General Electric Company

Nitika Bhaskar

Rahul Srivastava

Pritesh Jain

Sridhar Dasaratha

Krishna Kumar Swaminathan

Milesh Shrichandra Gogad

James Kenneth Aragones

Deepak Raj Sagi

Jonathan Mark Dunsdon

Hongwei Liao

Rahul Kumar Srivastava

Shishir Goel

Akshay Ambekar Krishnamurty

Akshay Krishnamurty Ambekar

The present discussion relates to generating power generation forecasts both on-site and remote to a wind farm, or other intermittent power generation asset, so as to increase the reliability of providing a forecast to interested parties, such as regulatory authorities. Forecasts may be separately generated at both the on-site and remote locations and, if both are available, one is selected for transmission to interested parties, such as regulatory authorities. If, due to circumstances, one forecast is unavailable, the other forecast may be used in its place locally and remotely, communications permitting.

Method and system to optimize availability, transmission, and accuracy of wind power forecasts and schedules

A wind power generation system includes one or both of a memory or storage device storing one or more processor-executable executable routines, and one or more processors configured to execute the one or more executable routines which, when executed, cause acts to be performed. The acts include receiving weather data, wind turbine system data, or a combination thereof; transforming the weather data, the wind turbine system data, or the combination thereof, into a data subset, wherein the data subset comprises a first time period data; selecting one or more wind power system models from a plurality of models; transforming the one or more wind power system models into one or more trained models at least partially based on the data subset; and executing the one or more trained models to derive a forecast, wherein the forecast comprises a predicted electrical power production for the wind power system.

Systems and methods for improved wind power generation

A method, medium, and system to receive a baseline airline schedule including details associated with at least one flight; optimize the baseline airline schedule in accordance with at least one specified optimization objective to generate an optimized airline schedule; evaluate a robustness of the optimized airline schedule based on an execution of a simulation based process to generate a set of quantitative metrics; and generate a record of the set of quantitative metrics.

Method and system for robust network planning optimization of airline flight operations

The present disclosure is directed to a system and method for controlling a wind farm. The method includes operating the wind farm based on multiple control settings over a plurality of time intervals. A next step includes collecting one or more wind parameters of the wind farm over the plurality of time intervals and one or more operating data points for each of the wind turbines in the wind farm for the plurality time intervals. The method also includes calculating a contribution of the operating data points for each of the wind turbines as a function of the one or more wind parameters. Further steps of the method include estimating an energy production for the wind farm for each of the control settings based at least in part on the contribution of the operating data points and controlling the wind farm based on optimal control settings.

System and method for controlling a wind farm

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Bengaluru, India

Nitika Bhaskar