A method for controlling a wind energy installation arrangement having at least one wind energy installation. The method includes determining pairs of values of a first quantity which depends on a wind speed, and a second quantity which depends on a power of the wind energy installation arrangement, and determining eigenvalues and/or eigenvectors of a covariance matrix of the pairs of determined values. The method may further include determining at least one intensity value that is dependent on a standard deviation and a mean value of a rotational speed and/or a torque of the wind energy installation arrangement and/or of a wind speed, and determining a value of a control parameter of the wind energy installation arrangement with the aid of an artificial intelligence based on the eigenvalues and/or eigenvectors and/or the at least one intensity value. The wind energy installation arrangement is controlled based on the control parameter value.

An adaptive dynamic planning control method and system for a large-scale energy storage station. The method comprises: setting a structure and control target parameters of an adaptive dynamic planning control system; initializing the parameters and importing an initial state of a controlled object; calculating an original wind electricity power fluctuation rate at a current moment t and smoothing the original wind electricity power according to a change rate control strategy; calculating a smoothed wind storage power fluctuation rate, a power of an energy storage system, and a state of charge (SOC) of the energy storage system; initializing and training an evaluation module and an execution module; calculating and saving a control strategy, a smoothed wind storage power fluctuation rate, an energy storage power and a (SOC) at each moment; and outputting the control strategy at each moment, the smoothed wind storage power fluctuation rate, the energy storage power and the (SOC).

The present disclosure provides a method and system for optimizing a control process. The method and system comprise using a sensor to generate a feedback signal that represents a measured performance index for an extremum seeking control (ESC) method and sending the feedback signal to an ESC conditioning circuit that applies a logarithmic transformation to the feedback signal to obtain a modified feedback signal. An ESC controller applies the modified feedback signal to the ESC method to generate an output value that is used to control an actuator to maximize the performance of a machine or process.

The present invention relates to a method of controlling a wind turbine by automatic online selection of a controller that minimizes the wind turbine fatigue. The method therefore relies on an (offline constructed) database (BDD) of simulations of a list (LIST) of controllers, and on an online machine learning step for determining the optimal controller in terms of wind turbine (EOL) fatigue. Thus, the method allows automatic selection of controllers online, based on a fatigue criterion, and switching between the controllers according to the measured evolution of wind condition.

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.

A method for monitoring a wind turbine where for each blade an activity signal is detected at subsequent time points, where for each time point predicting an activity signal of each blade at the respective time point by a separate data-driven model, where the predicted activity signal is an output value of the respective data-driven model and where one or more detected activity signals of blades other than the blade whose activity signal is the output value are input values of the respective data-driven model; b) determining for each data-driven model a residual between the predicted activity signal and the detected activity signal; checking a threshold criterion for one or more variables, where the values of the one or more variables depend on the residuals for all data-driven models determining an abnormal operation state of the turbine, if the threshold criterion is fulfilled, is provided.

A method of controlling a wind turbine including a plurality of rotor blades, a first controller for controlling an adaptive flow regulating system having a plurality of individually controllable adaptive flow regulating devices arranged on the rotor blades, and a second controller for controlling a pitch regulating system for regulating a pitch angle of each rotor blade. The method includes (a) determining a diagnostic value indicative of an operational efficiency of the adaptive flow regulating system, (b) determining a first gain value for the first controller and a second gain value for the second controller based on the diagnostic value, (c) applying the first gain value to control signals for the adaptive flow regulating system generated by the first controller, and (d) applying the second gain value to control signals for the pitch regulating system generated by the second controller, is provided.

A method for providing backup control for a supervisory controller of at least one wind turbine includes observing, via a learning-based backup controller of the at least one wind turbine, at least one operating parameter of the supervisory controller under normal operation. The method also includes learning, via the learning-based backup controller, one or more control actions of the at least one wind turbine based on the operating parameter(s). Further, the method includes receiving, via the learning-based backup controller, an indication that the supervisory controller is unavailable to continue the normal operation. Upon receipt of the indication, the method includes controlling, via the learning-based backup controller, the wind turbine(s) using the learned one or more control actions until the supervisory controller becomes available again. Moreover, the control action(s) defines a delta that one or more setpoints of the wind turbine(s) should be adjusted by to achieve a desired outcome.

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.

A method of controlling a wind turbine including a plurality of rotor blades, a first controller for controlling an adaptive flow regulating system having a plurality of individually controllable adaptive flow regulating devices arranged on the rotor blades, and a second controller for controlling a pitch regulating system for regulating a pitch angle of each rotor blade. The method includes (a) determining a diagnostic value indicative of an operational efficiency of the adaptive flow regulating system, (b) determining a first gain value for the first controller and a second gain value for the second controller based on the diagnostic value, (c) applying the first gain value to control signals for the adaptive flow regulating system generated by the first controller, and (d) applying the second gain value to control signals for the pitch regulating system generated by the second controller, is provided.