Offshore wind farm resource configuration method and apparatus, computer device, and storage medium

Abstract

The present application relates to the technical field of resource configuration, and discloses an offshore wind farm resource configuration method and apparatus, a computer device, and a storage medium. According to the present application, different adjustment rates are determined on the basis of a predicted wind energy dataset and a preset adjustment period, and the determined adjustment rates are used to adjust the layout of wind turbines in a wind power generation-based prediction twin model, thereby ensuring the stability of wind turbine groups during movement, and realizing a targeted adjustment mechanism for the wind turbines on the basis of different wind resource conditions. Optionally, on the basis of the established wind power generation-based prediction twin model, a target layout of the wind turbine groups in an offshore wind farm to be configured and a target orientation of each wind turbine can be obtained, and the positions and orientations of the wind turbines are adjusted at the same time, thereby maximizing offshore wind resource utilization, and improving the efficiency of wind power generation.

Description

Offshore wind farm resource configuration method, device, computer equipment and storage medium Technical Field

[0001] The present application relates to the technical field of resource allocation, and in particular to a method, apparatus, computer equipment, and storage medium for resource allocation at an offshore wind farm. Background Art

[0002] Offshore wind resources refer to energy resources that use wind power in ocean areas to generate electricity. Due to the strong and stable winds on the ocean, offshore wind energy is regarded as a renewable energy source with great potential. Offshore wind farms are usually built on some structure above the ocean surface, such as a fixed platform or floating equipment, to accommodate wind turbines. The resource allocation of offshore wind resources refers to the rational planning and configuration of the construction and layout of offshore wind farms based on the distribution and changes of offshore wind energy resources, and the optimization of their layout according to the characteristics of the sea area and the distribution of wind energy, aiming to maximize the utilization of offshore wind energy resources and improve power generation efficiency.

[0003] In the prior art, the adjustment of wind turbine groups is often carried out at a fixed rate, which may cause damage to the wind turbines themselves when the wind energy is relatively unstable. In addition, in the prior art, the wind turbines are often adjusted to the corresponding positions before adjusting their directions, which objectively reduces the efficiency of wind energy utilization.

[0004] Summary of the Invention

[0005] In view of this, the present application provides a method, device, computer equipment and storage medium for configuring offshore wind farm resources to solve the problem in the prior art of using a fixed rate to adjust the wind turbine group, which causes damage to the wind turbine itself when the wind energy is relatively unstable, and after adjusting the wind turbine to the corresponding position, the direction of the wind turbine is adjusted again, thereby reducing the efficiency of wind energy utilization.

[0006] In a first aspect, the present application provides a method for allocating resources of an offshore wind farm, the method comprising:

[0007] Obtain the initial measured wind energy dataset, the initial measured wind turbine dataset and the initial forecast wind energy dataset of the offshore wind farm to be configured; construct a prediction twin model based on wind power generation based on the initial measured wind energy dataset, the initial measured wind turbine dataset and the initial forecast wind energy dataset, and the prediction twin model based on wind power generation is used to obtain the simulated total power generation generated by different wind turbine layouts in the offshore wind farm to be configured under the condition of the predicted wind energy dataset; determine the adjustment rate based on the predicted wind energy dataset and the preset adjustment period; within the preset adjustment period, adjust the layout of each wind turbine in the prediction twin model based on wind power generation based on the predicted wind energy dataset and the adjustment rate until the simulated total power generation generated by the offshore wind farm to be configured is maximized, and obtain the target layout of the wind turbine group and the target orientation of each wind turbine in the offshore wind farm to be configured.

[0008] The offshore wind farm resource configuration method provided by the present application determines different adjustment rates by combining the predicted wind energy data set and the preset adjustment period, and uses the determined adjustment rate to adjust the layout of each wind turbine in the predictive twin model based on wind power generation, which is conducive to ensuring the stability of the wind turbine group during movement and can implement a targeted adjustment mechanism for the wind turbines according to different wind resource conditions. Optionally, according to the established predictive twin model based on wind power generation, the target layout of the wind turbine group and the target orientation of each wind turbine in the offshore wind farm to be configured can be obtained, realizing the simultaneous adjustment of the wind turbine position and wind turbine orientation, thereby maximizing the utilization of offshore wind resources and improving the efficiency of wind power generation.

[0009] In an optional embodiment, a wind power generation prediction twin model is constructed based on the initial measured wind energy dataset, the initial measured wind turbine dataset, and the initial forecast wind energy dataset, including:

[0010] Based on the initial measured wind energy dataset and the initial measured wind turbine dataset, a digital twin model based on wind power generation is constructed using digital twin technology; the initial forecast wind energy dataset is processed by a preset offshore wind energy prediction model to obtain a forecast wind energy dataset for the offshore wind farm to be configured; based on the forecast wind energy dataset and the digital twin model based on wind power generation, a forecast twin model based on wind power generation is constructed.

[0011] This application combines measured wind turbine data to construct a digital twin model based on wind power generation. Optionally, based on the digital twin model based on wind power generation and combined with predicted wind energy data, a corresponding predictive twin model based on wind power generation can be constructed, providing support for the subsequent determination of the target layout of the wind turbine group and the target orientation of each wind turbine in the offshore wind farm to be configured.

[0012] In an optional embodiment, based on the initial measured wind energy dataset and the initial measured wind turbine dataset, a digital twin model based on wind power generation is constructed using digital twin technology, including:

[0013] Obtain a characteristic dataset of wind turbines in the offshore wind farm to be configured; process the initial measured wind energy dataset and the initial measured wind turbine dataset respectively to obtain a target measured wind energy dataset and a target measured wind turbine dataset; construct a physical system model based on wind power generation based on the target measured wind turbine dataset and the characteristic dataset; and construct a digital twin model based on wind power generation based on the target measured wind energy dataset and the physical system model based on wind power generation.

[0014] This application combines the characteristic data set of wind turbines in the offshore wind farm to be configured to construct a digital twin model based on wind power generation, which can improve the accuracy of the model and provide support for improving the target layout of the wind turbine group in the offshore wind farm to be configured and the accuracy of determining the target orientation of each wind turbine.

[0015] In an optional embodiment, before processing the initial forecast wind energy dataset using a preset offshore wind energy prediction model to obtain a forecast wind energy dataset for the offshore wind farm to be configured, the method further includes:

[0016] Obtain a historical first measured wind energy dataset and a historical forecast wind energy dataset for the offshore wind farm to be configured; determine a historical second measured wind energy dataset corresponding to the historical forecast wind energy dataset in the historical first measured wind energy dataset; and construct a preset offshore wind energy prediction model based on the historical forecast wind energy dataset and the historical second measured wind energy dataset.

[0017] In an optional embodiment, determining the adjustment rate based on the predicted wind energy dataset and a preset adjustment period includes:

[0018] Based on the predicted wind energy data set and the preset adjustment period, a predicted wind energy coefficient is determined; the predicted wind energy coefficient is compared with a preset threshold, and an adjustment rate is determined according to the comparison result.

[0019] In an optional embodiment, within a preset adjustment period, the layout of each wind turbine in the wind power generation prediction twin model is adjusted based on the predicted wind energy dataset and the adjustment rate until the simulated total power generation generated by the offshore wind farm to be configured is maximized, thereby obtaining a target layout of the wind turbine group in the offshore wind farm to be configured and a target orientation of each wind turbine, including:

[0020] Within a preset adjustment period, the position of each wind turbine and the distance between different wind turbines in the prediction twin model based on wind power generation are adjusted based on the predicted wind energy data set and the adjustment rate, so as to obtain multiple layouts and multiple first simulated total power generation of the wind turbine group in the offshore wind farm to be configured; based on the multiple first simulated total power generation, the target layout of the wind turbine group in the offshore wind farm to be configured is determined in the multiple layouts, and the simulated total power generation generated by the wind turbine group in the offshore wind farm to be configured under the target layout is the largest among the multiple first simulated total power generation; based on the target layout, the target turning rate of each wind turbine in the offshore wind farm to be configured is determined; based on the target layout and the target turning rate of each wind turbine, the orientation of each wind turbine is adjusted in the prediction twin model based on wind power generation, so as to obtain multiple second simulated total power generation and multiple first orientations of each wind turbine; based on the multiple second simulated total power generation, the target orientation of each wind turbine in the offshore wind farm to be configured is determined in the multiple first orientations, and the simulated total power generation generated by the offshore wind farm to be configured under the target orientation of each wind turbine is the largest among the multiple second simulated total power generation.

[0021] By using the target turning rate, the present application can adjust the wind turbine groups in the offshore wind farm to be configured to the optimal layout while adjusting the individual wind turbines in the offshore wind farm to be configured to the optimal orientation. This can achieve simultaneous adjustment of the wind turbine position and wind turbine orientation, thereby improving the efficiency of wind power generation.

[0022] In an optional embodiment, determining a target turning rate for each wind turbine in the offshore wind farm to be configured based on the target layout includes:

[0023] Obtain the measured position and measured orientation of each wind turbine in the offshore wind farm to be configured; based on the target layout, adjust the orientation of each wind turbine in the predictive twin model based on wind power generation to obtain multiple third simulated total power generation and multiple second orientations of each wind turbine; based on the multiple third simulated total power generation, determine the third orientation of each wind turbine in the offshore wind farm to be configured among the multiple second orientations, and the simulated total power generation generated by the offshore wind farm to be configured under the third orientation of each wind turbine is the largest among the multiple third simulated total power generation; based on the measured orientation and third orientation of each wind turbine, determine the steering angle of each wind turbine; based on the target layout and the measured position, determine the adjustment time of each wind turbine; based on the adjustment time and steering angle of each wind turbine, determine the target steering rate of each wind turbine.

[0024] In a second aspect, the present application provides an offshore wind farm resource configuration device, the device comprising:

[0025] An acquisition module is used to obtain the initial measured wind energy data set, the initial measured wind turbine data set and the initial forecast wind energy data set of the offshore wind farm to be configured; a construction module is used to construct a prediction twin model based on wind power generation based on the initial measured wind energy data set, the initial measured wind turbine data set and the initial forecast wind energy data set. The prediction twin model based on wind power generation is used to obtain the simulated total power generation generated by different wind turbine layouts in the offshore wind farm to be configured under the condition of the predicted wind energy data set; a determination module is used to determine the adjustment rate based on the predicted wind energy data set and a preset adjustment period; an adjustment module is used to adjust the layout of each wind turbine in the prediction twin model based on wind power generation based on the predicted wind energy data set and the adjustment rate within the preset adjustment period, until the simulated total power generation generated by the offshore wind farm to be configured is maximized, and the target layout of the wind turbine group and the target orientation of each wind turbine in the offshore wind farm to be configured are obtained.

[0026] In a third aspect, the present application provides a computer device comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the offshore wind farm resource configuration method of the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0027] In a fourth aspect, the present application provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the offshore wind farm resource configuration method of the above-mentioned first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the specific implementation methods of the present application or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the specific implementation methods or the description of the prior art. Obviously, the drawings described below are some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0029] FIG1 is a schematic flow chart of a method for configuring offshore wind farm resources according to an embodiment of the present application;

[0030] FIG2 is a flow chart of another offshore wind farm resource configuration method according to an embodiment of the present application;

[0031] FIG3 is a flow chart of another offshore wind farm resource configuration method according to an embodiment of the present application;

[0032] FIG4 is a structural block diagram of an offshore wind farm resource configuration device according to an embodiment of the present application;

[0033] FIG5 is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present application. DETAILED DESCRIPTION

[0034] To make the purpose, technical solutions, and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without making creative efforts shall fall within the scope of protection of this application.

[0035] The embodiment of the present application provides a method for configuring offshore wind farm resources. Adjusting the layout of each wind turbine in a predictive twin model based on wind power generation through different adjustment rates is beneficial to ensuring the stability of the wind turbine group during movement, and can implement a targeted adjustment mechanism for the wind turbines according to different wind resource conditions. Optionally, based on the established predictive twin model based on wind power generation, the target layout of the wind turbine group and the target orientation of each wind turbine in the offshore wind farm to be configured can be obtained, achieving simultaneous adjustment of the wind turbine position and wind turbine orientation, thereby maximizing the utilization of offshore wind resources and improving the efficiency of wind power generation.

[0036] According to an embodiment of the present application, an embodiment of a method for configuring offshore wind farm resources is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0037] In this embodiment, a method for configuring offshore wind farm resources is provided. FIG1 is a flow chart of the method for configuring offshore wind farm resources according to an embodiment of the present application. As shown in FIG1 , the process includes the following steps:

[0038] Step S101 : obtaining an initial measured wind energy dataset, an initial measured wind turbine dataset, and an initial forecast wind energy dataset of an offshore wind farm to be configured.

[0039] Specifically, the initial measured wind energy dataset represents the wind energy data of the offshore wind farm to be configured that is currently measured and may include data such as wind speed, wind direction, wind speed frequency distribution, wind energy density, wind energy potential, and wind resource distribution.

[0040] Among them, wind speed and wind direction indicate the speed and direction of the wind; wind speed frequency distribution indicates the frequency of wind speeds within different wind speed ranges; wind energy density indicates the average energy of wind energy per unit area or volume; wind energy potential indicates the estimated value of wind energy in a given area or specific location; and wind resource distribution indicates the spatial distribution of wind energy in a specific area or wind farm.

[0041] Optionally, the initial measured wind turbine data set represents wind turbine data of the offshore wind farm to be configured obtained from current measurements, and may include data such as rated power, rated wind speed, cut-in and cut-out wind speeds, power curves, and wind direction performance.

[0042] Among them, the rated power indicates the power that the wind turbine can continuously and stably output under standard operating conditions; the rated wind speed indicates the minimum wind speed threshold at which the wind turbine starts to generate electricity; the cut-in and cut-out wind speeds indicate the minimum wind speed threshold at which the wind turbine starts to rotate and the maximum wind speed threshold at which the wind turbine stops generating electricity; the power curve indicates the output power of the wind turbine at different wind speeds; and the wind direction performance indicates the power generation performance and efficiency of the wind turbine under different wind direction conditions.

[0043] Optionally, the initial forecast wind energy data set represents forecast wind energy data at sea in different time periods, and can be obtained through weather forecast data from a weather station.

[0044] Step S102 : constructing a prediction twin model based on wind power generation based on the initial measured wind energy data set, the initial measured wind turbine data set, and the initial forecast wind energy data set.

[0045] Among them, the predictive twin model based on wind power generation can be used to obtain the simulated total power generation generated by different wind turbine layouts in the offshore wind farm to be configured under the conditions of the predicted wind energy data set.

[0046] Specifically, by obtaining the initial measured wind energy dataset, the initial measured wind turbine dataset and the initial forecast wind energy dataset, a corresponding wind power generation-based prediction twin model can be constructed.

[0047] Step S103: determining an adjustment rate based on the predicted wind energy data set and a preset adjustment period.

[0048] Specifically, a fixed rate is adopted to adjust the wind turbine group. However, this adjustment method may cause damage to the wind turbine itself when the wind energy is relatively unstable.

[0049] Therefore, in this embodiment, the corresponding adjustment rate is determined by predicting the wind energy data set and the preset adjustment period, and then different adjustment rates can provide support for subsequent adjustment of the wind turbine group.

[0050] Step S104: Within a preset adjustment period, the layout of each wind turbine in the predictive twin model based on wind power generation is adjusted based on the predicted wind energy data set and the adjustment rate, until the simulated total power generation generated by the offshore wind farm to be configured is maximized, and the target layout of the wind turbine group in the offshore wind farm to be configured and the target orientation of each wind turbine are obtained.

[0051] Specifically, within a preset adjustment period, the layout of each wind turbine is adjusted based on the predicted wind energy dataset and the adjustment rate. This ensures the stability of the wind turbine group during movement and enables targeted adjustment of wind turbines based on different wind resource conditions. Optionally, the adjustment process is performed within a constructed predictive twin model based on wind power generation, enabling simultaneous adjustment of wind turbine position and orientation, thereby maximizing the utilization of offshore wind resources and improving wind power generation efficiency.

[0052] The offshore wind farm resource configuration method provided in this embodiment combines the predicted wind energy data set and the preset adjustment period to determine different adjustment rates, and uses the determined adjustment rates to adjust the layout of each wind turbine in the predictive twin model based on wind power generation. This helps to ensure the stability of the wind turbine group during movement and can implement a targeted adjustment mechanism for the wind turbines based on different wind resource conditions. Optionally, based on the established predictive twin model based on wind power generation, the target layout of the wind turbine group and the target orientation of each wind turbine in the offshore wind farm to be configured can be obtained, achieving simultaneous adjustment of the wind turbine position and wind turbine orientation, thereby maximizing the utilization of offshore wind resources and improving the efficiency of wind power generation.

[0053] In this embodiment, a method for configuring offshore wind farm resources is provided. FIG2 is a flow chart of the method for configuring offshore wind farm resources according to an embodiment of the present application. As shown in FIG2 , the process includes the following steps:

[0054] Step S201: Acquire an initial measured wind energy dataset, an initial measured wind turbine dataset, and an initial forecast wind energy dataset for the offshore wind farm to be configured. For details, please refer to step S101 of the embodiment shown in FIG1 , which will not be described in detail here.

[0055] Step S202 : constructing a prediction twin model based on wind power generation based on the initial measured wind energy dataset, the initial measured wind turbine dataset, and the initial forecast wind energy dataset.

[0056] Specifically, the above step S202 includes:

[0057] Step S2021: Based on the initial measured wind energy data set and the initial measured wind turbine data set, a digital twin model based on wind power generation is constructed using digital twin technology.

[0058] Among them, digital twin technology refers to a technology that makes full use of physical models, sensor updates, operation history and other data, integrates multi-disciplinary, multi-physical quantity, multi-scale, and multi-probability simulation processes, completes mapping in virtual space, and thus reflects the entire life cycle process of the corresponding physical equipment.

[0059] Specifically, by combining the initial measured wind energy data set and the initial measured wind turbine data through digital twin technology, a digital twin model based on wind power generation can be constructed that reflects the simulated layout of the wind turbine group of the offshore wind farm to be configured and the entire life cycle process.

[0060] Step S2022: Acquire a first historical measured wind energy dataset and a historical forecast wind energy dataset of the offshore wind farm to be configured.

[0061] Specifically, the historical first measured wind energy dataset represents the measured wind energy data of the offshore wind farm to be configured within a historical period; the historical forecast wind energy dataset represents the forecast wind energy data of the offshore wind farm to be configured in different historical time periods, which can be obtained through the historical meteorological forecast data of the meteorological station.

[0062] Step S2023 : determining a second historical measured wind energy dataset corresponding to the historical forecast wind energy dataset in the first historical measured wind energy dataset.

[0063] Specifically, the actual wind energy data corresponding to the historical forecast wind energy data set, ie, the historical second measured wind energy data set, is determined in the historical first measured wind energy data set according to the historical forecast wind energy data set.

[0064] Step S2024: constructing a preset offshore wind energy prediction model based on the historical forecast wind energy dataset and the historical second measured wind energy dataset.

[0065] Specifically, the historical forecast wind energy dataset is used as the input value of the model, the historical second measured wind energy dataset is used as the output value of the model, and the model training is performed in combination with the correspondence between the historical forecast wind energy dataset and the historical second measured wind energy dataset, so as to construct a preset offshore wind energy prediction model for predicting wind energy data.

[0066] Step S2025 : Processing the initial forecast wind energy dataset using a preset offshore wind energy prediction model to obtain a forecast wind energy dataset for the offshore wind farm to be configured.

[0067] Specifically, the initial forecast wind energy dataset is input into a trained preset offshore wind energy prediction model, and wind energy data corresponding to the initial forecast wind energy dataset, ie, the forecast wind energy dataset, can be predicted.

[0068] Step S2026: construct a wind power generation-based prediction twin model based on the predicted wind energy data set and the wind power generation-based digital twin model.

[0069] Specifically, by inputting the predicted wind energy dataset into the constructed digital twin model, a predictive twin model based on wind power generation can be obtained.

[0070] In some optional implementations, the above step S2021 includes:

[0071] Step a1: Acquire a characteristic data set of wind turbine generator sets in the offshore wind farm to be configured.

[0072] Step a2: Process the initial measured wind energy dataset and the initial measured wind turbine dataset respectively to obtain a target measured wind energy dataset and a target measured wind turbine dataset.

[0073] Step a3: constructing a physical system model based on wind power generation based on the target measured wind turbine dataset and the characteristic dataset.

[0074] Step a4: construct a digital twin model based on wind power generation based on the target measured wind energy dataset and the physical system model based on wind power generation.

[0075] Specifically, outlier processing, missing value processing and normalization processing are performed on the initial measured wind energy dataset and the initial measured wind turbine dataset respectively to obtain the corresponding available target measured wind energy dataset and target measured wind turbine dataset.

[0076] Among them, outlier processing is used to clean up abnormal data, and the absolute median difference outlier processing method can be used; missing value processing is used to fill missing data, and the statistical filling method can be used; normalization processing is used to unify the format of data, and the Z-Score standardization method can be used.

[0077] Optionally, by utilizing the processed target measured wind turbine data set and combining it with the acquired characteristic data set of the wind turbine generator set, a corresponding physical system model based on wind power generation including the structure of the wind turbine and the generator set can be constructed.

[0078] Optionally, the processed target measured wind energy dataset is matched with the constructed physical system model based on wind power generation, and the target measured wind energy dataset is aligned or calibrated with the physical system model based on wind power generation, and a corresponding digital twin model based on wind power generation is constructed.

[0079] Among them, the digital twin model based on wind power generation is used to simulate the input conditions of wind energy, simulate the power generation output of the wind turbine and other performance indicators, compare and verify with the operating data of the actual wind power generation system, and adjust and optimize the model according to the verification results. The subsequently collected wind turbine data and wind energy data are connected to the digital twin model to update the model's input parameters in real time.

[0080] Step S203: Determine the adjustment rate based on the predicted wind energy data set and the preset adjustment period. Please refer to step S103 of the embodiment shown in FIG1 for details, which will not be repeated here.

[0081] In step S204, within a preset adjustment period, the layout of each wind turbine in the wind power generation prediction twin model is adjusted based on the predicted wind energy dataset and the adjustment rate until the simulated total power generation of the offshore wind farm to be configured is maximized, thereby determining the target layout of the wind turbine groups and the target orientation of each wind turbine in the offshore wind farm to be configured. For details, please refer to step S104 in the embodiment shown in Figure 1 and will not be repeated here.

[0082] The offshore wind farm resource configuration method provided in this embodiment combines the characteristic data set of the wind turbines in the offshore wind farm to be configured to construct a digital twin model based on wind power generation, which can improve the accuracy of the model. Optionally, a corresponding predictive twin model based on wind power generation can be constructed based on the digital twin model based on wind power generation in combination with the predicted wind energy data. The target layout of the wind turbine group and the target orientation of each wind turbine in the offshore wind farm to be configured can be obtained through the predictive twin model based on wind power generation, thereby realizing the simultaneous adjustment of the wind turbine position and wind turbine orientation, thereby maximizing the utilization of offshore wind resources and improving the efficiency of wind power generation.

[0083] In this embodiment, a method for configuring offshore wind farm resources is provided. FIG3 is a flow chart of the method for configuring offshore wind farm resources according to an embodiment of the present application. As shown in FIG3 , the process includes the following steps:

[0084] Step S301: Acquire an initial measured wind energy dataset, an initial measured wind turbine dataset, and an initial forecast wind energy dataset for the offshore wind farm to be configured. For details, refer to step S101 of the embodiment shown in FIG1 , which will not be described in detail here.

[0085] Step S302: Based on the initial measured wind energy dataset, the initial measured wind turbine dataset, and the initial forecast wind energy dataset, a wind power generation prediction twin model is constructed. For details, please refer to step S202 of the embodiment shown in FIG2 , which will not be repeated here.

[0086] Step S303: determining an adjustment rate based on the predicted wind energy data set and a preset adjustment period.

[0087] Specifically, the above step S303 includes:

[0088] Step S3031 : determining a predicted wind energy coefficient based on the predicted wind energy data set and a preset adjustment period.

[0089] First, obtain the number of wind direction changes in the predicted wind energy dataset within the preset adjustment period and mark it as F x .

[0090] The number of wind direction changes indicates the number of times the wind direction changes within a preset adjustment period.

[0091] Secondly, the corresponding predicted wind speed can be obtained based on the predicted wind energy data and marked as F s .

[0092] Then, the predicted wind speed F s and the number of wind direction changes F x Set wind energy weights separately: Set the predicted wind speed F s The wind energy weight is set to Q s ; Change the wind direction number F x The wind energy weight is set to Q x .

[0093] Finally, the predicted wind energy coefficient R is calculated according to the following relationship (1): R = F x Q x +F s Q s (1)

[0094] Step S3032: compare the predicted wind energy coefficient with a preset threshold, and determine the adjustment rate based on the comparison result.

[0095] Specifically, the predicted wind energy coefficient R is compared with the preset threshold R0. When R≤R0, the predicted wind energy coefficient R is marked as stable wind energy; when R>R0, ≤R0, the predicted wind energy coefficient R is marked as unstable wind energy.

[0096] Optionally, when the predicted wind energy coefficient R is marked as stable wind energy, the adjustment rate is determined to be a high adjustment rate; when the predicted wind energy coefficient R is marked as unstable wind energy, the adjustment rate is determined to be a low adjustment rate.

[0097] Step S304: Within a preset adjustment period, the layout of each wind turbine in the predictive twin model based on wind power generation is adjusted based on the predicted wind energy data set and the adjustment rate, until the simulated total power generation generated by the offshore wind farm to be configured is maximized, and the target layout of the wind turbine group in the offshore wind farm to be configured and the target orientation of each wind turbine are obtained.

[0098] Specifically, the above step S304 includes:

[0099] Step S3041: Within a preset adjustment period, the position of each wind turbine and the distance between different wind turbines in the predictive twin model based on wind power generation are adjusted based on the predicted wind energy data set and the adjustment rate to obtain multiple layouts of wind turbine groups in the offshore wind farm to be configured and multiple first simulated total power generation.

[0100] Specifically, in the predictive twin model based on wind power generation, the position of each wind turbine in the offshore wind farm to be configured and the distance between different wind turbines are adjusted according to the predicted wind energy data set and the adjustment rate. The simulated power generation of each wind turbine after each adjustment can be obtained, and then the simulated total power generation of the wind turbine group in the offshore wind farm to be configured can be obtained based on the simulated power generation of each wind turbine.

[0101] Optionally, by performing multiple adjustments within a preset adjustment period, multiple layouts of wind turbine groups in the offshore wind farm to be configured after adjustment and multiple first simulated total power generation can be obtained.

[0102] Step S3042: determining a target layout of wind turbine groups in the offshore wind farm to be configured from among multiple layouts based on the multiple first simulated total power generation.

[0103] Specifically, a corresponding broken line graph is constructed according to the obtained multiple first simulated total power generation amounts, and then the maximum simulated total power generation amount can be obtained according to the constructed broken line graph.

[0104] Optionally, the position of each wind turbine at the maximum simulated total power generation is recorded, and the recorded position of each wind turbine is marked as the optimal layout of the wind turbine group, that is, the simulated total power generation generated by the wind turbine group in the offshore wind farm to be configured under the target layout is the largest among multiple first simulated total power generation.

[0105] Step S3043: determining a target turning rate for each wind turbine in the offshore wind farm to be configured based on the target layout.

[0106] Specifically, based on the obtained optimal layout of the wind turbine group, the target turning rate of each wind turbine in the offshore wind farm to be configured can be further determined.

[0107] Step S3044: Based on the target layout and the target turning rate of each wind turbine, the orientation of each wind turbine is adjusted in the wind power generation prediction twin model to obtain multiple second simulated total power generation and multiple first orientations of each wind turbine.

[0108] Specifically, based on the obtained optimal layout of the wind turbine group, the orientation of each wind turbine is adjusted in the predictive twin model based on wind power generation using the determined target turning rate of each wind turbine. The simulated power generation of each wind turbine in different orientations can be obtained, and then the simulated total power generation of the wind turbine group in the offshore wind farm to be configured can be obtained based on the simulated power generation of each wind turbine.

[0109] Optionally, by performing multiple adjustments within a preset adjustment period, multiple second simulated total power generation of the wind turbine group in the offshore wind farm to be configured and multiple first orientations of each wind turbine after adjustment can be obtained.

[0110] Step S3045 : determining a target orientation of each wind turbine in the offshore wind farm to be configured from among the multiple first orientations based on the multiple second simulated total power generation amounts.

[0111] Specifically, the maximum second simulated total power generation can be obtained based on the multiple second simulated total power generation obtained, and the operating point of the maximum second simulated total power generation is marked as the maximum power point. The direction of the wind turbine at the maximum power point is obtained and marked as the optimal direction, i.e., the target direction.

[0112] In some optional implementations, step S3043 includes:

[0113] Step b1: obtaining the measured position and direction of each wind turbine in the offshore wind farm to be configured.

[0114] Step b2: Based on the target layout, adjust the orientation of each wind turbine in the wind power generation prediction twin model to obtain multiple third simulated total power generation and multiple second orientations of each wind turbine.

[0115] Step b3: determining a third orientation of each wind turbine in the offshore wind farm to be configured from the plurality of second orientations based on the plurality of third simulated total power generation amounts.

[0116] Step b4: determining the turning angle of each wind turbine based on the measured direction of each wind turbine and the third direction.

[0117] Step b5: Determine the adjustment time of each wind turbine based on the target layout and the measured position.

[0118] Step b6: determining a target turning rate for each wind turbine based on the adjustment duration and turning angle of each wind turbine.

[0119] First, based on the optimal layout of the wind turbine group, the orientation of each wind turbine is randomly adjusted in the predictive twin model based on wind power generation, so that the simulated power generation of each wind turbine in different orientations can be obtained. Then, based on the simulated power generation of each wind turbine, the simulated total power generation of the wind turbine group in the offshore wind farm to be configured can be obtained.

[0120] Optionally, by performing multiple adjustments within a preset adjustment period, multiple third simulated total power generation of the wind turbine group in the offshore wind farm to be configured and multiple second orientations of each wind turbine after adjustment can be obtained.

[0121] Secondly, the maximum third simulated total power generation can be obtained based on the multiple third simulated total power generation obtained, and the operating point of the maximum third simulated total power generation is marked as the maximum power point. The direction of the wind turbine at the maximum power point is obtained and marked as the optimal direction under the current random adjustment condition.

[0122] Optionally, the minimum angle required for each fan to be adjusted to the optimal direction, i.e., the steering angle, can be obtained based on the measured direction of each fan and the optimal direction under the current random adjustment, and marked as Z. j .

[0123] Then, based on the measured position of each wind turbine and the position of each wind turbine under the target layout, the adjustment distance of each wind turbine to the target layout can be obtained.

[0124] Optionally, the corresponding adjustment time can be obtained according to the adjustment distance and its corresponding adjustment rate, and is marked as T s .

[0125] Finally, according to the adjustment time and steering angle of each wind turbine, the target steering rate Z of each wind turbine can be obtained. s , as shown in the following relation (2):

[0126] The offshore wind farm resource configuration method provided in this embodiment combines the predicted wind energy data set and the preset adjustment cycle to determine different adjustment rates, and uses the determined adjustment rates to adjust the layout of each wind turbine in the wind power generation prediction twin model. This helps to ensure the stability of the wind turbine group during movement and can implement a targeted adjustment mechanism for the wind turbines according to different wind resource conditions. Optionally, through the target turning rate, while adjusting the wind turbine group in the offshore wind farm to be configured to the optimal layout, each wind turbine in the offshore wind farm to be configured can be adjusted to the optimal orientation at the same time, thereby achieving simultaneous adjustment of the wind turbine position and wind turbine orientation, thereby improving the efficiency of wind power generation.

[0127] This embodiment also provides an offshore wind farm resource configuration device for implementing the above-described embodiments and optional implementations. Details already described are omitted for clarity. As used below, the term "module" may refer to a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation using hardware, or a combination of software and hardware, is also possible and contemplated.

[0128] This embodiment provides an offshore wind farm resource configuration device, as shown in FIG4 , which includes:

[0129] The acquisition module 401 is used to acquire an initial measured wind energy dataset, an initial measured wind turbine dataset, and an initial forecast wind energy dataset of an offshore wind farm to be configured.

[0130] Construction module 402 is used to construct a prediction twin model based on wind power generation based on the initial measured wind energy data set, the initial measured wind turbine data set and the initial forecast wind energy data set. The prediction twin model based on wind power generation is used to obtain the simulated total power generation generated by different wind turbine layouts in the offshore wind farm to be configured under the condition of the predicted wind energy data set.

[0131] The determination module 403 is configured to determine an adjustment rate based on the predicted wind energy data set and a preset adjustment period.

[0132] The adjustment module 404 is used to adjust the layout of each wind turbine in the predictive twin model based on wind power generation based on the predicted wind energy data set and the adjustment rate within a preset adjustment period until the simulated total power generation generated by the offshore wind farm to be configured is maximized, and the target layout of the wind turbine group and the target orientation of each wind turbine in the offshore wind farm to be configured are obtained.

[0133] In some optional implementations, the building block 402 includes:

[0134] The first construction submodule is used to construct a digital twin model based on wind power generation using digital twin technology based on the initial measured wind energy data set and the initial measured wind turbine data set.

[0135] The processing submodule is used to process the initial forecast wind energy data set through a preset offshore wind energy prediction model to obtain a forecast wind energy data set for the offshore wind farm to be configured.

[0136] The second construction submodule is used to build a prediction twin model based on wind power generation based on the predicted wind energy dataset and the digital twin model based on wind power generation.

[0137] In some optional embodiments, the first building block includes:

[0138] The first acquisition unit is configured to acquire a characteristic data set of a wind turbine generator set in an offshore wind farm to be configured.

[0139] The processing unit is used to process the initial measured wind energy data set and the initial measured wind turbine data set respectively to obtain the target measured wind energy data set and the target measured wind turbine data set.

[0140] The first construction unit is used to construct a physical system model based on wind power generation based on a target measured wind turbine data set and a characteristic data set.

[0141] The second construction unit is used to build a digital twin model based on wind power generation based on the target measured wind energy data set and the physical system model based on wind power generation.

[0142] In some optional implementations, the building block 402 further includes:

[0143] The acquisition submodule is used to obtain the first historical measured wind energy dataset and the historical forecast wind energy dataset of the offshore wind farm to be configured.

[0144] The first determining submodule is configured to determine a second historical measured wind energy dataset corresponding to the historical forecast wind energy dataset from the first historical measured wind energy dataset.

[0145] The third construction submodule is used to construct a preset offshore wind energy prediction model based on the historical forecast wind energy dataset and the historical second measured wind energy dataset.

[0146] In some optional implementations, the determining module 403 includes:

[0147] The second determining submodule is configured to determine a predicted wind energy coefficient based on the predicted wind energy data set and a preset adjustment period.

[0148] The comparison and determination submodule is used to compare the predicted wind energy coefficient with the preset threshold and determine the adjustment rate based on the comparison result.

[0149] In some optional implementations, the adjustment module 404 includes:

[0150] The first adjustment submodule is used to adjust the position of each wind turbine and the distance between different wind turbines in the predictive twin model based on wind power generation based on the predicted wind energy data set and the adjustment rate within a preset adjustment period, so as to obtain multiple layouts of wind turbine groups in the offshore wind farm to be configured and multiple first simulated total power generation.

[0151] The third determination submodule is used to determine a target layout of the wind turbine group in the offshore wind farm to be configured among multiple layouts based on the multiple first simulated total power generation, and the simulated total power generation generated by the wind turbine group in the offshore wind farm to be configured under the target layout is the largest among the multiple first simulated total power generation.

[0152] The fourth determination submodule is configured to determine a target turning rate of each wind turbine in the offshore wind farm to be configured based on the target layout.

[0153] The second adjustment submodule is used to adjust the orientation of each wind turbine in the wind power generation prediction twin model based on the target layout and the target turning rate of each wind turbine, so as to obtain multiple second simulated total power generation and multiple first orientations of each wind turbine.

[0154] The fifth determination submodule is used to determine the target orientation of each wind turbine in the offshore wind farm to be configured among multiple first orientations based on multiple second simulated total power generation, and the simulated total power generation generated by the offshore wind farm to be configured under the target orientation of each wind turbine is the largest among the multiple second simulated total power generation.

[0155] In some optional implementations, the fourth determining submodule includes:

[0156] The second acquisition unit is used to acquire the measured position and measured orientation of each wind turbine in the offshore wind farm to be configured.

[0157] An adjustment unit is used to adjust the orientation of each wind turbine in a prediction twin model based on wind power generation based on a target layout, so as to obtain multiple third simulated total power generation and multiple second orientations of each wind turbine.

[0158] The first determination unit is used to determine, based on multiple third simulated total power generation, a third orientation of each wind turbine in the offshore wind farm to be configured among multiple second orientations, wherein the simulated total power generation generated by each wind turbine in the offshore wind farm to be configured under the third orientation is the largest among the multiple third simulated total power generation.

[0159] The second determining unit is configured to determine a turning angle of each wind turbine based on the measured direction of each wind turbine and the third direction.

[0160] The third determining unit is configured to determine an adjustment time for each wind turbine based on the target layout and the measured position.

[0161] The fourth determining unit is configured to determine a target turning rate for each wind turbine based on the adjustment duration and the turning angle of each wind turbine.

[0162] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0163] The offshore wind farm resource configuration device in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0164] An embodiment of the present application further provides a computer device having the offshore wind farm resource configuration device shown in FIG. 4 above.

[0165] Please refer to Figure 5, which is a structural diagram of a computer device provided by an optional embodiment of the present application. As shown in Figure 5, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. The various components are connected to each other using different buses and can be installed on a common motherboard or installed in other ways as needed. The processor can process instructions executed within the computer device, including instructions stored in or on the memory to display graphical information of a GUI on an external input / output device (such as a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 5 takes a processor 10 as an example.

[0166] The processor 10 may be a central processing unit (CPU), a network processor (NPU), or a combination thereof. The processor 10 may also include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device (PLD) may be a complex programmable logic device (CPLD), a field programmable gate array (FPGA), a general purpose array logic (GAL), or any combination thereof.

[0167] The memory 20 stores instructions that can be executed by at least one processor 10, so as to enable at least one processor 10 to execute the method shown in the above embodiment.

[0168] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created based on the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0169] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0170] The computer device further includes a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0171] The embodiments of the present application also provide a computer-readable storage medium. The above-mentioned method according to the embodiment of the present application can be implemented in hardware, firmware, or implemented as a computer code that can be recorded in a storage medium, or implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state drive, etc.; optionally, the storage medium can also include a combination of the above-mentioned types of memory. It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor or hardware, the method shown in the above embodiment is implemented.

[0172] Although the embodiments of the present application have been described with reference to the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present application, and such modifications and variations shall fall within the scope defined by the appended claims.

Claims

1. A method for configuring offshore wind farm resources, characterized in that: The method comprises: Obtaining an initial measured wind energy dataset, an initial measured wind turbine dataset, and an initial forecast wind energy dataset of an offshore wind farm to be configured; Based on the initial measured wind energy data set, the initial measured wind turbine data set and the initial forecast wind energy data set, a prediction twin model based on wind power generation is constructed, wherein the prediction twin model based on wind power generation is used to obtain the simulated total power generation generated by different wind turbine layouts in the offshore wind farm to be configured under the condition of the predicted wind energy data set; Determining an adjustment rate based on the predicted wind energy data set and a preset adjustment period; During the preset adjustment period, the layout of each wind turbine in the predictive twin model based on wind power generation is adjusted based on the predicted wind energy data set and the adjustment rate, until the target layout of the wind turbine group in the offshore wind farm to be configured and the target orientation of each wind turbine are obtained when the simulated total power generation generated by the offshore wind farm to be configured is maximized.

2. The method according to claim 1, characterized in that Based on the initial measured wind energy data set, the initial measured wind turbine data set and the initial forecast wind energy data set, a prediction twin model based on wind power generation is constructed, including: Based on the initial measured wind energy data set and the initial measured wind turbine data set, constructing a digital twin model based on wind power generation using digital twin technology; Processing the initial forecast wind energy data set through a preset offshore wind energy prediction model to obtain a forecast wind energy data set for the offshore wind farm to be configured; Based on the predicted wind energy data set and the digital twin model based on wind power generation, the predicted twin model based on wind power generation is constructed.

3. The method according to claim 2, characterized in that Based on the initial measured wind energy data set and the initial measured wind turbine data set, a digital twin model based on wind power generation is constructed using digital twin technology, including: Acquire a characteristic data set of wind turbine generator sets in the offshore wind farm to be configured; The initial measured wind energy data set and the initial measured wind turbine data set are processed respectively to obtain a target measured wind energy data set and a target measured wind turbine data set; Based on the target measured wind turbine data set and the characteristic data set, constructing a physical system model based on wind power generation; Based on the target measured wind energy data set and the physical system model based on wind power generation, the digital twin model based on wind power generation is constructed.

4. The method according to claim 2, characterized in that: Before processing the initial forecast wind energy data set through a preset offshore wind energy prediction model to obtain the forecast wind energy data set for the offshore wind farm to be configured, the method further includes: Acquire a first historical measured wind energy data set and a historical forecast wind energy data set for the offshore wind farm to be configured; Determining a historical second measured wind energy data set corresponding to the historical forecast wind energy data set in the historical first measured wind energy data set; The preset offshore wind energy prediction model is constructed based on the historical forecast wind energy data set and the historical second measured wind energy data set.

5. The method according to claim 1, characterized in that Based on the predicted wind energy data set and a preset adjustment period, determining an adjustment rate includes: Determining a predicted wind energy coefficient based on the predicted wind energy data set and the preset adjustment period; The predicted wind energy coefficient is compared with a preset threshold, and the adjustment rate is determined according to the comparison result.

6. The method according to claim 1, characterized in that Within the preset adjustment period, the layout of each wind turbine in the wind power generation prediction twin model is adjusted based on the predicted wind energy data set and the adjustment rate until the simulated total power generation generated by the offshore wind farm to be configured is maximized, and the target layout of the wind turbine group in the offshore wind farm to be configured and the target orientation of each wind turbine are obtained, including: Within the preset adjustment period, the position of each wind turbine and the distance between different wind turbines in the wind power generation prediction twin model are adjusted based on the predicted wind energy data set and the adjustment rate to obtain multiple layouts of the wind turbine groups in the offshore wind farm to be configured and multiple first simulated total power generation; Based on the multiple first simulated total power generation, determining the target layout of the wind turbine group in the offshore wind farm to be configured in the multiple layouts, the simulated total power generation generated by the wind turbine group in the offshore wind farm to be configured under the target layout being the largest among the multiple first simulated total power generation; Based on the target layout, determining a target turning rate for each wind turbine in the offshore wind farm to be configured; Based on the target layout and the target turning rate of each wind turbine, the orientation of each wind turbine is adjusted in the wind power generation-based prediction twin model to obtain a plurality of second simulated total power generation and a plurality of first orientations of each wind turbine; Based on the multiple second simulated total power capacities, the target orientation of each wind turbine in the offshore wind farm to be configured is determined among the multiple first orientations, and the simulated total power capacities generated by the offshore wind farm to be configured under the target orientation of each wind turbine are the largest among the multiple second simulated total power capacities.

7. The method according to claim 6, characterized in that Determining a target turning rate of each wind turbine in the offshore wind farm to be configured based on the target layout includes: Obtaining the measured position and measured orientation of each wind turbine in the offshore wind farm to be configured; Based on the target layout, adjusting the orientation of each wind turbine in the wind power generation-based prediction twin model to obtain a plurality of third simulated total power generation and a plurality of second orientations of each wind turbine; Based on the multiple third simulated total power generation, determining a third orientation of each wind turbine in the offshore wind farm to be configured in the multiple second orientations, the simulated total power generation generated by each wind turbine in the offshore wind farm to be configured in the third orientation being the largest among the multiple third simulated total power generation; Determining a steering angle of each wind turbine based on the measured orientation of each wind turbine and the third orientation; Based on the target layout and the measured position, determining the adjustment time of each wind turbine; The target turning rate of each wind turbine is determined based on the adjustment time and the turning angle of each wind turbine.

8. An offshore wind farm resource allocation device, characterized in that: The device comprises: An acquisition module, used to acquire an initial measured wind energy data set, an initial measured wind turbine data set and an initial forecast wind energy data set of an offshore wind farm to be configured; A construction module, used to construct a prediction twin model based on wind power generation based on the initial measured wind energy data set, the initial measured wind turbine data set and the initial forecast wind energy data set, wherein the prediction twin model based on wind power generation is used to obtain the simulated total power generation generated by different wind turbine layouts in the offshore wind farm to be configured under the condition of the forecast wind energy data set; A determination module, configured to determine an adjustment rate based on the predicted wind energy data set and a preset adjustment period; An adjustment module is used to adjust the layout of each wind turbine in the predictive twin model based on wind power generation based on the predicted wind energy data set and the adjustment rate within the preset adjustment period, until the target layout of the wind turbine group in the offshore wind farm to be configured and the target orientation of each wind turbine are obtained when the simulated total power generation generated by the offshore wind farm to be configured is maximized.

9. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the offshore wind farm resource configuration method according to any one of claims 1 to 7 by executing the computer instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the offshore wind farm resource configuration method according to any one of claims 1 to 7.

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