Systems and methods for optimizing the arrangement of wind turbines in a wind farm

A system, comprising:

• one or more electronic processors configured to execute a set of computer-executable instructions;
• and
• one or more non-transitory electronic data storage media containing the set of computer-executable instructions, wherein when executed, the instructions cause the one or more electronic processors or a device or apparatus in which they are contained to
• generate a parameterization to define the geometry of a wind turbine;
• execute a computational fluid dynamics (CFD) model using the defined geometry as an input to the CFD model, the CFD model generating an output representing a performance of the defined geometry;
• optimize the geometry of the wind turbine using the CFD model;
• convert at least some of the data generated during the optimization of the wind turbine into a format for use in training a Deep Learning Neural Network (DLNN), the data including a plurality of input geometries for the wind turbine, together with the CFD model output associated with each input geometry;
• train the Deep Learning Neural Network model, wherein the training data comprises at least some of the CFD model input geometries and CFD model outputs after conversion into the format for the Deep Learning Neural Network;
• iteratively adjust the Deep Learning Neural Network model to improve its accuracy, using a recursive process under control of an agent;
• use an optimized geometry of the wind turbine obtained from the data generated using the CFD model as an input to the DLNN model;
• perform further optimization of the geometry of the wind turbine using the DLNN model;
• validate the performance of an optimized geometry obtained from the Deep Learning Neural Network model by evaluating the optimized geometry with the CFD model
• define a layout for a set of N wind turbines in a wind farm;
• arrange N single wind turbine DLNN models into a wind farm ensemble surrogate model based on the defined layout;
• use the wind farm ensemble model to predict a total power output of the wind farm;
• optimize a layout for the wind farm by iteratively changing a position or specification for one or more of the wind turbines, subject to evaluation by a goal or objective;
• and
• use the wind farm ensemble surrogate model corresponding to the optimized layout for the wind farm as part of a control system for the wind farm.

One or more non-transitory computer-readable media comprising a set of computer-executable instructions that when executed by one or more programmed electronic processors, cause the processors or a device or apparatus in which they are contained to

• generate a parameterization to define the geometry of a wind turbine;
• execute a computational fluid dynamics (CFD) model using the defined geometry as an input to the CFD model, the CFD model generating an output representing a performance of the defined geometry;
• optimize the geometry of the wind turbine using the CFD model;
• convert at least some of the data generated during the optimization of the wind turbine into a format for use in training a Deep Learning Neural Network (DLNN), the data including a plurality of input geometries for the wind turbine, together with the CFD model output associated with each input geometry;
• train the Deep Learning Neural Network model, wherein the training data comprises at least some of the CFD model input geometries and CFD model outputs after conversion into the format for the Deep Learning Neural Network;
• iteratively adjust the Deep Learning Neural Network model to improve its accuracy, using a recursive process under control of an agent;
• use an optimized geometry of the wind turbine obtained from the data generated using the CFD model as an input to the DLNN model;
• perform further optimization of the geometry of the wind turbine using the DLNN model;
• validate the performance of an optimized geometry obtained from the Deep Learning Neural Network model by evaluating the optimized geometry with the CFD model define a layout for a set of N wind turbines in a wind farm;
• arrange N single wind turbine DLNN models into a wind farm ensemble surrogate model based on the defined layout;
• use the wind farm ensemble model to predict a total power output of the wind farm;
• optimize a layout for the wind farm by iteratively changing a position or specification for one or more of the wind turbines, subject to evaluation by a goal or objective;
• and
• use the wind farm ensemble surrogate model corresponding to the optimized layout for the wind farm as part of a control system for the wind farm.