. The control strategy reduces premature failure of components within the wind turbine assembly, and can be applied across an entire wind farm, even with only a subset of wind turbine assemblies being retrofitted.

Abstract: Embodiments of the present disclosure include a data processing and control augmentation system capable of identifying overloading of one or more wind turbine assemblies and providing information to a wind farm controller to reduce a power output of each overloaded turbine. The augmentation system thus reduces the power output of each overloaded turbine and, in turn, reduces loads applied to the wind turbine assembly, such as for a period of time until conditions favorably change. A described analysis of the present disclosure is able to utilize several incoming data streams from sensors so arranged to measure wind effects on blades to calculate and compare cyclic loads to threshold limits to o keep the loads within design limits. The control strategy reduces premature failure of components within the wind turbine assembly, and can be applied across an entire wind farm, even with only a subset of wind turbine assemblies being retrofitted.

Abstract: Embodiments of the present disclosure include a data processing and control augmentation system capable of identifying overloading of one or more wind turbine assemblies and providing information to a wind farm controller to reduce a power output of each overloaded turbine. The augmentation system thus reduces the power output of each overloaded turbine and, in turn, reduces loads applied to the wind turbine assembly, such as for a period of time until conditions favorably change. A described analysis of the present disclosure is able to utilize several incoming data streams from sensors so arranged to measure wind effects on blades to calculate and compare cyclic loads to threshold limits to o keep the loads within design limits. The control strategy reduces premature failure of components within the wind turbine assembly, and can be applied across an entire wind farm, even with only a subset of wind turbine assemblies being retrofitted.