A device for modeling blades comprises a data collection module configured to receive sensing data of a drone for a reference blade included in a wind turbine, and a modeling module configured to generate a blade model by performing modeling on the wind turbine based on the sensing data, wherein the modeling module comprises: a reference blade model generation unit configured to generate a reference blade model by performing modeling on the reference blade, and an other blade model generation unit configured to generate an other blade model for at least one other blade included in the wind turbine based on the reference blade model.

An optimized PLP placement method for plateau and mountain wind farms is provided. Starting from the outermost wind turbines, it measures turbine parameters. Firstly, install the first PLP to safeguard the outermost ones. Then, combine its parameters with those of the tallest turbine near the center to set up the second PLP, protecting turbines within a connection range. Next, use the previous step's parameters and those of the tallest turbine in the vertical area of the first two PLPs' plane to install the third PLP, shielding turbines in the area formed by three points. Repeat until covering the whole farm. Many cases prove its reliability, economy and suitability for wide application in optimizing PLP placement.

A system and method of creating 3D field data of at least one specimen of an engineering component includes obtaining a first set of 3D field data, defining at least one geometry parameter, training a variational autoencoder model (VAE), splitting the VAE into an encoder model and a decoder model, connecting a multilayer perceptron network model (MLP) to an input layer of the decoder model of the VAE to form a Hybrid Multilayer Perceptron-Variational Autoencoder model (MLP-VAE), training the MLP-VAE to map values, defining to at least partially define geometry data of at least one additional specimen of the engineering component, using the trained MLP-VAE to predict 3D field data related to the at least one additional specimen by directly mapping the respective at least one value of the at least one geometry parameter to respective predicted result data of the at least one specimen.

Disclosed is a wake-interaction network analysis model for wind farm optimization and method to manage and mitigate complex wake interactions in wind farms. Operationally, our method creates a dynamic network model where each wind turbine is treated as a node within a comprehensive network. The interactions between these nodes, representing the wake effects of one turbine on another, are mapped as weighted edges in the network. These weights are quantified based on sophisticated wake models, incorporating factors like wind speed, direction, and atmospheric conditions. Furthermore, our inventive method and model exhibits a dynamic adaptability to changing wind conditions. Unlike traditional static models, it recalculates the network's edges in real-time, reflecting the varying impact of wake interactions as wind direction and speed fluctuate. This dynamic adaption advantageously ensures that the model remains accurate and relevant under different environmental scenarios.

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.

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.

A device for modeling blades comprises a data collection module configured to receive sensing data of a drone for a reference blade included in a wind turbine, and a modeling module configured to generate a blade model by performing modeling on the wind turbine based on the sensing data, wherein the modeling module comprises: a reference blade model generation unit configured to generate a reference blade model by performing modeling on the reference blade, and an other blade model generation unit configured to generate an other blade model for at least one other blade included in the wind turbine based on the reference blade model.

Provided in the present disclosure are a power-generation performance evaluation method and apparatus for a wind generating set. The power-generation performance evaluation method comprises: acquiring respective historical operation data of a wind generating set within n historical time periods; according to the historical operation data, determining actual capacity coefficients and theoretical capacity coefficients; on the basis of the actual capacity coefficients and the theoretical capacity coefficients, determining historical power-generation performance coefficients, so as to obtain a power generation-performance change trend; and according to the power-generation performance change trend, determining an estimated power-generation performance coefficient within a target future time period.

A device for modeling blades comprises a data collection module configured to receive sensing data of a drone for a reference blade included in a wind turbine, and a modeling module configured to generate a blade model by performing modeling on the wind turbine based on the sensing data, wherein the modeling module comprises: a reference blade model generation unit configured to generate a reference blade model by performing modeling on the reference blade, and another blade model generation unit configured to generate another blade model for at least one other blade included in the wind turbine based on the reference blade model.

A simulation method for a marine seismic ground motion applicable to seismic analysis of offshore wind power includes: calculating a transfer function of a seismic ground motion for a bedrock site with an overlying seawater layer; modifying a response spectrum on the basis of a design modification factor (DMF) model, and calculating a power spectral density function of the seismic ground motion; calculating a spatially varying power spectral density matrix of the seismic ground motion; simulating the seismic ground motion in a frequency domain, and obtaining a non-stationary acceleration time history of the seismic ground motion; and using the simulated seismic ground motion as an input for seismic response analysis of an offshore wind power structure. The disclosure provides more accurate seismic ground motion inputs for seismic response analysis and seismic design of offshore wind power structures.