A method of generating a control schedule for a wind turbine is provided, the control schedule indicating how the turbine maximum power level varies over time, the method comprising: determining a value indicative of the current remaining fatigue lifetime of the turbine, or one or more turbine components, based on measured wind turbine site and/or operating data;applying an optimisation function that varies an initial control schedule to determine an optimised control schedule by varying the trade off between energy capture and fatigue life consumed by the turbine or the one or more turbine components until an optimised control schedule is determined, the optimisation including: estimating future fatigue lifetime consumed by the turbine or turbine component over the duration of the varied control schedule based on the current remaining fatigue lifetime and the varied control schedule; and constraining the optimisation of the control schedule according to one or more input constraints; wherein the input constraints include a maximum number of permitted component replacements for one or more turbine components and the optimisation further includes varying an initial value for a wind turbine lifetime to determine a target wind turbine lifetime.

A method for generating tool paths across a surface of a computer aided design (CAD) model having a complex shape. The method includes defining at least one reference point on the surface of the CAD model, generating a plurality of substantially equi-spaced reference points across the surface of the CAD model; connecting each of the reference points together with straight lines such that a first tool path is formed by a culmination of the straight lines; generating a second tool path across the surface of the CAD model; and, generating a plurality of intermediate tool paths that extend linearly between the first tool path and second tool path across the surface of a CAD model.

A method, a device and a storage medium for evaluating wind energy resources in complex terrain are provided, and the method includes: obtaining a climate field based on observation data of wind speed; obtaining an anomaly field; superimposing a climate field interpolation result and an outlier interpolation result with a consistent spatial resolution to obtain a wind speed interpolation result; performing a deviation correction on the wind speed interpolation result and the observation data of wind speed to obtain a final result; calculating an average effective wind power density; and estimating a wind power density based on a daily average wind speed. An accuracy of wind speed data is improved; the wind energy resources are evaluated in situations including complex terrain and lack of hourly wind speed data; and a high-precision data set of the wind energy resources is established to improve an evaluation accuracy of the wind energy resources.

A system for controlling the operation of a co-simulator comprising two or more sub-simulators, each sub-simulator being configured to simulate the behaviour of a respective sub-system of a second system, the co-simulator being configured to perform one or more iterations of a simulation in which the output of the sub-simulators is combined to determine a value of one or more properties of the second system, the system comprising: a controller configured to receive, at one or more time points during each iteration of the simulation, results of intermediate calculations performed by one or more of the sub-simulators, and to coordinate the input of the received results into other(s) of the sub-simulators for use in subsequent calculations to be performed during the course of the iteration; and an optimiser configured to determine, at each of the one or more time points and based on the results of the intermediate calculations, one or more elements of the co-simulator to adjust so as to optimise the process of determining the value of the one or more properties of the second system.

A method for predicting a day-ahead wind power of wind farms, comprising: constructing a raw data set based on a correlation between the to-be-predicted daily wind power, the numerical weather forecast meteorological feature and a historical daily wind power; obtaining a clustered data set and performing k-means clustering, obtaining a raw data set with cluster labels, and generating massive labeled scenes based on robust auxiliary classifier generative adversarial networks; determining the cluster label category of the to-be-predicted day based on the known historical daily wind power and numerical weather forecast meteorological feature, and screening out multiple scenes with high similarity to the to-be-predicted daily wind power based on the cluster label category; and obtaining the prediction results of the to-be-predicted daily wind power at a plurality of set times based on an average value, an upper limit value and a lower limit value of the to-be-predicted daily wind power.

Methods, apparatus, systems, and articles of manufacture are disclosed to improve fluid flow simulations. An example apparatus includes a property identifier to, prior to execution of the computer-based model, identify donors and recipients representative of one or more model regions of the computer-based model to simulate for a plurality of time steps including a first time step and a second time step, the donors having donor properties, in response to computing a flow field for the first time step of the computer-based model, a property extractor to extract the donor properties from extraction surfaces of the donors, and a property assignor to assign the donor properties to boundary surfaces of respective ones of the recipients, and a flow field generator to generate an output flow field for the second time step based on the recipients having the assigned donor properties.

Disclosed is a method, performed by an electronic device, for validation of a second version of a software or a second structure model for control of a wind turbine. The method comprises obtaining first load data from simulating a load of the wind turbine using a first structural model and a first version of the software. The method comprises obtaining second load data from simulating the load of the wind turbine using the first structural model and the second version of the software or using the first version of the software and a second structural model. The second version of the software is an update of the first version of the software. The second structural model is an update of the first structural model. The method comprises determining, based on the first load data and the second load data, a statistical parameter.

The present invention describes an electric-thermal-hydrogen multi-energy device planning method for zero energy buildings, including the following specific steps: firstly, constructing operation constraints of electric and thermal devices in the zero energy buildings; secondly, constructing operation constraints of hydrogen devices including the electrolyzer, the fuel cell and the hydrogen storage device; then, in view of constraints on annual zero energy of the buildings, establishing the robust electric-thermal-hydrogen multi-energy device planning model considering source-load uncertainties; and finally, solving the robust electric-thermal-hydrogen multi-energy device planning model of the zero energy buildings by adopting an alternating optimization procedure based column-and-constraint generation algorithm. By using the zero energy buildings, the planning method disclosed by the present disclosure plays important roles in aspects of promoting the development and utilization of renewable energy on the demand side, reducing energy consumption in the field of buildings, and reducing the emission of greenhouse gases.

Disclosed is a digital twinning method for monitoring an operation state of a tower of a wind turbine generator system online. The method includes: 1) constructing a simplified model of the tower of the wind turbine generator system, and discretizing the simplified model according to a finite element method to obtain a finite element model; 2) reducing an order of the finite element model of the tower according to proper orthogonal decomposition, analyzing precision of a reduced-order model under different orders, and selecting the reduced-order model having a smallest reduced order as a final reduced-order model on the premise that the precision satisfies actual engineering requirements; and 3) programming upper computer software in a computer, deploying the reduced-order model to the upper computer software, further building a physical entity of the tower, and monitoring a stress and a strain of the physical entity online through the reduced-order model.

A method and apparatus for evaluating vulnerability of monopile foundations of offshore wind turbines are provided. The method includes collecting offshore wind farm location data and wind-wave characteristic data, and simulating a wind-wave time course according to the offshore wind farm location data and the wind-wave characteristic data; determining a wind-wave dynamic load based on the wind-wave course; obtaining lateral soil resistance data of monopile foundations with a plurality of rock-soil strength parameters, and inputting the wind-wave dynamic load into a 3D finite element model; using the lateral soil resistance data of monopile foundations with a plurality of rock-soil strength parameters as boundary conditions of the 3D finite element model; and giving a limit state of monopile foundations, and determining vulnerability of monopile foundations on basis of the dynamic response result of the monopile foundations and the limit state of monopile foundations.