General Electric Company

General Electric Renovables Espana, S.l.

Siddhanth Chandrashekar

Megan Michela Wilson

Akshay Ambekar

Akshay Krishnamurty Ambekar

Stefan Kern

Peter Alan Gregg

Colin Craig McCulloch

Anup Menon

Subhankar Ghosh

Dongjai Lee

Vineel Chandrakanth Gujjar

Kalpit Vikrambhai Desai

Sara Simonne Delport

Vishal Cholapadi Ravindra

Krishnarao Dattaram Dhuri

Manisha Srivastava

Colin Craig Mcculloch

Megan Wilson

The present disclosure is directed to systems and methods for generating one or more farm-level power curves for a wind farm that can be used to validate an upgrade provided to the wind farm. The method includes operating the wind farm in a first operational mode. Another step includes collecting turbine-level operational data from one or more of the wind turbines in the wind farm during the first operational mode. The method also includes aggregating the turbine-level operational data into a representative farm-level time-series. Another step includes analyzing the operational data collected during the first second operational mode. Thus, the method also includes generating one or more farm-level power curves for the first operational mode based on the analyzed operational data.

System and method for validating optimization of a wind farm

The present disclosure is directed to a system and method for assessing farm-level performance of a wind farm. The method includes operating the wind farm in a first operational mode and identifying one or more pairs of wind turbines having wake interaction. The method also includes generating a pairwise dataset for the wind turbines pairs. Further, the method includes generating a first wake model based on the pairwise dataset and predicting a first farm-level performance parameter based on the first wake model. The method also includes operating the wind farm in a second operational mode and collecting operational data during the second operational mode. Moreover, the method includes predicting a first farm-level performance parameter for the second operational mode using the first wake model and the operational data from the second operational mode. The method further includes determining a second farm-level performance parameter during the second operational mode. Thus, the method includes determining a difference in the farm-level performance of the wind farm as a function of the first and second farm-level performance parameters.

System and method for assessing farm-level performance of a wind farm

A computer-implemented method for recalibrating nacelle-positions of a plurality of wind turbines in a wind park is implemented by a nacelle calibration computing device including a processor and a memory device coupled to the processor. The method includes identifying at least two associated wind turbines included within the wind park wherein each associated wind turbine includes location information, determining a plurality of predicted wake features for the associated wind turbines based at least partially on the location information of each associated wind turbine, retrieving a plurality of historical performance data related to the associated wind turbines, determining a plurality of current wake features based on the plurality of historical performance data, identifying a variance between the predicted wake features and the current wake features, and determining a recalibration factor for at least one of the associated wind turbines based on the identified variance.

Systems and methods for wind turbine nacelle-position recalibration and wind direction estimation

Methods and systems for optimizing operation of a wind farm are disclosed. The method includes providing a farm-level wake model for the wind farm based on historical wake parameters corresponding to reference sets of interacting wind turbines in the wind farm. Another step includes monitoring one or more real-time wake parameters for wind turbines in the wind farm. A further step includes identifying at least two interacting wind turbines from the reference sets based on the wake parameters. Another step includes determining a wake offset angle between the interacting wind turbines as a function of at least one of a wind direction, a geometry between the interacting wind turbines, or a wake meandering component. The method also includes continuously updating the wake model online based at least partially on the wake parameters and the wake offset angle and controlling the interacting wind turbines based on the updated wake model.

Systems and methods for optimizing operation of a wind farm

Embodiments of methods and systems for optimizing operation of a wind farm are presented. The method includes receiving new values corresponding to at least some wake parameters for wind turbines in the wind farm. The method further includes identifying new sets of interacting wind turbines from the wind turbines based on the new values. Additionally, the method includes developing a farm-level predictive wake model for the new sets of interacting wind turbines based on the new values and historical wake models determined using historical values of the wake parameters corresponding to reference sets of interacting wind turbines in the wind farm. Furthermore, the method includes adjusting one or more control settings for at least the new sets of interacting wind turbines based on the farm-level predictive wake model.

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Bengaluru, India

Siddhanth Chandrashekar