General Electric Renovables Espana, S.l.

General Electric Company

Ge Infrastructure Technology, LLC

Arunvenkataraman Subramanian

Scott Charles Evans

Samuel Bryan Shartzer

Samuel Davoust

Srinivas Bollapragada

Stefan Kern

Dhiraj Arora

Gerald Bowden Wise

Philip James Verzella

Frank William Ripple, Jr

Tapan Ravin Shah

Sachini Piyoni Ekanayake Ekanayake Mudiyanselage

Jeffrey Thomas Maskalunas

John Edmund LaFleche

Jr Frank William Ripple

John Edmund Lafleche

A computer system is provided. The computer system includes a scheduling computing device configured to receive computational task data defining a computational task to be performed, retrieve site data corresponding to each of a plurality of data processing computing devices, select, based on the computational task data and the site data, i) a first computational algorithm for executing the computational task, ii) a first data processing computing device of the plurality of data processing computing devices, and iii) at least one time period for executing the first computational algorithm by the first data processing computing device, wherein the first computational algorithm, the first data processing computing device, and the at least one time period are selected to facilitate reducing carbon dioxide emissions associated with executing the computational algorithm, and instruct the first data processing computing device to execute the first computational algorithm during the at least one time period.

Systems and methods for reducing carbon emissions from machine learning computational tasks

A system and method are provided for operating a wind farm includes determining a wind direction and identifying a turbine cluster, which is a subset of a plurality of wind turbines of the wind farm. The subset includes an upwind turbine and a downwind turbine that is affected by a wake emanating from the upwind turbine. The controller then determines a difference between a freestream maximal cluster power output and a wake-affected cluster power output for the turbine cluster. The controller determines a mitigation setpoint combination for the subset configured to establish a mitigated cluster power output. Based on the mitigation setpoint combination, an operating state of at least one wind turbine of the turbine cluster is changed.

Systems and methods for operating a wind farm

A system and method are provided for controlling a wind turbine of a wind farm. Accordingly, a controller implements a first model to determine a modeled performance parameter for the first wind turbine. The modeled performance parameter is based, at least in part, on an operation of a designated grouping of wind turbines of the plurality of wind turbines, which is exclusive of the first wind turbine. The controller then determines a performance parameter differential for the first wind turbine at multiple sampling intervals. The performance parameter differential is indicative of a difference between the modeled performance parameter and a monitored performance parameter for the first wind turbine. A second model is implemented to determine a predicted performance parameter of the first wind turbine at each of a plurality of setpoint combinations based, at least in part, on the performance parameter differential the first wind turbine. A setpoint combination is then selected based on the predicted performance parameter and an operating state of the first wind turbine is changed based on the setpoint combination.

Systems and methods for controlling a wind turbine

A method for controlling a wind turbine includes detecting a plurality of analytic outputs relating to power performance of the wind turbine from a plurality of different analytics. The method also includes analyzing the plurality of analytic outputs relating to power performance of the wind turbine. Further, the method includes generating at least one computer-based model of the power performance of the wind turbine using at least a portion of the analyzed plurality of analytic outputs. Moreover, the method includes training the computer-based model(s) of the power performance of the wind turbine using annotated analytic outputs relating to the power performance of the wind turbine. In addition, the method includes estimating a power magnitude of the wind turbine using the machine-learned computer-based model(s). As such, the method includes implementing a control action when the power magnitude of the wind turbine is outside of a selected range.

Machine-learning model-based analytic for monitoring wind farm power performance

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Dublin, CA, United States of America

Arunvenkataraman Subramanian