Risk Monitoring Method and Device for Wind Turbine Units Based on Extreme Wind Condition Spectrum Reconstruction

Abstract

This application provides a method and apparatus for wind turbine risk monitoring based on extreme wind condition spectrum reconstruction, relating to the field of wind turbine risk monitoring technology. The method includes: acquiring the first wind speed, first wind direction, and nacelle location of the target wind turbine's environment; obtaining the second wind speed, second wind direction, and turbulence intensity corresponding to each specified time window within a specified time period using a preset data processing model; determining the target turbulence intensity and constructing time-series data of the target turbulence intensity, wind speed based on the second wind speed, and wind direction based on the second wind direction; determining the time-varying wind speed distribution corresponding to the target wind turbine based on the target turbulence intensity time-series data and wind speed time-series data, and determining the prevailing wind direction based on the wind direction time-series data; and simulating the turbulence intensity at each wind turbine in the wind farm simulation model based on the wind speed distribution and prevailing wind direction. This application enables transient simulation of wind farms under extreme wind conditions.

Description

Technical Field

[0001] This application relates to the field of wind turbine risk monitoring technology, specifically to a wind turbine risk monitoring method based on extreme wind condition spectrum reconstruction, a wind turbine risk monitoring device based on extreme wind condition spectrum reconstruction, and a computer-readable storage medium. Background Technology

[0002] Currently, domestic wind farm development and design typically use commercial software WT and Windsim for micro-site selection simulation calculations. These methods offer short calculation cycles and relatively stable accuracy. However, as wind power development capacity gradually increases, the proportion of large-scale turbines with tall towers and long blades is significantly rising, placing higher demands on wind resource assessment and requiring a balance between accuracy and computational efficiency. Furthermore, statistical data shows that the vast majority of wind turbine failures are due to extreme wind conditions, but existing conventional commercial software cannot perform transient simulations of extreme wind conditions, thus failing to obtain information on wind resource conditions for wind turbines under extreme wind circumstances. Summary of the Invention

[0003] The purpose of this application is to provide a wind turbine risk monitoring method based on extreme wind condition spectrum reconstruction, a wind turbine risk monitoring device based on extreme wind condition spectrum reconstruction, and a computer-readable storage medium to solve the above-mentioned problems.

[0004] To achieve the above objectives, the first aspect of this application provides a wind turbine risk monitoring method based on extreme wind condition spectrum reconstruction, comprising:

[0005] The first wind speed, the first wind direction, and the nacelle position of the target wind turbine are obtained within a specified time period.

[0006] Based on the wind speed, wind direction, and cabin location within the specified time period, the second wind speed, second wind direction, and turbulence intensity corresponding to each specified time window within the specified time period are obtained through a preset data processing model.

[0007] The turbulence intensity greater than the first turbulence intensity threshold is determined as the target turbulence intensity. Time series data of all target turbulence intensities are constructed in time order. The second wind speed and second wind direction corresponding to each target turbulence intensity are obtained. Time series data of all second wind speeds and all second wind directions are constructed in time order.

[0008] Based on the time-series data of the target turbulence intensity and wind speed, the wind speed distribution corresponding to the target wind turbine is determined over time, and the prevailing wind direction is determined based on the time-series data of wind direction.

[0009] Based on the wind speed distribution and the prevailing wind direction, the turbulence intensity at each wind turbine in the pre-constructed wind farm simulation model is simulated. If the turbulence intensity at the current wind turbine is greater than the second turbulence intensity threshold, an alarm message is generated, wherein the first turbulence intensity threshold is greater than the second turbulence intensity threshold.

[0010] Optionally, after obtaining the second wind speed, second wind direction, and turbulence intensity corresponding to each specified time window within the specified time period through a preset data processing model, the method further includes:

[0011] The second wind speed obtained is determined to be abnormal if it is less than the first wind speed threshold or greater than the second wind speed threshold; and the second wind direction obtained is determined to be abnormal if it is less than the first angle threshold or greater than the second angle threshold.

[0012] Delete the abnormal wind speed data and abnormal wind direction data from the acquired second wind speed and second wind direction;

[0013] Wherein, the first wind speed threshold is less than the second wind speed threshold, and the first angle threshold is less than the second angle threshold.

[0014] Optionally, the preset data processing model includes: a wind speed mapping sub-model, a wind direction mapping sub-model, and a turbulence intensity calculation sub-model; based on the wind speed, wind direction, and nacelle position within the specified time period, the preset data processing model is used to obtain the second wind speed, second wind direction, and turbulence intensity corresponding to each specified time window within the specified time period, including:

[0015] Using the first wind speed within the specified time period as input, the second wind speed corresponding to each specified time window within the specified time period is obtained through the wind speed mapping sub-model;

[0016] Using the first wind direction and cabin position within the specified time period as input, the second wind direction corresponding to each specified time window within the specified time period is obtained through the wind direction mapping sub-model;

[0017] Using the first wind speed during the specified time period and the second wind speed corresponding to each specified time window within the specified time period as input, the turbulence intensity corresponding to each specified time window within the specified time period is obtained through the turbulence intensity calculation sub-model.

[0018] Optionally, the wind speed mapping sub-model includes:

[0019]

[0020] in, v is the second wind speed corresponding to the specified window. iLet n be the wind speed at the i-th second, n be the number of wind speeds within the specified time period, and m be the number of specified time windows within the specified time period.

[0021] Optionally, the wind direction mapping sub-model includes:

[0022]

[0023] in, For the second wind direction corresponding to the specified window, Y i Let P be the wind direction at the i-th second. i Let be the cabin position angle at the i-th second.

[0024] Optionally, the turbulence intensity calculation sub-model includes:

[0025]

[0026] in, The turbulence intensity corresponding to the specified window.

[0027] Optionally, determining the prevailing wind direction based on the wind direction time series data includes: determining the second wind direction that appears most frequently in the wind direction time series data as the prevailing wind direction;

[0028] Determining the time-varying wind speed distribution corresponding to the target wind turbine based on the target turbulence intensity time-series data and wind speed time-series data includes:

[0029] Determine the mean target turbulence intensity of the target turbulence intensity time series data and the mean second wind speed of the wind speed time series data;

[0030] Perform a Fourier transform on the wind speed time series data to obtain the spectrum corresponding to the wind speed time series data, calculate the power spectral density of the spectrum corresponding to the wind speed time series data to obtain the energy distribution of the second wind speed at different frequencies, and determine the peak frequency corresponding to different energies.

[0031] Based on the peak frequencies corresponding to different energies in the wind speed time series data, the average value of the target turbulence intensity, and the average value of the second wind speed, the wind speed distribution corresponding to the target wind turbine is obtained over time through a preset wind speed distribution model.

[0032] Optionally, the wind speed distribution model includes:

[0033]

[0034] Where U(t) represents the wind speed distribution over time, and m2 represents the number of peak frequencies. The target is the mean turbulence intensity. The second average wind speed, Lk f is the velocity integral scale parameter. i Let f be the i-th peak frequency, S(f) be the atmospheric power spectral density function, rand(-π, π) represent a random number in the interval [-π, π], and t represent time.

[0035] A second aspect of this application provides a wind turbine risk monitoring device based on extreme wind condition spectrum reconstruction, comprising:

[0036] The data acquisition module is configured to acquire the first wind speed, the first wind direction, and the nacelle position of the target wind turbine in the environment where the target wind turbine is located within a specified time period.

[0037] The first data processing module is configured to obtain the second wind speed, second wind direction and turbulence intensity corresponding to each specified time window within the specified time period based on the wind speed, wind direction and cabin position within the specified time period through a preset data processing model;

[0038] The second data processing module is configured to determine the turbulence intensity greater than the first turbulence intensity threshold as the target turbulence intensity, construct time series data of all target turbulence intensities arranged by time, obtain the second wind speed and second wind direction corresponding to each target turbulence intensity, and construct time series data of all second wind speeds arranged by time and time series data of all second wind directions arranged by time.

[0039] The third data processing module is configured to determine the wind speed distribution over time corresponding to the target wind turbine based on the target turbulence intensity time series data and wind speed time series data, and to determine the prevailing wind direction based on the wind direction time series data.

[0040] The simulation module is configured to simulate the turbulence intensity at each wind turbine in the pre-built wind farm simulation model based on the wind speed distribution and the prevailing wind direction. If the turbulence intensity at the current wind turbine is greater than the second turbulence intensity threshold, an alarm message is generated, wherein the first turbulence intensity threshold is greater than the second turbulence intensity threshold.

[0041] In a third aspect, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to execute the wind turbine risk monitoring method based on extreme wind condition spectrum reconstruction as described above.

[0042] In a fourth aspect, this application provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described wind turbine risk monitoring method based on extreme wind condition spectrum reconstruction.

[0043] The embodiments provided in this application have the following beneficial effects:

[0044] This application enables more refined transient simulation of wind farms under extreme wind conditions, thereby assessing the turbulence risk of wind turbine units, which is beneficial for early monitoring of risky units and reducing potential safety hazards.

[0045] Other features and advantages of the embodiments or implementations of this application will be described in detail in the following detailed description section. Attached Figure Description

[0046] The accompanying drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the following detailed description to explain the embodiments of this application, but do not constitute a limitation on the embodiments of this application. In the drawings:

[0047] Figure 1 This illustration schematically shows a flowchart of a wind turbine risk monitoring method based on extreme wind condition spectrum reconstruction according to an embodiment of this application.

[0048] Figure 2 This illustration shows an extreme wind condition diagram of an embodiment of the present application;

[0049] Figure 3 A schematic diagram illustrating the peak frequency of an embodiment of this application is shown.

[0050] Figure 4 This illustration schematically shows an extreme wind condition diagram obtained by inversion of an embodiment of this application;

[0051] Figure 5 A simulation diagram illustrating an embodiment of this application is shown schematically;

[0052] Figure 6 This illustration shows a schematic block diagram of a wind turbine risk monitoring device based on extreme wind condition spectrum reconstruction according to an embodiment of this application;

[0053] Figure 7 The schematic diagram illustrates a terminal device structure according to an embodiment of this application.

[0054] Explanation of reference numerals in the attached figures

[0055] 10 - Terminal device, 100 - Processor, 101 - Memory, 102 - Computer program. Detailed Implementation

[0056] The specific embodiments of this application will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the embodiments of this application.

[0057] To solve the above problems, such as Figure 1 As shown, the first aspect of this application provides a method for risk monitoring of wind turbine units based on spectrum reconstruction under extreme wind conditions, including:

[0058] S100: Obtain the first wind speed, first wind direction and nacelle position of the target wind turbine in the environment where the target wind turbine is located within a specified time period.

[0059] S200: Based on the wind speed, wind direction and nacelle position within a specified time period, the second wind speed, second wind direction and turbulence intensity corresponding to each specified time window within the specified time period are obtained through a preset data processing model.

[0060] S300. Determine the turbulence intensity greater than the first turbulence intensity threshold as the target turbulence intensity, construct time series data of all target turbulence intensities arranged by time, obtain the second wind speed and second wind direction corresponding to each target turbulence intensity, and construct time series data of all second wind speeds arranged by time and time series data of all second wind directions arranged by time.

[0061] S400. Determine the wind speed distribution of the target wind turbine over time based on the time series data of the target turbulence intensity and wind speed, and determine the prevailing wind direction based on the time series data of the wind direction.

[0062] S500 simulates the turbulence intensity at each wind turbine in the pre-built wind farm simulation model based on wind speed distribution and prevailing wind direction. If the turbulence intensity at the current wind turbine is greater than the second turbulence intensity threshold, an alarm message is generated, wherein the first turbulence intensity threshold is greater than the second turbulence intensity threshold.

[0063] Thus, this application enables more refined transient simulation of wind farms under extreme wind conditions, thereby assessing the turbulence risk of wind turbine units, which is beneficial for early monitoring of risky units and reducing potential safety hazards.

[0064] In step S100, the target wind turbine can be any wind turbine in the wind farm. The first wind speed, first wind direction, and nacelle position data of the target wind turbine can be directly obtained through the wind farm's SCADA (Supervisory Control and Data Acquisition) system, i.e., the data acquisition and monitoring control system. The specified time period can be the past year. For example, the SCADA system can obtain the second-level measured data of the target wind turbine for the entire past year, i.e., the wind speed, wind direction, and nacelle position corresponding to each second in the past year. The nacelle position refers to the angle between the wind turbine nacelle and the horizontal plane when the wind turbine is running. It is understandable that during the rotation of the wind turbine, the nacelle will change due to the torsional force, resulting in different nacelle positions, which will have a certain impact on the performance and stability of the generator.

[0065] In step S200, the specified time window can be 1 minute, 10 minutes, etc. For example, the data such as the first wind speed, first wind direction, and cabin position are synthesized and processed by the data processing model to generate wind speed, wind direction, and turbulence intensity data for a specified time window, such as every 10 minutes. In this application, the preset data processing model includes: a wind speed mapping sub-model, a wind direction mapping sub-model, and a turbulence intensity calculation sub-model; based on the wind speed, wind direction, and cabin position within a specified time period, the preset data processing model is used to obtain the second wind speed, second wind direction, and turbulence intensity corresponding to each specified time window within the specified time period, including:

[0066] S210. Using the first wind speed within a specified time period as input, the second wind speed corresponding to each specified time window within the specified time period is obtained through a wind speed mapping sub-model; the obtained time series SCADA data of the target wind turbine can be represented as: wind speed (v1, v2...v...). n ), wind direction (Y1, Y2...Y n ), cabin positions (P1, P2...P n In this model, v1 represents the measured wind speed at the first sampling time, Y1 represents the measured wind direction at the first sampling time, and P1 represents the measured nacelle position at the first sampling time. That is, the wind speed, wind direction, and nacelle position correspond one-to-one at each sampling time. The wind speed mapping sub-model can map the wind speed time series data to the wind speed within a specified time window, such as 10 minutes. The wind speed mapping sub-model includes:

[0067]

[0068] in, The average wind speed corresponding to the specified window, i.e., the second wind speed, v i Let n be the wind speed at second i, n be the number of wind speeds within a specified time period, and m be the number of specified time windows within a specified time period.

[0069] S220. Taking the first wind direction and cabin position within a specified time period as input, the second wind direction corresponding to each specified time window within the specified time period is obtained through the wind direction mapping sub-model; the wind direction mapping sub-model can map the wind direction time series data to the wind direction of a specified time window, such as 10 minutes. The wind direction mapping sub-model includes:

[0070]

[0071] in, Y represents the average wind direction corresponding to the specified window, i.e., the second wind direction. i Let P be the wind direction at the i-th second. i Let be the cabin position angle at the i-th second;

[0072] S230. Taking the first wind speed during a specified time period and the second wind speed corresponding to each specified time window within the specified time period as input, the turbulence intensity corresponding to each specified time window within the specified time period is obtained through the turbulence intensity calculation sub-model; wherein, the turbulence intensity calculation sub-model includes:

[0073]

[0074] in, Specifies the turbulence intensity corresponding to the specified window.

[0075] By following the steps above, the processed 10-minute wind speed (ῡ1,ῡ2...ῡ) can be obtained. m ), wind direction (Ŷ1, Ŷ2...Ŷ) m ) and turbulence intensity data (σ1,σ2...σ m )

[0076] In this application, after obtaining the second wind speed, second wind direction, and turbulence intensity corresponding to each specified time window within a specified time period through a preset data processing model, the method further includes:

[0077] S240. Determine that among the acquired second wind speeds, those less than the first wind speed threshold or greater than the second wind speed threshold are abnormal wind speed data; and determine that among the acquired second wind directions, those less than the first angle threshold or greater than the second angle threshold are abnormal wind direction data, wherein the first wind speed threshold is less than the second wind speed threshold, and the first angle threshold is less than the second angle threshold; in this application, the first wind speed threshold is 3 m / s, the second wind speed threshold is 30 m / s, the first angle threshold is 0°, and the second angle threshold is 360°, that is, determine that the wind speed does not satisfy 3 ≤ ῡ j ≤30m / s, wind direction does not meet 0°≤Ŷ j Data ≤360° indicates abnormal wind speed or abnormal wind direction;

[0078] S250. Delete abnormal wind speed data and abnormal wind direction data from the acquired second wind speed and second wind direction to remove data that does not meet the requirements, leaving data with normal wind speed and wind direction, thereby eliminating the interference of abnormal data and improving the accuracy of subsequent simulations.

[0079] In step S300, the first turbulence intensity threshold is the turbulence intensity threshold defined in the IEC extreme turbulence standard for identifying extreme turbulence standard wind conditions. This application extracts wind conditions with turbulence intensities exceeding the extreme turbulence standard from the IEC extreme turbulence standard to analyze their spectral characteristics and deduce the periodically changing extreme wind speed curves with these spectral characteristics. Specifically, as shown... Figure 2 As shown, this application determines the extreme turbulence of the target wind turbine at different wind speeds based on the turbine model parameters, turbulence design standards, rotor diameter, and other parameters, as well as the extreme turbulence standards in the IEC standard. Based on this, wind conditions exceeding the extreme turbulence standards are selected, and all wind condition segments are integrated into a continuous wind condition sequence, forming a set of extreme wind condition time series wind speeds (u1, u2...u ... m1 ), wind direction (y1, y2...y m1 ) and turbulence intensity data (σ1,σ2...σ m1 ), where m1 is less than m and m>10000, to ensure the accuracy of the spectrum analysis.

[0080] In step S400, the prevailing wind direction is determined based on the wind direction time series data, including: determining the second most frequently occurring wind direction in the wind direction time series data as the prevailing wind direction; and determining the time-varying wind speed distribution corresponding to the target wind turbine based on the target turbulence intensity time series data and wind speed time series data, including:

[0081] S410. Average the target turbulence intensity in the target turbulence intensity time series data to determine the mean value of the target turbulence intensity in the target turbulence intensity time series data, and average the wind speed in the wind speed time series data to determine the second mean value of the wind speed in the wind speed time series data.

[0082] S420. Perform a Fourier transform on the wind speed time-series data to obtain the corresponding spectrum. Calculate the power spectral density (PSD) of the spectrum to obtain the energy distribution of the second wind speed at different frequencies and determine the peak frequencies corresponding to different energies. The steps of performing a Fourier transform on the wind speed time-series data to obtain the corresponding spectrum and calculating the PSD can be implemented using existing technologies, such as directly through MATLAB software. For example, the FFT function in MATLAB can be used to convert the wind speed time-series data into frequency domain data, providing the spectrum of the wind speed data. Then, MATLAB software can be used to perform spectral analysis on the extreme wind speed sequence data to find the PSD (Power Spectral Density) peak values ​​and obtain the peak frequencies (f1, f2...f) for different energies. m2 ),like Figure 3 As shown, the peak value refers to the highest power value at a specific frequency, and the power spectral density can represent the energy distribution of a signal across its various frequency components; the process of obtaining the peak frequency is existing technology and is not limited here.

[0083] S430. Based on the peak frequency, average target turbulence intensity, and average second wind speed corresponding to different energies in the wind speed time series data, the wind speed distribution corresponding to the target wind turbine is obtained through a preset wind speed distribution model. This application generates non-IEC wind conditions with their spectral and turbulence characteristics based on the peak frequency and average wind speed ū of different energies in the extreme wind condition time series, according to the atmospheric power spectral density function, through inverse Fourier transform. Specifically, the wind speed distribution model of this application includes:

[0084]

[0085]

[0086] Where U(t) represents the wind speed distribution over time, and m2 represents the number of peak frequencies. The target is the mean turbulence intensity. The second average wind speed, L k f is the velocity integral scale parameter. i Let S(f) be the i-th peak frequency, S(f) be the atmospheric power spectral density function, rand(-π, π) represent a random number in the interval [-π, π], and t represent time. In this application, the first 20 peak frequencies are selected for wind speed inversion. Combined with the average wind speed, a periodically changing non-IEC standard wind condition is formed as the loading data for the FLUENT simulation. The periodic boundary of the inverted extreme wind condition U(t) is as follows: Figure 4 As shown.

[0087] In step S500, this application uses FLUENT simulation software to simulate the turbulence risk of wind turbine generators. Specifically, firstly, a simulation model of the wind farm is constructed based on the topographic map of the wind farm, and the mesh generation and simulation settings are determined. The mesh quality standards are: two-dimensional mesh quality > 0.7; mesh skewness ratio < 200; kerndant number < 5. The FLUENT parameters can be configured as follows: Select the Ke turbulence model for steady-state simulation; set boundary conditions; determine the velocity inlet, pressure outlet, and wall boundaries; set monitoring points at each turbine location in the wind farm, primarily monitoring the velocity and turbulence changes over time; initialize the computational domain; set the time step and calculation step; use the wind speed distribution obtained in step S400 (i.e., the non-IEC wind speed curve) as the velocity inlet of the model; perform FLUENT numerical simulation calculations on the two-dimensional model at different turbine locations throughout the wind farm; simulate the main wind energy direction under extreme wind conditions for each wind turbine in the wind farm; monitor the simulation results at each turbine location, i.e., the wind speed and turbulence changes over time at each turbine location; and determine the turbulence at the corresponding wind speed at the turbine location according to the IEC extreme turbulence standard. If the turbulence exceeds the extreme turbulence standard, the turbine is considered to be at risk, and an alarm message is generated; if it is below the extreme turbulence standard, the turbine is considered to be without risk. Figure 5 The image shown is a simulation example of wind condition cloud map and extreme shear value at the wind turbine location.

[0088] like Figure 6 As shown, in a second aspect, this application provides a wind turbine risk monitoring device based on extreme wind condition spectrum reconstruction, comprising:

[0089] Obtain the first wind speed, first wind direction, and nacelle location of the target wind turbine within a specified time period;

[0090] Based on the wind speed, wind direction and cabin location within a specified time period, the second wind speed, second wind direction and turbulence intensity corresponding to each specified time window within the specified time period are obtained through a preset data processing model;

[0091] The turbulence intensity greater than the first turbulence intensity threshold is determined as the target turbulence intensity. Time series data of all target turbulence intensities are constructed in time order. The second wind speed and second wind direction corresponding to each target turbulence intensity are obtained. Time series data of all second wind speeds and all second wind directions are constructed in time order.

[0092] Based on the time series data of the target turbulence intensity and wind speed, the wind speed distribution corresponding to the target wind turbine is determined over time, and the prevailing wind direction is determined based on the wind direction time series data.

[0093] Based on wind speed distribution and prevailing wind direction, the turbulence intensity at each wind turbine in the pre-built wind farm simulation model is simulated. If the turbulence intensity at the current wind turbine is greater than the second turbulence intensity threshold, an alarm message is generated, wherein the first turbulence intensity threshold is greater than the second turbulence intensity threshold.

[0094] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0095] In a third aspect, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to execute the wind turbine risk monitoring method based on extreme wind condition spectrum reconstruction as described above.

[0096] In a fourth aspect, this application provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described wind turbine risk monitoring method based on extreme wind condition spectrum reconstruction.

[0097] like Figure 7 The diagram shown is a schematic representation of a terminal device provided in an embodiment of this application. Figure 7 As shown, the terminal device 10 of this embodiment includes a processor 100, a memory 101, and a computer program 102 stored in the memory 101 and executable on the processor 100. When the processor 100 executes the computer program 102, it implements the steps in the above method embodiments. Alternatively, when the processor 100 executes the computer program 102, it implements the functions of each module / unit in the above device embodiments.

[0098] For example, computer program 102 may be divided into one or more modules / units, one or more of which are stored in memory 101 and executed by processor 100 to complete this application. One or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of computer program 102 in terminal device 10.

[0099] Terminal device 10 may be a computing device such as a desktop computer, laptop, handheld computer, or cloud server. Terminal device 10 may include, but is not limited to, a processor 100 and a memory 101. Those skilled in the art will understand that... Figure 7 This is merely an example of terminal device 10 and does not constitute a limitation on terminal device 10. It may include more or fewer components than shown, or combine certain components, or different components. For example, terminal device may also include input / output devices, network access devices, buses, etc.

[0100] The processor 100 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0101] The memory 101 can be an internal storage unit of the terminal device 10, such as a hard disk or RAM of the terminal device 10. The memory 101 can also be an external storage device of the terminal device 10, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, or FlashCard equipped on the terminal device 10. Furthermore, the memory 101 can include both internal and external storage units of the terminal device 10. The memory 101 is used to store computer programs and other programs and data required by the terminal device 10. The memory 101 can also be used to temporarily store data that has been output or will be output.

[0102] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0103] It should also be noted that 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 process, method, article, or apparatus. Unless otherwise specified, 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 that element.

[0104] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for risk monitoring of wind turbine units based on spectrum reconstruction under extreme wind conditions, characterized in that, include: The first wind speed, the first wind direction, and the nacelle position of the target wind turbine are obtained within a specified time period. Based on the wind speed, wind direction, and cabin location within the specified time period, the second wind speed, second wind direction, and turbulence intensity corresponding to each specified time window within the specified time period are obtained through a preset data processing model. The turbulence intensity greater than the first turbulence intensity threshold is determined as the target turbulence intensity. Time series data of all target turbulence intensities are constructed in time order. The second wind speed and second wind direction corresponding to each target turbulence intensity are obtained. Time series data of all second wind speeds and all second wind directions are constructed in time order. Based on the time-series data of the target turbulence intensity and wind speed, the wind speed distribution corresponding to the target wind turbine is determined over time, and the prevailing wind direction is determined based on the time-series data of wind direction. Based on the wind speed distribution and the prevailing wind direction, the turbulence intensity at each wind turbine in the pre-built wind farm simulation model is simulated. If the turbulence intensity at the current wind turbine is greater than the second turbulence intensity threshold, an alarm message is generated, wherein the first turbulence intensity threshold is greater than the second turbulence intensity threshold. Determining the prevailing wind direction based on the wind direction time series data includes: determining the second wind direction that appears most frequently in the wind direction time series data as the prevailing wind direction; Determining the time-varying wind speed distribution corresponding to the target wind turbine based on the target turbulence intensity time-series data and wind speed time-series data includes: Determine the mean target turbulence intensity of the target turbulence intensity time series data and the mean second wind speed of the wind speed time series data; Perform a Fourier transform on the wind speed time series data to obtain the spectrum corresponding to the wind speed time series data, calculate the power spectral density of the spectrum corresponding to the wind speed time series data to obtain the energy distribution of the second wind speed at different frequencies, and determine the peak frequency corresponding to different energies. Based on the peak frequency corresponding to different energies in the wind speed time series data, the mean value of the target turbulence intensity, and the mean value of the second wind speed, the wind speed distribution corresponding to the target wind turbine is obtained through a preset wind speed distribution model. The wind speed distribution model includes: Where U(t) represents the wind speed distribution over time, and m2 represents the number of peak frequencies. The target is the mean turbulence intensity. The second average wind speed, L k f is the velocity integral scale parameter. i Let f be the i-th peak frequency, S(f) be the atmospheric power spectral density function, rand(-π, π) represent a random number in the interval [-π, π], and t represent time.

2. The wind turbine risk monitoring method based on extreme wind condition spectrum reconstruction according to claim 1, characterized in that, After obtaining the second wind speed, second wind direction, and turbulence intensity corresponding to each specified time window within the specified time period through a preset data processing model, the method further includes: The second wind speed obtained is determined to be abnormal if it is less than the first wind speed threshold or greater than the second wind speed threshold; and the second wind direction obtained is determined to be abnormal if it is less than the first angle threshold or greater than the second angle threshold. Delete the abnormal wind speed data and abnormal wind direction data from the acquired second wind speed and second wind direction; Wherein, the first wind speed threshold is less than the second wind speed threshold, and the first angle threshold is less than the second angle threshold.

3. The wind turbine risk monitoring method based on extreme wind condition spectrum reconstruction according to claim 1, characterized in that, The preset data processing model includes: a wind speed mapping sub-model, a wind direction mapping sub-model, and a turbulence intensity calculation sub-model; based on the wind speed, wind direction, and nacelle position within the specified time period, the preset data processing model obtains the second wind speed, second wind direction, and turbulence intensity corresponding to each specified time window within the specified time period, including: Using the first wind speed within the specified time period as input, the second wind speed corresponding to each specified time window within the specified time period is obtained through the wind speed mapping sub-model; Using the first wind direction and cabin position within the specified time period as input, the second wind direction corresponding to each specified time window within the specified time period is obtained through the wind direction mapping sub-model; Using the first wind speed during the specified time period and the second wind speed corresponding to each specified time window within the specified time period as input, the turbulence intensity corresponding to each specified time window within the specified time period is obtained through the turbulence intensity calculation sub-model.

4. The wind turbine risk monitoring method based on extreme wind condition spectrum reconstruction according to claim 3, characterized in that, The wind speed mapping sub-model includes: in, v is the second wind speed corresponding to the specified time window. i Let n be the wind speed at the i-th second, n be the number of wind speeds within the specified time period, and m be the number of specified time windows within the specified time period.

5. The wind turbine risk monitoring method based on extreme wind condition spectrum reconstruction according to claim 4, characterized in that, The wind direction mapping sub-model includes: in, For the second wind direction corresponding to the specified time window, Y i Let P be the wind direction at the i-th second. i Let be the cabin position angle at the i-th second.

6. The wind turbine risk monitoring method based on extreme wind condition spectrum reconstruction according to claim 5, characterized in that, The turbulence intensity calculation sub-model includes: in, The turbulence intensity corresponding to the specified time window.

7. A wind turbine risk monitoring device based on extreme wind condition spectrum reconstruction, employing the wind turbine risk monitoring method based on extreme wind condition spectrum reconstruction as described in any one of claims 1-6, characterized in that, include: The data acquisition module is configured to acquire the first wind speed, the first wind direction, and the nacelle position of the target wind turbine in the environment where the target wind turbine is located within a specified time period. The first data processing module is configured to obtain the second wind speed, second wind direction and turbulence intensity corresponding to each specified time window within the specified time period based on the wind speed, wind direction and cabin position within the specified time period through a preset data processing model; The second data processing module is configured to determine the turbulence intensity greater than the first turbulence intensity threshold as the target turbulence intensity, construct time series data of all target turbulence intensities arranged by time, obtain the second wind speed and second wind direction corresponding to each target turbulence intensity, and construct time series data of all second wind speeds arranged by time and time series data of all second wind directions arranged by time. The third data processing module is configured to determine the wind speed distribution over time corresponding to the target wind turbine based on the target turbulence intensity time series data and wind speed time series data, and to determine the prevailing wind direction based on the wind direction time series data. The simulation module is configured to simulate the turbulence intensity at each wind turbine in the pre-built wind farm simulation model based on the wind speed distribution and the prevailing wind direction. If the turbulence intensity at the current wind turbine is greater than the second turbulence intensity threshold, an alarm message is generated, wherein the first turbulence intensity threshold is greater than the second turbulence intensity threshold.

8. A computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to perform the wind turbine risk monitoring method based on extreme wind condition spectrum reconstruction as described in any one of claims 1-6.

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