A wind farm wake distribution simulation method, device and storage medium based on wind box splicing

Abstract

The present invention discloses a method, device and storage medium for simulating the wake distribution of a wind farm based on wind box splicing, wherein the method includes: based on an automated simulation interface, simulating and generating a single wind turbine wake sample; constructing a single wind turbine wake model based on the single wind turbine wake sample; constructing a wind farm wake model based on the single wind turbine wake model and the actual wind farm distribution; based on the wind farm wake model, using a wind box splicing mode to simulate and analyze the wake distribution of the actual wind farm, wherein the wind box splicing mode is a mode of splitting the actual wind farm into several spliced ​​wind boxes. The present invention can effectively reduce the simulation calculation time, thereby effectively improving the efficiency of the simulation analysis of the wind farm wake distribution; and the embodiments of the present invention can effectively reduce the wind energy loss caused by the wake effect in the wind farm, thereby effectively improving the overall output of the wind farm.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a method, device and storage medium for simulating the wake distribution of a wind farm based on wind box splicing. Background Art

[0002] As an important renewable energy source, wind energy is experiencing rapid growth and boasts broad prospects worldwide. Large-scale wind farms often consist of dozens or even hundreds of wind turbines arranged side by side in a network. Upwind turbines absorb wind energy and generate power, which reduces the incoming wind speed from downstream turbines. This phenomenon, known as the wake effect, significantly impacts the power generation efficiency of downstream turbines.

[0003] Existing wind farm wake distribution simulation methods are mainly divided into two types: one is to simulate and analyze the wind farm wake distribution based on the mechanism model of computational fluid dynamics. However, the mechanism model based on computational fluid dynamics requires the wind farm to be finely discretized. After reading the specific environmental information and wind turbine information of the wind farm, CFD simulation is performed through a large amount of grid calculation to obtain the specific spatiotemporal wind distribution of the entire wind farm. As a result, the numerical simulation of the mechanism model requires extremely long computing time, and the efficiency of the wind farm wake distribution simulation analysis is low. Summary of the Invention

[0004] The present invention provides a wind farm wake distribution simulation method, device and storage medium based on wind box splicing, so as to solve the technical problem that the existing wind farm wake distribution simulation method based on the mechanism model of computational fluid dynamics requires fine discretization of the wind farm, resulting in extremely long calculation time for the numerical simulation of the mechanism model and low efficiency of the wind farm wake distribution simulation analysis.

[0005] The present invention provides a wind farm wake distribution simulation method based on wind box splicing, comprising:

[0006] Based on the automated simulation interface, a single wind turbine wake sample is simulated and generated;

[0007] Constructing a single wind turbine wake model based on the single wind turbine wake sample;

[0008] Constructing a wind farm wake model based on the single wind turbine wake model and actual wind farm distribution;

[0009] Based on the wind farm wake model, a wind box splicing mode is adopted to simulate and analyze the wake distribution of an actual wind farm, wherein the wind box splicing mode is a mode in which the actual wind farm is split into a plurality of spliced ​​wind boxes.

[0010] Furthermore, the automated simulation interface includes a batch simulation interface for the SOWFA container and a post-processing interface for the SOWFA container. The simulation generates a single wind turbine wake sample based on the automated simulation interface, including:

[0011] Performing simulation calculations based on a batch simulation interface of a SOWFA image and a SOWFA container to obtain SOWFA batch simulation results; wherein the SOWFA image is constructed based on a pre-built Docker text, and the Docker text includes a base image command, a SOWFA installation command, and a SOWFA dependency command;

[0012] The post-processing interface of the SOWFA container is determined using Paraview, and the SOWFA batch simulation results are processed according to the post-processing interface to obtain a single fan wake sample.

[0013] Furthermore, the single wind turbine wake model is constructed based on the single wind turbine wake sample, and the single wind turbine wake model is constructed, including:

[0014] Determine the state parameters at the next moment according to the current state parameters of the actual wind farm;

[0015] Sampling the single fan wake sample according to a wind box sampling algorithm to obtain a single fan wake data set;

[0016] Based on the single wind turbine wake data set and the next moment state parameters, the single wind turbine wake model is pre-trained and re-trained to construct a single wind turbine wake model.

[0017] Furthermore, determining the state parameters at the next moment according to the actual state parameters of the wind farm at the current moment includes:

[0018] Determine the current state parameters of the actual wind farm based on a wind box sampling algorithm, and extract the spatial features of the current state parameters;

[0019] Determine the spatial characteristics of the state parameters at the next moment according to the spatial characteristics of the state parameters at the current moment, the incoming flow conditions and the fan parameters;

[0020] The next moment parameters of the actual wind farm are determined according to the spatial characteristics of the next moment state parameters.

[0021] Furthermore, the loss function of the pre-training is:

[0022]

[0023] Among them, L re1 is the loss function of pre-training; mean 0,2,3 is the average value over dimensions 0, 2, and 3; is the self-predicted tail flow of the Autoencoder network; is the actual wake; t is the time.

[0024] Furthermore, the loss function of the retraining is:

[0025]

[0026] L ls =||Z t+1 +Z t-1 -2Z t || 2

[0027] Among them, L re2 and L ls Both are loss functions for retraining; mean 0,2,3 is the average value over dimensions 0, 2, and 3; is the wind field state parameter at time t predicted by the neural network based on the wind field state parameter at time t-1; The actual wind field state parameters determined by the simulation process at time t; is the actual wind field state parameter at time t Wind field parameters mapped by the Autoencoder network; L ls To constrain the neural network to maintain a linear loss function in the latent space; Z t+1 , Z t , Z t-1 are the eigenvectors mapped in the latent space by the actual wind field state parameters at time t+1, t, and t-1 respectively.

[0028] Furthermore, the wind farm wake model is constructed based on the single wind turbine wake model and the actual wind farm distribution, including:

[0029] Determine a wind box model according to the actual distribution of the wind farm, wherein the wind box model includes several wind turbine wind box models and / or a wind turbine-free wind box model;

[0030] Determining the boundary flow conditions of the actual wind farm based on the wind box model;

[0031] A wind farm wake model of an actual wind farm is determined according to the single wind turbine wake model and the boundary incoming flow condition.

[0032] The present invention also provides a wind farm wake distribution simulation device based on wind box splicing, comprising:

[0033] Single wind turbine wake sample generation module, used to simulate and generate single wind turbine wake samples based on the automated simulation interface;

[0034] A single wind turbine wake model construction module, configured to construct a single wind turbine wake model based on the single wind turbine wake sample;

[0035] A wind farm wake model construction module is used to construct a wind farm wake model based on the single wind turbine wake model and the actual wind farm distribution;

[0036] The wake distribution simulation analysis module is used to simulate and analyze the wake distribution of an actual wind farm based on the wind farm wake model using a wind box splicing mode, wherein the wind box splicing mode is a mode in which the actual wind farm is split into several spliced ​​wind boxes.

[0037] The present invention also provides a terminal device, comprising: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the above-mentioned wind farm wake distribution simulation method based on wind box splicing.

[0038] The present invention also provides a computer-readable storage medium, which includes a stored computer program; wherein, when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the above-mentioned wind farm wake distribution simulation method based on wind box splicing.

[0039] The present invention generates a single wind turbine wake sample and constructs a single wind turbine wake model based on an automated simulation interface. On this basis, a wind farm wake model is constructed in combination with the actual wind farm distribution. The wake distribution of the actual wind farm is simulated and analyzed using a risk splicing mode. The wake distribution simulation results of the entire wind farm can be accurately obtained without finely discretizing the wind farm and performing a large number of grid calculations for CFD simulation. The simulation calculation time can be effectively reduced, thereby effectively improving the efficiency of the wind farm wake distribution simulation analysis. In addition, the embodiments of the present invention can effectively reduce the wind energy loss caused by the wake effect in the wind farm, thereby effectively improving the overall output of the wind farm.

[0040] Furthermore, the present invention groups the related wind turbines into two groups of wind turbine models that interfere with each other. This can ignore the complex spatial structure that may exist between the wind turbines with wake correlation, thereby converting the complex multi-wind turbine wind field problem into a dual-wind turbine model problem, thereby effectively simplifying the model and effectively enhancing the versatility of the model. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 1 is a flow chart of a wind farm wake distribution simulation method based on wind box splicing provided by an embodiment of the present invention;

[0042] Figure 2Schematic diagram of the principle of a single wind turbine wake model provided by an embodiment of the present invention;

[0043] Figure 3 Schematic diagram of the principle of a wind turbine-free wake model provided by an embodiment of the present invention;

[0044] Figure 4 Schematic diagram of the principle of bellows splicing provided by an embodiment of the present invention;

[0045] Figure 5 It is a structural schematic diagram of a wind farm wake distribution simulation device based on wind box splicing provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0047] In the description of this application, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature specified as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0048] In the description of this application, it should be noted that, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integral connections; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; and internal connections between two components. Those skilled in the art will understand the specific meanings of the above terms in this application based on the specific circumstances.

[0049] See also Figure 1 The present invention provides a wind farm wake distribution simulation method based on wind box splicing, comprising:

[0050] S1. Based on the automated simulation interface, simulate and generate a single wind turbine wake sample;

[0051] In an embodiment of the present invention, the automated simulation interface can be the automated simulation interface of SOWFA, including a batch simulation interface and a post-processing interface, wherein SOWFA (Simulator for Wind Farm Applications) is a high-fidelity wind farm simulation engine developed by the Renewable Energy Laboratory based on the OpenFOAM computational fluid dynamics library.

[0052] S2. Construct a single wind turbine wake model based on the single wind turbine wake sample;

[0053] In an embodiment of the present invention, a single wind turbine wake model may be constructed based on a single wind turbine wake sample and a preset simulation NS equation.

[0054] S3. Construct a wind farm wake model based on the single wind turbine wake model and the actual wind farm distribution;

[0055] S4. Based on the wind farm wake model, the wind box splicing mode is used to simulate and analyze the wake distribution of the actual wind farm. The wind box splicing mode is a mode in which the actual wind farm is split into several spliced ​​wind boxes.

[0056] The embodiment of the present invention generates a single wind turbine wake sample and constructs a single wind turbine wake model based on an automated simulation interface. On this basis, a wind farm wake model is constructed in combination with the actual wind farm distribution. The risk splicing mode is used to simulate and analyze the wake distribution of the actual wind farm. Without the need for fine discretization of the wind farm and a large number of grid calculations for CFD simulation, the wake distribution simulation results of the entire wind farm can be accurately obtained, which can effectively reduce the simulation calculation time and thus effectively improve the efficiency of the wind farm wake distribution simulation analysis.

[0057] In one embodiment, the automated simulation interface includes a batch simulation interface for the SOWFA container and a post-processing interface for the SOWFA container. Step S1, based on the automated simulation interface, simulates and generates a single wind turbine wake sample, including:

[0058] S11. Perform simulation calculations according to the batch simulation interface of the SOWFA image and the SOWFA container to obtain SOWFA batch simulation results; wherein the SOWFA image is constructed according to a pre-built Docker text, and the Docker text includes a base image command, a SOWFA installation command, and a SOWFA dependency command;

[0059] In an embodiment of the present invention, a Docker text may be constructed based on Docker, that is, Docker may be used to manage the SOWFA container, so as to generate a SOWFA image that is easy to transplant and deploy according to the Docker text.

[0060] S12. Use Paraview to determine the post-processing interface of the SOWFA container, process the SOWFA batch simulation results according to the post-processing interface, and obtain a single fan wake sample.

[0061] It should be noted that SOWFA is a high-fidelity wind farm simulation engine developed by the Renewable Energy Laboratory based on the OpenFOAM computational fluid dynamics library. As a member of the mechanism model, after reading the specific environmental information and wind turbine information of the wind farm, SOWFA performs CFD simulation through a large amount of grid calculations to accurately obtain the specific spatiotemporal wind distribution of the entire wind farm. SOWFA is a wind farm simulation tool with powerful functions, but due to its complex operation and complicated parameters, it is difficult to apply. In this embodiment, Docker is first used to implement the virtualized container management of SOWFA to form a SOWFA image that is easy to transplant and deploy. Then, a batch simulation interface of the SOWFA container is designed and implemented, and parameter editing, calculation process management, and batch simulation are implemented in combination with the SOWFA image to obtain SOWFA batch simulation results. Finally, a post-processing interface is constructed based on Paraview to process the SOWFA batch simulation results into data samples (single wind turbine wake samples) that meet business needs.

[0062] In this embodiment of the present invention, the SOWFA image can be loaded on any operating system that supports Docker to build a SOWFA container. Secondly, this embodiment of the present invention develops a batch simulation interface to connect to the container and manage SOWFA parameters and calculation processes. This batch simulation interface enables batch simulation. Finally, a Python script is written to export the batch simulation results. These results are then post-processed using the ParaView library's post-processing interface to construct the required sample dataset.

[0063] In this embodiment of the present invention, SOWFA is installed in a Docker container and communicates with the host server via SSH. Compared to traditional virtual machines, Docker offers many advantages, including fast startup, efficient resource allocation, flexible resource sharing between the container and the host, and simple deployment and migration. Therefore, building a Docker image for SOWFA is intuitive and extremely convenient. The steps for configuring the SOWFA image are as follows:

[0064] First, pull the complete OpenFoam Docker image and download the scotch installation package. After configuring the system path, create a Docker container using the OpenFoam Docker image. Next, start and enter the container. Download the SOWFA installation package and install SOWFA. Set the SOWFA_DIR and OpenFoam environment variables. Next, install OpenSSH Server. Start the SSH service and set the connection username and password. Finally, build a Docker image using the current container.

[0065] After building the SOWFA Docker image, you can use it to create a Docker container. File sharing is achieved by mounting folders on the host server into the container. By setting up port mapping between the host and the container, operations within the container can be directly controlled remotely via SSH. To control the SOWFA computing process within the container, embodiments of the present invention also include the development of an SSHConnecter module, which can execute commands and transfer data between Docker and the host.

[0066] In the embodiment of the present invention, the SOWFA wind farm simulation can be divided into two stages: an atmospheric boundary layer (ABL) simulation stage and a wind turbine simulation stage.

[0067] The purpose of the ABL simulation phase is to simulate a fully developed turbulent flow field, excluding the wind turbines. After the simulation is complete, boundary data for multiple time steps is extracted to provide boundary conditions for the second phase of the simulation. The ABL simulation only needs to be performed once for each operating condition with a specific wind speed and direction. The simulation results can be reused to simulate all wind turbines in the same wind farm under the same operating conditions.

[0068] The wind turbine simulation phase, based on the previously described fully developed turbulent flow field, simulates the operation of the wind turbine within the eddy field by incorporating a simplified wind turbine model. During this phase, the wind turbine is added to the ABL case study, and the wind farm simulation is re-run. Simplified wind turbine models include the Action Line Model (ALM) and the Action Disk Model (ADM). The difference between these two models is that the ALM uses a more detailed three-blade model to represent the wind turbine, while the ADM uses a disk model. To meet different requirements, Python APIs have been developed for both models.

[0069] This embodiment uses Paraview as the post-processing platform for the example. Paraview has a Python interface, and its recording function can be used to generate batch scripts to process the massive number of previously generated examples. The examples generated in this embodiment are all in the format of (600, 600, 300). Paraview scripts are used for processing, and the wind speed data of the examples are extracted and generated into CSV files as learning samples for the single wind turbine proxy model. Data in CSV format cannot be directly called by neural networks, so this embodiment of the present invention separately writes a Python script to process the CSV file. According to the geometric relationship of the example image, the data in the CSV file is saved as a .npy file to prepare for subsequent deep learning.

[0070] In summary, the embodiments of the present invention can effectively solve the problems of SOWFA, such as the difficulty of installation, complex parameter setting, and cumbersome calculation process management. The proposed batch simulation tool is an important supplement to SOWFA. It can be foreseen that this tool can promote the application of SOWFA in actual power system engineering scenarios.

[0071] In one embodiment, step S2, constructing a single wind turbine wake model based on a single wind turbine wake sample, comprises:

[0072] S21. Determine the state parameters at the next moment according to the current state parameters of the actual wind farm;

[0073] In an embodiment of the present invention, the time and space terms of a preset simulation NS equation can be decoupled using a preset neural network to convert the problem of solving a partial differential equation into the problem of solving an ordinary differential equation. The preset neural network includes an encoder and a compiler. The encoder is configured to extract the spatial characteristics of the wind farm's state parameters at the current moment based on the wind farm's state parameters at the current moment; the compiler determines the next moment's parameters based on the spatial characteristics of the state parameters at the next moment.

[0074] In an embodiment of the present invention, the NS equations of the mechanism model describe the physical equations of the flow problem, wherein the mass conservation equation is:

[0075]

[0076] The momentum conservation equation is:

[0077]

[0078] Where u is the wind speed vector; p is the pressure distribution; ρ is the air density; is the gradient sign (gas momentum is affected by pressure gradient); t is time; The gas viscosity term that prevents relative sliding between fluid layers is generally written as (Shear stress is proportional to velocity gradient).

[0079] In an embodiment of the present invention, a preset neural network includes an encoder and a compiler. The encoder and decoder will extract the spatial characteristics of the wind field state parameters, fix the spatial correlation of the fluid field to the encoder neural network, and extract the wind speed distribution of the actual wind field state parameters as a feature vector that can characterize all the information of the wind field at that moment. After the encoder extracts all the spatial characteristics of the wind field state parameters, the model will perform temporal deduction in the latent space of the feature vector learned by the encoder according to the incoming flow conditions, wind turbine parameters and other conditions to obtain the feature vector of the wind field at the next moment, and translate it through the decoder network to obtain the actual wind speed distribution of the wind field at the next moment. In the above manner, in the embodiment of the present invention, the neural network fixes the spatial characteristics to the encoder and decoder networks, and only performs time-based deduction in the latent space where the feature vector is located, thereby realizing the decoupling of time and space of the wind field.

[0080] Regarding the encoder's extraction of wind field state parameter features, the principle is that the wind speed distribution of the wake usually shows a certain regularity. Taking the steady-state wake model as an example, the parameter model summarizes the Gaussian distribution law of wind speed and the law of yaw angle and wake offset through empirical observation. There is information redundancy in high-dimensional wind speed state variables. There is a certain regular relationship between the wind speeds at adjacent grid points in space. The universal approximation theorem of the embodiment of the present invention ensures that a neural network with enough hidden units and linear output layers can represent any arbitrary function. Therefore, during the model training process, the encoder will find the general laws of the wind field, summarize the spatial information of the model through the laws, and thus extract the wind field state parameters with a huge amount of data as feature parameters containing all wind field information.

[0081] After the spatial characteristics are fixed, it can be seen from the comparison with the NS equation that only the momentum equation remains in the equation group The characteristics of the quality equation have been completely summarized in relation to time. In the latent space constructed by the encoder based on spatial features, the characteristic vectors extracted from the wind field state parameters only have time correlation. It can be pointed out that in the latent space, the NS equation is transformed from a partial differential equation to a first-order ordinary differential equation that is only related to time. In the latent space, the characteristic vectors extracted from the wind field state parameters at each time should satisfy the characteristics of linear deduction. The present invention designs a linear neural network in the latent space, introduces parameter conditions, constrains the time step deduction process in the latent space to be linear deduction, and ultimately achieves the goal of using neural network simulation.

[0082] The simulation idea of ​​the neural network in the embodiment of the present invention is to calculate the state parameter X of the wind farm at time t. t , boundary flow condition V t , fan parameters U t As input, predict the wind field state parameters at time t+1 In this iterative manner, the development process of the wind farm wake at a later time is deduced based on the state parameters of the wind farm at the initial moment, thereby determining the wind farm wake model of the actual wind farm.

[0083] In the embodiment of the present invention, the state parameters at the next moment are determined according to the current state parameters of the actual wind farm, for example:

[0084] S211. Determine the current state parameters of the actual wind farm based on a wind box sampling algorithm, and extract the spatial features of the current state parameters;

[0085] S212, determining the spatial characteristics of the state parameters at the next moment based on the spatial characteristics of the state parameters at the current moment, the incoming flow conditions, and the fan parameters;

[0086] S213: Determine the next moment parameters of the actual wind farm according to the spatial characteristics of the state parameters at the next moment.

[0087] In the embodiment of the present invention, the input samples of the neural network can be taken in the form of wind box sampling. The reason for selecting wind box sampling is that the wind speed distribution of the wind field is highly coupled in time and space, and wind box sampling is required to preserve the spatiotemporal information of the wind field.

[0088] During the sampling process of a three-dimensional wind field, neural networks are unable to accurately determine the wake propagation time and coordinates due to the high coupling between wake propagation time and space. Specifically, at the same sampling interval, the wake propagation distance varies for different wind conditions; and at the same sampling distance, the wake propagation time varies for different wind conditions. In real wind fields, wind speeds at higher altitudes are higher than those at lower altitudes, and even the same sampling distance cannot account for the wind speed differences along the vertical axis.

[0089] To this end, embodiments of the present invention employ a windbox sampling method. By dividing the wind field into a fine grid and uniformly sampling at fixed intervals, this method addresses the problem of varying propagation distances for winds of varying speeds within the same time interval. Windbox sampling preserves detailed wake propagation information, ensuring that the neural network input samples fully capture the spatiotemporal characteristics of the NS equations, and providing the information foundation for subsequent neural network feature extraction.

[0090] In an embodiment of the present invention, the simulation data can be sampled according to the wind box sampling method, and the obtained wind box data (single wind turbine wake data set) can be flattened into a plane according to spatial logic as learning data input for the neural network.

[0091] In order to achieve better training results of the single wind turbine wake model based on neural network, the training process of the neural network is divided into two parts: pre-training and re-training.

[0092] Pre-training uses 36,900 sets of wind speed data from all examples at each time point as input and uses the Autoencoder network as output. The goal of pre-training is to pre-constrain the Autoencoder network to a latent space where it can express features. This pre-constrains the neural network's parameters before formal training, reduces the complexity of formal training, and makes it easier to optimize the network to the target position.

[0093] During pre-training, examples are blurred and masked with noise to increase sample diversity. This exploits sample imperfections to enhance model robustness, while missing data ensures that the feature vectors in the latent space accurately represent the wind farm state parameters, ensuring the completeness of the latent space. Based on the pre-training objectives, a pre-training loss function is designed.

[0094] Next, retraining was performed using 369 examples of wind speed data at t = 20002s, along with the turbine's yaw angle and incoming flow conditions at subsequent times, and iterating 99 times in the latent space. All iteration results were decoded through the Decoder network and compared with the actual results to verify the model's accuracy. The error between all predicted wake results obtained during the iterations and the simulation results of the mechanism model was compared to design a retraining loss function.

[0095] In the embodiment of the present invention, the current state data of the actual wind farm may be determined according to a wind turbine sampling algorithm.

[0096] S22. Sampling a single fan wake sample according to a wind box sampling algorithm to obtain a single fan wake data set;

[0097] S23. Based on the single wind turbine wake data set and the next moment state parameters, pre-train and re-train the single wind turbine wake model to construct a single wind turbine wake model.

[0098] See also Figure 2 , which is a schematic diagram of the principle of a single wind turbine wake model provided in an embodiment of the present invention.

[0099] In one embodiment, the loss function for pre-training is:

[0100]

[0101] Among them, L re1 is the loss function of pre-training; mean 0,2,3 is the average value over dimensions 0, 2, and 3; is the self-predicted tail flow of the Autoencoder network; is the actual wake; t is the time.

[0102] In one embodiment, the loss function for retraining is:

[0103]

[0104] L ls =||Z t+1 +Z t-1 -2Z t || 2

[0105] Among them, L re2 and L ls Both are loss functions for retraining; mean 0,2,3 is the average value over dimensions 0, 2, and 3; is the wind field state parameter at time t predicted by the neural network based on the wind field state parameter at time t-1; The actual wind field state parameters determined by the simulation process at time t; is the actual wind field state parameter at time t Wind field parameters mapped by the Autoencoder network; L ls To constrain the neural network to maintain a linear loss function in the latent space; Z t+1 , Z t , Z t-1 are the eigenvectors mapped in the latent space by the actual wind field state parameters at time t+1, t, and t-1 respectively.

[0106] In one embodiment, step S3, constructing a wind farm wake model based on a single wind turbine wake model and actual wind farm distribution, includes:

[0107] S31. Determine a wind box model according to the actual distribution of the wind farm, wherein the wind box model includes several wind turbine wind box models and / or a wind turbine-free wind box model;

[0108] S32. Determine the boundary flow conditions of the actual wind farm based on the wind box model;

[0109] S33. Determine a wind farm wake model of an actual wind farm based on a single wind turbine wake model and boundary flow conditions.

[0110] In an embodiment of the present invention, a large-scale wind farm is often composed of dozens or even hundreds of wind turbines arranged side by side to form a network, and the wake model of a single wind turbine is obviously not applicable to actual projects. In this embodiment, based on the training foundation of a small wind farm, the wind farm simulated by the mechanism model is expanded to a wind farm containing multiple wind turbines that is more similar to an actual wind farm. Through the study of multi-wind turbine wind farms, a wind box model design is proposed, and the expanded wind farm is divided into wind boxes with wind turbines and wind boxes without wind turbines. By constructing a neural network for the two wind box models, the actual wind farm is restored by splicing the wind boxes, thereby achieving the versatility of the model.

[0111] Specifically, in large wind farms, under normal inflow conditions, not all upstream wind turbine wakes will affect downstream wind turbines. Only wind turbines within the upstream wind turbine's wake radiation zone are truly affected by the wake. During the initial design and construction of large wind farms, annual inflow conditions at the construction site are studied, and wind turbines are arranged and constructed based on actual conditions. In reality, many wind turbines in wind farms are not affected by wakes. Based on initial planning for wind farm construction, wind turbines that are actually affected by wakes are often small wind farms consisting of only a few turbines. Therefore, when studying wakes in large wind farms, it is sufficient to model the wakes of only the few interfering wind turbines in front and behind.

[0112] In this embodiment of the present invention, an actual wind farm can be divided into the space near the wind turbines and the area without wind turbines, thereby dividing a large wind farm into wind farms composed of multiple wind boxes. The wind box model in this embodiment of the present invention has two wind box modes: a wind box mode with wind turbines and a wind box mode without wind turbines. These two wind box modes can be combined to form any wind farm. The aerodynamic description of a wind box without wind turbines can also be expressed using the NS equations.

[0113]

[0114] Unlike a bellows with a fan, the above equations do not include the fan's volume force term, making them simpler. Therefore, a similar neural network structure to that used for a bellows with a fan can be used to construct a bellows model.

[0115] See also Figure 3 , which is a schematic diagram of the principle of a no-extension wake model provided by an embodiment of the present invention. For the windbox itself, its overall wind speed characteristics are still extracted using the wind speed distribution in the windbox at time t. Regarding the fan parameters, this item does not exist for a no-extension windbox. Therefore, this part of the neural network was deleted during the design process of the no-extension wake model corresponding to the no-extension windbox mode.

[0116] During single-turbine model training, the boundary flow conditions for the wind field are naturally distributed turbulent winds, which can be assumed to remain constant over long timescales. This is significantly different from the wind box model. The multi-wind box model focuses on simulating wind turbine wakes. Therefore, for the wind box model, the wind field flow is no longer a stable atmospheric flow, but rather a nonuniform flow that includes multiple possible wind turbine wakes.

[0117] See also Figure 4, which is a schematic diagram of the principle of wind box splicing provided by an embodiment of the present invention. The wind box model needs to consider the splicing problem between multiple wind boxes, and how to accurately describe the boundary conditions of the wind box flow becomes an important issue in the research process. The wind speed distribution in three-dimensional space is not uniform. This is especially true for the wake flow that actually contains multiple turbulences. For the uniform atmospheric flow, the fluid at the same vertical interface at a certain moment will change over time. If there is a wake at the interface, the changes in the propagation process will be more complicated.

[0118] Therefore, when considering the wake effect, the wind field sampled by the wind box cannot only give a specific interface when giving the boundary conditions to characterize all the characteristics of the boundary flow. To solve this problem, starting from the basis of the mechanism model, the NS equation is discretized to obtain:

[0119]

[0120] The subscript of the parameter represents the position of the parameter after spatial discretization, and the superscript represents the time after temporal discretization. is the wind speed at point i at time n+1; is the wind speed at point i at time n; is the pressure at point i+1 / 2 at time n; is the pressure at point i-1 / 2 at time n; μ is the dynamic viscosity of the fluid; is the wind speed at point i-1 at time n.

[0121] According to the discrete form, it can be seen from the difference format of the NS equation that the state parameter of the wind speed at (x0, y0, z0) at time t depends on the state parameter of the wind speed at (x0-u x Δt,y0-u y Δt,z0-u z For the discretized wind box data, this means that the data of the boundary condition selection area needs to include the envelope surface of the wind speed range within the Δt time. In other words, it is necessary to include the farthest distance that can be reached within the Δt time at the highest wind speed in the interface. Based on the above analysis, this embodiment adopts the method of extracting features from multiple interfaces as boundary conditions, and encompasses all the information of the boundary conditions through the numerical discretization of multiple interfaces.

[0122] The boundary conditions of the rear wind box will be the last few air outlet interfaces of the front wind box as the boundary conditions v t, input into the neural network model. The neural network responsible for boundary flow condition analysis will extract the boundary condition features of several wind outlet interfaces, map them into the latent space, and complete the deduction of the overall state parameters of the wind field. From a physical perspective, the principle of this design is that within the Δt time, the wake of the interface outside the selected interface cannot propagate to the rear wind box, so the wind speed data has no correlation with the rear wind box. The characteristics of all wind speed interfaces that may propagate wind speed to the rear wind box have been processed by the boundary condition neural network.

[0123] Given a standard atmospheric flow, SOWFA is used to generate samples of two wind turbines with a size of (1800m, 600m, 300m) and a spacing of 1200m. The forward input of the wind box is the standard atmospheric flow, and the internal wind turbines have different yaw angles. The entire wind field is divided into three wind boxes. With each wind turbine as the center and 300m before and after the three-dimensional wind speed data, 738 single-wind turbine wind box samples with a size of (600m, 600m, 300m) are constructed. 369 wind boxes without wind turbines with a size of (600m, 600m, 300m) are selected based on the 600m spacing between the two wind turbine wind boxes. A sampling point is selected every 20m, and the three-dimensional wind speed data at the data point is extracted. A total of (31, 31, 16) data points are obtained for each wind box.

[0124] A total of 369 dual-turbine samples were generated, with wind speeds ranging from 8 to 16 m / s, incoming wind speeds from due west, and turbine yaw angles between 250° and 290°. After fully developing the turbulent wind field for 20,000 s, wind speed samples were extracted at 2-s intervals from 20,002 s to 20,200 s, for a total of 100 data points.

[0125] Since the training data for the bellows model consists of three bellows, and considering that the first bellows has a uniform inflow, the boundary conditions are given differently from those of the subsequent bellows, the actual model training method is to train the three bellows separately. In this embodiment, an independent bellows model is constructed for each bellows. In addition, the neural network structure of each bellows is still relatively complex, and the actual training process involves multiple iterations. To achieve better training results, the neural network training process is divided into two parts: pre-training and retraining.

[0126] Pre-training for the three wind turbines involved training with individual examples for each wind turbine. The Autoencoder network was trained using 36,900 sets of wind speed data from each wind turbine at each time point as input, and the network itself as output. Similar to the pre-training goal for the single-turbine wake model, pre-training pre-constrained the Autoencoder network to a latent space capable of expressing features. This pre-constrained the neural network's parameter values ​​for retraining, reducing retraining complexity and making it easier to optimize the network to the target position.

[0127] During pre-training, the three wind turbine samples were blurred and masked with noise to increase sample diversity. The goal is to increase model robustness by exploiting sample imperfections, and to ensure that the feature vectors in the latent space accurately represent the wind farm state parameters through data loss, thereby ensuring the completeness of the latent space.

[0128] A total of 369 training samples participated in the formal training of the three wind boxes, of which 80% were used as training data and 20% as test data. During the deduction training process, the wind speed state parameters of the wind field at t = 20002s were used as the basis. The first wind box uses fixed parameters as the incoming flow conditions, and the second and third wind boxes use the last three air outlet interfaces of the previous wind box as the incoming flow conditions. The yaw angles of the fans inside the first and third wind boxes are used as fan parameters, and the second wind box has no fan parameters. Based on the initial wind field state parameters and subsequent parameter conditions, the wind field iterates 99 times in the latent space formed by the Autoencoder network. All iteration results are decoded by the Decoder network and compared with the actual results to verify the accuracy of the model. The errors of all predicted wake results obtained in the iterative process are compared with the simulation results of the mechanism model. The loss function of the model design is the same as that of the single wind turbine wake model.

[0129] The implementation of the embodiments of the present invention has the following beneficial effects:

[0130] The embodiment of the present invention generates a single wind turbine wake sample and constructs a single wind turbine wake model based on an automated simulation interface. On this basis, a wind farm wake model is constructed in combination with the actual wind farm distribution situation. The risk splicing mode is used to simulate and analyze the wake distribution of the actual wind farm. The wake distribution simulation results of the entire wind farm can be accurately obtained without fine discretization of the wind farm and a large number of grid calculations for CFD simulation. The simulation calculation time can be effectively reduced, thereby effectively improving the efficiency of the wind farm wake distribution simulation analysis. The embodiment of the present invention can effectively reduce the wind energy loss caused by the wake effect in the wind farm, thereby effectively improving the overall output of the wind farm.

[0131] Furthermore, the embodiment of the present invention groups the wind turbines in pairs, and divides several related wind turbines into multiple groups of wind turbine models that interfere with each other. This can ignore the complex spatial structure that may exist between wind turbines with wake correlation, thereby converting the complex multi-wind turbine wind field problem into a dual-wind turbine model problem, thereby effectively simplifying the model and effectively enhancing the versatility of the model.

[0132] See also Figure 5 Based on the same inventive concept as the above embodiment, the present invention further provides a wind farm wake distribution simulation device based on wind box splicing, comprising:

[0133] A single wind turbine wake sample generation module 10 is used to simulate and generate a single wind turbine wake sample based on an automated simulation interface;

[0134] A single wind turbine wake model construction module 20 is used to construct a single wind turbine wake model based on a single wind turbine wake sample;

[0135] A wind farm wake model building module 30 is used to build a wind farm wake model based on a single wind turbine wake model and actual wind farm distribution;

[0136] The wake distribution simulation analysis module 40 is used to simulate and analyze the wake distribution of an actual wind farm based on a wind farm wake model and using a wind box splicing mode, wherein the wind box splicing mode is a mode in which the actual wind farm is split into several spliced ​​wind boxes.

[0137] In one embodiment, the automated simulation interface includes a batch simulation interface of the SOWFA container and a post-processing interface of the SOWFA container, and the single wind turbine wake sample generation module 10 is further used to:

[0138] Perform simulation calculations based on the batch simulation interface of the SOWFA image and SOWFA container to obtain SOWFA batch simulation results. The SOWFA image is built based on a pre-built Docker text, which includes the base image command, SOWFA installation command, and SOWFA dependency command.

[0139] The post-processing interface of the SOWFA container was determined using Paraview. The SOWFA batch simulation results were processed according to the post-processing interface to obtain a single fan wake sample.

[0140] In one embodiment, the single wind turbine wake model building module 20 is further configured to:

[0141] Determine the state parameters at the next moment according to the current state parameters of the actual wind farm;

[0142] The single fan wake samples are sampled according to the wind box sampling algorithm to obtain the single fan wake data set;

[0143] Based on the single wind turbine wake data set and the next moment state parameters, the single wind turbine wake model is pre-trained and retrained to construct a single wind turbine wake model.

[0144] In one embodiment, determining the state parameters at the next moment according to the actual state parameters of the wind farm at the current moment includes:

[0145] Determine the current state parameters of the actual wind farm based on the wind box sampling algorithm and extract the spatial characteristics of the current state parameters;

[0146] Determine the spatial characteristics of the state parameters at the next moment based on the spatial characteristics of the state parameters at the current moment, the incoming flow conditions and the fan parameters;

[0147] The next moment parameters of the actual wind farm are determined according to the spatial characteristics of the state parameters at the next moment.

[0148] In one embodiment, the loss function for pre-training is:

[0149]

[0150] Among them, L re1 is the loss function of pre-training; mean 0,2,3 is the average value over dimensions 0, 2, and 3; is the self-predicted tail flow of the Autoencoder network; is the actual wake; t is the time.

[0151] In one embodiment, the loss function for retraining is:

[0152]

[0153] L ls =||Z t+1 +Z t-1 -2Z t || 2

[0154] Among them, L re2 and L ls Both are loss functions for retraining; mean 0,2,3 is the average value over dimensions 0, 2, and 3; is the wind field state parameter at time t predicted by the neural network based on the wind field state parameter at time t-1; The actual wind field state parameters determined by the simulation process at time t; is the actual wind field state parameter at time t Wind field parameters mapped by the Autoencoder network; L ls To constrain the neural network to maintain a linear loss function in the latent space; Z t+1 , Z t , Z t-1 are the eigenvectors mapped in the latent space by the actual wind field state parameters at time t+1, t, and t-1 respectively.

[0155] In one embodiment, the wake distribution simulation analysis module 40 is further configured to:

[0156] Determine a wind box model according to the actual distribution of the wind farm, wherein the wind box model includes several wind turbine wind box models and / or a wind turbine-free wind box model;

[0157] Determine the boundary flow conditions of the actual wind farm based on the wind box model;

[0158] According to the single wind turbine wake model and boundary flow conditions, the wind farm wake model of the actual wind farm is determined.

[0159] Accordingly, an embodiment of the present invention also provides a terminal device, comprising: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the wind farm wake distribution simulation method based on wind box splicing as described in any one of the above embodiments.

[0160] The terminal device of this embodiment includes: a processor, a memory, and a computer program and computer instructions stored in the memory and capable of running on the processor. When the processor executes the computer program, each step in the above embodiment 1 is implemented, such as Figure 1 Alternatively, when the processor executes the computer program, the functions of each module / unit in the above-mentioned device embodiment are realized, such as the wind farm wake model building module 30 .

[0161] For example, a computer program can be divided into one or more modules / units, one or more of which are stored in a memory and executed by a processor to implement the present invention. One or more modules / units can be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program in a terminal device. For example, the wind farm wake model construction module 30 is used to construct a wind farm wake model based on a single wind turbine wake model and the actual wind farm distribution.

[0162] Terminal devices can be computing devices such as desktop computers, laptops, PDAs, and cloud servers. Terminal devices may include, but are not limited to, processors and memory. Those skilled in the art will appreciate that the schematic diagrams are merely examples of terminal devices and do not limit the scope of terminal devices. Terminal devices may include more or fewer components than shown, or combinations of certain components, or different components. For example, terminal devices may also include input / output devices, network access devices, buses, and the like.

[0163] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting various parts of the entire terminal device using various interfaces and lines.

[0164] The memory can be used to store computer programs and / or modules. The processor implements various functions of the terminal device by running or executing the computer programs and / or modules stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area. The program storage area can store an operating system, at least one application required for a function, etc.; the data storage area can store data created based on the use of the mobile terminal, etc. In addition, the memory can include high-speed random access memory and non-volatile memory, such as a hard disk, internal memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0165] If the module / unit integrated into the terminal device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention can implement all or part of the process in the above-mentioned embodiment method, and can also be completed by a computer program to instruct the relevant hardware. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, it can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media does not include electric carrier signals and telecommunication signals.

[0166] Accordingly, an embodiment of the present invention further provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the wind farm wake distribution simulation method based on wind box splicing as described in any one of the above embodiments.

[0167] The above specific embodiments further illustrate the objectives, technical solutions, and beneficial effects of the present invention. It should be understood that the above are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention for those skilled in the art.

Claims

1. A wind farm wake distribution simulation method based on wind box splicing, characterized in that: include: Based on the automated simulation interface, a single wind turbine wake sample is simulated and generated; The automated simulation interface includes a batch simulation interface of the SOWFA container and a post-processing interface of the SOWFA container. The simulation generates a single wind turbine wake sample based on the automated simulation interface, including: performing simulation calculations according to the SOWFA image and the batch simulation interface of the SOWFA container to obtain SOWFA batch simulation results; wherein the SOWFA image is constructed according to a pre-built Docker text, and the Docker text includes a basic image command, a SOWFA installation command, and a SOWFA dependency command; using Paraview to determine the post-processing interface of the SOWFA container, and processing the SOWFA batch simulation results according to the post-processing interface to obtain a single wind turbine wake sample; Constructing a single wind turbine wake model based on the single wind turbine wake sample; constructing the single wind turbine wake model based on the single wind turbine wake sample includes: determining a next-moment state parameter based on a current-moment state parameter of an actual wind farm; sampling the single wind turbine wake sample according to a windbox sampling algorithm to obtain a single wind turbine wake dataset; pre-training and re-training the single wind turbine wake model based on the single wind turbine wake dataset and the next-moment state parameter to construct a single wind turbine wake model; Constructing a wind farm wake model based on the single wind turbine wake model and the actual wind farm distribution; constructing the wind farm wake model based on the single wind turbine wake model and the actual wind farm distribution includes: determining a wind box model based on the actual wind farm distribution, wherein the wind box model includes a plurality of wind turbine wind box models and / or a wind box model without a wind turbine; determining a boundary flow condition of the actual wind farm based on the wind box model; determining a wind farm wake model of the actual wind farm based on the single wind turbine wake model and the boundary flow condition; Based on the wind farm wake model, a wind box splicing mode is adopted to simulate and analyze the wake distribution of an actual wind farm, wherein the wind box splicing mode is a mode in which the actual wind farm is split into a plurality of spliced ​​wind boxes.

2. The wind farm wake distribution simulation method based on wind box splicing according to claim 1, characterized in that: The determining of the state parameters at the next moment according to the current state parameters of the actual wind farm includes: Determine the current state parameters of the actual wind farm based on a wind box sampling algorithm, and extract the spatial features of the current state parameters; Determine the spatial characteristics of the state parameters at the next moment according to the spatial characteristics of the state parameters at the current moment, the incoming flow conditions and the fan parameters; The next moment parameters of the actual wind farm are determined according to the spatial characteristics of the next moment state parameters.

3. The wind farm wake distribution simulation method based on wind box splicing according to claim 1, characterized in that: The loss function of the pre-training is: Among them, L re1 is the loss function of pre-training; mean 0,2,3 is the average value over dimensions 0, 2, and 3; is the self-predicted tail flow of the Autoencoder network; is the actual wake; t is the time.

4. The wind farm wake distribution simulation method based on wind box splicing according to claim 1, characterized in that: The loss function of the retraining is: L ls =‖Z t+1 +Z t-1 -2Z t ‖ 2 Among them, L re2 and L ls Both are loss functions for retraining; mean 0,2,3 is the average value over dimensions 0, 2, and 3; is the wind field state parameter at time t predicted by the neural network based on the wind field state parameter at time t-1; The actual wind field state parameters determined by the simulation process at time t; is the actual wind field state parameter at time t Wind field parameters mapped by the Autoencoder network; L ls To constrain the neural network to maintain a linear loss function in the latent space; Z t+1 , Z t , Z t-1 are the eigenvectors mapped in the latent space by the actual wind field state parameters at time t+1, t, and t-1 respectively.

5. A wind farm wake distribution simulation device based on wind box splicing, characterized in that: include: Single wind turbine wake sample generation module, used to simulate and generate single wind turbine wake samples based on the automated simulation interface; The automated simulation interface includes a batch simulation interface of the SOWFA container and a post-processing interface of the SOWFA container. The simulation generates a single wind turbine wake sample based on the automated simulation interface, including: performing simulation calculations according to the SOWFA image and the batch simulation interface of the SOWFA container to obtain SOWFA batch simulation results; wherein the SOWFA image is constructed according to a pre-built Docker text, and the Docker text includes a basic image command, a SOWFA installation command, and a SOWFA dependency command; using Paraview to determine the post-processing interface of the SOWFA container, and processing the SOWFA batch simulation results according to the post-processing interface to obtain a single wind turbine wake sample; A single wind turbine wake model construction module is configured to construct a single wind turbine wake model based on the single wind turbine wake sample; the construction of the single wind turbine wake model based on the single wind turbine wake sample includes: determining a next-moment state parameter based on a current-moment state parameter of an actual wind farm; sampling the single wind turbine wake sample according to a windbox sampling algorithm to obtain a single wind turbine wake dataset; and pre-training and re-training the single wind turbine wake model based on the single wind turbine wake dataset and the next-moment state parameter to construct a single wind turbine wake model; a wind farm wake model construction module, configured to construct a wind farm wake model based on the single wind turbine wake model and the actual wind farm distribution; constructing the wind farm wake model based on the single wind turbine wake model and the actual wind farm distribution comprises: determining a wind box model based on the actual wind farm distribution, wherein the wind box model comprises a plurality of wind turbine wind box models and / or a wind box model without a wind turbine; determining a boundary flow condition of the actual wind farm based on the wind box model; and determining a wind farm wake model of the actual wind farm based on the single wind turbine wake model and the boundary flow condition; The wake distribution simulation analysis module is used to simulate and analyze the wake distribution of an actual wind farm based on the wind farm wake model using a wind box splicing mode, wherein the wind box splicing mode is a mode in which the actual wind farm is split into several spliced ​​wind boxes.

6. A terminal device, characterized in that: include: A processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the method for simulating the wind farm wake distribution based on wind box splicing according to any one of claims 1 to 4 is implemented.

7. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored computer program; wherein, when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the wind farm wake distribution simulation method based on wind box splicing according to any one of claims 1 to 4.

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