A wind power prediction method, device and medium

Abstract

This application relates to the field of wind power generation technology and discloses a wind power prediction method, device, and medium. Compared with the current technology, which uses shutdown to ensure controllable power generation when wind farms experience icing, resulting in reduced power generation revenue and hindering the development of clean energy, this technical solution uses meteorological information sent by a meteorological forecasting platform to first predict the standard output power of the wind turbine under normal operating conditions. Then, it predicts the icing situation of the wind turbine based on the meteorological information and determines the power conversion factor of the wind turbine based on the icing situation. The power conversion factor is used to convert the power generation of the wind turbine under normal conditions to the power generation under icing conditions, thereby realizing the prediction of wind turbine power generation. This is beneficial for dispatchers to schedule wind farms according to different power generation conditions and promotes the development of clean energy.

Description

Technical Field

[0001] This application relates to the field of wind power generation technology, and in particular to a wind power prediction method, device and medium. Background Technology

[0002] A wind farm is a power station that utilizes wind power for electricity generation. It consists of a group of wind turbine generators or a cluster of wind turbine generators. In practice, the power output and production of a wind farm are subject to unpredictable factors such as its geographical location and temperature. These fluctuations are significant and highly random, making scheduling difficult. This problem is exacerbated during the icing season, when the wind farm's power generation capacity is more easily disrupted. Currently, most wind farms shut down their turbines after the blades become icy. This approach is detrimental to the development of clean energy and also impacts the profitability of the wind farm.

[0003] Therefore, how to accurately predict the power output of wind farms under icy weather conditions so that dispatchers can make timely adjustments is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0004] The purpose of this application is to provide a wind power prediction method, device, and medium to solve the problem in the current technology that shutdown is carried out during icing weather in order to ensure the controllability of wind farm output, which is detrimental to the development of clean energy.

[0005] To address the aforementioned technical problems, this application provides a wind power prediction method, comprising:

[0006] Obtain meteorological information sent by the meteorological forecasting platform;

[0007] The power prediction model is invoked to predict the standard output power of the wind turbine under normal operating conditions based on the first meteorological data in the meteorological information.

[0008] The icing model is invoked to predict the icing thickness of the wind turbine based on the second meteorological data in the meteorological information;

[0009] The power conversion factor is determined based on the ice thickness.

[0010] The output power of the wind turbine under icing conditions is calculated based on the power conversion factor and the standard output power.

[0011] Preferably, the step of determining the power conversion factor based on the ice thickness includes:

[0012] Based on the ice thickness, the lift coefficient and drag coefficient of the wind turbine are determined by calling the correlation model;

[0013] The power conversion factor of the fan is determined based on the lift coefficient and the drag coefficient.

[0014] The establishment of the association model includes:

[0015] The historical data obtained includes the wind speed, ice thickness, wind turbine power, lift coefficient, and drag coefficient of the wind turbine under icing conditions.

[0016] Confirm the correspondence between the icing thickness of the wind turbine and its lift and drag coefficients;

[0017] The association model is established based on the correspondence.

[0018] Preferably, the establishment of the power prediction model includes:

[0019] Acquire the historical operating data of the wind turbine and the corresponding meteorological data for the time period;

[0020] The power prediction model is established based on the correspondence between the meteorological data and the historical operating data.

[0021] Preferably, the first meteorological data includes temperature, humidity, and wind speed;

[0022] The second meteorological data includes: temperature, humidity, and liquid water content.

[0023] Preferably, the icing thickness of the wind turbine is the thickness at 95% spanwise section of the wind turbine blades.

[0024] Preferably, the formula for calculating the power conversion factor is:

[0025]

[0026]

[0027]

[0028] P k =a+bC lk +cC dk +dC lk 2 +eC lk C dk +fC dk 2 ;

[0029] Among them, P k P is the power conversion factor. i P represents the power output of the wind turbine under icing conditions. o C represents the fan power during normal operation. lk C is the lift conversion factor.li C is the lift coefficient under icing conditions. lo C is the lift coefficient during normal operation. dk C is the drag conversion factor. di C is the drag coefficient under icing conditions. do The resistance coefficient is the coefficient of resistance during normal operation, where a, b, c, d, e, and f are all constants.

[0030] Preferred options also include:

[0031] Confirm the ice shape and wind speed based on the meteorological information;

[0032] Correspondingly, the step of calling the association model to confirm the lift coefficient and drag coefficient of the wind turbine is as follows: calling the association model corresponding to the ice shape and wind speed to confirm the lift coefficient and drag coefficient of the wind turbine.

[0033] To address the aforementioned technical problems, this application also provides a wind power prediction device, comprising:

[0034] The acquisition module is used to acquire meteorological information sent by the meteorological forecasting platform;

[0035] The standard output power prediction module is used to call the power prediction model and predict the standard output power of the wind turbine under normal operating conditions based on the first meteorological data in the meteorological information.

[0036] The icing thickness prediction module is used to call the icing model and predict the icing thickness of the wind turbine based on the second meteorological data in the meteorological information.

[0037] The confirmation module is used to confirm the power conversion factor based on the ice thickness.

[0038] The calculation module is used to calculate the output power of the wind turbine under icing conditions based on the power conversion factor and the standard output power.

[0039] To solve the above-mentioned technical problems, this application also provides another wind power prediction device, including a memory for storing a computer program;

[0040] A processor is used to execute the computer program to implement the steps of the wind power prediction method as described above.

[0041] To address the aforementioned technical problems, this application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the wind power prediction method described above.

[0042] The wind power prediction method provided in this application obtains meteorological information sent by a meteorological forecasting platform; calls a power prediction model to predict the standard output power of the wind turbine under normal operating conditions based on the first meteorological data in the meteorological information; calls an icing model to predict the icing thickness of the wind turbine based on the second meteorological data in the meteorological information; determines the power conversion factor based on the icing thickness; and calculates the output power of the wind turbine under icing conditions based on the power conversion factor and the standard output power. Compared with the current technology, which uses shutdown to ensure controllable power generation when wind farms experience icing, resulting in reduced power generation revenue and hindering the development of clean energy, this technical solution, by predicting power generation under icing conditions, facilitates dispatchers to manage and schedule wind farms based on the power generation status of the wind turbines. In this technical solution, meteorological information sent by a meteorological forecasting platform is used to first predict the standard output power of the wind turbine under normal operating conditions. Then, the icing situation of the wind turbine is predicted based on the meteorological information, and the power conversion factor of the wind turbine is determined based on the icing situation. The power conversion factor is used to convert the power generation of the wind turbine under normal conditions to the power generation under icing conditions, thereby realizing the prediction of the wind turbine's power generation. This is beneficial for dispatchers to schedule wind farms according to different power generation conditions and is conducive to the development of clean energy.

[0043] Furthermore, the wind power prediction device and medium provided in this application correspond to the wind power prediction method described above and have the same effect. Attached Figure Description

[0044] To more clearly illustrate the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 A flowchart of a wind power prediction method provided in this application embodiment;

[0046] Figure 2 A schematic diagram of the leading edge ice thickness of a blade provided in an embodiment of this application;

[0047] Figure 3 A streamline ice power fitting error diagram provided for embodiments of this application;

[0048] Figure 4 An error map of power fitting for angular ice provided in an embodiment of this application;

[0049] Figure 5 A structural diagram of a wind power prediction device provided in an embodiment of this application;

[0050] Figure 6 A structural diagram of another wind power prediction device provided in an embodiment of this application. Detailed Implementation

[0051] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the protection scope of this application.

[0052] The core of this application is to provide a wind power prediction method, device, and medium for accurately predicting the power output of wind farms under icing weather conditions, so as to facilitate timely adjustments by dispatchers.

[0053] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0054] Figure 1 A flowchart of a wind power prediction method provided in this application embodiment is shown below. Figure 1 As shown, the method includes:

[0055] S10: Obtain meteorological information sent by the meteorological forecasting platform;

[0056] S11: Call the power prediction model to predict the standard output power of the wind turbine under normal operating conditions based on the first meteorological data in the meteorological information;

[0057] S12: Call the icing model and predict the icing thickness of the wind turbine based on the second meteorological data in the meteorological information;

[0058] S13: Determine the power conversion factor based on the ice thickness;

[0059] S14: Calculate the output power of the fan under icing conditions based on the power conversion factor and the standard output power.

[0060] Firstly, the wind power prediction method provided in this application is applied to a wind power prediction device, which can specifically be in the form of a processor or a host computer. It is mainly used in wind farms to predict the power generation of each wind turbine under icing conditions. This allows dispatchers to schedule whether each turbine is generating power, ensuring the overall stable and controllable power generation of the wind farm without requiring shutdowns, thus guaranteeing the power generation efficiency of the wind farm and benefiting the development of clean energy. In specific implementation, a wind farm is a place that utilizes wind power for power generation, also known as a "wind farm," and consists of a group of wind turbine generators or a cluster of wind turbine generators. Wind farms can be installed on land or at sea.

[0061] The wind power prediction method provided in this embodiment is mainly used to predict the power generation of wind turbines under icing conditions. In practical applications, wind farms are affected by uncertain factors such as geographical location, temperature, humidity, wind direction, and wind speed, causing variations in wind farm power generation and output. These variations are significant and highly random, which is detrimental to dispatching operations. Especially during the icing season, the icing of wind turbine blades disrupts power generation, thus affecting the wind farm's overall power generation capacity. Current technology involves shutting down wind farms after blade icing, which is detrimental to the development of clean energy and impacts wind farm profitability. Therefore, this application, by predicting the power generation of wind farms under icing conditions, helps dispatchers adjust the operating status of each wind turbine in a timely manner, achieving stable and controllable wind farm output and meeting users' electricity needs.

[0062] It is understood that the purpose of this application is to predict the power generation of wind turbines under icing conditions. Since the power generation of wind turbines is strongly correlated with meteorological conditions, in practical applications, this embodiment first requires obtaining meteorological information sent by a meteorological forecasting platform. This meteorological information can be obtained via the internet. It is understood that the purpose of this application is to predict the power generation of wind turbines so that dispatchers can adjust the turbine's operating status. Therefore, the meteorological information obtained in step S10 should be future meteorological information. After obtaining the meteorological information, the power prediction model can be invoked to predict the standard output power of the wind turbine under normal operating conditions based on the first meteorological data in the meteorological information. It is understood that the output power of the wind turbine varies with different icing thicknesses. Therefore, this application predicts the standard output power of the wind turbine under normal operating conditions and then converts the output power under icing conditions based on the icing thickness. The meteorological information obtained in this embodiment should include temperature, humidity, wind speed, and other information used subsequently.

[0063] This embodiment also provides a specific power prediction model. The establishment of this model includes: acquiring historical operating data of the wind turbine and corresponding meteorological data; and establishing a power prediction model based on the correspondence between the meteorological data and the historical operating data. In this embodiment, the power prediction model is established using the historical records of the wind farm. Specifically, it is established based on the historical operating data of the wind turbine, such as its output power, and the corresponding meteorological data. The meteorological data selected uses meteorological parameters with a high correlation to wind turbine power as input features for the wind turbine power prediction model under normal weather conditions. The power prediction model is then constructed based on machine learning. Specifically, temperature, humidity, and wind speed can be selected as input features for the wind turbine power prediction model under normal weather conditions.

[0064] To predict wind turbine icing, step S12 requires calling an icing model to predict the icing thickness based on the second meteorological data in the meteorological information. It should be noted that steps S11 and S12 are not sequential. In this embodiment, the icing model is called to predict possible icing conditions of the wind turbine and calculate the icing thickness. Specifically, when calculating the icing thickness, based on experiments, the thickness at the 95% spanwise section of the wind turbine blades is selected as the icing thickness. This embodiment also provides a method for establishing the icing model, selecting temperature, humidity, and liquid water content as feature quantities as inputs to the model. The icing thickness at the leading edge of the blade at the 95% spanwise section is used as the feature quantity characterizing the overall icing degree of the blades and is output to the model. Experiments are conducted on the wind turbine to obtain the corresponding parameters during icing. A correlation model between meteorological parameters and icing thickness is established based on deep learning, and the icing thickness is predicted using temperature, humidity, and liquid water content parameters. Figure 2 This is a schematic diagram of the leading edge ice thickness of a blade, provided as an embodiment of this application.

[0065] After predicting the icing thickness, a power conversion factor is determined based on the icing thickness. This power conversion factor is the ratio of the wind turbine's output power under normal weather conditions to its output power under icing conditions. It's understandable that the power conversion factor will differ for different icing thicknesses; therefore, a matching power conversion factor should be selected after determining the icing thickness. Finally, the wind turbine's output power under icing conditions is calculated based on the power conversion factor and the standard output power.

[0066] As can be understood from the above introduction, the meteorological information required for power prediction models and icing models is different in application. Therefore, in specific implementation, the first meteorological data includes temperature, humidity, and wind speed; the second meteorological data includes temperature, humidity, and liquid water content.

[0067] The wind power prediction method provided in this embodiment first establishes a wind turbine power prediction model under normal weather conditions, predicts the wind turbine power under normal weather conditions using meteorological information, and then uses meteorological information and icing thickness to convert weather forecast meteorological parameters into icing degree. Based on the mapping relationship between icing thickness and power conversion factor, the power conversion factor under different conditions is obtained, and the wind turbine power under non-icing conditions is converted into the wind turbine power under icing conditions based on the power conversion factor.

[0068] The wind power prediction method provided in this application obtains meteorological information sent by a meteorological forecasting platform; calls a power prediction model to predict the standard output power of the wind turbine under normal operating conditions based on the first meteorological data in the meteorological information; calls an icing model to predict the icing thickness of the wind turbine based on the second meteorological data in the meteorological information; determines the power conversion factor based on the icing thickness; and calculates the output power of the wind turbine under icing conditions based on the power conversion factor and the standard output power. Compared with the current technology, which uses shutdown to ensure controllable power generation when wind farms experience icing, resulting in reduced power generation revenue and hindering the development of clean energy, this technical solution, by predicting power generation under icing conditions, facilitates dispatchers to manage and schedule wind farms based on the power generation status of the wind turbines. In this technical solution, meteorological information sent by a meteorological forecasting platform is used to first predict the standard output power of the wind turbine under normal operating conditions. Then, the icing situation of the wind turbine is predicted based on the meteorological information, and the power conversion factor of the wind turbine is determined based on the icing situation. The power conversion factor is used to convert the power generation of the wind turbine under normal conditions to the power generation under icing conditions, thereby realizing the prediction of the wind turbine's power generation. This is beneficial for dispatchers to schedule wind farms according to different power generation conditions and is conducive to the development of clean energy.

[0069] Based on the above embodiments, this embodiment provides a specific method for determining the power conversion factor. Determining the power conversion factor based on icing thickness includes: using an association model to confirm the lift coefficient and drag coefficient of the wind turbine based on the icing thickness; and determining the power conversion factor of the wind turbine based on the lift coefficient and drag coefficient. The establishment of the association model includes: acquiring historical data on wind speed, icing thickness, wind turbine power, lift coefficient, and drag coefficient of the wind turbine under icing conditions; confirming the correspondence between the icing thickness of the wind turbine and the lift coefficient and drag coefficient; and establishing an association model based on the correspondence.

[0070] Understandably, in practice, due to factors such as weather and wind speed, the ice shape under icing conditions varies, and the power generation of wind turbines will also differ under different ice shapes. Therefore, in practice, wind power prediction methods also include: confirming the ice shape and wind speed based on meteorological information; and correspondingly, calling the correlation model to confirm the lift coefficient and drag coefficient of the wind turbine: calling the correlation model corresponding to the ice shape and wind speed to confirm the lift coefficient and drag coefficient of the wind turbine.

[0071] In this embodiment, the power conversion factor is determined by calculating the lift coefficient and drag coefficient of the wind turbine. Specifically, the formula for calculating the power conversion factor is as follows:

[0072]

[0073]

[0074]

[0075] P k =a+bC lk +cC dk +dC lk 2 +eC lk C dk +fC dk 2 ;

[0076] Among them, P k P is the power conversion factor. i P represents the power output of the wind turbine under icing conditions. o C represents the fan power during normal operation. lk C is the lift conversion factor. li C is the lift coefficient under icing conditions. lo C is the lift coefficient during normal operation. dk C is the drag conversion factor. di C is the drag coefficient under icing conditions. do The resistance coefficient is the coefficient of resistance during normal operation, where a, b, c, d, e, and f are all constants.

[0077] In this embodiment, the power conversion factor is calculated by fitting the above three equations. For ease of intuitive understanding, as a specific embodiment, Table 1 shows the parameter values ​​when the streamline ice icing degree is 5%.

[0078] Table 1

[0079]

[0080] In this embodiment, the power conversion factor under different operating conditions is predicted according to the fitting formula of three conversion factors. Combined with the predicted power of the wind turbine under normal weather conditions, the power of the wind turbine under icing conditions is then predicted. It is understandable that different ice formations have different effects on the output power of the wind turbine, and the fitting effect will also be different. Figure 3 This application provides a streamline ice power fitting error diagram as an embodiment. Figure 4 An example of a power fitting error map for angular ice provided in this application embodiment, such as... Figure 3 and Figure 4 As shown, the power prediction accuracy differs between streamlined ice and angular ice. Streamlined ice has a better power fitting effect, while the power prediction accuracy for angular ice is also above 90%.

[0081] The wind power prediction method has been described in detail in the above embodiments. This application also provides embodiments corresponding to the wind power prediction device. It should be noted that this application describes the embodiments of the device from two perspectives: one is based on the functional modules, and the other is based on the hardware.

[0082] Figure 5 A structural diagram of a wind power prediction device provided in an embodiment of this application is shown below. Figure 5 As shown, the wind power prediction device includes:

[0083] The acquisition module 10 is used to acquire meteorological information sent by the meteorological forecasting platform;

[0084] The standard output power prediction module 11 is used to call the power prediction model and predict the standard output power of the wind turbine under normal operating conditions based on the first meteorological data in the meteorological information.

[0085] The icing thickness prediction module 12 is used to call the icing model and predict the icing thickness of the wind turbine based on the second meteorological data in the meteorological information.

[0086] Confirmation module 13 is used to confirm the power conversion factor based on the icing thickness;

[0087] The calculation module 14 is used to calculate the output power of the wind turbine under icing conditions based on the power conversion factor and the standard output power.

[0088] Since the embodiments of the apparatus and the embodiments of the method correspond to each other, please refer to the description of the embodiments of the method for the embodiments of the apparatus, which will not be repeated here.

[0089] The wind power prediction device provided in this application addresses the problem in current technologies where wind farms are shut down to maintain controllable power generation after icing, which negatively impacts revenue and hinders clean energy development. This solution predicts power generation under icing conditions, enabling dispatchers to manage wind farms based on turbine performance. The solution utilizes meteorological information from a weather forecasting platform to first predict the standard output power of wind turbines under normal operating conditions. Then, it predicts the icing situation and determines the power conversion factor based on the icing conditions. This conversion factor allows for the conversion of power generation from normal to icing conditions, thus enabling accurate prediction of wind turbine power generation. This facilitates dispatchers' management of wind farms based on different power generation statuses, promoting clean energy development.

[0090] Figure 6 A structural diagram of another wind power prediction device provided in the embodiments of this application is shown below. Figure 6 As shown, the device includes: a memory 20 for storing computer programs;

[0091] The processor 21 is used to execute a computer program to implement the steps of the wind power prediction method as described in the above embodiments.

[0092] The wind power prediction device provided in this embodiment may include, but is not limited to, smartphones, tablets, laptops, or desktop computers.

[0093] The processor 21 may include one or more processing cores, such as a quad-core processor or an octa-core processor. The processor 21 may be implemented using at least one of the following hardware forms: Digital Signal Processor (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 21 may also include a main processor and a coprocessor. The main processor, also known as the Central Processing Unit (CPU), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, the processor 21 may integrate a Graphics Processing Unit (GPU), which is responsible for rendering and drawing the content to be displayed on the screen. In some embodiments, the processor 21 may also include an Artificial Intelligence (AI) processor, which is used to handle computational operations related to machine learning.

[0094] The memory 20 may include one or more computer-readable storage media, which may be non-transitory. The memory 20 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In this embodiment, the memory 20 is used to store at least the following computer program 201, which, after being loaded and executed by the processor 21, is capable of implementing the relevant steps of the wind power prediction method disclosed in any of the foregoing embodiments. In addition, the resources stored in the memory 20 may also include an operating system 202 and data 203, and the storage method may be temporary or permanent storage. The operating system 202 may include Windows, Unix, Linux, etc. The data 203 may include, but is not limited to, meteorological information.

[0095] In some embodiments, the wind power prediction device may further include a display screen 22, an input / output interface 23, a communication interface 24, a power supply 25, and a communication bus 26.

[0096] Those skilled in the art will understand that Figure 6 The structure shown does not constitute a limitation on the wind power forecasting device and may include more or fewer components than shown.

[0097] The wind power prediction device provided in this application includes a memory and a processor. When the processor executes the program stored in the memory, it can perform the following methods: acquiring meteorological information sent by a meteorological forecasting platform; calling a power prediction model to predict the standard output power of the wind turbine under normal operating conditions based on the first meteorological data in the meteorological information; calling an icing model to predict the icing thickness of the wind turbine based on the second meteorological data in the meteorological information; confirming the power conversion factor based on the icing thickness; and calculating the output power of the wind turbine under icing conditions based on the power conversion factor and the standard output power.

[0098] The wind power prediction device provided in this application addresses the problem in current technologies where wind farms are shut down to maintain controllable power generation after icing, which negatively impacts revenue and hinders clean energy development. This solution predicts power generation under icing conditions, enabling dispatchers to manage wind farms based on turbine performance. The solution utilizes meteorological information from a weather forecasting platform to first predict the standard output power of wind turbines under normal operating conditions. Then, it predicts the icing situation and determines the power conversion factor based on the icing conditions. This conversion factor allows for the conversion of power generation from normal to icing conditions, thus enabling accurate prediction of wind turbine power generation. This facilitates dispatchers' management of wind farms based on different power generation statuses, promoting clean energy development.

[0099] Finally, this application also provides an embodiment corresponding to a computer-readable storage medium. The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps described in the above method embodiments.

[0100] It is understood that if the methods in the above embodiments are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and executes all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0101] The computer-readable storage medium provided in this application addresses the problem in current technologies where wind farms are shut down to maintain controllable power generation after icing, which negatively impacts revenue and hinders clean energy development. This technical solution, by predicting power generation under icing conditions, allows dispatchers to better manage wind farms based on turbine performance. This solution utilizes meteorological information from a weather forecasting platform to first predict the standard output power of wind turbines under normal operating conditions. Then, it predicts the icing situation and determines the power conversion factor based on the icing condition. This conversion factor allows for the conversion between the power generation under normal conditions and the power generation under icing conditions, thus enabling power generation prediction and facilitating wind farm management based on different power generation statuses, ultimately promoting clean energy development.

[0102] The wind power prediction method, apparatus, and medium provided in this application have been described in detail above. The various embodiments in the specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section. It should be noted that those skilled in the art can make several improvements and modifications to this application without departing from the principles of this application, and these improvements and modifications also fall within the protection scope of the claims of this application.

[0103] It should also be noted that, in this specification, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

Claims

1. A wind power prediction method, characterized by, The method comprises the following steps: obtaining meteorological information sent by a meteorological prediction platform; confirming ice shape and wind speed according to the meteorological information; calling a power prediction model to predict standard output power of a wind turbine in a normal operating state according to first meteorological data in the meteorological information; calling an icing model to predict icing thickness of the wind turbine according to second meteorological data in the meteorological information; confirming a power conversion coefficient according to the icing thickness; calculating output power of the wind turbine in an icing state according to the power conversion coefficient and the standard output power; the step of confirming the power conversion coefficient according to the icing thickness comprises the following steps: calling an associated model to confirm lift coefficient and drag coefficient of the wind turbine according to the icing thickness; correspondingly, the step of calling the associated model to confirm the lift coefficient and the drag coefficient of the wind turbine is: calling an associated model corresponding to the ice shape and the wind speed to confirm the lift coefficient and the drag coefficient of the wind turbine; confirming the power conversion coefficient of the wind turbine according to the lift coefficient and the drag coefficient; wherein, the establishment of the associated model comprises the following steps: obtaining wind speed, icing thickness, wind turbine power, lift coefficient and drag coefficient of the wind turbine in an icing state in historical data; confirming the corresponding relationship between the icing thickness and the lift coefficient and the drag coefficient of the wind turbine; establishing the associated model according to the corresponding relationship; the calculation formula of the power conversion coefficient is: ； ； ； ； wherein, is a power reduction factor, is the fan power in the iced state, is the fan power in normal operation, is a lift reduction factor, is the lift coefficient in the iced state, is the lift coefficient in normal operation, is a drag reduction factor, is the drag coefficient in the iced state, is the drag coefficient in normal operation, a, b, c, d, e, f are constants.

2. The wind power prediction method according to claim 1, characterized in that, the establishment of the power prediction model comprises the following steps: obtaining historical operating data of the wind turbine and meteorological data corresponding to the time; establishing the power prediction model according to the corresponding relationship between the meteorological data and the historical operating data.

3. The wind power prediction method according to claim 1, characterized in that, the first meteorological data comprises temperature, humidity and wind speed; the second meteorological data comprises temperature, humidity and liquid water content.

4. The wind power prediction method according to any one of claims 1 to 3, characterized in that, the icing thickness of the wind turbine is the thickness of the 95% section of the blade span of the wind turbine.

5. A wind power prediction device, characterized by, The method comprises the following steps: an obtaining module is configured to obtain meteorological information sent by a meteorological prediction platform; a standard output power prediction module is configured to call a power prediction model to predict standard output power of a wind turbine in a normal operating state according to first meteorological data in the meteorological information; an icing thickness prediction module is configured to call an icing model to predict icing thickness of the wind turbine according to second meteorological data in the meteorological information; a confirming module is configured to confirm a power conversion coefficient according to the icing thickness; a calculating module is configured to calculate output power of the wind turbine in an icing state according to the power conversion coefficient and the standard output power; the step of confirming the power conversion coefficient according to the icing thickness comprises the following steps: calling an associated model to confirm lift coefficient and drag coefficient of the wind turbine according to the icing thickness; correspondingly, the step of calling the associated model to confirm the lift coefficient and the drag coefficient of the wind turbine is: calling an associated model corresponding to the ice shape and the wind speed to confirm the lift coefficient and the drag coefficient of the wind turbine; confirming the power conversion coefficient of the wind turbine according to the lift coefficient and the drag coefficient; wherein, the establishment of the associated model comprises the following steps: obtaining wind speed, icing thickness, wind turbine power, lift coefficient and drag coefficient of the wind turbine in an icing state in historical data; confirming the corresponding relationship between the icing thickness and the lift coefficient and the drag coefficient of the wind turbine; establishing the associated model according to the corresponding relationship; The correlation model is established according to the correspondence relationship; The calculation formula of the power conversion coefficient is: ； ； ； ； wherein, is a power reduction factor, is the fan power in the iced state, is the fan power in normal operation, is a lift reduction factor, is the lift coefficient in the iced state, is the lift coefficient in normal operation, is a drag reduction factor, is the drag coefficient in the iced state, is the drag coefficient in normal operation, a, b, c, d, e, f are constants.

6. A wind power prediction device, characterized by, The computer program is stored in the memory. The processor is configured to execute the computer program to implement the steps of the wind power prediction method according to any one of claims 1 to 4.

7. A computer readable storage medium characterized in that, The computer program is stored on the computer readable storage medium, and the computer program is executed by the processor to implement the steps of the wind power prediction method according to any one of claims 1 to 4.

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