The present invention relates to determining and setting wind turbine type maximum power level (301) and individual wind turbine maximum power level (308) for over-rating control.

A system for generating power includes an environmental engine that determines performance metrics for a plurality of wind turbines deployed at a plurality of windfarms, such that each windfarm includes a corresponding subset of the plurality of windfarms. The performance metrics for a given wind turbine of the plurality of wind turbines characterizes wind flowing over blades of the given wind turbine. The system includes an artificial intelligence (AI) ensemble engine operating on the one or more computing devices that generates a set of models for each wind turbine of the plurality of wind turbines, wherein each model of each set of models is generated with a different machine learning algorithm and selects, for each respective set of models, a model with a highest efficiency metric. The AI engine provides edge computing systems operating at the plurality of windfarms with a selected model and corresponding recommended operating parameters.

A method to design the kits and layup the reinforcement layers and core using projection system, comprising a mold having a contoured surface; a layup projection generator which: defines a plurality of mold sections; identifies the dimensions and location for a plurality of layup segments. A model-based calibration method for alignment of laser projection system is provided in which mold features are drawn digitally, incorporated into the plug(s) which form the wind turbine blade mold, and transferred into the mold. The mold also includes reflective targets which are keyed to the molded geometry wherein their position is calculated from the 3D model. This method ensures the precision level required from projection system to effectively assist with fabrication of wind turbine blades. In this method, digital location of reflectors is utilized to compensate for the mold deformations.

A computer system optimizes site planning for turbines at a wind farm site, by obtaining land-based characteristics of the wind farm site, wind-based characteristics of the wind farm site, including a distribution of wind velocity and wind direction across the wind farm site over time; a number of turbines to be added; and characteristics for each of the turbines. For each turbine, the system simulates a plurality of wakes at a plurality of locations based on the wind-based characteristics and the turbine-based characteristics; determines power outputs based on the simulated wakes and aforementioned characteristics, determines successive locations of the turbine locations according by applying a gradient descent to the power outputs. The system aggregates the maximum power outputs of each turbine and displays an optimized location and final yaw angle corresponding to each turbine.

A method for determining locations for wind turbine installations at a site. The method includes receiving data from a site, wherein the data includes geotechnical data such as borehole measurements and geophysical data such as high resolution shallow seismic data, magnetometer data, and sonar images. The method includes conditioning the received data by interpreting the received data for key soil boundaries and mechanical properties that are related to a shallow subsurface of the site. The method also includes generating a ground model of the site. The ground model may be continuously updated with newly received data or interpretation updates. The ground model may also be displayed on a screen. The method also includes performing a site action based on the ground model including selecting where to install one or more heavy wind turbine structures within the site.

An engine component for a turbine engine, the engine component comprising a wall bounding an interior; a panel portion defining a portion of the wall, the panel portion comprising: an outer wall; an inner wall spaced from the outer wall to define a wall gap; and a structural segment formed within the wall gap comprising at least one structural element. The apparatus formed from a method including calculating a factor and adjusting a variable until the factor is between a given range.

A system for generating power includes an environmental engine that determines performance metrics for a plurality of wind turbines deployed at a plurality of windfarms, such that each windfarm includes a corresponding subset of the plurality of windfarms. The performance metrics for a given wind turbine of the plurality of wind turbines characterizes wind flowing over blades of the given wind turbine. The system includes an artificial intelligence (AI) ensemble engine operating on the one or more computing devices that generates a set of models for each wind turbine of the plurality of wind turbines, wherein each model of each set of models is generated with a different machine learning algorithm and selects, for each respective set of models, a model with a highest efficiency metric. The AI engine provides edge computing systems operating at the plurality of windfarms with a selected model and corresponding recommended operating parameters.

A computer-implemented method for online calibration of power system model against a power system includes iteratively approximating the power system model, at sequential optimization steps, around a moving design point defined by parameter values of a set of calibration parameters of the power system model. At each optimization step, an approximated system model is used to transform a dynamic input signal into a model output signal, which is compared with measurement signals obtained from measurement devices installed in the power system that define an actual power system output signal generated in response to the dynamic input signal. Parameter values of the calibration parameters adjusted in a direction to minimize an error between the model output signal and the actual power system output signal. The power system model is calibrated against the power system based on resulting optimal values of the calibration parameters.

The invention provides a method for determining a wind turbine layout in a wind power plant comprising a plurality of wind turbines. The method comprises the steps of generating a plurality of random layout candidates fulfilling a set of basic requirements, and then performing a pre screening process on each of the plurality of random layout candidates. Based on the pre-screening process, a subset of layout candidates is selected and detailed optimization is performed on the layout candidates of the selected subset of layout candidates. Based on the detailed optimization, an optimized layout for the wind power plant is selected among the optimized layout candidates of the subset of layout candidates.

A method of determining an average duration for a time period that the at least one first asset, or a second asset operatively connected to the at least one first asset, is inoperable is described. The method comprises obtaining a location of at least one first asset associated with energy generation, transmission or storage. The method comprises obtaining a first probability of a natural adverse event occurring at the location of the at least one first asset. The method comprises obtaining a second probability of a non-natural adverse event occurring at the location of the at least one first asset. The method comprises determining a third probability of at least one damage state of the at least one first asset from the first and second probabilities. The method comprises determining an average duration for a time period that the at least one first asset, or a second asset operatively connected to the at least one first asset, is inoperable based on the third probability of the at least one damage state of the first asset.