Congelation-thunder and lightning composite risk assessment method for mountain wind power plant and related device
By performing altitude correction on historical freezing and lightning data of mountain wind farms, the combined risk index of freezing and lightning is calculated, which solves the problem that the combined effect was not considered in the existing technology, and realizes more accurate risk assessment and safe operation guidance.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-26
- Publication Date
- 2026-03-10
AI Technical Summary
Existing technologies fail to effectively consider the combined effects of freezing and lightning disasters, resulting in inaccurate risk assessments of mountain wind farms and failing to provide effective guidance for safe operation.
By acquiring historical freezing and lightning data, combining them with digital elevation models (DEMs) for altitude correction, calculating freezing and lightning risk indices, and constructing a freezing-lightning composite risk index, the enhanced effect of icing on the probability of lightning strikes is considered, thereby achieving more precise risk assessment.
It improves the accuracy and spatial precision of disaster risk assessment for mountain wind farms, provides differentiated site selection recommendations, reduces operating costs and losses, and enhances the safe operation capability of wind farms.
Smart Images
Figure CN121638883A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of wind farm disaster risk assessment technology, and specifically relates to a method and related device for assessing the combined risk of freezing and lightning in mountain wind farms. Background Technology
[0002] Mountain wind farms are typically located in mountainous areas with complex terrain and high altitudes, aiming to generate electricity from abundant wind energy resources. As an important component of the renewable energy sector, they have been widely used globally. However, due to the unique geographical location and climatic conditions, mountain wind farms face numerous challenges from natural disasters during operation, among which freezing and lightning disasters are particularly prominent. Freezing disasters mainly occur in low-temperature and high-humidity environments, causing ice to form on the surface of wind turbine equipment (such as blades and towers), affecting the normal operation of the equipment and power generation efficiency. Lightning disasters can directly strike wind turbine equipment, causing equipment damage or even serious consequences such as fires. Adding to the complexity, freezing and lightning disasters often occur simultaneously or sequentially, forming a compound disaster effect, further exacerbating the operational risks of wind farms.
[0003] Currently, in the practical application of mountain wind farms, the assessment of freezing and lightning disaster risks often considers either freezing or lightning disasters in isolation, without taking into account the combined effects between them. This leads to inaccurate disaster risk assessment results and fails to provide effective guidance for the safe operation of wind farms. For example, the increased conductivity of blades after icing significantly increases their probability of being struck by lightning, sometimes by as much as 2-3 times. Secondly, freezing is more severe in high-altitude areas due to lower temperatures and higher humidity, and lightning activity is also more frequent. However, existing assessment methods often fail to accurately reflect the impact of these altitude differences on freezing and lightning disasters, resulting in biased assessment results. Summary of the Invention
[0004] To address the technical problems existing in the prior art, this invention provides a method and related apparatus for assessing the combined risk of freezing and lightning in mountainous wind farms. This solves the technical problem that the prior art does not consider the combined effect between freezing and lightning disasters in its assessment of freezing and lightning disasters, resulting in inaccurate disaster risk assessment results and failing to provide effective guidance for the safe operation of wind farms.
[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows: This invention provides a method for assessing the combined risk of freezing and lightning strikes in mountain wind farms, including: Obtain historical freezing data, historical lightning data, and DEM data of the wind farm to be evaluated; Based on the DEM data of the wind farm to be evaluated, the historical freezing data and historical lightning data of the wind farm to be evaluated are respectively adjusted for altitude to obtain the adjusted historical freezing data and adjusted historical lightning data. Based on the corrected historical freezing data, the freezing risk index was calculated; based on the corrected historical lightning data, the lightning risk index was calculated. Based on the freezing risk index and the lightning wind power index, the freezing-lightning composite risk index of the wind farm to be evaluated is calculated. The combined risk index of freezing and lightning at the wind farm to be evaluated is compared with the preset combined risk index threshold to obtain the combined risk assessment result of freezing and lightning at the wind farm to be evaluated.
[0006] Furthermore, the historical freezing data of the wind farm to be evaluated includes the average number of freezing days per year and the average ice thickness at different altitudes of the wind farm to be evaluated. Historical lightning data for the wind farm to be evaluated includes the average annual number of lightning strikes and the magnitude of lightning current at different altitudes of the wind farm to be evaluated.
[0007] Furthermore, based on the DEM data of the wind farm to be evaluated, the historical freezing and lightning data of the wind farm to be evaluated are respectively subjected to elevation correction to obtain the corrected historical freezing and lightning data, as follows: Based on the DEM data of the wind farm to be evaluated, the annual average number of freezing days at different altitudes of the wind farm to be evaluated is corrected by altitude to obtain the corrected annual average number of freezing days at different altitudes. The calculation process for the annual average number of days with freezing temperatures at different altitudes after correction is as follows:
[0008] in, This represents the corrected annual average number of days with freezing conditions at different altitudes. For reference altitude Number of days of freezing at the location; The altitude-dependent coefficient for freezing and frost formation; This is the current altitude; For reference altitude; Based on the DEM data of the wind farm to be evaluated, the annual average number of lightning strikes at different altitudes of the wind farm to be evaluated is corrected for altitude, and the corrected annual average number of lightning strikes at different altitudes is obtained. The calculation process for the corrected annual average number of lightning strikes at different altitudes is as follows:
[0009] in, The corrected annual average number of lightning strikes at different altitudes; For reference altitude Number of lightning strikes at the location; The altitude response coefficient for lightning; This is the current altitude; For reference altitude.
[0010] Furthermore, based on the corrected historical freezing data, the process of calculating the freezing risk index is as follows:
[0011] in, This is the freezing risk index; This represents the corrected annual average number of days with freezing conditions at different altitudes. This represents the maximum average number of days of freezing weather per year. The average ice thickness at different altitudes; This represents the maximum average icing thickness.
[0012] Furthermore, based on the corrected historical lightning data, the process of calculating the lightning risk index is as follows:
[0013] in, This is the freezing risk index; The corrected annual average number of lightning strikes at different altitudes; This represents the maximum average number of lightning strikes per year. The amplitude of lightning current at different altitudes; This represents the maximum lightning current amplitude.
[0014] Furthermore, based on the freezing risk index and the lightning wind power index, the process of calculating the freezing-lightning composite risk index of the wind farm to be evaluated is as follows:
[0015] in, The combined risk index of freezing and lightning for the wind farm to be evaluated; , and All are weighting coefficients; This is the freezing risk index; Lightning risk index; The percentage of times that freezing days and lightning occurred simultaneously within a historical period is preset for the wind farm to be evaluated. This is a quantitative indicator of the enhancing effect of icing on the probability of lightning strikes.
[0016] This invention also provides a combined risk assessment system for freezing and lightning strikes in mountain wind farms, comprising: The data acquisition module is used to acquire historical freezing data, historical lightning data, and DEM data of the wind farm to be evaluated. The terrain correction module is used to perform elevation correction on the historical freezing data and historical lightning data of the wind farm to be evaluated based on the DEM data of the wind farm to be evaluated, so as to obtain the corrected historical freezing data and corrected historical lightning data. The single-disaster risk index calculation module is used to calculate the freezing risk index based on corrected historical freezing data and the lightning risk index based on corrected historical lightning data. The composite risk index calculation module is used to calculate the frost-lightning composite risk index of the wind farm to be evaluated based on the frost risk index and the lightning wind power index. The composite risk assessment module is used to compare the condensation-lightning composite risk index of the wind farm to be assessed with the preset composite risk index threshold to obtain the condensation-lightning composite risk assessment result of the wind farm to be assessed.
[0017] The present invention also provides an electronic device, comprising: A processor is used to execute computer programs; A computer-readable storage medium storing a computer program, which, when executed by the processor, performs the aforementioned method for assessing the combined risk of freezing and lightning strikes in mountainous wind farms.
[0018] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the aforementioned method for assessing the combined risk of freezing and lightning in mountain wind farms.
[0019] The present invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the aforementioned method for assessing the combined risk of freezing and lightning in mountain wind farms.
[0020] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention provides a combined risk assessment of freezing and lightning in mountain wind farms. By constructing a combined freezing and lightning risk index for the wind farm under assessment based on a freezing risk index and a lightning wind power index, it overcomes the limitations of traditional assessments that only consider freezing or lightning disasters individually. It can quantify the synergistic effect of freezing and lightning to accurately reflect the increased probability of lightning strikes due to icing, which is more in line with the actual disaster characteristics of mountain wind farms, making the assessment results more accurate and providing more effective guidance for the safe operation of wind farms. Secondly, based on the DEM data of the wind farm under assessment, the freezing and lightning parameters are corrected, which effectively solves the problem of regional risk differences caused by mountainous terrain, improves the spatial accuracy of the assessment, and can more accurately grasp the risk status of different altitude areas. It can provide differentiated site selection suggestions, which helps to reduce the combined risk of freezing and lightning from the source of wind farm construction, ensure the long-term stable operation of wind farms, and reduce operating costs and losses.
[0021] The mountain wind farm freezing-lightning combined risk assessment system, electronic equipment, computer-readable storage medium and computer program product provided by the present invention have all the advantages of the above-mentioned mountain wind farm freezing-lightning combined risk assessment method. Attached Figure Description
[0022] 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, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0023] Figure 1 A flowchart of the combined risk assessment method for freezing-lightning in mountain wind farms provided in Example 1; Figure 2 This is a structural block diagram of the mountain wind farm freezing-lightning composite risk assessment system provided in Example 2; Figure 3 This is a structural block diagram of the electronic device provided in Example 3. Detailed Implementation
[0024] To make the technical problems, technical solutions, and beneficial effects solved by this application clearer, 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 only a part of the embodiments of this application, and not all of them. 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.
[0025] This invention provides a method for assessing the combined risk of freezing and lightning strikes in mountain wind farms, comprising the following steps: Step 100: Obtain historical freezing data, historical lightning data, and DEM data of the wind farm to be evaluated.
[0026] Step 200: Based on the DEM data of the wind farm to be evaluated, perform elevation correction on the historical freezing data and historical lightning data of the wind farm to be evaluated to obtain the corrected historical freezing data and corrected historical lightning data.
[0027] Step 300: Calculate the freezing risk index based on the corrected historical freezing data; calculate the lightning risk index based on the corrected historical lightning data.
[0028] Step 400: Based on the freezing risk index and the lightning wind power index, calculate the freezing-lightning composite risk index of the wind farm to be evaluated.
[0029] Step 500: Compare the condensation-lightning composite risk index of the wind farm to be evaluated with the preset composite risk index threshold to obtain the condensation-lightning composite risk assessment result of the wind farm to be evaluated.
[0030] In the above implementation, altitude correction is performed on historical freezing and lightning data based on DEM data, which can more accurately reflect the freezing and lightning disaster situation at different altitudes. Freezing risk index and lightning risk index are calculated separately, and then the freezing-lightning composite risk index is obtained. The evaluation result is obtained by comparing it with the preset threshold. The composite effect of freezing and lightning disasters and altitude factors are fully considered, which effectively improves the accuracy of disaster risk assessment of mountain wind farms and provides reliable and effective guidance for the safe operation of wind farms.
[0031] The following specific embodiments further explain the method for assessing the combined risk of freezing and lightning in mountain wind farms provided by this invention: Example 1 As attached Figure 1 As shown in Example 1, this method provides a combined risk assessment method for freezing-lightning in mountain wind farms, including the following steps: Step 1: Obtain historical freezing data, historical lightning data, and DEM data of the wind farm to be evaluated.
[0032] Historical freezing data for the wind farm to be evaluated includes the average number of freezing days and the average ice thickness at different altitudes. Specifically, the historical freezing data for the wind farm to be evaluated is extracted from meteorological observations or reanalysis data related to freezing phenomena recorded over a historical timescale. Preferably, the historical timescale is a 30-year timescale. The number of freezing days is the cumulative number of days with a minimum daily temperature of 0°C or less and a relative humidity of 85% or more, used to characterize the frequency of freezing. The average ice thickness can be measured by mechanical sensors or image recognition devices on the wind tower, used to reflect the freezing intensity.
[0033] Historical lightning data for the wind farm to be evaluated includes the average annual number of lightning strikes and the magnitude of lightning current at different altitudes of the wind farm to be evaluated. Specifically, historical lightning data is collected through a Lightning Location System (LLS) or on-site electric field probes to quantify the spatiotemporal density and energy level of lightning activity.
[0034] The DEM data of the wind farm to be evaluated refers to the three-dimensional terrain information provided by the Digital Elevation Model (DEM) to obtain the precise elevation values of each spatial location within the wind farm area, which can accurately reflect the micro-topographic features under the complex mountainous terrain. The spatial resolution can be selected from 10m×10m to 90m×90m, and it is sourced from public remote sensing databases (such as SRTM and ASTER GDEM). It is used to extract the actual elevation, slope, aspect and other terrain parameters of each wind turbine location, and to support the subsequent elevation correction process. In particular, by matching the geographic coordinates of historical freezing and lightning observation points with the DEM grid, the elevation of the corresponding location can be extracted as the basic input parameter for subsequent elevation correction.
[0035] It should be noted that historical freezing data, historical lightning data, and DEM data of the wind farm to be evaluated can be collected directly by local monitoring equipment, or obtained from meteorological bureaus, power grid companies, or third-party data service providers to ensure the integrity of the time series and spatial representativeness. When local measured data is missing, interpolation can be used to fill in the gaps and improve data continuity.
[0036] Step 2: Based on the DEM data of the wind farm to be evaluated, perform altitude correction on the historical freezing data and historical lightning data of the wind farm to be evaluated to obtain the corrected historical freezing data and corrected historical lightning data. Since the vertical lapse rate of atmospheric temperature and humidity and the development height of thunderstorm clouds are significantly controlled by terrain, the raw data obtained in step 1, if not adapted to terrain, cannot accurately reflect the risk level at different elevations. Therefore, in this embodiment 1, based on the DEM data of the wind farm to be evaluated, an exponential elevation correction model is constructed to spatially normalize the annual average number of freezing days and the annual average number of lightning strikes at different elevations of the wind farm to be evaluated. Specifically, a predetermined reference elevation is used... (e.g., the altitude of the wind farm entrance or the location of the monitoring station) is used as a benchmark, based on the pre-determined freezing altitude influence coefficient. and lightning altitude response coefficient The average number of freezing days and the average number of lightning strikes per year at different altitudes of the wind farms to be evaluated are subjected to exponential transformation so that all data are uniformly mapped to the target altitude for comparison.
[0037] Specifically, the steps are as follows: Step 21: Based on the DEM data of the wind farm to be evaluated, perform altitude correction on the annual average number of freezing days at different altitudes of the wind farm to be evaluated, and obtain the corrected annual average number of freezing days at different altitudes; the calculation process of the corrected annual average number of freezing days at different altitudes is as follows:
[0038] in, This represents the corrected annual average number of days with freezing conditions at different altitudes. For reference altitude Number of days of freezing at the location; The coefficient representing the influence of altitude on freezing and frost conditions. ; This is the current altitude; For reference altitude.
[0039] Step 22: Based on the DEM data of the wind farm to be evaluated, perform altitude correction on the annual average number of lightning strikes at different altitudes of the wind farm to be evaluated, and obtain the corrected annual average number of lightning strikes at different altitudes; the calculation process for the corrected annual average number of lightning strikes at different altitudes is as follows:
[0040] in, The corrected annual average number of lightning strikes at different altitudes; For reference altitude Number of lightning strikes at the location; The altitude response coefficient for lightning. For every 100m increase in altitude, the number of lightning strikes increases by 5%-10%. For reference altitude.
[0041] Step 2 above achieves refined topographic correction of historical freezing and lightning data for mountain wind farms. By introducing an altitude-dependent correction model based on DEM, the problem of underestimation of risk in high-altitude areas due to the limited distribution of observation points in traditional assessments is overcome. The altitude effect is quantified by an exponential function, improving the scientific rigor and operability of parameter extrapolation. The proposed dual-parameter correction framework takes into account the unique response patterns of both freezing and lightning disasters, providing a reliable data foundation for the accurate calculation of subsequent composite risk indices, thereby effectively supporting the safe site selection and protection design of mountain wind farms.
[0042] Step 3: Calculate the freezing risk index based on the corrected historical freezing data; calculate the lightning risk index based on the corrected historical lightning data.
[0043] Specifically, the steps are as follows: Step 31: The process of calculating the freezing risk index based on the corrected historical freezing data is as follows:
[0044] in, This is the freezing risk index; This represents the corrected annual average number of days with freezing conditions at different altitudes. This represents the maximum average number of days of freezing weather per year. The average ice thickness at different altitudes; This represents the maximum average icing thickness.
[0045] It should be noted that the freezing risk index is used to comprehensively reflect the frequency and intensity of freezing disasters in mountain wind farms within a specific area. Traditional assessments typically use only a single indicator (such as the average number of freezing days per year or the average ice thickness) for judgment, which is insufficient to fully characterize the impact of freezing on the operational safety of wind turbines. In this Example 1, the corrected average number of freezing days per year at different altitudes is used. Average ice thickness at corresponding altitude They were normalized separately and then merged into a unified freezing risk index through a product. Among these, the normalization operation eliminates the magnitude bias caused by differences in geographical location and climate background, making it possible to make horizontal comparisons of freezing risks between different regions. Secondly, the use of a product rather than a weighted sum means that the final risk index will only increase significantly when both freezing frequency and ice thickness are at a high level, reflecting a double threshold effect. That is, short-term light icing or long-term slight icing is not enough to constitute a high risk, while long-term and severe icing events are identified as core threat scenarios.
[0046] Through step 31 above, the two key factors of the frequency of freezing and the physical intensity of icing were synergistically modeled, and the superposition effect under extreme conditions was highlighted by nonlinear combination. This enabled a multi-dimensional quantitative assessment of the risk of freezing disasters in mountain wind farms, solving the technical problem of one-sided assessment results caused by relying on a single parameter in traditional methods. This more realistically reflects the multiple impacts of freezing on the aerodynamic performance degradation of wind turbine blades, increased mechanical load, and increased de-icing energy consumption under complex terrain conditions, achieving the technical effect of improving the accuracy of risk identification and supporting differentiated operation and maintenance decisions.
[0047] Step 32: The process of calculating the lightning risk index based on the corrected historical lightning data is as follows:
[0048] in, This is the freezing risk index; The corrected annual average number of lightning strikes at different altitudes; This represents the maximum average number of lightning strikes per year. The amplitude of lightning current at different altitudes; This represents the maximum lightning current amplitude.
[0049] It should be noted that the lightning risk index is a key indicator used to quantify the degree of lightning threat to mountain wind farms. It is a normalized composite evaluation parameter constructed by comprehensively considering two dimensions: the frequency of lightning strikes and the energy intensity of a single lightning strike. The harm of lightning to wind turbine generators is not only reflected in the number of occurrences, but also closely related to the energy released. For example, high-amplitude lightning currents may cause blade ablation, damage to the control system, or even fire. Therefore, relying solely on the frequency of lightning strikes for risk assessment is difficult to accurately reflect the actual threat level. To this end, this embodiment 1 introduces a two-factor product model, which jointly models the annual average number of lightning strikes and the amplitude of lightning current to improve the precision of the assessment.
[0050] Through the above steps 32, this embodiment 1 introduces the lightning current amplitude and constructs a product-type risk index, so that the assessment results can not only reflect the activity of thunderstorms, but also capture the potential threat of strong energy impacts. This achieves a multi-dimensional, normalized, and quantitative assessment of lightning risks in mountain wind farms, thereby solving the problem of incomplete lightning hazard assessment and achieving the technical effect of improving the accuracy of lightning risk identification and supporting differentiated protection configuration.
[0051] Step 4: Based on the freezing risk index and the lightning wind power index, calculate the freezing-lightning composite risk index of the wind farm to be evaluated. The process for calculating the freezing-lightning composite risk index of the wind farm to be evaluated is as follows:
[0052] in, The combined risk index of freezing and lightning for the wind farm to be evaluated; , and All are weighting coefficients; This is the freezing risk index; Lightning risk index; The percentage of times that freezing days and lightning occur simultaneously within a historical period is preset for the wind farm to be evaluated; this is referred to as the composite frequency percentage. The composite intensity is a quantitative indicator of the enhancing effect of icing on the probability of lightning strikes.
[0053] In this embodiment 1, the weighting coefficient , and These are used to adjust the relative importance of single-hazard risk items and compound effect items in the overall risk assessment; among them, the weighting coefficients... and The weighting coefficient can be dynamically set according to the regional climate characteristics; preferably, the weighting coefficient... Take 0.4 as the weighting coefficient. Set to 0.4; weighting coefficient The control factor for the composite coupling term is specifically designed to enhance the additional risk contribution in the context of concurrent freezing-lightning scenarios; preferably, the weighting coefficient... A value of 0.2 is chosen to highlight the amplification effect of the combined effect under extreme weather conditions; optionally, the weighting coefficient... , and The model can be determined through expert scoring, analytic hierarchy process (AHP), or regression fitting based on historical fault data, ensuring that the model has good adaptability and engineering applicability.
[0054] The percentage of times that freezing days and lightning occurred simultaneously within the preset historical period of the wind farm to be evaluated. This represents the statistical result of the number of times freezing events and lightning events co-occur within the same time window (usually on a daily basis) within a preset historical period (such as the past 30 years), used to quantify the spatiotemporal overlap probability of the two types of disasters; this parameter can be obtained by simultaneously analyzing data from meteorological observation stations, lightning location systems, and icing monitoring devices; among which, the percentage of the frequency of freezing days and lightning occurring simultaneously within the preset historical period of the wind farm to be evaluated. The calculation process is as follows:
[0055] in, The frequency of simultaneous occurrence of freezing days and lightning within a historical period is preset for the wind farm to be evaluated.
[0056] Quantitative indicators of the enhancing effect of icing on the probability of lightning strikes This study aims to quantify the enhancing effect of icing on the probability of lightning strikes, modeling the coupling effect at the physical mechanism level. Specifically, when a conductive ice layer forms on the surface of wind turbine blades, local electric field distortion intensifies, significantly increasing the lightning attraction capability. The quantification index of the enhancing effect of icing on the probability of lightning strikes is as follows: The calculation process is as follows:
[0057] in, This is the lightning strike probability enhancement factor, indicating that for every 1mm increase in icing thickness, the probability of a lightning strike increases. .
[0058] By taking into account the co-occurrence frequency of freezing and lightning events and the increased probability of lightning strikes caused by icing in step 4 above, a refined model of the combined risk of freezing and lightning in mountain wind farms is achieved. This solves the problem that the combined risk is underestimated because the traditional assessment method simply superimposes the single disaster risk. It can more realistically reflect the comprehensive threat level faced by wind power facilities under complex terrain conditions, and thus provide a scientific basis for wind farm site selection, protection system design and operation and maintenance strategy formulation.
[0059] Step 5: Compare the condensation-lightning combined risk index of the wind farm to be evaluated with a preset combined risk index threshold to obtain the condensation-lightning combined risk assessment result of the wind farm to be evaluated. Specifically, compare the condensation-lightning combined risk index of the wind farm to be evaluated with a preset first combined risk index threshold and a preset second combined risk index threshold; wherein, the preset first combined risk index threshold is less than the preset second combined risk index threshold; preferably, the preset first combined risk index threshold is 0.3 and the preset second combined risk index threshold is 0.6.
[0060] If the combined risk index of freezing and lightning of the wind farm to be evaluated is less than the preset first combined risk index threshold, the combined risk assessment result of freezing and lightning of the wind farm to be evaluated is low combined risk. In this case, conventional blade de-icing measures such as electric heating and independent lightning rods are adopted.
[0061] If the combined freezing-lightning risk index of the wind farm to be evaluated is greater than the preset first combined risk index threshold, but less than the preset second combined risk index threshold, then the combined freezing-lightning risk assessment result of the wind farm to be evaluated is a medium combined risk. In this case, protective measures such as linking the de-icing system with the lightning warning and installing lightning protection strips on the blades are adopted. Among them, the de-icing system and the lightning warning are activated 30 minutes before the warning to reduce the ice thickness.
[0062] If the combined risk index of freezing and lightning of the wind farm to be evaluated is greater than the preset second combined risk index, then the combined risk assessment result of freezing and lightning of the wind farm to be evaluated is a high combined risk. In this case, protective measures such as adopting an integrated de-icing and lightning protection device (the de-icing heating wire also serves as a lightning induction channel) and avoiding the wind turbine site selection on mountain ridges with an altitude greater than 1800m are adopted.
[0063] The mountain wind power freezing-lightning combined risk assessment method described in Example 1 introduces a freezing-lightning combined risk index for the wind farm to be assessed, quantifying the synergistic effect of icing enhancing lightning strike probability. This overcomes the limitations of traditional single-hazard assessment and better reflects the actual characteristics of mountain hazards. Secondly, by introducing a terrain correction model and using a pre-determined freezing altitude influence coefficient... and lightning altitude response coefficient The system corrects freezing and lightning parameters to address regional risk differences caused by mountainous terrain and improves the spatial accuracy of assessments. In addition, it links the de-icing system with lightning protection measures (such as early warning-triggered de-icing), which improves protection efficiency by more than 20% and reduces overall costs compared to independent protection.
[0064] Example 2 As attached Figure 2 As shown in the figure, this embodiment 2 provides a composite risk assessment system for freezing-lightning in mountain wind farms, including a data acquisition module, a terrain correction module, a single disaster risk index calculation module, a composite risk index calculation module, and a composite risk assessment module.
[0065] The data acquisition module is used to acquire historical freezing data, historical lightning data, and DEM data of the wind farm to be evaluated.
[0066] The terrain correction module is used to perform elevation correction on the historical freezing data and historical lightning data of the wind farm to be evaluated based on the DEM data of the wind farm to be evaluated, so as to obtain the corrected historical freezing data and corrected historical lightning data.
[0067] The single disaster risk index calculation module is used to calculate the freezing risk index based on corrected historical freezing data and the lightning risk index based on corrected historical lightning data.
[0068] The composite risk index calculation module is used to calculate the frost-lightning composite risk index of the wind farm to be evaluated based on the frost risk index and the lightning wind power index.
[0069] The composite risk assessment module is used to compare the condensation-lightning composite risk index of the wind farm to be assessed with the preset composite risk index threshold to obtain the condensation-lightning composite risk assessment result of the wind farm to be assessed.
[0070] Example 3 As attached Figure 3 As shown, this embodiment 3 provides an electronic device, including: a memory for storing a computer program; a processor for executing the computer program to implement the steps of the method for assessing the combined risk of freezing and lightning in mountain wind farms; or, the processor for executing the computer program to implement the functions of each module in the above-mentioned combined risk assessment system for freezing and lightning in mountain wind farms.
[0071] For example, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a preset function, the instruction segments describing the execution process of the computer program in the electronic device.
[0072] The electronic device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The electronic device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that the above are examples of electronic devices and do not constitute a limitation on the electronic device. It may include more components than described above, or combine certain components, or different components. For example, the electronic device may also include input / output devices, network access devices, buses, etc.
[0073] The processor can be a central processing unit, or other general-purpose processors, digital signal processors, application-specific integrated circuits, off-the-shelf programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor, or any conventional processor, etc. The processor is the control center of the electronic device, connecting various parts of the entire electronic device through various interfaces and lines.
[0074] The memory can be used to store the computer program and / or module. The processor implements various functions of the electronic device by running or executing the computer program and / or module stored in the memory and by calling the data stored in the memory.
[0075] The memory may primarily include a program storage area and a data storage area. The program storage area may store the operating system and at least one application program required for a given function (such as sound playback, image playback, etc.). The data storage area may store data created based on the use of the mobile phone (such as audio data, phonebook, etc.). Furthermore, the memory may include high-speed random access memory and non-volatile memory, such as hard disks, RAM, plug-in hard disks, smart memory cards, secure digital cards, flash memory cards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.
[0076] Example 4 This embodiment 4 also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the method for assessing the combined risk of freezing and lightning in a mountain wind farm.
[0077] If the modules / units integrated in the mountain wind farm freezing-lightning composite risk assessment system are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium.
[0078] Based on this understanding, the present invention can implement all or part of the processes in the above-mentioned method for assessing the combined risk of freezing and lightning in mountain wind farms. This can also be accomplished by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of the above-mentioned method for assessing the combined risk of freezing and lightning in mountain wind farms. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or a preset intermediate form, etc.
[0079] The computer-readable storage medium may include any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory, random access memory, electrical carrier signal, telecommunication signal, and software distribution medium, etc.
[0080] Example 5 This embodiment 5 provides a computer product, which includes a computer program stored in a computer-readable storage medium. The processor of the electronic device reads the computer program from the computer-readable storage medium and executes the computer program, so that the electronic device can execute the mountain wind farm freezing-lightning combined risk assessment method described in embodiment 1, which will not be repeated here.
[0081] It should be noted that those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above methods.
[0082] The method for assessing the combined risk of freezing and lightning strikes in mountainous wind farms described in this invention is based on historical freezing data, lightning data, and a digital elevation model. It utilizes an altitude correction model to spatially correct the number of freezing days and lightning strikes at different altitudes, improving data accuracy. Through normalization, it calculates the single-hazard risk indices for freezing and lightning separately, and then integrates these indices with the frequency of combined events and the icing enhancement effect to construct a comprehensive composite risk index. Finally, it classifies risk levels through threshold comparison. This invention overcomes the limitations of traditional single-hazard assessments, fully considers the influence of terrain and the disaster coupling mechanism, and significantly improves the spatial accuracy and practical applicability of disaster risk assessment in mountainous wind farms, providing a scientific basis for wind turbine site selection and collaborative protection.
[0083] The above embodiments are merely one of the implementation methods for achieving the technical solution of the present invention. The scope of protection claimed by the present invention is not limited to this embodiment, but also includes any variations, substitutions and other implementation methods that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention.
Claims
1. A method for evaluating the freezing-thunder complex risk of a mountain wind farm, characterized in that, The method comprises the following steps: obtain historical icing data, historical lightning data and DEM data of a wind farm to be evaluated; based on the DEM data of the wind farm to be evaluated, correct the historical icing data and the historical lightning data of the wind farm to be evaluated respectively according to the altitude, and obtain corrected historical icing data and corrected historical lightning data; based on the corrected historical icing data, calculate the icing risk index; based on the corrected historical lightning data, calculate the lightning risk index; based on the icing risk index and the lightning risk index, calculate the icing-lightning combined risk index of the wind farm to be evaluated; compare the icing-lightning combined risk index of the wind farm to be evaluated with a preset combined risk index threshold, and obtain the icing-lightning combined risk evaluation result of the wind farm to be evaluated.
2. The method according to claim 1, wherein, The historical icing data of the wind farm to be evaluated includes the annual average icing days and the average icing thickness of different altitudes of the wind farm to be evaluated. The historical lightning data of the wind farm to be evaluated includes the annual average lightning stroke times and the lightning current amplitude of different altitudes of the wind farm to be evaluated.
3. The method according to claim 2, wherein, Based on the DEM data of the wind farm to be evaluated, correct the historical icing data and the historical lightning data of the wind farm to be evaluated respectively according to the altitude, and obtain corrected historical icing data and corrected historical lightning data, as follows: Based on the DEM data of the wind farm to be evaluated, correct the annual average icing days of different altitudes of the wind farm to be evaluated according to the altitude, and obtain the corrected annual average icing days of different altitudes; The calculation process of the corrected annual average icing days of different altitudes is as follows: wherein, is the corrected annual number of freezing days at different altitudes; is the freezing day number at the reference altitude; is the freezing day number at the reference altitude; is the freezing altitude influence coefficient; is the current altitude; is the reference altitude; Based on the DEM data of the wind farm to be evaluated, correct the annual average lightning stroke times of different altitudes of the wind farm to be evaluated according to the altitude, and obtain the corrected annual average lightning stroke times of different altitudes; The calculation process of the corrected annual average lightning stroke times of different altitudes is as follows: wherein, is the corrected annual number of lightning strikes at different altitudes; is the number of lightning strikes at a reference altitude is the number of lightning strikes at a reference altitude is the lightning altitude response coefficient; is the current altitude; is the reference altitude.
4. The method according to claim 1, wherein, Based on the corrected historical icing data, calculate the icing risk index, as follows: wherein, is the freeze risk index; is the corrected average annual freeze days at different altitudes; is the maximum average annual freeze days; is the average ice cover thickness at different altitudes; is the maximum average ice cover thickness.
5. The icing-thunder complex risk assessment method for a mountain wind farm according to claim 1, characterized in that, Based on the corrected historical lightning data, calculate the lightning risk index, as follows: wherein, is the freeze risk index; is the corrected annual average number of lightning strikes at different altitudes; is the maximum annual average number of lightning strikes; is the lightning current amplitude at different altitudes; is the maximum lightning current amplitude.
6. The icing-thunder complex risk assessment method for a mountain wind farm according to claim 1, characterized in that, Based on the icing risk index and the lightning risk index, calculate the icing-lightning combined risk index of the wind farm to be evaluated, as follows: wherein, is the icing-lightning combined risk index of the wind farm to be evaluated; , and are weight coefficients; is the icing risk index; is the lightning risk index; is the frequency ratio of icing days and lightning at the same time in the preset historical period of the wind farm to be evaluated; is the quantitative index of the icing enhancement effect on lightning probability.
7. A system for assessing the combined risk of icing and lightning for a mountain wind farm, characterized in that The method comprises the following steps: a data acquisition module for acquiring historical icing data, historical lightning data and DEM data of a wind farm to be evaluated; a terrain correction module for correcting the historical icing data and the historical lightning data of the wind farm to be evaluated according to the altitude based on the DEM data of the wind farm to be evaluated, and obtaining corrected historical icing data and corrected historical lightning data; a single disaster risk index calculation module for calculating the icing risk index based on the corrected historical icing data; a lightning risk index calculation module for calculating the lightning risk index based on the corrected historical lightning data; a combined risk index calculation module for calculating the icing-lightning combined risk index of the wind farm to be evaluated based on the icing risk index and the lightning risk index; a combined risk evaluation module for comparing the icing-lightning combined risk index of the wind farm to be evaluated with a preset combined risk index threshold, and obtaining the icing-lightning combined risk evaluation result of the wind farm to be evaluated.
8. An electronic device, comprising: The method comprises the following steps: a processor suitable for executing a computer program; A computer readable storage medium, having stored therein a computer program, which, when executed by the processor, performs the mountain wind farm freezing-thunder complex risk assessment method according to any one of claims 1-6.
9. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 8. The computer program, when executed by the processor, implements the mountain wind farm freezing-thunder complex risk assessment method according to any one of claims 1-6.
10. A computer program product, characterised in that, The computer program product comprises a computer program, which, when executed by the processor, implements the mountain wind farm freezing-thunder complex risk assessment method according to any one of claims 1-6.