General Electric Company

General Electric Renovables Espana, S.l.

Akshay Krishnamurty Ambekar

Megan Michela Wilson

Stefan Kern

Siddhanth Chandrashekar

Deepak Raj Sagi

Benoit Philippe Petitjean

Dipankar Deb

Dongjai Lee

Subhankar Ghosh

Sara Simonne Delport

Kalpit Vikrambhai Desai

Soeren Georg

Vishal Cholapadi Ravindra

Oladodja Achraf Gbadamassi

Benoit Philippe Armand Petitjean

Megan Wilson

The present disclosure is directed to a method for detecting a mass imbalance in a rotor of a wind turbine. The method includes receiving, with a computing device, sensor data indicative of an operating characteristic of the wind turbine. The method also includes determining, with the computing device, a mean amplitude of a designated frequency component of the operating characteristic. Furthermore, the method includes determining, with the computing device, when a mass imbalance is present within the rotor based on the mean amplitude of the designated frequency component.

Method and system for detecting a mass imbalance in a wind turbine rotor

A method implemented using at least one processor module includes receiving a plurality of operational parameters corresponding to a plurality of wind turbines and obtaining a plurality of source sound power values corresponding to the plurality of wind turbines. The method further includes obtaining a receptor sound pressure value corresponding to a receptor location and estimating an attenuation model based on the plurality of source sound power values, and the receptor sound pressure value. The attenuation model disclosed herein comprises a plurality of attenuation coefficients. The method also includes determining at least one turbine set-point corresponding to at least one wind turbine among the plurality of wind turbines based on the plurality of attenuation coefficients, and the plurality of turbine operational parameters.

System and method for optimal operation of wind farms

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 determining wake losses of a wind farm. The wind farm includes a plurality of wind turbines. The method includes operating the wind farm in a first operational mode. Another step includes collecting turbine-level data from at least one upstream wind turbines in the wind farm during the first operational mode. The method also includes estimating a freestream farm-level power output for the wind farm during first operational mode based, at least in part, on the collected turbine-level data. A further step includes measuring an actual farm-level power output for the wind farm for the first operational mode. Thus, the method also includes determining the wake losses of the wind farm for the first operational mode as a function of the measured actual farm-level power output and the estimated freestream farm-level power output.

System and method for determining wind farm wake loss

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 system and method for simultaneously coordinating a wake control system and a noise control system of a wind farm having a plurality of wind turbines is disclosed. The method includes determining, via a farm controller, one or more wake control set points for the wake control system. Further, the method includes determining, via the farm controller, one or more noise control set points for the noise control system. The method also includes selecting, via the farm controller, between the wake control set points and the noise control set points for each of the plurality of wind turbines. Thus, the method also includes sending, via the farm controller, the selected control set points to local controllers of the plurality of wind turbines and operating the wind farm based on the selected control set points.

System and method for coordinating wake and noise control systems of a wind farm

A method for optimizing a hybrid wind system including a wind farm having a plurality of wind turbines and one or more energy storage units, is presented. The method includes acquiring actual wind power data associated with one or more dispatch windows. The method includes determining forecasted wind farm power estimates corresponding to the dispatch windows using a plurality of forecast schemes. The method includes computing difference values by comparing the forecasted wind farm power estimates to the actual wind power data. The method includes identifying a wind power forecast scheme based at least in part on the computed difference values and balancing a penalty to the grid with life consumption of the energy storage units while regulating the wind turbines and the energy storage units based at least in part on a subsequent forecasted wind farm power estimate generated using the identified wind power forecast scheme.

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Mauldin, SC, United States of America

Akshay Krishnamurty Ambekar