General Electric Company

Ge Infrastructure Technology, LLC

General Electric Renovables Espana, S.l.

Zhanpan Zhang

Peter Alan Gregg

Scott Charles Evans

Robert August Kaucic

John Joseph Mihok

Necip Doganaksoy

Guangliang Zhao

Subhankar Ghosh

Jin Xia

Hongyan Liu

Kevin Wayne Kinzie

Megan Michela Wilson

Bouchra Bouqata

Jay Zhiqiang Cao

Frank William Ripple, Jr

David William Eldridge

Satish Giridhar Iyengar

Kyle Raymond Barden

Alvaro Enrique Gil

Abhirup Mondal

Andrew T Ferree

Ricardo Zetina Benignos

Manuel Rodolfo Valdez

Matthew David Pepple

William Arthur Flodder

Mark Nicolaas Jonkhof

Jr Frank William Ripple

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 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 method of correcting turbine underperformance includes calculating a power production curve using monitored data, detecting changes between the monitored data and a baseline power production curve, generating operability curves for paired operational variables from the monitored data, detecting changes between the operability curves and corresponding baseline operability curves, comparing the changes to a respective predetermined metric, and if the change exceeds the metric, providing feedback to a turbine control system identifying at least one of the paired operational variables for each paired variable in excess of the metric. A system and a non-transitory computer-readable medium are also disclosed.

System and method for detecting turbine underperformance and operation anomaly

The system and method described herein relate to production of power from the wind farm that incorporate tunable power production forecasts for optimal wind farm performance, where the wind farm power production is controlled at least in part by the power production forecasts. The system and method use a tunable power forecasting model to generate tunable coefficients based on asymmetric loss function applied on actual power production data, along with tuning factor(s) that tune forecast towards under forecasting or over forecasting. The power production forecasts are generated using the tunable coefficientsand power characteristic features that are derived from actual power production data. The power production forecasts are monitored for any degradation, and a control action to regenerate the coefficients or retune the model is undertaken if degradation is observed.

System and method for controlling power production from a wind farm

The present disclosure is directed to a system and method for forecasting a farm-level power output of a wind farm having a plurality of wind turbines. The method includes collecting actual operational data and/or site information for the wind farm. The method also includes predicting operational data for the wind farm for a future time period. Further, the method includes generating a model-based power output forecast based on the actual operational data, the predicted operational data, and/or the site information. In addition, the method includes measuring real-time operational data from the wind farm and adjusting the power output forecast based on the measured real-time operational data. Thus, the method also includes forecasting the farm-level power output of the wind farm based on the adjusted power output forecast.

System and method for forecasting power output of a wind farm

The present disclosure is directed to systems and methods for validating wind farm performance improvements so as to optimize wind farm performance. In one embodiment, the method includes operating, via a controller, the wind farm in a first operating mode. Another step includes collecting a first set of operating data, via a processor, during the first operating mode. A further step includes operating, via the controller, the wind farm in a second operating mode. The method also includes collecting a second set of operating data, via the processor, during the second operating mode. Next, the method includes normalizing the first and second sets of operating data based on wind speed distributions. As such, another step includes comparing, via the processor, the normalized first and second sets of operating data so as to validate one or more wind farm performance measurements.

Systems and methods for validating wind farm performance improvements

The present disclosure is directed to systems and methods for validating and/or identifying wind farm performance measurements so as to optimize wind farm performance. The method includes measuring operating data from one or more wind turbines of the farm. Another step includes generating a plurality of baseline models of performance of the wind farm from at least a portion of the operating data. Thus, each of the baseline models of performance is developed from a different portion of operating data so as to provide comparable models. The method also includes selecting an optimal baseline model and comparing the optimal baseline model with actual performance of the wind farm. In a particular embodiment, the actual performance of the wind farm is determined after one or more wind turbines of the wind farm is modified by one or more upgrades.

Systems and methods for validating wind farm performance measurements

Growing community of inventors

Niskayuna, NY, United States of America

Zhanpan Zhang