Wind power plant icing shutdown prediction result determination method and device

Abstract

The embodiment of the invention provides a wind power plant icing shutdown prediction result determination method and device, and relates to the technical field of wind power generation. The method comprises the following steps: acquiring numerical weather forecast data of a wind power plant in a future preset time period and actually measured meteorological data of the wind power plant in a historical icing shutdown time period; determining an icing shutdown prediction sample pair according to the numerical weather forecast data of the wind power plant in the future preset time period and the actually measured meteorological data of the wind power plant in the historical icing shutdown time period; and inputting the icing shutdown prediction sample pair into the icing shutdown prediction model, processing the icing shutdown prediction sample pair based on the icing shutdown prediction model, and outputting a wind power plant icing shutdown prediction result. By adopting the technical scheme, the fan shutdown time in different typical areas under the icing working condition can be accurately predicted, and then corresponding emergency plans and cold-proof and anti-freezing measures can be made in advance, so that the economic loss caused by fan shutdown events is reduced.

Description

Technical Field

[0001] The disclosed embodiments relate to the technical field of wind power generation, and in particular to a method and device for determining icing shutdown prediction results of a wind farm. Background Art

[0002] As a renewable energy source, wind energy has been rapidly developed around the world. my country's wind turbines are mainly concentrated in the Northeast, North China, Northwest China and coastal areas. The wind turbines in these areas are in cold and humid environments for a long time. At the same time, they often encounter freezing rain, freezing fog and snowfall, accompanied by icing of wind turbine power generation equipment. Blade icing is the main reason for reducing the overall structural performance and power generation efficiency of wind turbines. Ice on the surface of wind turbine blades will change the aerodynamic performance, thereby reducing the power generation efficiency of wind turbines. At the same time, icing will cause the blade load to increase and the mass distribution to be unbalanced, thereby changing the natural frequency of the structure itself, which may cause the wind turbine blades to produce resonance response and shutdown during operation.

[0003] Therefore, low temperature and freezing weather are typical conditions that cause wind turbine shutdown, especially in northern China. Under emergency shutdown conditions, the dynamic characteristics of the wind turbine will change, increasing the impact load on the unit.

[0004] Therefore, there is an urgent need for a method to determine the prediction results of wind farm icing shutdown, which can accurately predict the timing of wind turbine shutdown in different typical areas under icing conditions, and then make corresponding emergency plans and anti-cold and anti-freeze measures in advance to reduce the economic losses caused by wind turbine shutdown events. Summary of the invention

[0005] In order to solve the above technical problems or at least partially solve the above technical problems, the embodiments of the present disclosure provide a method and device for determining wind farm icing shutdown prediction results.

[0006] A first aspect of an embodiment of the present disclosure provides a method for determining a wind farm icing shutdown prediction result, the method comprising:

[0007] Obtain numerical weather forecast data for the wind farm in the future preset time period and measured meteorological data of the wind farm during the historical icing shutdown period;

[0008] Determine an icing shutdown prediction sample pair based on numerical weather forecast data of the wind farm in a preset future time period and measured meteorological data of the wind farm during the historical icing shutdown period;

[0009] The icing shutdown prediction sample pairs are input into an icing shutdown prediction model, and based on the icing shutdown prediction model, the icing shutdown prediction sample pairs are processed to output a wind farm icing shutdown prediction result.

[0010] In one example, the icing shutdown prediction model is used to process the icing shutdown prediction sample pair, and output the wind farm icing shutdown prediction result, including:

[0011] Based on the icing shutdown prediction model, obtaining the time accumulation characteristics and similarity characteristics of the icing shutdown prediction sample pairs;

[0012] Determining category information based on the time accumulation feature and the similarity feature;

[0013] The wind farm icing shutdown prediction result is output according to the category information.

[0014] In one example, the icing shutdown prediction model is obtained by training with historical wind farm measured meteorological data; wherein the historical wind farm measured meteorological data includes: a first meteorological sequence data when the wind turbine is in an icing shutdown state and a second meteorological sequence data when the wind turbine is in an icing non-shutdown state.

[0015] In one example, the icing shutdown prediction model is obtained by training with historical wind farm measured meteorological data, and includes:

[0016] Extracting a first meteorological change feature of the first meteorological sequence data and extracting a second meteorological change feature of the second meteorological sequence data;

[0017] Retrieving first weight information of the first meteorological change feature and second weight information of the second meteorological change feature;

[0018] The icing shutdown prediction model is trained according to the coupling relationship between the first meteorological change feature and the second meteorological change feature, the first weight information, and the second weight information.

[0019] In one example, the first meteorological sequence data includes at least one of the following: temperature sequence data, humidity sequence data, wind speed sequence data, pressure sequence data, and wind direction sequence data; the second meteorological sequence data includes at least one of the following: temperature sequence data, humidity sequence data, wind speed sequence data, pressure sequence data, and wind direction sequence data.

[0020] A second aspect of an embodiment of the present disclosure provides a device for determining a wind farm icing shutdown prediction result, the device comprising:

[0021] An acquisition module is used to obtain numerical weather forecast data for the wind farm in a preset time period in the future and measured meteorological data of the wind farm during the historical icing shutdown period;

[0022] A determination module, configured to determine an icing shutdown prediction sample pair based on numerical weather forecast data of the wind farm in a preset future time period and measured meteorological data of the wind farm during the historical icing shutdown period;

[0023] The output module is used to input the icing shutdown prediction sample pair into the icing shutdown prediction model, process the icing shutdown prediction sample pair based on the icing shutdown prediction model, and output the wind farm icing shutdown prediction result.

[0024] A third aspect of an embodiment of the present disclosure provides an electronic device, comprising: a processor and a memory, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the method of the first aspect above.

[0025] A fourth aspect of an embodiment of the present disclosure provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the method of the first aspect described above can be implemented.

[0026] The disclosed embodiment provides a method and device for determining wind farm icing shutdown prediction results, the method comprising: obtaining numerical weather forecast data for a preset time period in the future of the wind farm and measured meteorological data of the wind farm during the historical icing shutdown period; determining an icing shutdown prediction sample pair based on the numerical weather forecast data for the preset time period in the future of the wind farm and the measured meteorological data of the wind farm during the historical icing shutdown period; inputting the icing shutdown prediction sample pair into an icing shutdown prediction model, processing the icing shutdown prediction sample pair based on the icing shutdown prediction model, and outputting the wind farm icing shutdown prediction result. By adopting this technical solution, the timing of wind turbine shutdown in different typical areas under icing conditions can be accurately predicted, and then corresponding emergency plans and anti-cold and anti-freeze measures can be made in advance to reduce the economic losses caused by wind turbine shutdown events. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0028] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0029] Figure 1 It is a flow chart of a method for determining wind farm icing shutdown prediction results provided by an embodiment of the present disclosure;

[0030] Figure 2 It is a flow chart of a method for determining wind farm icing shutdown prediction results provided by an embodiment of the present disclosure;

[0031] Figure 3 It is a structural schematic diagram of a device for determining wind farm icing shutdown prediction results provided by an embodiment of the present disclosure;

[0032] Figure 4 It is a structural schematic diagram of an electronic device in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0033] In order to more clearly understand the above-mentioned objectives, features and advantages of the present disclosure, the scheme of the present disclosure will be further described below. It should be noted that the embodiments of the present disclosure and the features in the embodiments can be combined with each other without conflict.

[0034] In the following description, many specific details are set forth to facilitate a full understanding of the present disclosure, but the present disclosure may also be implemented in other ways different from those described herein; it is obvious that the embodiments in the specification are only part of the embodiments of the present disclosure, rather than all of the embodiments.

[0035] Regarding the prediction of icing shutdown based on numerical weather forecast data using traditional machine learning methods, on the one hand, the probability of wind turbines being covered with ice is low, the scale and degree of each cold wave are different, and the number of historical samples of icing shutdowns is extremely small, so there is a lack of sufficient sample accumulation; on the other hand, from a physical point of view, icing shutdowns mainly occur when certain climatic conditions are met and the process of cumulative changes over time is met, and there are complex coupling relationships between various subtle meteorological conditions, so they cannot be simply classified and judged directly based on the meteorological conditions at a certain moment.

[0036] Due to the above reasons, for the NWP-based icing shutdown prediction using traditional machine learning methods, on the one hand, it is difficult to ensure the generalization ability of the model when using historical data of icing with a small number of samples to train models with complex coupling relationships under various subtle meteorological conditions. On the other hand, the accuracy of icing predictions cannot be guaranteed when using the trained model for predictions. That is, under the premise of small samples, there are limitations in the problem of NWP-based icing shutdown prediction using traditional machine learning methods. It is difficult to ensure the generalization ability of the model during model training and the accuracy of icing shutdown predictions cannot be guaranteed when making predictions.

[0037] Figure 1 1 is a flow chart of a method for determining wind farm icing shutdown prediction results provided by an embodiment of the present disclosure, and the method can be performed by an electronic device. The electronic device can be exemplarily understood as a device such as a mobile phone, a tablet computer, a laptop computer, a desktop computer, a smart TV, etc. Figure 1 As shown, the method provided in this embodiment includes the following steps:

[0038] S101. Obtain numerical weather forecast data for a wind farm in a preset future time period and measured meteorological data for the wind farm during a historical icing shutdown period.

[0039] In one example, the numerical weather forecast data of the wind farm in the future preset time period at least includes the spatial distribution information and time variation information of the temperature variable, humidity variable, wind speed variable, pressure variable, and wind direction variable; wherein the future preset time period may be the next 1h, the next 2h, the next 4h, the next 1 day, the next 2 days, the next N days, etc. wherein N is a positive integer; the wind farm measured meteorological data during the historical icing shutdown period may be the period of 1h, 2h, 3h, ... Nh, etc. before and after the occurrence of icing, determined according to the accuracy of the wind turbine icing shutdown prediction, wherein N is a positive integer; the more samples of the numerical weather forecast data of the wind farm in the future preset time period and the wind farm measured meteorological data during the historical icing shutdown period, the better.

[0040] S102. Determine an icing shutdown prediction sample pair based on numerical weather forecast data of the wind farm in a preset future time period and measured meteorological data of the wind farm during a historical icing shutdown period.

[0041] In an example, an icing shutdown prediction sample pair may be composed of numerical weather forecast data for a preset future time period of a wind farm and measured meteorological data of the wind farm during the same type of historical icing shutdown period, or may be composed of numerical weather forecast data for a preset future time period of a wind farm and measured meteorological data of the wind farm during different types of historical icing shutdown periods.

[0042] S103: Input the icing shutdown prediction sample pairs into the icing shutdown prediction model, process the icing shutdown prediction sample pairs based on the icing shutdown prediction model, and output the wind farm icing shutdown prediction results.

[0043] In one example, due to the scale and forecast model of numerical weather forecasts, there is a certain systematic deviation between NWP and the actual meteorological conditions at the height of the wind turbine hub. In order to eliminate the above deviation, linear correction and polynomial correction are performed on NWP to ensure that the numerical range is the same as that at the height of the wind turbine hub.

[0044] In one example, before determining an icing shutdown prediction sample pair based on numerical weather forecast data for a future preset time period of the wind farm and measured meteorological data of the wind farm during historical icing shutdown periods, the method includes: performing linear correction and / or polynomial correction on the numerical weather forecast data for the future preset time period of the wind farm to obtain numerical weather forecast data adapted to the wind turbine hub height.

[0045] The disclosed embodiment provides a method for determining wind farm icing shutdown prediction results, the method comprising: obtaining numerical weather forecast data for a preset time period in the future of the wind farm and measured meteorological data of the wind farm during the historical icing shutdown period; determining icing shutdown prediction sample pairs according to the numerical weather forecast data for the preset time period in the future of the wind farm and measured meteorological data of the wind farm during the historical icing shutdown period; inputting the icing shutdown prediction sample pairs into an icing shutdown prediction model, processing the icing shutdown prediction sample pairs based on the icing shutdown prediction model, and outputting wind farm icing shutdown prediction results. By adopting this technical solution, the timing of wind turbine shutdown in different typical areas under icing conditions can be accurately predicted, and then corresponding emergency plans and anti-cold and anti-freeze measures can be made in advance to reduce the economic losses caused by wind turbine shutdown events.

[0046] Figure 2 The flowchart of a method for determining wind farm icing shutdown prediction results provided by the embodiment of the present disclosure is shown. The embodiment of the present disclosure is optimized on the basis of the above embodiment, and the embodiment of the present disclosure can be combined with various optional solutions in one or more of the above embodiments.

[0047] like Figure 2 As shown, the method for determining the wind farm icing shutdown prediction result may include the following steps:

[0048] S201. Obtain numerical weather forecast data for the wind farm in a preset future time period and measured meteorological data for the wind farm during a historical icing shutdown period.

[0049] In one example, please refer to the content of step S101, which will not be repeated here.

[0050] S202: Determine an icing shutdown prediction sample pair based on numerical weather forecast data of the wind farm in a preset future time period and measured meteorological data of the wind farm during a historical icing shutdown period.

[0051] In one example, please refer to the content of step S102, which will not be repeated here.

[0052] S203: Input the icing shutdown prediction sample pairs into the icing shutdown prediction model, and obtain the time accumulation features and similarity features in the icing shutdown prediction sample pairs based on the icing shutdown prediction model.

[0053] In one example, an icing shutdown prediction model is obtained by training with historical wind farm measured meteorological data; wherein the historical wind farm measured meteorological data includes: a first meteorological sequence data when the wind turbine is in an icing shutdown state and a second meteorological sequence data when the wind turbine is in an icing non-shutdown state.

[0054] In one example, the icing shutdown prediction model is trained with historical wind farm measured meteorological data, including:

[0055] Extracting a first meteorological change feature of the first meteorological sequence data and extracting a second meteorological change feature of the second meteorological sequence data;

[0056] Retrieving first weight information of a first meteorological change feature and second weight information of a second meteorological change feature;

[0057] An icing shutdown prediction model is trained according to the coupling relationship between the first meteorological change feature and the second meteorological change feature, the first weight information, and the second weight information.

[0058] In one example, since the occurrence of icing shutdown requires certain climatic conditions to meet a cumulative change process over time, and there are complex coupling relationships among various subtle meteorological conditions in the above cumulative change process, in order to determine the cumulative change process of the first meteorological series data over the above time and the time cumulative effect of the complex coupling relationship, as well as the cumulative change process of the second meteorological series data over the above time and the time cumulative effect of the complex coupling relationship.

[0059] In one example, when training the model, the gated recurrent unit (GRU) is used to extract the meteorological change characteristics of each type of meteorological sequence data, wherein the update gate and the reset gate are mainly used to retain historical information and forget unnecessary information.

[0060] In dealing with the temporal cumulative effect of the mutual coupling of the first meteorological change feature and the second meteorological change feature, special attention is paid to the cumulative change pattern of the first meteorological change feature and the second meteorological change feature during the model training stage. In view of the complex interaction between various subtle meteorological conditions during the icing process, a multi-head attention mechanism is introduced to parse the deep relationship in the multidimensional sequence feature data. This process involves the use of multiple sets of attention mechanisms to dynamically adjust the first weight information and the second weight information, aiming to accurately capture the correlation between different meteorological feature sequences. Finally, multiple weighted subsequences are fused through a linear layer to generate a comprehensive output sequence. This method significantly improves the significance of meteorological features that affect icing shutdowns and can more effectively grasp the complex interdependencies between multidimensional features.

[0061] The icing shutdown prediction model in this embodiment not only improves the model's sensitivity to key meteorological characteristics, but also enhances its ability to process multi-dimensional and complex meteorological data, thereby laying a solid foundation for achieving accurate icing shutdown prediction.

[0062] In one example, the first meteorological sequence data includes at least one of the following: temperature sequence data, humidity sequence data, wind speed sequence data, pressure sequence data, and wind direction sequence data; the second meteorological sequence data includes at least one of the following: temperature sequence data, humidity sequence data, wind speed sequence data, pressure sequence data, and wind direction sequence data.

[0063] In one example, an icing shutdown prediction model includes two weight-sharing neural network branches and a similarity measurement module.

[0064] S204: Determine category information based on the time accumulation feature and the similarity feature.

[0065] In one example, whether the information is of the same category is determined based on the time accumulation feature and the similarity feature.

[0066] S205: Output wind farm icing shutdown prediction results based on the category information.

[0067] In one example, the same category information can be a sample pair constructed from multiple categories of meteorological series data under icing shutdown and a sample pair constructed from multiple categories of meteorological series data under no shutdown, and different category information can be a sample constructed from multiple categories of meteorological series data under icing shutdown and another sample constructed from multiple categories of meteorological series data under no shutdown. Based on the specific category information, the wind farm icing shutdown prediction result is output.

[0068] The disclosed embodiment provides a method for determining a wind farm icing shutdown prediction result, the method comprising: inputting an icing shutdown prediction sample pair into an icing shutdown prediction model, and obtaining a time accumulation feature and a similarity feature in the icing shutdown prediction sample pair based on the icing shutdown prediction model. Based on the time accumulation feature and the similarity feature, determine category information. Based on the category information, output a wind farm icing shutdown prediction result. The use of this technical solution solves the problem that the traditional shutdown prediction method cannot guarantee the shutdown prediction accuracy during prediction.

[0069] Figure 3 1 is a schematic diagram of a wind farm icing shutdown prediction result determination device provided by an embodiment of the present disclosure. The wind farm icing shutdown prediction result determination device can be understood as the above electronic device or a part of the functional modules in the above electronic device. Figure 3 As shown, the wind farm icing shutdown prediction result determination device 30 includes:

[0070] The acquisition module 301 is used to acquire numerical weather forecast data of the wind farm within a preset time period in the future and the wind farm measured meteorological data during the historical icing shutdown period.

[0071] The determination module 302 is used to determine an icing shutdown prediction sample pair based on numerical weather forecast data of the wind farm in a preset future time period and measured meteorological data of the wind farm during a historical icing shutdown period.

[0072] The output module 303 is used to input the icing shutdown prediction sample pairs into the icing shutdown prediction model, process the icing shutdown prediction sample pairs based on the icing shutdown prediction model, and output the wind farm icing shutdown prediction results.

[0073] In one example, the output module 303 includes:

[0074] An acquisition submodule is used to acquire the time accumulation characteristics and similarity characteristics of the ice-covered shutdown prediction sample pairs based on the ice-covered shutdown prediction model;

[0075] A determination submodule is used to determine category information based on time accumulation features and similarity features;

[0076] The output submodule is used to output the wind farm icing shutdown prediction results according to the category information.

[0077] In one example, an icing shutdown prediction model is obtained by training with historical wind farm measured meteorological data; wherein the historical wind farm measured meteorological data includes: a first meteorological sequence data when the wind turbine is in an icing shutdown state and a second meteorological sequence data when the wind turbine is in an icing non-shutdown state.

[0078] In one example, the icing shutdown prediction model is trained with historical wind farm measured meteorological data, including:

[0079] Extracting a first meteorological change feature of the first meteorological sequence data and extracting a second meteorological change feature of the second meteorological sequence data;

[0080] Retrieving first weight information of a first meteorological change feature and second weight information of a second meteorological change feature;

[0081] An icing shutdown prediction model is trained according to the coupling relationship between the first meteorological change feature and the second meteorological change feature, the first weight information, and the second weight information.

[0082] In one example, the first meteorological sequence data includes at least one of the following: temperature sequence data, humidity sequence data, wind speed sequence data, pressure sequence data, and wind direction sequence data; the second meteorological sequence data includes at least one of the following: temperature sequence data, humidity sequence data, wind speed sequence data, pressure sequence data, and wind direction sequence data.

[0083] The device provided in this embodiment can execute the method of any of the above embodiments, and its execution method and beneficial effects are similar, which will not be repeated here.

[0084] An embodiment of the present disclosure further provides an electronic device, which includes: a memory, in which a computer program is stored; and a processor, which is used to execute the computer program. When the computer program is executed by the processor, the method of any of the above embodiments can be implemented.

[0085] For example, Figure 4 Schematic diagram of the structure of an electronic device in the embodiment of the present disclosure. Figure 4 , which shows a schematic diagram of the structure of an electronic device 1000 suitable for implementing the embodiment of the present disclosure. The electronic device 1000 in the embodiment of the present disclosure may include but is not limited to mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 4 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0086] like Figure 4 As shown, the electronic device 1000 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 1001, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1008 into a random access memory (RAM) 1003. In the RAM 1003, various programs and data required for the operation of the electronic device 1000 are also stored. The processing device 1001, the ROM 1002, and the RAM 1003 are connected to each other via a bus 1004. An input / output (I / O) interface 1005 is also connected to the bus 1004.

[0087] Typically, the following devices may be connected to the I / O interface 1005: an input device 1006 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 1007 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 1008 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 may allow the electronic device 1000 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 4 The electronic device 1000 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead.

[0088] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device 1009, or installed from a storage device 1008, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment of the present disclosure are executed.

[0089] It should be noted that the computer-readable medium disclosed above may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0090] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0091] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0092] The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: obtains numerical weather forecast data within a preset time period in the future of the wind farm and measured meteorological data of the wind farm during the historical icing shutdown period; determines icing shutdown prediction sample pairs according to the numerical weather forecast data within the preset time period in the future of the wind farm and the measured meteorological data of the wind farm during the historical icing shutdown period; inputs the icing shutdown prediction sample pairs into the icing shutdown prediction model, processes the icing shutdown prediction sample pairs based on the icing shutdown prediction model, and outputs the wind farm icing shutdown prediction results. Computer program codes for performing the operations of the present disclosure can be written in one or more programming languages ​​or a combination thereof, and the programming languages ​​include but are not limited to object-oriented programming languages—such as Java, Smalltalk, C++, and also conventional procedural programming languages—such as "C" language or similar programming languages. The program code can be executed completely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0093] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0094] The units involved in the embodiments described in the present disclosure may be implemented by software or hardware, wherein the name of a unit does not, in some cases, limit the unit itself.

[0095] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0096] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0097] The embodiments of the present disclosure also provide a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the method of any of the above embodiments can be implemented. The execution method and beneficial effects are similar and will not be repeated here.

[0098] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0099] The above are only specific embodiments of the present disclosure, so that those skilled in the art can understand or implement the present disclosure. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present disclosure. Therefore, the present disclosure will not be limited to these embodiments herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for determining wind farm icing shutdown prediction results, characterized in that: The method comprises: Obtain numerical weather forecast data for the wind farm in the future preset time period and measured meteorological data of the wind farm during the historical icing shutdown period; Determine an icing shutdown prediction sample pair based on numerical weather forecast data of the wind farm in a preset future time period and measured meteorological data of the wind farm during the historical icing shutdown period; The icing shutdown prediction sample pairs are input into an icing shutdown prediction model, and based on the icing shutdown prediction model, the icing shutdown prediction sample pairs are processed to output a wind farm icing shutdown prediction result.

2. The method according to claim 1, characterized in that The step of processing the icing shutdown prediction sample pair based on the icing shutdown prediction model and outputting the wind farm icing shutdown prediction result includes: Based on the icing shutdown prediction model, obtaining the time accumulation characteristics and similarity characteristics of the icing shutdown prediction sample pairs; Determining category information based on the time accumulation feature and the similarity feature; The wind farm icing shutdown prediction result is output according to the category information.

3. The method according to claim 1, characterized in that The icing shutdown prediction model is obtained after training with historical wind farm measured meteorological data; wherein the historical wind farm measured meteorological data includes: a first meteorological sequence data when the wind turbine is in an icing shutdown state and a second meteorological sequence data when the wind turbine is in an icing non-shutdown state.

4. The method according to claim 3, characterized in that The icing shutdown prediction model is obtained by training with historical wind farm measured meteorological data, and includes: Extracting a first meteorological change feature of the first meteorological sequence data and extracting a second meteorological change feature of the second meteorological sequence data; Retrieving first weight information of the first meteorological change feature and second weight information of the second meteorological change feature; The icing shutdown prediction model is trained according to the coupling relationship between the first meteorological change feature and the second meteorological change feature, the first weight information, and the second weight information.

5. The method according to claim 1, characterized in that The first meteorological sequence data includes at least one of the following: temperature sequence data, humidity sequence data, wind speed sequence data, pressure sequence data, and wind direction sequence data; the second meteorological sequence data includes at least one of the following: temperature sequence data, humidity sequence data, wind speed sequence data, pressure sequence data, and wind direction sequence data.

6. A device for determining wind farm icing shutdown prediction results, characterized in that: The device comprises: An acquisition module is used to obtain numerical weather forecast data for the wind farm in a preset time period in the future and measured meteorological data of the wind farm during the historical icing shutdown period; A determination module, configured to determine an icing shutdown prediction sample pair based on numerical weather forecast data of the wind farm in a preset future time period and measured meteorological data of the wind farm during the historical icing shutdown period; The output module is used to input the icing shutdown prediction sample pair into the icing shutdown prediction model, process the icing shutdown prediction sample pair based on the icing shutdown prediction model, and output the wind farm icing shutdown prediction result.

7. The device according to claim 6, characterized in that The output module comprises: An acquisition submodule, configured to acquire, based on the icing shutdown prediction model, a time accumulation feature and a similarity feature in the icing shutdown prediction sample pair; A determination submodule, used to determine category information based on the time accumulation feature and the similarity feature; The output submodule is used to output the wind farm icing shutdown prediction result according to the category information.

8. The device according to claim 6, characterized in that The icing shutdown prediction model is obtained after training with historical wind farm measured meteorological data; wherein the historical wind farm measured meteorological data includes: a first meteorological sequence data when the wind turbine is in an icing shutdown state and a second meteorological sequence data when the wind turbine is in an icing non-shutdown state.

9. An electronic device, characterized in that: include: A processor and a memory, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the method according to any one of claims 1 to 5.

10. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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