Abstract: A system for optimizing performance of a wind turbine during noise reduced operation includes a supervisory controller and a converter controller. The converter controller is configured to perform a plurality of operations including operating the wind turbine at a first optimization mode or a second optimization mode. The first optimization mode includes actively adjusting a power output of the wind turbine based on grid parameter(s) so as to maximize energy production of the wind turbine without accelerating consumption of life of component(s) thereof. The second optimization mode includes operating the wind turbine at a maximum power output rating while tracking remaining life of the component(s) based on a plurality of parameters. When the remaining life of the component(s) exceeds a predetermined threshold, the converter controller generates a notification to indicate that a maintenance action is needed.

Abstract: 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.

Abstract: 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.