Wind generating set risk early warning method, device and equipment under sand storm weather and storage medium

Abstract

The invention discloses a wind generating set risk early warning method, device and equipment in sand storm weather and a storage medium, and relates to the technical field of wind power generation, and the wind generating set risk early warning method in sand storm weather at least comprises the following steps: collecting multi-source meteorological data in a target area, judging whether a preset sand storm early warning condition is triggered or not according to the multi-source meteorological data; in response to the condition of triggering sand storm early warning, simulating the motion condition of sand and dust according to the multi-source meteorological data and the digital elevation model of the target area, and predicting to obtain sand and dust settlement distribution information in the target area; determining at least one to-be-protected generator set according to a preset wind generating set distribution diagram and the sand and dust sedimentation distribution information; and executing a protection control strategy on the to-be-protected generator set according to the multi-source meteorological data and the sand and dust settlement distribution information. The protection effect on the wind generating set in the sand storm weather is improved, and power generation output is guaranteed.

Description

Technical Field

[0001] This application relates to the field of wind power generation technology, and in particular to a method, device, equipment and computer-readable storage medium for risk warning of wind turbine generator sets under sandstorm weather. Background Technology

[0002] Wind energy, as a clean and renewable energy source, is being developed and utilized more and more widely, with many large-scale wind farms being built in areas prone to sandstorms, such as Northwest and North China. The extreme conditions accompanying sandstorms, such as strong winds, high concentrations of particulate matter, low visibility, and sudden changes in air pressure, pose a severe challenge to the stable operation of wind turbine generators. Currently, wind farms rely heavily on macro-meteorological early warning systems and passive human intervention to cope with sandstorms, such as uniformly shutting down turbines before a sandstorm arrives or conducting maintenance and repairs afterward.

[0003] However, the aforementioned dust storm protection measures are too rudimentary and lack specificity, potentially leading to over-protection of some units or under-protection of others. Secondly, while the simple and direct shutdown protection strategy avoids equipment impact, it results in significant power generation losses, failing to meet the needs of wind farms in extreme dust storm weather where both generator protection and power output are required.

[0004] The information disclosed in this background section is only for understanding the background technology of the present application concept, and therefore may contain information that does not constitute prior art. Summary of the Invention

[0005] The main purpose of this application is to provide a method, device, equipment and computer-readable storage medium for risk warning of wind turbine generator sets under sandstorm weather, which aims to improve the protection effect of wind turbine generator sets under sandstorm weather and ensure power generation output.

[0006] To achieve the above objectives, this application provides a method for risk warning of wind turbine generators during sandstorms, the method comprising: Collect multi-source meteorological data within the target area, and determine whether the preset sandstorm warning conditions are triggered based on the multi-source meteorological data; In response to triggering the dust storm warning conditions, based on the multi-source meteorological data and the digital elevation model of the target area, the movement of dust is simulated to predict the dust deposition distribution information within the target area; Based on the preset wind turbine generator distribution map and the dust deposition distribution information, at least one generator unit to be protected is identified. Based on the multi-source meteorological data and the dust deposition distribution information, a protection control strategy is implemented for the generator set to be protected, wherein the protection control strategy includes at least reducing operating power and adjusting blade angle.

[0007] In one embodiment, the multi-source meteorological data includes at least air pressure, humidity, predicted wind speed, PM10 concentration, and visibility. The step of determining whether a preset sandstorm warning condition is triggered based on the multi-source meteorological data includes: If the air pressure value is found to decrease by more than a preset value within a preset time and / or the humidity value is found to be lower than a preset humidity threshold, then it is determined whether the predicted wind speed is greater than a preset wind speed threshold, whether the PM10 concentration is greater than a first preset concentration threshold, and whether the visibility is less than a preset visibility threshold. If at least a preset number of conditions are met among the three conditions—the predicted wind speed being greater than a preset wind speed threshold, the PM10 concentration being greater than a preset concentration threshold, and the visibility being less than a preset visibility threshold—then the conditions for triggering the sandstorm warning are determined.

[0008] In one embodiment, the step of simulating the movement of dust storms based on the multi-source meteorological data and the digital elevation model of the target area, and predicting the dust deposition distribution information within the target area, includes: Based on the digital elevation model and the coordinate information of each wind turbine in the target area, a CFD simulation model is constructed. The wind speed and wind direction information from the multi-source meteorological data are used as the boundary conditions of the CFD simulation model to obtain the physical property parameters of the dust particles. Based on the CFD simulation model, the boundary conditions, and the physical property parameters, the motion trajectory of dust particles in the target area and the dust deposition distribution information in the target area are simulated.

[0009] In one embodiment, the dust deposition distribution information includes at least dust concentration information, and the step of determining at least one generator set to be protected based on a preset wind turbine generator set distribution map and the dust deposition distribution information includes: Based on the dust deposition distribution information, areas in the target region where the dust concentration is higher than a preset concentration threshold are identified as risk areas; Based on the wind turbine distribution map, the wind turbines located in the risk area are identified as turbines to be protected.

[0010] In one embodiment, the step of identifying wind turbines located in risk areas as turbines to be protected based on the wind turbine distribution map includes: Extract the coverage area of ​​each wind turbine generator set from the wind turbine generator set distribution map; Identify the risky wind turbine generators whose coverage areas overlap with the risk areas. Calculate the ratio of the overlap area between the coverage area and the risk area of ​​each risk generator set to the coverage area, and obtain the risk score corresponding to each risk generator set; Generator sets with risk scores greater than a preset score threshold are identified as generator sets to be protected.

[0011] In one embodiment, after the step of identifying risky wind turbine generators whose coverage areas overlap with the risk areas, the method further includes: The overlapping area between the coverage area and the risk area of ​​each risk generator set is determined sequentially to include a preset sensitive component, wherein the sensitive component includes at least one of blades, gearbox, bearing, sensor and cooling device; If included, the risky generator set is identified as the generator set to be protected.

[0012] In one embodiment, the step of implementing a protection control strategy for the generator set to be protected based on the multi-source meteorological data and the dust deposition distribution information includes: Analyze the dust deposition distribution information to determine the direction of dust flow; The plane of rotation of the wind turbine blades of the generator set to be protected is controlled to form a preset angle with the direction of sand and dust flow; When the PM10 concentration in the multi-source meteorological data is higher than the second preset concentration threshold, the output power of the generator set to be protected is limited to a control power range, wherein the upper limit of the control power range is lower than the rated power of the generator set to be protected.

[0013] In addition, this application also provides a wind turbine generator risk early warning device, the wind turbine generator risk early warning device comprising: The condition triggering module is used to collect multi-source meteorological data within the target area and determine whether to trigger the preset sandstorm warning conditions based on the multi-source meteorological data. The dust simulation module is used to simulate the movement of dust in response to the triggering of the dust storm warning conditions, based on the multi-source meteorological data and the digital elevation model of the target area, and to predict the dust deposition distribution information within the target area. The protection determination module is used to determine at least one generator set to be protected based on a preset wind turbine generator set distribution map and the dust deposition distribution information. The strategy execution module is used to execute a protection control strategy on the generator set to be protected based on the multi-source meteorological data and the dust deposition distribution information, wherein the protection control strategy includes at least reducing the operating power and adjusting the blade angle.

[0014] In addition, this application also provides a wind turbine generator risk warning device, which includes at least: a memory, a processor, and a computer program stored in the memory and executable on the processor. The computer program is configured to implement the steps of the wind turbine generator risk warning method under sandstorm weather as described above.

[0015] In addition, to achieve the above objectives, this application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the risk warning method for wind turbine generator sets under sandstorm weather as described above.

[0016] In addition, to achieve the above objectives, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described method for risk warning of wind turbine generator sets during sandstorm weather.

[0017] This application provides a method for risk warning of wind turbine generators under sandstorm weather. The method includes: firstly, collecting multi-source meteorological data of a target area; secondly, determining whether a preset sandstorm warning condition has been triggered based on the multi-source meteorological data; and thirdly, in response to the triggering of the sandstorm warning condition, simulating the movement of sand and dust based on the multi-source meteorological data and a digital elevation model (DEM) of the target area to predict the sand and dust deposition distribution information within the target area. The DEM reflects the topographic features of the target area. In this scheme, the method first determines whether a sandstorm warning condition has been triggered based on the multi-source meteorological data of the target area, and then, if the sandstorm warning condition is triggered, performs sand and dust deposition analysis based on the DEM. Dust movement simulation is used to predict dust deposition distribution information, ensuring that risk warnings are based on real-time and comprehensive meteorological changes. This avoids the lag and inaccuracy of traditional methods that rely on macroscopic warnings, and effectively saves computational resources. It eliminates the need for constant prediction of current dust deposition distribution information, which reflects the dust distribution in the target area, providing a reliable data foundation for subsequent generator protection. Based on a preset wind turbine distribution map and the dust deposition distribution information, at least one generator to be protected is identified. Protection control strategies are implemented for the generator to be protected based on the multi-source meteorological data and the dust deposition distribution information. These strategies include at least reducing operating power and adjusting blade angles. This technical solution combines meteorological dynamics and the topographic features of the target area, improving the accuracy of dust impact prediction and providing a reliable basis for subsequent protection. Identifying at least one generator to be protected based on a preset wind turbine distribution map and dust deposition distribution information, by matching the generator location with dust risk areas, achieves accurate identification of high-risk generators, preventing over-protection or under-protection caused by uniform shutdowns. Based on multi-source meteorological data and dust deposition distribution information, targeted protection and control strategies are implemented for the generator sets to be protected, such as reducing operating power and adjusting blade angles. This allows for dynamic adjustment of operating parameters, ensuring the safety of the generator set equipment while maintaining power generation capacity, thus balancing protection and power generation needs. Attached Figure Description

[0018] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating the risk warning method for wind turbine generators during sandstorms, as described in this application embodiment. Figure 2 This is a flowchart illustrating the process of predicting dust deposition distribution information in the target area in an embodiment of this application. Figure 3 This is a schematic diagram illustrating the process of determining the generator sets to be protected from the wind turbine generator sets in the risk area in an embodiment of this application; Figure 4 This is a schematic diagram of the wind turbine generator risk warning device in the embodiments of this application; Figure 5 This is a schematic diagram of the equipment structure of the device involved in the risk warning method for wind turbine generators under sandstorm weather in the embodiments of this application.

[0021] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0022] To make the above-mentioned objectives, features, and advantages of this application more apparent and understandable, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0023] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0024] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0025] Traditional wind turbine dust storm protection technologies rely on macro-meteorological early warnings and passive manual intervention, resulting in a lack of targeted protection strategies. Specifically, the risk assessment process fails to accurately match meteorological dynamics with terrain features, leading to some wind turbines facing over-protection or under-protection. Furthermore, while a uniform shutdown strategy avoids equipment impact, it cannot maintain power generation output, affecting the operational efficiency and equipment safety of the wind farm.

[0026] For example, during the actual operation of a wind farm, a sandstorm occurred, and the meteorological monitoring system detected increased wind speed and particulate matter concentration. Because the protection decisions were based solely on regional average meteorological data without more refined risk warnings, the wind turbines located in the concentrated sandstorm deposition area failed to identify the risk in time, while those in the leeward area were forced to shut down. Consequently, turbines in high-risk areas experienced increased mechanical wear due to sand and dust particle impact, while the power generation capacity of low-risk areas was interrupted, resulting in a loss of power generation potential. If these problems are not addressed, the operation of wind turbines under extreme sandstorm conditions will lead to an increased probability of equipment failure, increased maintenance needs, and a continuous decline in power generation efficiency. In the long term, this could reduce the overall reliability of the wind farm and affect the stable power supply capacity of the power grid.

[0027] To address this issue, this application proposes a risk warning method for wind turbine generators during sandstorms, comprising: collecting multi-source meteorological data within a target area; determining whether a preset sandstorm warning condition has been triggered based on the multi-source meteorological data; responding to the triggering of the sandstorm warning condition; simulating the movement of sand and dust based on the multi-source meteorological data and a digital elevation model of the target area; predicting the sand and dust deposition distribution information within the target area; identifying at least one generator generator to be protected based on a preset wind turbine generator distribution map and the sand and dust deposition distribution information; and implementing a protection control strategy for the generator generator to be protected based on the multi-source meteorological data and the sand and dust deposition distribution information, wherein the protection control strategy includes at least reducing operating power and adjusting blade angle.

[0028] For ease of understanding, the following explains some key terms used in this embodiment: Multi-source meteorological data refers to a collection of various meteorological parameters acquired from different sources and types of sensors or observation stations. This data may include, but is not limited to, wind speed, wind direction, air pressure, temperature, humidity, precipitation, visibility, and particulate matter concentration in the air, and is used to comprehensively reflect the real-time meteorological conditions of a target area.

[0029] Dust storm warning conditions: These refer to a set of pre-set criteria for identifying an impending or ongoing dust storm. These conditions are typically based on the analysis of multi-source meteorological data, and a dust storm warning is triggered when meteorological parameters reach or exceed specific thresholds.

[0030] A Digital Elevation Model (DEM) is a model that represents ground elevation information in digital form. It stores the three-dimensional coordinate data of the Earth's surface through a series of regular grid points or irregular triangular meshes, accurately describing the topographic features of a target area and providing a topographic basis for simulating dust storm movements.

[0031] Dust deposition distribution information: Data obtained through simulation or prediction regarding the concentration, coverage, deposition amount, and spatial distribution of dust particles within a target area over time. This information can visually demonstrate the extent of the impact of dust on different areas and facilities.

[0032] Wind turbine distribution map: A geospatial map within the target area, showing the specific location, model, altitude, and other information of all wind turbines. This distribution map, combined with dust deposition distribution information, can be used to assess the dust risk faced by each turbine.

[0033] Wind turbine generators requiring protection: These are wind turbine generators identified as facing a high risk of dust storms and requiring specific protective measures, based on information on dust deposition distribution and wind turbine generator distribution maps.

[0034] Protection and control strategies: These are a series of operational adjustments taken to mitigate the damage caused by sandstorms to generator units requiring protection. These strategies aim to balance equipment protection with power generation efficiency, for example, by adjusting operating parameters to reduce risk.

[0035] This application provides a risk warning method for wind turbine generators during sandstorms, referring to... Figure 1 , Figure 1 This is a flowchart illustrating an embodiment of a risk warning method for wind turbine generators during sandstorms. The risk warning method for wind turbine generators during sandstorms may include: Step S10: Collect multi-source meteorological data within the target area, and determine whether the preset sandstorm warning conditions are triggered based on the multi-source meteorological data; The target area refers to the wind farm, which contains multiple wind turbine generators. The target area can be understood as the region requiring risk warnings for these wind turbine generators during sandstorms. In practical applications, multi-source meteorological data can be acquired through various meteorological sensors deployed in and around the wind farm, such as anemometers, wind vanes, barometers, hygrometers, visibility meters, and PM10 (particulate matter suspended in the air with an aerodynamic equivalent diameter of no more than 10 micrometers) concentration monitors. These sensors can monitor and transmit meteorological parameters in real time. Determining whether sandstorm warning conditions are triggered can employ pre-set simple rules; for example, a sandstorm warning is considered triggered when the monitored wind speed consistently exceeds a certain fixed threshold.

[0036] Step S20: In response to triggering the sandstorm warning conditions, based on multi-source meteorological data and the digital elevation model of the target area, the movement of sand and dust is simulated, and the sand and dust deposition distribution information in the target area is predicted. In this embodiment, the movement of dust storms can be simulated based on the aforementioned multi-source meteorological data and the digital elevation model of the target area, thereby predicting the dust deposition distribution information within the target area. When simulating dust movement, simplified physical models can be used, such as empirical formulas or statistical models, to estimate dust diffusion and deposition. For example, a simple regression model can be established based on historical dust storm event data, inputting current meteorological data such as wind speed and direction, and combining this with the average altitude of the target area to roughly predict the potential impact range and approximate deposition intensity of the dust storm. The digital elevation model can be used as a background map to visualize the predicted path of the dust storm.

[0037] Step S30: Based on the preset wind turbine generator distribution map and dust deposition distribution information, determine at least one generator unit to be protected. The wind turbine distribution map can be a simple two-dimensional plan view, showing the geographical coordinates of all turbines within the wind farm. For example, when identifying turbines to be protected, a direct comparison method can be used. For instance, predicted dust deposition distribution information (e.g., a general dust impact area) can be directly overlaid onto the wind turbine distribution map. Any turbine whose location falls within this dust impact area is identified as a turbine to be protected. It should be noted that this method does not consider specific differences in dust concentration; any turbine located within the impact area is considered a risk turbine.

[0038] Step S40: Implement a protection control strategy for the generator unit to be protected based on multi-source meteorological data and dust deposition distribution information. The protection control strategy includes at least reducing operating power and adjusting blade angle.

[0039] Finally, based on the obtained multi-source meteorological data and dust deposition distribution information, protective control strategies are implemented for the aforementioned generator sets to be protected. These protective control strategies include at least reducing operating power and adjusting blade angles.

[0040] For example, when implementing a protection and control strategy, a uniform, pre-set strategy can be adopted. For instance, once a generator set is identified as a generator set to be protected, regardless of the specific dust concentration or wind speed it faces, its operating power is uniformly reduced to a fixed percentage of its rated power (e.g., a uniform 20% reduction), and its blade angle is adjusted to a pre-set fixed angle (e.g., uniformly adjusted to 90° perpendicular to the wind direction) to reduce dust impact. It should be noted that this strategy does not dynamically adjust based on the specific dust intensity or direction.

[0041] To facilitate understanding, the following is a more specific example to illustrate the above technical solution in greater detail: Assume a wind farm at location A (the target area) has its wind turbine distribution map pre-stored. On a certain day, the meteorological monitoring system begins collecting multi-source meteorological data for the area. For example, it detects a continuous increase in wind speed and a decrease in visibility. At this point, the system makes a judgment based on preset sandstorm warning conditions. If the preset condition is only "wind speed exceeding 20 meters per second," the system determines that a sandstorm warning has been triggered when the wind speed reaches this threshold.

[0042] In response to the warning, the system begins simulating the movement of the dust storm. For example, based on current wind speed and direction data, combined with a digital elevation model of location A, the system uses a simplified empirical model to predict that the dust storm will enter the wind farm from the northwest and roughly cover the northern area of ​​the wind farm. Thus, the system predicts the dust deposition distribution information for this northern area, for example, a general dust impact range. Next, the system compares the predicted dust impact range with a preset wind turbine distribution map. Assuming the wind farm has 100 turbines, and 30 of them are located within the predicted dust impact range, the system identifies these 30 turbines as those requiring protection. Finally, based on current multi-source meteorological data (e.g., persistent high wind speeds) and dust deposition distribution information (e.g., the dust impact range), the system implements protective control strategies for these 30 turbines requiring protection. Specifically, the system can send commands to these generator sets to uniformly reduce their operating power to 70% of their rated power, and simultaneously adjust the blade angle to a 90° angle with the current wind direction to reduce the direct impact of sand and dust on the blades. Meanwhile, the 70 generator sets located outside the sandstorm's impact area continue to operate at normal power to maintain power output.

[0043] As can be seen from the above examples, the technical solution of this application embodiment can identify sandstorm threats in a timely manner by dynamically collecting meteorological data and making early warning judgments. By combining terrain models to simulate sandstorm movement, the deposition distribution of sandstorms can be predicted, thus providing a basis for subsequent protective measures. Based on sandstorm deposition distribution information and generator set distribution maps, generator sets that need protection can be identified in a targeted manner, avoiding the crude approach of uniformly protecting all units. Finally, by implementing protective control strategies such as reducing operating power and adjusting blade angles for specific units, the power generation capacity of the wind farm is maintained as much as possible while ensuring equipment safety.

[0044] Compared to traditional dust storm protection measures, the technical solution of this application demonstrates significant technological contributions. Traditional methods often rely on macro-meteorological early warnings and adopt a crude strategy of uniform shutdown or post-event maintenance. For example, in the above example, if traditional methods are used, once the wind speed reaches the warning threshold, all 100 generator units in the entire wind farm may be required to shut down, resulting in a huge loss of power generation. This application, by introducing dust movement simulation and sedimentation distribution prediction, can more accurately identify the affected areas and units, for example, identifying only 30 generator units to be protected. This refined risk assessment makes the protective measures more targeted, avoiding over-protection of unaffected units. In addition, by dynamically adjusting the operating power and blade angle of the generator units to be protected, rather than simply shutting them down, this application embodiment can maintain partial power output while ensuring equipment safety, effectively balancing the needs of equipment protection and power generation efficiency. This shift from passive and crude to proactive and refined significantly improves the operational resilience and economic efficiency of wind turbine units during dust storms.

[0045] Furthermore, in a feasible embodiment, the multi-source meteorological data includes at least air pressure, humidity, predicted wind speed, PM10 concentration, and visibility. The step of determining whether to trigger a preset sandstorm warning condition based on the multi-source meteorological data may include: Step S11: If the air pressure value drops by more than a preset value within a preset time and / or the humidity value is lower than a preset humidity threshold, then determine whether the predicted wind speed is greater than a preset wind speed threshold, whether the PM10 concentration is greater than a first preset concentration threshold, and whether the visibility is less than a preset visibility threshold. It should be noted that air pressure refers to the numerical value of atmospheric pressure, usually obtained through air pressure sensors. A rapid drop in air pressure is often one of the early signs of an approaching dust storm. It can be obtained in real-time through sensors at meteorological stations deployed in the target area, or through meteorological satellite data or numerical weather prediction models. Humidity refers to the water vapor content in the air, usually obtained through humidity sensors. During dust storms, the air is typically abnormally dry. It can also be obtained in real-time through sensors at meteorological stations deployed in the target area, or through meteorological satellite data or numerical weather prediction models. Forecasted wind speed refers to the predicted wind speed value for the target area over a future period, usually obtained through meteorological forecasting models. Strong winds are a crucial driving force for the formation and development of dust storms. It can be obtained through short-term weather forecast data issued by meteorological departments, or through calculations using wind speed prediction models within wind farms combined with historical and real-time data. PM10 concentration refers to the mass concentration of particulate matter with a diameter of 10 micrometers or less in the air, usually measured by particulate matter sensors, and is one of the key indicators for assessing the severity of a dust storm. The data can be obtained in real-time through air quality monitoring stations deployed in the target area, or estimated using remote sensing satellite data. Visibility refers to the maximum distance at which a human eye or instrument can clearly identify a target object under specific meteorological conditions. It is typically measured using visibility sensors. Sandstorms cause a sharp increase in the concentration of particulate matter in the air, thus significantly reducing visibility. Visibility can also be obtained in real-time through meteorological station sensors deployed in the target area, or through meteorological observation data from transportation departments or airports.

[0046] Step S12: If at least a preset number of conditions are met among the three conditions of predicted wind speed greater than preset wind speed threshold, PM10 concentration greater than preset concentration threshold, and visibility less than preset visibility threshold, the conditions for triggering a sandstorm warning are determined.

[0047] In this embodiment, monitoring shows that the decrease in air pressure exceeds a preset amplitude and / or the humidity value falls below a preset humidity threshold within a preset time period, aiming to identify early or precursory meteorological characteristics of sandstorms. A rapid drop in air pressure or a significant decrease in humidity are typical signals of an approaching sandstorm. By setting preset time, preset amplitude, and preset humidity thresholds, these meteorological changes can be quantitatively assessed, thereby providing preliminary warnings before a sandstorm fully forms or its impact area expands.

[0048] Only when the air pressure value drops by more than a preset value within a preset time and / or the humidity value is lower than a preset humidity threshold will the comparison of wind speed, PM10 concentration and visibility be performed. This reduces the amount of data processing and eliminates the need to constantly determine whether to trigger a sandstorm warning based on multi-source meteorological data.

[0049] For example, the preset time can be set to 1 hour, the preset amplitude can be set to 5 hPa, and the preset humidity threshold can be set to 20%. The purpose of determining whether the predicted wind speed is greater than the preset wind speed threshold, whether the PM10 concentration is greater than the first preset concentration threshold, and whether the visibility is less than the preset visibility threshold is to further confirm the core meteorological characteristics of the dust storm. Strong winds, high particulate matter concentration, and low visibility are direct manifestations of dust storms. By thresholding these indicators, the intensity and impact of the current dust storm can be quantified. For example, the preset wind speed threshold can be set to 8 m / s, the first preset concentration threshold can be set to 500 μg / m³, and the preset visibility threshold can be set to 1000 meters. If at least a preset number of the above three conditions—predicted wind speed greater than the preset wind speed threshold, PM10 concentration greater than the preset concentration threshold, and visibility less than the preset visibility threshold—are met, the conditions for triggering a dust storm warning are determined. In this embodiment, the introduction of the "preset number of conditions" judgment logic increases the flexibility and robustness of the warning judgment. The characteristics of dust storms can vary by region and intensity, and not all indicators will reach extreme values ​​simultaneously. By allowing warnings to be triggered only when certain conditions are met, missed warnings due to a single indicator failing to meet the standard can be avoided, as well as false alarms caused by overly lenient conditions. For example, the preset quantity can be set to 2, meaning that a warning can be triggered if any two of the above three conditions are met.

[0050] This application embodiment introduces multi-source meteorological data and constructs a multi-stage, multi-indicator judgment logic to achieve precise triggering of sandstorm warning conditions. Specifically, firstly, multi-source meteorological data within the target area is collected. This data includes at least air pressure, humidity, predicted wind speed, PM10 concentration, and visibility. This meteorological data covers key physical indicators of sandstorm occurrence, providing a comprehensive information foundation for subsequent accurate judgment. When determining whether to trigger the preset sandstorm warning conditions, the method employs a hierarchical and progressive judgment mechanism. First, the system monitors whether the decrease in air pressure within a preset time exceeds a preset range, and / or whether the humidity is below a preset humidity threshold. This preliminary judgment aims to capture early or precursory meteorological characteristics of sandstorm arrival. For example, a sudden drop in air pressure often indicates the occurrence of severe convective weather, while extremely low humidity is an important environmental condition for sandstorm formation. Through this stage of judgment, potential sandstorm risks can be effectively preliminarily screened, reducing the computational load of subsequent complex judgments and providing the possibility of obtaining a longer warning time.

[0051] In one feasible embodiment, such as Figure 2 As shown, the step of simulating the movement of dust storms and predicting the dust deposition distribution information within the target area based on multi-source meteorological data and a digital elevation model of the target area may include: Step S21: Based on the digital elevation model and the coordinate information of each wind turbine generator in the target area, construct a CFD simulation model; Step S22: Use the wind speed and wind direction information from the multi-source meteorological data as the boundary conditions of the CFD simulation model to obtain the physical property parameters of the dust particles. Step S23: Based on the CFD simulation model, boundary conditions, and physical property parameters, simulate the movement trajectory of dust particles in the target area and the dust deposition distribution information in the target area.

[0052] A digital elevation model (DEM) is a model that represents ground elevation information in digital form, accurately describing the undulating shape of the Earth's surface. Its function is to provide topographic data, serving as the basis for considering the influence of terrain when simulating dust storm movements. DEMs can be obtained in various ways. For example, high-precision mapping of the target area can be performed using LiDAR scanning technology to generate detailed 3D point cloud data, which can then be used to construct the DEM. Alternatively, aerial photogrammetry or satellite remote sensing imagery data can be processed in conjunction with ground control points to extract surface elevation information for establishing the DEM.

[0053] The coordinate information of each wind turbine in the target area refers to the precise location data of the wind turbine in geographic space, usually expressed in the form of latitude and longitude or projected coordinates. This information is crucial for accurately identifying the location of the turbine in the simulation model in order to assess the specific impact of dust on it. Obtaining this coordinate information can include: directly obtaining its precise geographic coordinates by conducting on-site measurements of each wind turbine using positioning system equipment; or, extracting existing turbine location data from wind farm planning and design drawings or Geographic Information System (GIS) databases. CFD (Computational Fluid Dynamics) simulation models are tools that use numerical methods and computers to simulate physical phenomena such as fluid flow, heat transfer, and mass transfer. In dust simulation, CFD simulation models can accurately simulate airflow fields and the movement of dust particles within them. CFD simulation models can be constructed in various ways; for example, commercial CFD software can be used, with its built-in physical models and solvers for setup and calculation; or, customized development can be based on an open-source CFD platform to meet the dust simulation requirements of this application embodiment.

[0054] Wind speed and direction information from multi-source meteorological data are key meteorological parameters describing airflow states. As boundary conditions in CFD simulation models, they directly determine the initial movement trend and path of dust particles. Boundary conditions refer to the constraints imposed on physical quantities (such as velocity, pressure, and temperature) at the boundaries of the computational domain in a CFD simulation model. In dust simulation, wind speed and direction, as boundary conditions, define the external environment when dust enters or leaves the simulation area. Setting boundary conditions can include directly inputting measured or predicted wind speed and direction data as the velocity profile at the inlet boundary into the CFD model, and specifying the outlet boundary as a pressure outlet. It should be noted that the physical properties of dust particles refer to the inherent attributes that affect the movement and settling characteristics of dust particles in the air, such as particle size distribution, density, and shape factor. These physical properties play a crucial role in accurately simulating the movement trajectory and settling process of dust. There are various methods to obtain the physical property parameters of dust particles. For example, laboratory analysis of actual dust samples can be performed, using a laser particle size analyzer to measure the particle size distribution and a densitometer to measure the particle density. Alternatively, the average particle size and density range of dust particles can be estimated by inverting remote sensing data or referring to observation data of historical dust storm events.

[0055] Specifically, simulating the trajectory of dust particles in the target area refers to predicting the three-dimensional spatial movement path of single or multiple dust particles under the influence of a wind field using computational fluid dynamics methods. This helps in understanding how dust diffuses and is transported by airflow. For example, this simulation can be implemented by: employing the Lagrangian particle tracking method, treating dust particles as a discrete phase, and calculating the motion equations of each particle under the influence of fluid forces, gravity, etc.; or by using an Eulerian multiphase flow model, treating dust particles as a continuous phase, and describing their overall motion by solving the momentum and mass conservation equations for the particle phase.

[0056] Dust deposition distribution information refers to the spatial distribution of dust particles ultimately deposited on the Earth's surface within a target area. It is typically expressed as the mass of dust per unit area or the number of particles, and is one of the important parameters for assessing the impact of dust on wind turbine generators. For example, obtaining dust deposition distribution information can include: after a CFD simulation, statistically analyzing the amount of dust particles deposited within each grid cell and visualizing it as a dust concentration map or deposition thickness map; or, calculating the deposition velocity and residence time of dust particles in the air, combined with wind field distribution, to predict dust accumulation in different areas.

[0057] The method described in this application integrates multiple factors such as topography, meteorology, and the characteristics of dust particles themselves. Through numerical calculations, it accurately tracks the movement path of dust particles and predicts their final deposition location and concentration distribution. This approach enables the prediction of highly accurate dust deposition distribution information, providing a reliable data foundation for subsequently identifying generator units to be protected and implementing protective control strategies. This refined simulation method significantly improves the accuracy of dust deposition prediction, allowing the entire risk warning method to more effectively identify high-risk areas and formulate more targeted protection strategies. It avoids the problems of inaccurate predictions leading to crude or ineffective protective measures in traditional methods.

[0058] In one feasible embodiment, the dust deposition distribution information includes at least dust concentration information, and the step of determining at least one generator set to be protected based on a preset wind turbine generator set distribution map and dust deposition distribution information may include: Step S31: Based on the dust deposition distribution information, areas in the target area where the dust concentration is higher than the preset concentration threshold are identified as risk areas; Dust deposition distribution information refers to the spatial distribution data of dust within a target area obtained through simulation or monitoring. Its core function is to quantify the impact of dust storms on different locations within the target area. Dust concentration information is a key component of this distribution information, specifically referring to the mass or quantity of dust particles contained in a unit volume of air, usually expressed as PM10 concentration (the number of micrograms of particulate matter with a diameter of less than 10 micrometers per cubic meter of air). This information provides an objective and quantitative basis for subsequent risk assessment, making the identification of risk areas no longer reliant on vague qualitative judgments. It can originate from the output of computational fluid dynamics (CFD) simulation models, or be obtained in real-time through the deployment of multiple dust monitoring sensors within the target area, or be estimated by combining satellite remote sensing data. A preset concentration threshold is a pre-set critical value for dust concentration used to distinguish the degree of dust hazard. When the dust concentration in a certain area exceeds this threshold, the area is considered to have a high dust risk. Specifically, this threshold can be set based on historical dust storm data, wind turbine tolerance standards, industry regulations, or expert experience. For example, it can be set at a PM10 concentration of 500 micrograms per cubic meter, or different thresholds can be set according to the sensitivity of different components. A risk area refers to the geographical range within the target area where the dust concentration exceeds the preset concentration threshold.

[0059] Step S32: Based on the wind turbine distribution map, identify the wind turbines located in the risk area as the turbines to be protected.

[0060] Specifically, risk areas can be identified by overlaying dust concentration data with the geographic coordinates of the target area using Geographic Information System (GIS) software, automatically drawing the boundaries of high-concentration areas. A wind turbine distribution map displays the precise geographical location and layout of all wind turbines within the target area, providing spatial positioning information and enabling the association of risk areas with specific turbines. Turbines requiring protection are those identified as being located in high-dust-risk areas based on dust deposition distribution information and the wind turbine distribution map, requiring specific protective measures.

[0061] Identifying the wind turbines to be protected from among the wind turbines in the target area is a crucial step in achieving targeted protection, avoiding indiscriminate protection for all turbines. For example, this identification process can be achieved through spatial analysis, such as determining whether the center point of the wind turbine or its coverage area overlaps with an identified risk area.

[0062] This application's embodiments combine the geographical location information of the wind turbine units, directly mapping risk areas to specific units. This ensures that the selection of protection targets is spatially targeted, thereby optimizing resource allocation, avoiding interference with low-risk units, and improving overall protection efficiency. The above scheme, combined with dust deposition distribution information predicted by the dust simulation module, transforms the abstract dust distribution data from the simulation results into quantifiable risk areas with geospatial attributes. This allows for precise location of affected wind turbine units, enabling subsequent protection and control strategies to be implemented more effectively and improving protection outcomes.

[0063] Furthermore, in one feasible embodiment, such as Figure 3 As shown, the step of identifying wind turbine generators located in risk areas as generators to be protected based on the wind turbine generator distribution map may include: Step S321: Extract the coverage area of ​​each wind turbine generator set from the wind turbine generator set distribution map; The coverage area refers to the spatial range actually occupied or affected by each wind turbine. This area can be determined by calculating a circular region based on the turbine's base location and blade rotation radius to simulate its maximum operating range; or by defining a polygonal region based on the turbine's actual footprint and safety distance to more accurately reflect its physical boundaries. The purpose of the coverage area is to perform spatial overlay analysis with the risk area, thereby quantifying the degree to which the turbine is affected by dust storms.

[0064] Step S322: Identify the risky generator sets whose coverage areas overlap with the risk areas among the various wind turbine generator sets; Step S323: Calculate the ratio of the overlap area between the coverage area and the risk area of ​​each risk generator set to the coverage area, and obtain the risk score corresponding to each risk generator set. The overlapping area refers to the spatially shared portion of the wind turbine's coverage area and the risk area. This area can be calculated using the spatial analysis functions of a Geographic Information System (GIS), such as performing an intersection analysis to accurately calculate the intersection area of ​​two polygonal regions; or using pixel-level or grid-level calculation methods to count the number of overlapping units and multiply by the area of ​​each unit. The overlapping area directly reflects the degree of exposure of the turbine to dust storms and is a key indicator for quantifying risk. Furthermore, the risk score quantifies the degree of dust storm risk faced by each wind turbine. This score can be simply calculated as the ratio of the overlapping area to the coverage area, reflecting the proportion of turbines affected; further, it can be weighted by combining other risk factors such as dust concentration and wind speed from multi-source meteorological data to form a more complex comprehensive risk index. The risk score provides a comparable value for screening turbines requiring priority protection.

[0065] Step S324: The generator sets with risk scores greater than the preset score threshold are identified as generator sets to be protected.

[0066] A preset scoring threshold is a critical value used to distinguish between high-risk and low-risk generator units. This threshold can be set based on historical data analysis, expert judgment, or risk tolerance assessment; or it can be optimized by simulating the protection effects and costs under different thresholds. The purpose of a preset scoring threshold (e.g., 0.3) is to filter out generator units that require focused protection based on this threshold, thereby avoiding resource waste or insufficient protection.

[0067] The technical solution of this application introduces a risk scoring mechanism to more accurately quantify the risk exposure level of each wind turbine, thereby optimizing protection decisions and avoiding over- or under-protection caused by simple location judgments. Specifically, when determining the turbines to be protected, the coverage area of ​​each wind turbine is first extracted from the wind turbine distribution map. This step obtains the spatial range information of each turbine, providing basic data for subsequent overlap analysis and ensuring that the assessment is based on the actual geographical distribution. Next, the wind turbines with overlapping coverage areas and risk areas are identified, identifying potential risk objects rather than all turbines located in risk areas. This narrows the assessment scope, focusing on the truly affected equipment. Based on this, the ratio of the overlap area between the coverage area and the risk area of ​​each risk turbine to the coverage area is calculated to obtain a risk score. The risk level is quantified by the area ratio, reflecting the exposure intensity of the turbine, rather than a simple binary judgment. This solves the inaccuracy problem in partially overlapping scenarios. Finally, the wind turbines with risk scores greater than a preset score threshold are identified as turbines to be protected. High-risk turbines are screened based on the preset threshold to ensure that the protection measures are highly targeted and to avoid resource waste or omissions. Through the above steps, the embodiments of this application can closely integrate the information on sand and dust deposition distribution with the actual spatial layout of wind turbine generators, thereby achieving a refined assessment of the risk of individual units and providing a more accurate basis for decision-making in subsequent protection and control strategies.

[0068] In one feasible embodiment, after the step of identifying risky wind turbine generators whose coverage areas overlap with the risk areas, the method may further include: Step S325: Sequentially determine whether the overlapping area between the coverage area of ​​each risk generator set and the risk area includes a preset sensitive component. The sensitive component includes at least one of the following: blades, gearbox, bearing, sensor, and cooling device. Step S326, if included, then the risky generator set is identified as the generator set to be protected.

[0069] In this embodiment, it is determined sequentially whether the overlapping area between the coverage area of ​​each risky generator set and the risk area includes preset sensitive components. The aim is to identify, through refined spatial analysis, whether key vulnerable components inside the wind turbine generator set are directly exposed to the risk of sandstorms. This can be achieved by performing spatial overlay analysis between a detailed three-dimensional model of the wind turbine generator set (containing precise geometric information of each component) and the risk area in the sandstorm deposition distribution information.

[0070] For example, computer-aided design (CAD) models or building information models (BIM) can be used to represent the structure of the generator set, and the spatial query function of a geographic information system (GIS) can be used to determine whether the geometric boundaries of sensitive components intersect with or are contained within the geometric boundaries of the risk area.

[0071] Another feasible implementation is to pre-mark the precise coordinate ranges of sensitive components in the digital twin model of the wind turbine generator, and then compare these coordinate ranges with the geographical coordinate ranges of the dust risk area to determine if there is any overlap. It should be noted that the pre-defined sensitive components refer to key components of the wind turbine generator that are particularly sensitive to dust abrasion, blockage, impact, or corrosion; damage to these components would severely affect the generator's performance, lifespan, or lead to shutdown. The pre-determining of these components can be based on the wind turbine generator's design specifications, historical fault data, operation and maintenance experience, as well as the component's material properties (such as wear resistance and hardness) and operating principles (such as precision fit and heat dissipation requirements).

[0072] In one feasible embodiment, in addition to blades, gearboxes, bearings, sensors, and cooling devices, sensitive components may also include pitch systems, yaw systems, generators, hydraulic system piping, and electrical cabinet seals. If these are included, the generator set at risk is identified as the generator set to be protected, meaning that once a sensitive component of the generator set is found to be within a dust storm risk area, the generator set is prioritized as an object requiring protective measures.

[0073] Furthermore, if a generator set's sensitive components overlap with a risk area, its priority is raised to the "to be protected" level, regardless of its overall risk score. Alternatively, a decision tree or expert system can be used to directly trigger the identification of "generator sets to be protected" by using "whether sensitive components are within a risk area" as a high-weight judgment condition.

[0074] Through the above scheme, even if the overall overlap area between a wind turbine and the risk area is small, if its core vulnerable components happen to be located in a high-risk area for sandstorms, the turbine will be immediately identified and designated as a turbine requiring protection. This judgment mechanism ensures that protection resources are prioritized for the turbines most in need of protection, especially those whose critical components are directly threatened. This avoids protection blind spots that may result from relying solely on the overall overlap area or risk score, allowing protection strategies to be more accurately applied to the most vulnerable aspects during sandstorms.

[0075] In one feasible embodiment, the step of implementing a protection control strategy for the generator unit to be protected based on multi-source meteorological data and dust deposition distribution information may include: Step S41: Analyze the dust deposition distribution information and determine the direction of dust flow; The purpose of this step is to identify the main movement trends and directions of dust within the target area through in-depth analysis of dust deposition distribution data. This provides a precise basis for subsequent wind turbine blade adjustments, ensuring the targeted nature of protective measures. Specifically, spatial interpolation and gradient analysis of dust deposition distribution information can be used to identify the direction of most significant dust concentration changes, thereby inferring the main flow direction of the dust. Furthermore, fluid dynamics models or machine learning algorithms, combined with historical dust deposition data and real-time meteorological data, can be used to predict the trajectory of dust and thus determine its flow direction.

[0076] Step S42: Control the plane of rotation of the wind turbine blades of the generator set to be protected to form a preset angle with the direction of sand and dust flow; Step S43: When the PM10 concentration in the multi-source meteorological data is higher than the second preset concentration threshold, the output power of the generator set to be protected is limited to within the control power range, wherein the upper limit of the control power range is lower than the rated power of the generator set to be protected.

[0077] The purpose of the protection strategy is to reduce the direct impact and wear of dust particles on the windward side of the blades, thereby extending the blades' lifespan and reducing maintenance costs. Specifically, this can be achieved through the coordinated operation of the wind turbine's yaw and pitch systems. The yaw system adjusts the nacelle orientation so that the rotor plane is roughly facing the direction of dust flow, while the pitch system fine-tunes the blade angle to achieve a preset angle (e.g., a value between 30° and 90°). When the PM10 concentration in multi-source meteorological data exceeds a second preset concentration threshold (e.g., 800 μg / m³, where the second preset threshold is greater than the first preset threshold), the output power of the protected turbine is limited to a controlled power range. The upper limit of this controlled power range is lower than the rated power of the protected turbine. This step aims to dynamically adjust the wind turbine's operating status based on the dust concentration. When the PM10 concentration reaches a certain level, limiting the output power reduces the turbine's operating load and blade speed, thereby reducing wear from dust particles on internal and external components and preventing overload operation in harsh environments. The upper limit of the control power range (e.g., 50% to 70% of the rated power) is lower than the rated power in order to maintain a certain power output as much as possible while protecting the equipment. Specifically, the central control system of the wind turbine can receive PM10 concentration data. When the concentration exceeds a second preset concentration threshold, the power limiting mode is automatically triggered, and the output power is controlled within the preset range by adjusting the pitch angle or the generator excitation current.

[0078] In another feasible embodiment, other protective measures are also included for sensitive components such as gearboxes, bearings, sensors, and cooling devices. For example, for the cooling devices of the generator set to be protected, a compressed air purging system can be automatically activated to clean the cooling devices of the wind turbine generator set after the sandstorm warning conditions are triggered.

[0079] Through the above technical solutions, the embodiments of this application effectively solve the problem that traditional protective measures fail to fully consider the direction and specific concentration of sand and dust flow, resulting in insufficient precision in protection and an inability to maximize power generation output while effectively protecting equipment. Specifically, by analyzing sand and dust deposition distribution information to determine the direction of sand and dust flow, a precise basis is provided for blade adjustment, avoiding blind operation; by controlling the rotation plane of the wind turbine blades to form a preset angle with the direction of sand and dust flow, the direct impact and wear of sand and dust on the blades can be effectively reduced, extending equipment life; at the same time, the output power of the generator set is dynamically limited based on the PM10 concentration in multi-source meteorological data, allowing the protection strategy to be adjusted according to the actual degree of harm from sand and dust, avoiding equipment overload damage under high concentrations of sand and dust, while maintaining some power generation capacity when the risk is controllable. Overall, the embodiments of this application achieve precise and dynamic protection for wind turbine generator sets, effectively protecting equipment while meeting power generation needs during sandstorms, significantly improving the operational reliability and economic benefits of wind farms.

[0080] This application also provides a wind turbine generator risk early warning device, such as... Figure 4 As shown, the wind turbine generator risk early warning device includes: The condition triggering module 10 is used to collect multi-source meteorological data within the target area and determine whether to trigger the preset sandstorm warning conditions based on the multi-source meteorological data. The dust simulation module 20 is used to simulate the movement of dust and predict the dust deposition distribution information within the target area in response to the triggering of the dust storm warning conditions, based on the multi-source meteorological data and the digital elevation model of the target area. The protection determination module 30 is used to determine at least one generator set to be protected based on the preset wind turbine generator set distribution map and the sand and dust deposition distribution information. The strategy execution module 40 is used to execute a protection control strategy on the generator set to be protected based on the multi-source meteorological data and the dust deposition distribution information, wherein the protection control strategy includes at least reducing the operating power and adjusting the blade angle.

[0081] The wind turbine generator risk warning device provided in this application adopts the wind turbine generator risk warning method under sandstorm weather described in the above embodiments, which can improve the protection effect of wind turbine generators under sandstorm weather and ensure power generation output. Compared with the prior art, the beneficial effects of the wind turbine generator risk warning device provided in this application are the same as the beneficial effects of the wind turbine generator risk warning method under sandstorm weather provided in the above embodiments, and other technical features in the wind turbine generator risk warning device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0082] This application also provides a wind turbine generator risk warning device, which includes at least: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the wind turbine generator risk warning method under sandstorm weather described in the above embodiments.

[0083] The following is for reference. Figure 5 It shows a structural schematic diagram of a wind turbine generator risk warning device suitable for implementing the embodiments of this application. Figure 5 The wind turbine generator risk warning device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0084] like Figure 5As shown, the wind turbine generator risk warning device may include a processing unit 1001 (e.g., a central processing unit, a graphics processor, etc.), 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 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the wind turbine generator risk warning device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the wind turbine risk warning device to communicate wirelessly or wiredly with other devices to exchange data. Although the figure shows a wind turbine risk warning device with various systems, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems can be implemented alternatively.

[0085] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments of this application.

[0086] The wind turbine generator risk warning device provided in this application adopts the wind turbine generator risk warning method under sandstorm weather described in the above embodiments, which can improve the protection effect of wind turbine generators under sandstorm weather and ensure power generation output. Compared with the prior art, the beneficial effects of the wind turbine generator risk warning device provided in this application are the same as the beneficial effects of the wind turbine generator risk warning method under sandstorm weather provided in the above embodiments, and other technical features in the wind turbine generator risk warning device are the same as the features disclosed in the methods of the above embodiments, and will not be repeated here.

[0087] It should be understood that various parts of the embodiments of this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0088] The above description is merely a specific implementation of the embodiments of this application, but the protection scope of the embodiments of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the protection scope of the embodiments of this application. Therefore, the protection scope of the embodiments of this application should be determined by the protection scope of the above claims.

[0089] This application also provides a computer-readable storage medium storing a computer program that can run on a processor. The computer program is used to execute the wind turbine generator risk warning method under sandstorm weather described in the above embodiments.

[0090] The computer-readable storage medium provided in this application embodiment may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0091] The aforementioned computer-readable storage medium may be included in the wind turbine generator risk warning device; or it may exist independently and not be installed in the wind turbine generator risk warning device.

[0092] The aforementioned computer-readable storage medium carries one or more programs. When these programs are executed by the wind turbine generator risk warning device, the wind turbine generator risk warning device performs the following actions: collecting multi-source meteorological data within a target area; determining whether a preset sandstorm warning condition is triggered based on the multi-source meteorological data; responding to the triggering of the sandstorm warning condition; simulating the movement of sand and dust based on the multi-source meteorological data and a digital elevation model of the target area; predicting the sand and dust deposition distribution information within the target area; identifying at least one generator unit to be protected based on a preset wind turbine generator distribution map and the sand and dust deposition distribution information; and implementing a protection control strategy for the generator unit to be protected based on the multi-source meteorological data and the sand and dust deposition distribution information, wherein the protection control strategy includes at least reducing operating power and adjusting blade angle.

[0093] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via 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., via the Internet using an Internet service provider).

[0094] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0095] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0096] The computer-readable storage medium provided in this application embodiment stores computer-readable program instructions for executing the aforementioned risk warning method for wind turbine generator sets during sandstorms, which can improve the protection effect of wind turbine generator sets during sandstorms and ensure power generation output. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application embodiment are the same as the beneficial effects of the risk warning method for wind turbine generator sets during sandstorms provided in the above embodiments, and will not be repeated here.

[0097] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described method for risk warning of wind turbine generator sets during sandstorm weather.

[0098] The computer program product provided in this application can improve the protection effect of wind turbine generators during sandstorms and ensure power generation output. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as the beneficial effects of the wind turbine generator risk warning method under sandstorms provided in the above embodiments, and will not be repeated here.

[0099] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent scope of this application.

Claims

1. A method for risk early warning of wind turbine generators under sandstorm weather, characterized in that, The risk warning method for wind turbine generators during sandstorms includes: Collect multi-source meteorological data within the target area, and determine whether the preset sandstorm warning conditions are triggered based on the multi-source meteorological data; In response to triggering the dust storm warning conditions, based on the multi-source meteorological data and the digital elevation model of the target area, the movement of dust is simulated to predict the dust deposition distribution information within the target area; Based on the preset wind turbine generator distribution map and the dust deposition distribution information, at least one generator unit to be protected is identified. Based on the multi-source meteorological data and the dust deposition distribution information, a protection control strategy is implemented for the generator set to be protected, wherein the protection control strategy includes at least reducing operating power and adjusting blade angle.

2. The risk warning method for wind turbine generator sets under sandstorm weather as described in claim 1, characterized in that, The multi-source meteorological data includes at least air pressure, humidity, predicted wind speed, PM10 concentration, and visibility. The step of determining whether to trigger a preset sandstorm warning condition based on the multi-source meteorological data includes: If the air pressure value is found to decrease by more than a preset value within a preset time and / or the humidity value is found to be lower than a preset humidity threshold, then it is determined whether the predicted wind speed is greater than a preset wind speed threshold, whether the PM10 concentration is greater than a first preset concentration threshold, and whether the visibility is less than a preset visibility threshold. If at least a preset number of conditions are met among the three conditions—the predicted wind speed being greater than a preset wind speed threshold, the PM10 concentration being greater than a preset concentration threshold, and the visibility being less than a preset visibility threshold—then the conditions for triggering the sandstorm warning are determined.

3. The risk warning method for wind turbine generators under sandstorm weather as described in claim 1, characterized in that, The step of simulating the movement of dust storms and predicting the dust deposition distribution information within the target area based on the multi-source meteorological data and the digital elevation model of the target area includes: Based on the digital elevation model and the coordinate information of each wind turbine in the target area, a CFD simulation model is constructed. The wind speed and wind direction information from the multi-source meteorological data are used as the boundary conditions of the CFD simulation model to obtain the physical property parameters of the dust particles. Based on the CFD simulation model, the boundary conditions, and the physical property parameters, the motion trajectory of dust particles in the target area and the dust deposition distribution information in the target area are simulated.

4. The risk warning method for wind turbine generator sets under sandstorm weather as described in claim 1, characterized in that, The dust deposition distribution information includes at least dust concentration information. The step of determining at least one generator set to be protected based on the preset wind turbine generator set distribution map and the dust deposition distribution information includes: Based on the dust deposition distribution information, areas in the target region where the dust concentration is higher than a preset concentration threshold are identified as risk areas; Based on the wind turbine distribution map, the wind turbines located in the risk area are identified as turbines to be protected.

5. The risk warning method for wind turbine generator sets under sandstorm weather as described in claim 4, characterized in that, The step of identifying wind turbine generators located in risk areas as generators to be protected based on the wind turbine generator distribution map includes: Extract the coverage area of ​​each wind turbine generator set from the wind turbine generator set distribution map; Identify the risky wind turbine generators whose coverage areas overlap with the risk areas. Calculate the ratio of the overlap area between the coverage area and the risk area of ​​each risk generator set to the coverage area, and obtain the risk score corresponding to each risk generator set; Generator sets with risk scores greater than a preset score threshold are identified as generator sets to be protected.

6. The risk warning method for wind turbine generator sets under sandstorm weather as described in claim 5, characterized in that, After the step of identifying risky wind turbine generators whose coverage areas overlap with the risk areas, the method further includes: The overlapping area between the coverage area and the risk area of ​​each risk generator set is determined sequentially to include a preset sensitive component, wherein the sensitive component includes at least one of blades, gearbox, bearing, sensor and cooling device; If included, the risky generator set is identified as the generator set to be protected.

7. The method for risk warning of wind turbine generators under sandstorm weather as described in any one of claims 1 to 6, characterized in that, The step of implementing a protection control strategy for the generator set to be protected based on the multi-source meteorological data and the dust deposition distribution information includes: Analyze the dust deposition distribution information to determine the direction of dust flow; The plane of rotation of the wind turbine blades of the generator set to be protected is controlled to form a preset angle with the direction of sand and dust flow; When the PM10 concentration in the multi-source meteorological data is higher than the second preset concentration threshold, the output power of the generator set to be protected is limited to a control power range, wherein the upper limit of the control power range is lower than the rated power of the generator set to be protected.

8. A risk early warning device for wind turbine generator sets, characterized in that, The wind turbine generator risk early warning device includes: The condition triggering module is used to collect multi-source meteorological data within the target area and determine whether to trigger the preset sandstorm warning conditions based on the multi-source meteorological data. The dust simulation module is used to simulate the movement of dust in response to the triggering of the dust storm warning conditions, based on the multi-source meteorological data and the digital elevation model of the target area, and to predict the dust deposition distribution information within the target area. The protection determination module is used to determine at least one generator set to be protected based on a preset wind turbine generator set distribution map and the dust deposition distribution information. The strategy execution module is used to execute a protection control strategy on the generator set to be protected based on the multi-source meteorological data and the dust deposition distribution information, wherein the protection control strategy includes at least reducing the operating power and adjusting the blade angle.

9. A risk early warning device for wind turbine generator sets, characterized in that, The wind turbine generator risk warning device includes at least: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the wind turbine generator risk warning method under sandstorm weather as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program for implementing a risk warning method for wind turbine generators under sandstorm weather. The program for implementing the risk warning method for wind turbine generators under sandstorm weather is executed by a processor to implement the steps of the risk warning method for wind turbine generators under sandstorm weather as described in any one of claims 1 to 7.

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