General Electric Company

Lockheed Martin Corporation

General Electric Renovables Espana, S.l.

Ge Infrastructure Technology, LLC

Nbcuniversal Media, LLC

Ge Vernova Infrastructure Technology LLC

Ge Intellectual Property Licensing, LLC

Abb Schweiz Ag

Scott Charles Evans

Thomas Stephen Markham

Marc Robert Pearlman

John Erik Hershey

Samuel Bryan Shartzer

Jeremy Impson

Samuel Davoust

Ertugrul Berkcan

William James Premerlani

Emad Andarawis Andarawis

Daniel White Sexton

Bruce Gordon Barnett

Austars Raymond Schnore, Jr

Benjamin Edward Beckmann

Peter Alan Gregg

Charles Scott Sealing

Terry Michael Topka

Eric R Steinbrecher

Ishan Prabhath Weerakoon

Arunvenkataraman Subramanian

Wolfgang Daum

Michael James Hartman

Weizhong Yan

John William Carbone

Nurali Virani

Zhanpan Zhang

Mingxiao Jiang

Ping Liu

Dhiraj Arora

Edward E Eiland

Stephen Francis Bush

Harold Woodruff Tomlinson, Jr

Kenneth B Welles, Ii

Andrew Soliz Torres

John Steven Holmes

William Hull Bicknell

Mark Fredrick Culler

Robert J Mitchell, Jr

Philip Paul Beauchamp

Budhaditya Deb

Stephen J Dill

Stefan Kern

Scott Alan Horning

Bernhard Joseph Scholz

Tanveer Iqbal

Sara Simonne Louisa Delport

Adam Edgar Klingbeil

Kenneth Brakeley Welles

John Anderson Fergus Ross

Dustin R Garvey

Srinivas Chukka

Ralph Thomas Hoctor

John James Dougherty

David Michael Davenport

John Kenneth Hooker

Gary Jude Saulnier

Michael Joseph Dell'Anno

Stephen Michael Hladik

Robert Marten Bultman

Waseem Ibrahim Faidi

Danian Zheng

Kevin Wayne Kinzie

Sergei Ivanovich Dolinsky

Richard D Baertsch

Abha Moitra

Srinivas Bollapragada

Anil Varma

Thomas F Papallo

Changting Wang

Roger J Morgan

Brock Estel Osborn

Ziqiang Hu

Andrew Walter Crapo

Joseph Lawrence Chacon

Kasi Viswanadha Raju Gadiraju

Steven Matt Gustafson

Samantha Rao

Bernard P Landa

Colin Craig McCulloch

Megan Michela Wilson

Timothy Patrick Kelliher

Michael C Von Kannewurff

Ramakrishna Rao

Shobhana Mani

Stephen Eric Zingelewicz

Brian Allen Rittenhouse

Robert James Boring

Suresh K Iyer

Lang Tong

Martin W Egan

Yogesh Kesrinath Potdar

Eugene Joseph Orlowski, Jr

Lei Wu

Richard Brownell Arthur

Gerald Bowden Wise

Somashekhar Basavaraj

Bouchra Bouqata

Frank William Ripple, Jr

Abhishek Srivastav

Steven Jay Tyber

A method for optimizing performance of a wind farm having at least one wind turbine includes maneuvering a first unmanned aerial vehicle (UAV) having at least one sensor to a first location near the at least one wind turbine of the wind farm; collecting, via the at least one sensor of the first UAV, data corresponding to one or more wind conditions at the at least one wind turbine; receiving the data corresponding to the one or more wind conditions at the at least one wind turbine via a controller; generating a control action for the at least one wind turbine using the data corresponding to the one or more wind condition at the at least one wind turbine; and implementing, via the controller, the control action.

System and method for optimizing wind farm performance using unmanned aerial vehicles

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

According to some embodiments, a system to facilitate hierarchical data exchange may include an aggregation platform data store containing electronic records. A data aggregation platform may collect, from a plurality of data source devices, information associated with a plurality of data sources and store the collected information into the aggregation platform data store. The data aggregation platform may also receive a data request from a data consumer device, and, responsive to the received data request, determine a precision tier associated with the data request. The data aggregation platform may then automatically calculate a resource value for the data request based on the precision tier. It may then be arranged for information from the aggregation platform data store to be modified and transmitted to the data consumer device.

Hierarchical data exchange management system

A system and method are provided for operating a power generating asset. Accordingly, at least one external data set indicative of a plurality of variables affecting the performance of the power generating asset is received by the controller. The controller also receives at least one operational data set indicative of the performance of the power generating asset. A plurality of production-assessment models for the power generating asset are generated and trained based on the data sets. A performance prediction is then generated for each of a plurality of model-variable combinations and a control action is implemented based on one of the performance predictions.

Systems and methods for operating power generating assets

A system and method are provided for controlling a wind turbine of a wind farm. Accordingly, a controller prepares a yaw bias correction function based, at least in part, on a yaw offset function, and on wind speed measurement data and wind direction reference data of a wind event acting on at least a portion of the wind farm. The controller also applies the yaw bias correction function based at least in part on position data of a nacelle of the wind turbine, to yaw the nacelle of the wind turbine.

Systems and methods for controlling a wind turbine

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.

A system and method are provided for controlling a wind turbine of a wind farm. Accordingly, a controller determines a performance differential for the wind turbine at multiple sampling intervals of a yaw event. The controller determines a trendline for the wind turbine correlating the performance differential to a deviation of a wind direction at each of the multiple sampling intervals from an first yaw angle. A difference between an angle associated with the vertex of the trendline and the first yaw angle are utilized by the controller to determine a yaw angle offset. The yaw angle offset is used to adjust a second yaw angle of the wind turbine.

A control system is disclosed. The control system includes a wind turbine, at least one sensor configured to detect at least one property of the wind turbine to generate measurement data, and a controller communicatively coupled to the wind turbine and the at least one sensor. The controller includes at least one processor in communication with at least one memory device. The at least one processor is configured to control, during a training phase, the wind turbine according to at least one test parameter, receive, from the at least one sensor, during the training phase, first measurement data, generate, based on the at least one test parameter and the received first measurement data, a control model, receive, during an operating phase, second measurement data from the at least one sensor, and update the control model continuously based on the second measurement data.

Systems and methods for continuous machine learning based control of wind turbines

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

A method for assessing or validating wind turbine or wind farm performance produced by one or more upgrades is provided. Measurements of operating data from wind turbines in a wind farm are obtained. Baseline models of performance are generated, and each of the baseline models is developed from a different portion of the operating data. A generating step filters the wind turbines so that they are in a balanced randomized state. An optimal baseline model of performance is selected from the baseline models and the optimal baseline model includes direction. The optimal baseline model of performance and an actual performance of the wind farm or wind turbine is compared. The comparing step determines a difference between an optimal baseline model of power output and an actual power output of the wind farm/turbine. The difference is reflective of a change in the power output produced by the upgrades.

System and method for assessing and validating wind turbine and wind farm performance

A method for controlling operation of a wind turbine includes collecting training data, training a machine learning model, obtaining recent data, and applying the machine learning model the recent data to output a reference power or reference power differential corresponding to the recent data. The machine learning model is then applied to the recent data to output at least one of estimated power or estimated power differential corresponding to values of the pitch setpoints and the tip speed ratio setpoints which differ from the recent data. A turbine setting is determined by comparing the estimated power or estimated power differential to the reference power or reference power differential, and then applying the turbine setting to the wind turbine if the estimated power or estimated power differential is greater than or equal to a threshold amount above the reference power or reference power differential.

Method for controlling operation of a wind turbine

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Burnt Hills, NY, United States of America

Scott Charles Evans