General Electric Company

General Electric Renovables Espana, S.l.

Fabiano Daher Adegas

Fernando Javier D'Amato

Masoud Abbaszadeh

Conner Brooks Shane

Justin Barton

Dale Robert Mashtare

Pranav Agarwal

Thomas Stephen Markham

Samuel Davoust

Xiongzhe Huang

Sara Simonne L Delport

Junqiang Zhou

Carlo Luigi Bottasso

Stefano Cacciola

Alexander Luenenschloss

A system for computing wind turbine estimated operational parameters and/or control commands, includes sensors monitoring the wind turbine, a control processor implementing a model performing a linearization evaluation to obtain a structural component dynamic behavior, a fluid component dynamic behavior, and/or a combined structural and fluid component dynamic behavior of wind turbine operation, and a module performing a calculation utilizing the linearization evaluation of the structural component dynamic behavior, the fluid component dynamic behavior, and/or the combined structural and fluid component dynamic behavior. The module being at least one of an estimation module and a multivariable control module. The estimation module generating signal estimates of turbine or fluid states. The multivariable control module determining actuator commands that include wind turbine commands that maintain operation of the wind turbine at a predetermined setting in real time. A method and a non-transitory medium are also disclosed.

Wind turbine model based control and estimation with accurate online models

Systems and methods are provided for the robust, multivariable control of a power generating asset via H-infinity loop shaping using coprime factorization. Accordingly, a controller of the power generating asset computes a gain value for an H-infinity (H&#x221e;) module in real-time at predetermined sampling intervals using an actuator dynamic model. The controller then determines an acceleration factor based, at least in part, on the gain value of the H&#x221e; module. Based, at least in part on the acceleration vector, the controller generates a control vector. An operating state of at least one component of the power generating asset is changed based on the control vector.

Systems and methods for multivariable control of a power generating system

A system for wind turbine control includes a state dependent quadratic regulator (SDQR) control unit, a linear quadratic regulator (LQR) generating control acceleration commands for wind turbine speed and wind turbine power regulation, an actuator dynamic model computing a gain value for the LQR at predetermined sampling intervals and augmenting the actuator dynamic model with a wind turbine model. The wind turbine model either an analytical linearization model or a precomputed linear model, where the precomputed linear model is selected from a model bank based on a real-time scheduling operation, and the analytical linearization model is computed using an online linearization operation in real-time at time intervals during operation of the wind turbine based on current wind turbine operating point values present at about the time of linearization. A method and a non-transitory medium are also disclosed.

System and method for wind turbine multivariable control using state dependent quadratic regulator

A control system for a wind turbine is provided. The wind turbine includes at least one stationary component. The control system includes at least one mechanical load measurement sensor coupled to the at least one stationary component. The system also includes at least one modeling device configured to generate and transmit at least one wind turbine regulation device command signal to at least one wind turbine regulation device to regulate operation of the wind turbine based upon at least one wind inflow parameter.

System and method for controlling wind turbines

Methods, apparatus, systems and articles of manufacture are disclosed to provide wind turbine control and compensate for wind induction effects. An example method includes receiving wind speed data from a Light Detecting and Ranging (LIDAR) sensor. The example method includes receiving operating data indicative of wind turbine operation. The example method includes determining an apriori induction correction for wind turbine operating conditions with respect to the LIDAR wind speed data based on the operating data. The example method includes estimating a wind signal from the LIDAR sensor that is adjusted by the correction. The example method includes generating a control signal for a wind turbine based on the adjusted LIDAR estimated wind signal.

Systems and methods to correct induction for LIDAR-assisted wind turbine control

A control method and a control system for a wind turbine are disclosed. The control method comprises measuring wind turbine blade pitch angles; obtaining a wind turbine rotor acceleration value; determining whether a blade pitch runaway fault condition is occurring; and during the blade pitch runaway fault condition, adjusting a pitch angle command based at least in part on the rotor acceleration value, a pitch angle of at least one faulted blade and a pitch angle of a healthy blade; and controlling wind turbine blades based at least in part on the adjusted pitch angle command.

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São Paulo, Brazil

Fabiano Daher Adegas