General Electric Company

Subhankar Ghosh

Robert August Kaucic

Necip Doganaksoy

Zhanpan Zhang

Hongyan Liu

Megan Michela Wilson

Akshay Krishnamurty Ambekar

Stefan Kern

Tapan Shah

Babu Ozhur Narayanan

Siddhanth Chandrashekar

Dongjai Lee

Aditya Ramkrishna Karnik

Jay Zhiqiang Cao

Sara Simonne Delport

Alexander Turner Graf

Paul Alex Ardis

Abhirup Mondal

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 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

A method for creating predictive damage models includes receiving a first predictive damage model, identifying latent space between a first and a second domain asset, building a regression model from first domain asset projected source data, creating target dependent variables of a second model, applying classification or regression techniques to determine a function expressing the dependent variables, determining data points from the function to develop a second regression model, applying the second regression model to data points to predict target dependent variables, evaluating the second predictive damage model using the predicted target dependent variables, performing a sensitivity study to determine a directionality parameter of the second predictive damage model, and if the results are within an acceptable predetermined range, providing maintenance or servicing recommendations generated by the second predictive model to a user platform display, else repeating the process by rebuilding the regression model to further refine the regression model.

Creating predictive damage models by transductive transfer learning

A rail vehicle wheel flat warning system comprising a first sensor, a second sensor and a controller. The first sensor may be located adjacent to a first side of a rail to provide data associated with a rail vehicle wheel passing over the first side of the rail. The second sensor may be located adjacent to the first side of the rail to provide data associated with the rail vehicle wheel passing over the first side of the rail. Furthermore, the controller may be in communication with the first sensor and the second sensor to receive data from the first sensor and the second sensor. The controller may determine a potential wheel deformity based on the data received from the first sensor and the second sensor.

Wheel deformity warning system

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Bengaluru, India

Subhankar Ghosh