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. The overall effect of the coordinated yaw control of wind turbines across the whole or partial site is intended to keep the wake loss of the wind turbines from the upstream wind turbines to the minimum and to maximize the production of turbines that are not waking other turbines.

A method for operating a wind farm having a first wind power installation and a second wind power installation, to an associated wind power installation and to an associated wind farm. The second wind power installation is located in the wake of the first wind power installation in at least one wake wind direction. A tip-speed coefficient is determined from the ratio of a second tip-speed ratio of the second wind power installation and a first tip-speed ratio of the first wind power installation and a pitch-angle coefficient is determined from the ratio of a second pitch angle of the second wind power installation and a first pitch angle of the first wind power installation. The method comprises: determining a turbulence metric, in particular a turbulence intensity, at the second wind power installation; operating the first wind power installation and the second wind power installation in the wake wind direction in a part-load range, wherein the tip-speed coefficient and/or the pitch-angle coefficient are/is a function of the turbulence metric at the second wind power installation and are/is greater than one.

Disclosed herein are methods, systems, and devices for utilizing distributed reinforcement learning and consensus control to most effectively generate and utilize energy. In some embodiments, individual turbines within a wind farm may communicate to reach a consensus as to the desired yaw angle based on the wind conditions.

Systems, methods, and non-transitory computer readable media including instructions for synchronizing a plurality of geographically-associated fluid turbines. Synchronizing a plurality of geographically-associated fluid turbines includes receiving first signals indicative of a phase of a rotational cycle of first rotating blades of a first turbine configured to generate a downstream fluid flow; receiving second signals indicative of a phase of a rotational cycle of second rotating blades of a second turbine configured to receive at least a portion of the downstream fluid flow and generate a differential power output; determining from the first and second signals that greater aggregate power output is achievable through blade phase coordination; determining a phase correction between the first and second rotating blades based on the first and second signals to achieve the greater aggregate power output; calculating coordinating signals based on the phase correction; and outputting the coordinating signals to impose the phase correction.

Provided is an apparatus and method for cooperative controlling wind turbines of a wind farm, wherein the wind farm includes at least one pair of turbines aligned along a common axis approximately parallel to a current wind direction and having an upstream turbine and a downstream turbine. The method includes the steps of: a) providing a data driven model trained with a machine learning method and stored in a database, b) determining a decision parameter for controlling at least one of the upstream turbine and the downstream turbine by feeding the data driven model with the current power production of the upstream turbine which returns a prediction value indicating whether the downstream turbine will be affected by wake, and/or the temporal evolvement of the current power production of the upstream turbine; c) based on the decision parameter, determining control parameters for the upstream turbine and/or the downstream turbine.

An offshore wind power plant designed to reduce a fatigue load of a wind turbine according to an exemplary embodiment of the present invention includes an offshore structure, a plurality of wind turbines which is installed above the offshore structure to be spaced apart from each other with a predetermined distance and is supplied with a power through wind to produce electrical energy; and a turbine controller which controls at least one of a pitch movement and a yaw movement for the plurality of wind turbines.

A method and a system for controlling a wind turbine based on sectors. Original sectors of the wind turbine are reconstructed based on a wind resource parameter and a wake-flow effect. A load is calculated and superposed for a new sector obtained from the sector reconstruction. An optimization algorithm is applied, under a condition that a constraint condition of a fatigue load is met, to find an operation parameter for maximum power generation amount of the wind turbine.

Disclosed herein are methods, systems, and devices for utilizing distributed reinforcement learning and consensus control to most effectively generate and utilize energy. In some embodiments, individual turbines within a wind farm may communicate to reach a consensus as to the desired yaw angle based on the wind conditions.

Disclosed is a method of establishing a wake management of a wind farm. The method comprises acts of monitoring one or more wake conditions using one or more sensors from one or more wind turbine generators (WTGs); and establishing a wake management of the wind farm as a function of the wake conditions. Disclosed is also a method of optimising operation of a wind turbine park based on wake management and a system for generating wake management.

A wind farm/park having a plurality of spatially distributed wind turbines, including at least one upstream wind turbine and at least one downstream wind turbine. Each wind turbine includes a rotor with at least two blades. At least one downstream wind turbine is affected under certain wind conditions by a wake region generated by the upstream wind turbine and containing helical vortex structures formed at the tip of the blades of the upstream wind turbine. A geometry or configuration of one or more of the rotor blades of the upstream wind turbine is different from a geometry or configuration of the other blade(s) of the upstream wind turbine thereby creating a fixed asymmetry in the blade configuration so as to accelerate destruction of vortices in the wake of the rotor of the upstream wind turbine by exciting a natural instability of the blade tip vortices.