General Electric Company

Ge Infrastructure Technology, LLC

Frank William Ripple, Jr

John Joseph Mihok

Kyle Raymond Barden

Scott Charles Evans

Zhanpan Zhang

Guangliang Zhao

Michael James Rizzo

Arunvenkataraman Subramanian

Gerald Bowden Wise

Alvaro Enrique Gil

Jin Xia

Christopher Albert Jordan

John Edmund LaFleche

A system and method are provided for operating a power generating asset. Accordingly, a plurality of operational data sets are received by a controller. The operational data sets include at least one indication of a performance anomaly. A plurality of predictive models are implemented by the controller to determine a plurality of potential root causes of the performance anomaly and a plurality of corresponding probabilities for each of the potential root causes. A consolidation model is generated for classifying the plurality of potential root causes and corresponding probabilities. The consolidation model is trained via a training data set to correlate the plurality of potential root causes to an actual root cause for the performance anomaly. The consolidation model is implemented by the controller to determine the actual root cause of the performance anomaly based on the plurality of potential root causes and corresponding probabilities.

Systems and methods for operating a power generating asset

A method for detecting a fault in a direct current (DC) battery of a pitch system includes receiving, via a server, a plurality of voltage signals of the battery over at least one time period. The method also includes storing, via a database of the server, the plurality of voltage signals of the battery for the predetermined time period. Further, the method includes determining, via the server, a state of the battery as a function of the plurality of voltage signals. When the state of the battery is indicative of a battery fault, the method includes implementing a corrective action for the battery.

System and method for detecting battery faults in a pitch system of 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

Growing community of inventors

Broadalbin, NY, United States of America

Frank William Ripple, Jr