A method for associating geographic location information with a power system model. The method generally includes the steps of receiving known geographic information for a plurality of power system assets within the model and automatically calculating the geographic information for other power system assets with unknown geographic information based on the imported geographic information and architectural information available within the model. The step of calculating the geographic location information may be performed iteratively initially using only imported geographic information and subsequently using both imported and calculated geographic information. The geographic location of each power system asset with an unknown location may be determined through multilateration based on known locations and branch connection lengths of two or more connections with known geolocations.

Evaluating expected performance of a wind farm during a voltage event of a power grid, using a simulation tool. The tool initiates a simulated voltage event, at time, t.sub.0; initiates simulation of a subsequent response to the voltage event by the farm; retrieves information regarding an initial voltage state of a model of the farm, at t.sub.0, and predicts a final voltage state of the model of the wind farm, based on the retrieved information regarding the initial voltage state. At t.sub.0, a simulated output current, I, of the farm is lowered, based on the retrieved information and the predicted final voltage state, and a simulated output voltage, V, of the model of the farm, is monitored. The simulated output current, I, of the farm is subsequently adjusted, based on V, while monitoring V, I, and/or simulated output power of the model. Expected performance of the farm is then evaluated.

A computing device for distributed power system model calibration is provided. The computing device is programmed to receive event data and model response data associated with a model to simulate, wherein the model includes a plurality of parameters, divide the event data into a plurality of sets, wherein each set includes associated parameters, and transmit the plurality of sets of event data to a plurality of client nodes. Each client node of the plurality of client nodes is programmed to analyze a corresponding set of event data to determine updated parameters for the model. The computing device is further programmed to receive a plurality of updated parameters for the model from the plurality of client nodes and analyze the received plurality of updated parameters to determine at least one adjusted parameter.

An example method comprises receiving historical sensor data from sensors of components of wind turbines, training a set of models to predict faults for each component using the historical sensor data, each model of a set having different observation time windows and lead time windows, evaluating each model of a set using standardized metrics, comparing evaluations of each model of a set to select a model with preferred lead time and accuracy, receive current sensor data from the sensors of the components, apply the selected model(s) to the current sensor data to generate a component failure prediction, compare the component failure prediction to a threshold, and generate an alert and report based on the comparison to the threshold.

The present invention relates to a method of determining the wind speed in the plane of a rotor (PR) of a wind turbine (1), by measuring (MES2) the rotational speed of the rotor, the angle of the blades and the generated power. The method according to the invention uses a wind turbine model (MOD) constructed from wind speed measurements (LID), and by use of measurement clustering (GRO) and regressions (REG).

The disclosure discloses a numerical simulation method of an influence of a polytetrafluoroethylene (PTFE)-based membrane on an aerodynamic characteristic of a wind turbine blade, and relates to the technical field of polymer composites. The simulation method comprises the following steps: selecting a wind turbine generator, a blade airfoil and a PTFE-based nano functional membrane; setting a numerical simulation computation network and a computation area of a wind energy capture area; determining main computation parameters and a Reynolds number for aerodynamic characteristic computation; establishing a geometrical model whose airfoil boundary extends by 0.26 mm (membrane thickness) along a normal direction to obtain a new computational geometry; computing by using a hydrodynamic computation method and a finite volume method; and obtaining an influence number simulation computation result.

Techniques for conducting an air flow simulation for a wind turbine are described. The techniques include importing a file containing a digitized representation of a three-dimensional blade geometry, extracting from the file, blade constructive parameters, and calculating a low-order air flow past a wind-turbine that includes the blade, based on a Blade Element Momentum Theory (BEMT) to determine sectional angle of attack and free-stream velocity, boundary layer transition, and acoustic noise results. The techniques also include performing air flow simulation for a given number of blade sections, and generating virtual microphone rings. The process also includes computing noise spectra at the virtual microphone rings and blending the noise spectra generated and generating synthetic noise signals from each section by inverse Fourier transform of the noise spectra and converting the noise spectra into an audio track.

The invention relates to a method for determining design parameters (41) of a rotor blade (3, 4, 5) of a machine interacting with a fluid, in particular of a wind turbine, in which quality parameters (39) of the rotor blade are determined non-destructively, in particular by way of measurements, in which target parameters of the rotor blade (40) are determined, and in which the determined target parameters are predefined in an optimization process (33), wherein the design parameters are varied in the optimization process in such a way that the target parameters are achieved, taking the determined quality parameters into consideration. In this way, it is possible to determine the parameters of a present rotor blade which can be non-destructively determined, so as to determine the parameters that cannot be determined non-destructively, or that are difficult to determine, by way of a computer model.

A method and a device for calculating a power generation of a wind farm is provided. The method includes: determining whether a terrain complexity of a wind farm field exceeds a predetermined complexity; determining a representativeness of anemometer tower data in the wind farm field in a case where the terrain complexity exceeds the predetermined complexity; performing a mesoscale numerical simulation of a meteorological variable in the wind farm field in a case where the anemometer tower data is unrepresentative; extracting mesoscale numerical simulation data as virtual anemometer tower data; and calculating the power generation of the wind farm by using the virtual anemometer tower data.

A method of modeling an equivalent wind turbine generator (WTG) system for a wind farm having a plurality of WTG units includes determining an impact factor of each WTG unit of the plurality of WTG units, determining an equivalent single WTG unit model parameters of the wind farm based on the impact factor of each WTG unit, and determining an effective wind speed of the wind farm to use as the equivalent WTG input wind speed. The method produces a model of static and/or dynamic wind farm behavior. Additionally, a software configured to execute a method of modeling an equivalent wind turbine generator (WTG) system for a wind farm having a plurality of WTG units.