Fuzzy probability-based regional wind power icing disaster weather identification method and system

Abstract

The present application belongs to the technical field of power weather disaster prevention and reduction, and discloses a regional wind power icing disaster weather identification method and system based on fuzzy probability, comprising: acquiring meteorological element data of multiple positions in a target region, the meteorological elements including temperature, humidity and wind speed; using a fuzzy information distribution method, respectively calculating the probability distribution characteristics of each meteorological element during the icing disaster, and based on the probability distribution characteristics, obtaining the cumulative probability distribution; based on the numerical range corresponding to the cumulative probability distribution of each meteorological element reaching the preset threshold value, determining the meteorological condition index of the icing disaster weather of the target region; using the meteorological condition index to identify the wind power icing disaster weather process of the target region. The present application clearly reflects the whole process of wind power abnormal output incubation period, significant influence period and influence subsidence, thereby helping relevant parties to take measures in advance, reducing large-scale power loss caused by extreme weather, and improving the safety and reliability of power system operation.

Description

Technical Field

[0001] This invention belongs to the field of power meteorological disaster prevention and mitigation technology, and specifically relates to a method and system for identifying regional wind power freezing weather disasters based on fuzzy probability. Background Technology

[0002] Against the backdrop of climate change, extreme weather events are becoming more frequent and intense. Both domestically and internationally, there have been instances where extreme weather events have caused drastic fluctuations in wind power output, affecting reliable power supply. Among these, low-temperature freezing events triggered by cold air passages have a wide impact and long duration, attracting widespread attention from the power industry.

[0003] Currently, scholars both domestically and internationally have conducted numerical simulations and experimental studies on the specific impacts of wind turbine blade icing. Blade icing alters the geometry of wind turbine blades, thereby reducing their wind energy utilization coefficient and leading to power loss. Furthermore, icing can cause uneven blade load distribution, increasing the risk of mechanical failure. A severe wind turbine icing event typically includes the pre-icing phase, the icing operation phase, the icing shutdown phase, and the post-icing phase. During the pre-icing phase, wind turbine units do not experience power reduction, while the remaining phases exhibit significant power losses. Many factors contribute to icing formation on wind turbine surfaces during freezing disasters, but meteorological conditions are the most important. Generally, the key meteorological condition leading to icing is the simultaneous occurrence of low temperature and high humidity. Therefore, the icing process of wind turbines can be analyzed by establishing the relationship between freezing weather and meteorological elements. In addition, combining physical models with numerical weather prediction is also an important method for studying wind turbine icing. However, physical icing models require information such as liquid water content, cloud droplet density, and median volume diameter, most of which can only be obtained under laboratory conditions, greatly limiting the application of physical models. Overall, current technologies still suffer from problems such as high subjectivity, insufficient model expressive power leading to inaccurate predictions, and high difficulty. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for identifying regional wind power freezing weather disasters based on fuzzy probability, so as to solve the problems of inaccurate predictions and high difficulty caused by strong subjectivity and insufficient model expression ability in the existing technology.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for identifying regional wind power freezing weather disasters based on fuzzy probability, including: Acquire meteorological element data for multiple locations within the target area, including temperature, humidity, and wind speed; Using the fuzzy information allocation method, the probability distribution characteristics of each meteorological element during the freezing disaster are calculated respectively, and the cumulative probability distribution is obtained based on the cumulative probability distribution characteristics. Based on the numerical range corresponding to the cumulative probability distribution of each meteorological element when it reaches the preset threshold, the meteorological condition indicators of freezing disaster weather in the target area are determined. Meteorological condition indicators are used to identify wind power freezing weather processes in the target area.

[0006] Furthermore, acquiring meteorological element data from multiple locations within the target area includes: Acquire observation data from multiple meteorological stations or new energy power stations within the target area. The observation data includes icing, 2m air temperature, humidity, dew point temperature, and 10m wind speed data. The humidity-related parameter is the dew point temperature difference, which is used to measure humidity.

[0007] Furthermore, dew point temperature refers to the temperature at which air cools to saturation under conditions where the water vapor content and air pressure remain unchanged; that is, the temperature at which water vapor condenses under unchanged atmospheric conditions. When using the dew point temperature difference to measure humidity, the smaller the dew point temperature difference, the greater the humidity. When the dew point temperature difference approaches 0°C, it indicates that the water vapor in the air has reached a state of near saturation.

[0008] Furthermore, the method of using fuzzy information allocation to calculate the probability distribution characteristics of each key meteorological element during the freezing disaster specifically includes: The sample data of each meteorological element are divided into multiple level intervals; for each sample, the probability of fuzzy information allocation in each level interval is calculated using the following formula:

[0009] in, Assign probabilities to fuzzy information in each level interval. For the first One sample, For the number of samples, For the first The median value of each grade range, Allocate the length of the information interval; Based on the cumulative probability distribution characteristics, the cumulative probability distribution is obtained. The probabilities of fuzzy information assigned to each sample in each level interval are accumulated to obtain the cumulative probability value of the meteorological element in each level:

[0010] in, This represents the cumulative probability distribution.

[0011] Furthermore, the determination of meteorological condition indicators for freezing disaster weather in the target area based on the numerical range corresponding to the cumulative probability distribution of each meteorological element reaching a preset threshold includes: The cumulative probability distributions of air temperature, dew point temperature, and dew point temperature difference are divided into intervals of 1 ℃, and wind speed is divided into intervals of 0.5 m / s. When a freezing disaster occurs, if the cumulative probability of air temperature exceeds the threshold, the corresponding air temperature value range is between -5 and 1 ℃; if the cumulative probability of dew point temperature exceeds the threshold, the corresponding dew point temperature value range is greater than -8 ℃; if the cumulative probability of dew point temperature difference exceeds the threshold, the corresponding dew point temperature difference value range is less than 5 ℃; and if the cumulative probability of wind speed reaches the threshold, the corresponding wind speed value range is less than 9 m / s.

[0012] Furthermore, the expression for the meteorological condition index for determining the freezing disaster weather in the target area is as follows:

[0013] in, For temperature, Dew point temperature, This refers to wind speed.

[0014] Furthermore, the identification of wind power freezing disaster weather processes in the target area using meteorological condition indicators includes: Based on the satisfaction of the meteorological conditions indicators over time, the duration of freezing weather in the target area is assessed; when the temperature is between -5℃ and 1℃, the dew point temperature is less than -8℃, the dew point temperature difference is less than 5℃, and the wind speed is less than 9m / s, it is identified as the formation or development stage of freezing weather.

[0015] Secondly, the present invention provides a regional wind power freezing weather identification system based on fuzzy probability, comprising: The data acquisition module is used to acquire meteorological element data from multiple locations within the target area, including temperature, humidity, and wind speed. The probability calculation module is used to calculate the probability distribution characteristics of each meteorological element during the freezing disaster using the fuzzy information allocation method, and to obtain the cumulative probability distribution based on the cumulative probability distribution characteristics. The indicator determination module is used to determine the meteorological condition indicators of freezing disaster weather in the target area based on the numerical range corresponding to the cumulative probability distribution of each meteorological element when it reaches a preset threshold. The identification output module is used to identify wind power freezing weather processes in the target area using meteorological condition indicators.

[0016] Furthermore, acquiring meteorological element data from multiple locations within the target area includes: Acquire observation data from multiple meteorological stations or new energy power stations within the target area. The observation data includes icing, 2m air temperature, humidity, dew point temperature, and 10m wind speed data. The humidity-related parameter is the dew point temperature difference, which is used to measure humidity.

[0017] Furthermore, dew point temperature refers to the temperature at which air cools to saturation under conditions where the water vapor content and air pressure remain unchanged; that is, the temperature at which water vapor condenses under unchanged atmospheric conditions. When using the dew point temperature difference to measure humidity, the smaller the dew point temperature difference, the greater the humidity. When the dew point temperature difference approaches 0°C, it indicates that the water vapor in the air has reached a state of near saturation.

[0018] Furthermore, the method of using fuzzy information allocation to calculate the probability distribution characteristics of each key meteorological element during the freezing disaster specifically includes: The sample data of each meteorological element are divided into multiple level intervals; for each sample, the probability of fuzzy information allocation in each level interval is calculated using the following formula:

[0019] in, Assign probabilities to fuzzy information in each level interval. For the first One sample, For the number of samples, For the first The median value of each grade range, Allocate the length of the information interval; Based on the cumulative probability distribution characteristics, the cumulative probability distribution is obtained. The probabilities of fuzzy information assigned to each sample in each level interval are accumulated to obtain the cumulative probability value of the meteorological element in each level:

[0020] in, This represents the cumulative probability distribution.

[0021] Furthermore, the determination of meteorological condition indicators for freezing disaster weather in the target area based on the numerical range corresponding to the cumulative probability distribution of each meteorological element reaching a preset threshold includes: The cumulative probability distributions of air temperature, dew point temperature, and dew point temperature difference are divided into intervals of 1 ℃, and wind speed is divided into intervals of 0.5 m / s. When a freezing disaster occurs, if the cumulative probability of air temperature exceeds the threshold, the corresponding air temperature value range is between -5 and 1 ℃; if the cumulative probability of dew point temperature exceeds the threshold, the corresponding dew point temperature value range is greater than -8 ℃; if the cumulative probability of dew point temperature difference exceeds the threshold, the corresponding dew point temperature difference value range is less than 5 ℃; and if the cumulative probability of wind speed reaches the threshold, the corresponding wind speed value range is less than 9 m / s.

[0022] Furthermore, the expression for the meteorological condition index for determining the freezing disaster weather in the target area is as follows:

[0023] in, For temperature, Dew point temperature, This refers to wind speed.

[0024] Furthermore, the identification of wind power freezing disaster weather processes in the target area using meteorological condition indicators includes: Based on the satisfaction of the meteorological conditions indicators over time, the duration of freezing weather in the target area is assessed; when the temperature is between -5℃ and 1℃, the dew point temperature is less than -8℃, the dew point temperature difference is less than 5℃, and the wind speed is less than 9m / s, it is identified as the formation or development stage of freezing weather.

[0025] Thirdly, the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the regional wind power freezing disaster weather identification method based on fuzzy probability.

[0026] Fourthly, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the regional wind power freezing disaster weather identification method based on fuzzy probability.

[0027] Compared with the prior art, the present invention has the following technical effects: This invention collects key meteorological elements such as temperature, humidity, and wind speed from multiple stations or reanalysis data within a target area, and uses a fuzzy information allocation method to calculate the probability distribution characteristics of each element during freezing disasters. This method does not require prior assumptions about the overall distribution of the data and can directly and objectively extract the joint statistical regularities of disaster-causing conditions such as low temperature and high humidity from historical samples, thereby determining meteorological condition indicators for freezing disasters with clear numerical ranges. Compared to qualitative analysis relying on forecasters' subjective judgment and physical models requiring laboratory data such as liquid water content, this scheme is entirely based on conventionally available meteorological observations or reanalysis data, avoiding the problems of strong subjectivity and limited regional-scale application, and achieving objective and quantitative identification of regional wind power freezing disaster weather. Furthermore, by examining the satisfaction of meteorological condition indicators over time, this invention can further assess the duration of freezing disaster weather, providing more accurate information on the evolution of disaster-causing processes for grid dispatching and wind farm operation and maintenance. It can effectively diagnose the onset of the impact of freezing disasters on wind farms and the total provincial wind power output, clearly reflecting the entire process of the incubation period, significant impact period and the decline of the impact of abnormal wind power output. This helps relevant parties take measures in advance to reduce the large-scale power loss caused by extreme weather and improve the safety and reliability of power system operation. Attached Figure Description

[0028] Figure 1 This invention relates to the fuzzy probability distribution and cumulative probability distribution of meteorological elements for freezing and non-freezing disasters.

[0029] Figure 2 This is a schematic diagram illustrating the impact of a certain freezing weather event on wind power output.

[0030] Figure 3 This is a flowchart of the present invention. Detailed Implementation

[0031] The present invention will be further described below with reference to the accompanying drawings: Example 1, please refer to Figure 3 This invention provides a method for identifying regional wind power freezing weather disasters based on fuzzy probability, including: S1, acquire meteorological element data for multiple locations within the target area, including temperature, humidity, and wind speed; This involves acquiring meteorological data from multiple locations within the target area. The target area refers to a contiguous wind power development zone where freezing disaster identification is needed. Multiple locations refer to scattered ground-based meteorological observation stations, wind farm anemometer towers, or monitoring points at new energy power plants within the area. Meteorological elements specifically include near-surface air temperature, humidity parameters characterizing air saturation, and 10-meter wind speed. One approach is to directly collect historical hourly observation records from each station within the area, extracting 2-meter air temperature, relative humidity or dew point temperature, and 10-meter wind speed, while simultaneously acquiring real-time records of icing or freezing rain as true-value labels for disaster occurrence. When sparse station coverage leads to insufficient spatial representativeness, freezing rain observation data can be supplemented for analysis. Identification can be completed using only conventional meteorological elements, without relying on physical quantities that require laboratory conditions such as liquid water content. The data sources are flexible, ensuring spatial coverage and temporal continuity at the regional scale.

[0032] S2. Using the fuzzy information allocation method, the probability distribution characteristics of each meteorological element during the freezing disaster are calculated respectively, and the cumulative probability distribution is obtained based on the cumulative probability distribution characteristics. The fuzzy information allocation method is used to calculate the probability distribution of each meteorological element during a freezing disaster, and then sums them to obtain the cumulative probability distribution. The fuzzy information allocation method does not require a pre-defined overall data distribution; it approximates the true probability density by assigning each sample value to adjacent intervals based on distance. Specifically, based on the confirmed freezing disaster period, air temperature, dew point temperature, dew point temperature difference, and wind speed samples from all stations or grid points are collected. The ranges for air temperature, dew point temperature, and dew point temperature difference are divided into intervals of 1°C, and the range for wind speed is divided into intervals of 0.5 m / s. The allocation probability of each sample at the midpoint of each interval is calculated, and then the allocation probabilities of all samples are summed for each interval to form the cumulative probability. This method effectively avoids estimation bias caused by the setting of interval boundaries in histograms, and even with a limited number of samples, it can obtain relatively smooth and stable probability curves, objectively presenting the distribution characteristics of each element under disaster conditions.

[0033] S3, based on the numerical range corresponding to the cumulative probability distribution of each meteorological element when it reaches the preset threshold, determine the meteorological condition indicators of freezing disaster weather in the target area; Based on the numerical range corresponding to the cumulative probability distribution of each meteorological element reaching a preset threshold, the meteorological condition indicators for freezing disaster weather are determined. The preset threshold is usually a high cumulative probability level that can cover the vast majority of disaster samples, such as 90%. The element values ​​corresponding to the first time the cumulative probability of each element reaches 90% are read from the cumulative probability curve obtained in step S2: for air temperature, the range where the cumulative probability exceeds 90% is concentrated between -5℃ and 1℃; the portion where the cumulative probability of dew point temperature exceeds 90% is greater than -8℃; the portion where the cumulative probability of dew point temperature difference exceeds 90% is less than 5℃; and the corresponding value for wind speed with a cumulative probability close to 90% is approximately below 9 m / s. Finally, the conditions can be determined as follows: simultaneously satisfying an air temperature between -5℃ and 1℃, a dew point temperature above -8℃, a dew point temperature difference less than 5℃, and a wind speed below 9 m / s. By directly using the threshold combination corresponding to the cumulative probability of 90% mentioned above, an identification criterion applicable to the formation and development stage of freezing disasters in this region is formed. The index established in this way is completely derived from historical disaster data, avoiding the arbitrariness of subjective experience, and can quantitatively distinguish between freezing and non-freezing meteorological environments. Moreover, the combined conditions of multiple factors accurately correspond to the core disaster characteristic of low temperature and high humidity.

[0034] S4 uses meteorological condition indicators to identify wind power freezing disaster weather processes in the target area.

[0035] The aforementioned meteorological indicators are used to identify wind power freezing weather processes in the target area. Threshold combinations of air temperature, dew point temperature, dew point temperature difference, and wind speed are applied to hourly data from various stations or grid points within the analysis period. If all conditions are met at any given time, that point is determined to be under the influence of freezing weather. The number of consecutive hours that the conditions are met represents the duration of the freezing weather at that point. A freezing weather time series is generated for all observation stations in the region. The duration of the process at each station is statistically analyzed, and a spatial distribution map is plotted, directly reflecting the range and intensity differences of the disaster's impact. This method can objectively define the start and end times of freezing weather processes, providing quantitative information on the duration of impact for grid dispatch, helping wind farms to formulate operation and maintenance strategies in advance, and reducing the impact of large-scale power losses on system reliability.

[0036] Example 2: This invention provides a method for identifying regional wind power freezing weather disasters based on fuzzy probability, including: Data preparation Focus on key meteorological data, including icing at meteorological stations or renewable energy power plants within the region, 2-meter air temperature, humidity, dew point temperature, and 10-meter wind speed. It should be noted that dew point temperature refers to the temperature at which air cools to saturation under constant water vapor content and air pressure; that is, the temperature at which water vapor condenses under unchanged atmospheric conditions. Considering the sparse and spatially discrete distribution of icing observation data, freezing rain observation data can be collected as a supplement, where possible. If actual observation data for key meteorological elements is lacking, reanalysis data for the corresponding time period can be used instead. For example, hourly reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis 5th generation (ERA5) can be used. It should be noted that since ERA5 lacks humidity data, humidity can be measured using the dew point temperature difference. A smaller dew point temperature difference indicates higher humidity; when the dew point temperature difference is close to 0°C, it indicates that the water vapor in the air is nearly saturated.

[0037] A method for identifying freezing disasters based on fuzzy probability The fuzzy information allocation method can obtain a relatively close probability distribution of the sequence without assuming a population distribution function, and its error is smaller compared to the histogram probability distribution method. Specifically, suppose there is a set of data... , including The samples were divided into Each interval For the first One sample, For the first The median value of each grade range, Given the length of the information allocation interval, the probability of fuzzy information allocation for a certain value. The calculation method is as follows:

[0038] The cumulative probability value of the j-th level It can be obtained through the following formula.

[0039]

[0040] Figure 1 The probability distribution of air temperature, dew point temperature, dew point temperature difference, and wind speed at multiple meteorological monitoring stations in a certain region during a freezing disaster is presented.

[0041] Ice-related disasters are represented in blue, while non-ice-related disasters are represented in gray. The fuzzy probability distribution of meteorological elements is shown as a bar chart, and the cumulative probability distribution is shown as a solid line. The cumulative probability distributions of air temperature, dew point temperature, and dew point temperature difference were divided into intervals of 1 °C, while wind speed was divided into intervals of 0.5 m / s. Overall, the probability distribution characteristics of various meteorological elements during freezing disasters differed significantly from those during non-freezing periods. During freezing disasters, the cumulative probability of air temperature between -5 and 1 °C exceeded 90%; the cumulative probability of dew point temperature greater than -8 °C exceeded 90%; and the cumulative probability of dew point temperature difference less than 5 °C exceeded 90%. Regarding wind speed, it was higher during freezing disasters compared to non-freezing periods. This is related to the fact that freezing disasters are often triggered by cold waves, which increase wind speed. Considering that excessively high wind speeds are unfavorable for wind turbine icing and the wind speed corresponding to a cumulative probability of around 90%, a wind speed threshold of less than 9 m / s was determined.

[0042] The above analysis shows that areas experiencing freezing disasters are typically characterized by low temperatures and high humidity. Therefore, the meteorological conditions for freezing disasters in these areas are as follows:

[0043] It should be noted that this indicator only focuses on the formation and development stages of freezing disasters, and does not include the maintenance and melting stages of freezing disasters.

[0044] Identification effect evaluation In a certain month, a severe freezing disaster occurred in Northeast my country. The freezing disaster was mainly concentrated in the border area between Inner Mongolia Autonomous Region and Liaoning Province, as well as the central region of Jilin Province; the freezing disaster was mainly concentrated in the central region of Liaoning Province, the central region of Jilin Province, and the southern region of Heilongjiang Province.

[0045] To verify the effectiveness of the proposed method, the distribution of freezing disaster durations largely corresponds to the observed areas of freezing rain and icing, but the affected areas are larger, mainly due to limited and spatially discontinuous observational data. Furthermore, the duration of freezing disasters can, to some extent, quantify the intensity of icing disasters. For example, in areas with icing exceeding 10 mm, the duration of freezing disasters exceeds 15 hours.

[0046] It can effectively identify freezing disaster weather processes in the region based on the threshold range of key meteorological elements.

[0047] The identification results can be directly applied to regional wind farm operation and maintenance and power grid dispatching departments, thereby improving the power system's ability to cope with freezing weather disasters and ensuring the safe and stable operation of the system.

[0048] This study selects freezing weather events and analyzes the actual and predicted power output of individual wind farms and the total wind power output in Jilin Province. Figure 2 This verifies the effectiveness of the freezing weather identification method proposed in this patent.

[0049] In another embodiment of the present invention, a regional wind power freezing weather identification system based on fuzzy probability is provided, which can be used to implement the above-mentioned regional wind power freezing weather identification method based on fuzzy probability. Specifically, the system includes: The data acquisition module is used to acquire meteorological element data from multiple locations within the target area, including temperature, humidity, and wind speed. The probability calculation module is used to calculate the probability distribution characteristics of each meteorological element during the freezing disaster using the fuzzy information allocation method, and to obtain the cumulative probability distribution based on the cumulative probability distribution characteristics. The indicator determination module is used to determine the meteorological condition indicators of freezing disaster weather in the target area based on the numerical range corresponding to the cumulative probability distribution of each meteorological element when it reaches a preset threshold. The identification output module is used to identify wind power freezing weather processes in the target area using meteorological condition indicators.

[0050] The module division in this embodiment of the invention is illustrative and represents only one logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional modules in the various embodiments of the invention can be integrated into a single processor, exist as separate physical entities, or be integrated into a single module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0051] In another embodiment of the present invention, a computer device is provided, comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions from the computer storage medium to achieve a corresponding method flow or corresponding function. The processor described in this embodiment of the present invention can be used in the operation of a regional wind power freezing weather identification method based on fuzzy probability.

[0052] In another embodiment of the present invention, a storage medium is provided, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, the storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the regional wind power freezing weather identification method based on fuzzy probability in the above embodiments.

[0053] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0054] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0055] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0056] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0057] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for identifying regional wind power freezing weather disasters based on fuzzy probability, characterized in that, include: Acquire meteorological element data for multiple locations within the target area, including temperature, humidity, and wind speed; Using the fuzzy information allocation method, the probability distribution characteristics of each meteorological element during the freezing disaster are calculated respectively, and the cumulative probability distribution is obtained based on the cumulative probability distribution characteristics. Based on the numerical range corresponding to the cumulative probability distribution of each meteorological element when it reaches the preset threshold, the meteorological condition indicators of freezing disaster weather in the target area are determined. Meteorological condition indicators are used to identify wind power freezing weather processes in the target area.

2. The method for identifying regional wind power freezing weather disasters based on fuzzy probability according to claim 1, characterized in that, The acquisition of meteorological element data at multiple locations within the target area includes: Acquire observation data from multiple meteorological stations or new energy power stations within the target area. The observation data includes data on icing, air temperature, humidity, dew point temperature, and wind speed. The humidity-related parameter is the dew point temperature difference, which is used to measure humidity.

3. The method for identifying regional wind power freezing weather disasters based on fuzzy probability according to claim 2, characterized in that, Dew point temperature refers to the temperature at which air cools to saturation under constant conditions of water vapor content and air pressure. In other words, it is the temperature at which water vapor condenses when atmospheric conditions remain unchanged. When using the dew point temperature difference to measure humidity, the smaller the dew point temperature difference, the greater the humidity. When the dew point temperature difference approaches 0°C, it indicates that the water vapor in the air has reached a state of near saturation.

4. The method for identifying regional wind power freezing weather disasters based on fuzzy probability according to claim 1, characterized in that, The method of using fuzzy information allocation to calculate the probability distribution characteristics of each key meteorological element during the freezing disaster specifically includes: The sample data of each meteorological element are divided into multiple level intervals; for each sample, the probability of fuzzy information allocation in each level interval is calculated using the following formula: in, Assign probabilities to fuzzy information in each level interval. For the first One sample, For the number of samples, For the first The median value of each grade range, Allocate the length of the information interval; Based on the cumulative probability distribution characteristics, the cumulative probability distribution is obtained. The probabilities of fuzzy information assigned to each sample in each level interval are accumulated to obtain the cumulative probability value of the meteorological element in each level: in, This represents the cumulative probability distribution.

5. The method for identifying regional wind power freezing weather disasters based on fuzzy probability according to claim 1, characterized in that, The meteorological condition indicators for determining freezing disaster weather in the target area are the numerical range corresponding to the cumulative probability distribution of each meteorological element reaching a preset threshold, including: The cumulative probability distributions of air temperature, dew point temperature, and dew point temperature difference are divided into intervals of 1 ℃, and wind speed is divided into intervals of 0.5 m / s. When a freezing disaster occurs, if the cumulative probability of air temperature exceeds the threshold, the corresponding air temperature value range is between -5 and 1 ℃; if the cumulative probability of dew point temperature exceeds the threshold, the corresponding dew point temperature value range is greater than -8 ℃; if the cumulative probability of dew point temperature difference exceeds the threshold, the corresponding dew point temperature difference value range is less than 5 ℃; and if the cumulative probability of wind speed reaches the threshold, the corresponding wind speed value range is less than 9 m / s.

6. The method for identifying regional wind power freezing weather disasters based on fuzzy probability according to claim 5, characterized in that, The expression for the meteorological condition index for determining the freezing disaster weather in the target area is as follows: in, For temperature, Dew point temperature, This refers to wind speed.

7. The method for identifying regional wind power freezing weather disasters based on fuzzy probability according to claim 5, characterized in that, The identification of wind power freezing disaster weather processes in the target area using meteorological condition indicators includes: Based on the satisfaction of the meteorological conditions indicators over time, the duration of freezing weather in the target area is assessed; when the temperature is between -5℃ and 1℃, the dew point temperature is less than -8℃, the dew point temperature difference is less than 5℃, and the wind speed is less than 9m / s, it is identified as the formation or development stage of freezing weather.

8. A regional wind power freezing weather identification system based on fuzzy probability, characterized in that, include: The data acquisition module is used to acquire meteorological element data from multiple locations within the target area, including temperature, humidity, and wind speed. The probability calculation module is used to calculate the probability distribution characteristics of each meteorological element during the freezing disaster using the fuzzy information allocation method, and to obtain the cumulative probability distribution based on the cumulative probability distribution characteristics. The indicator determination module is used to determine the meteorological condition indicators of freezing disaster weather in the target area based on the numerical range corresponding to the cumulative probability distribution of each meteorological element when it reaches a preset threshold. The identification output module is used to identify wind power freezing weather processes in the target area using meteorological condition indicators.

9. The regional wind power freezing weather identification system based on fuzzy probability according to claim 8, characterized in that, The data acquisition module is specifically used for: Acquire observation data from multiple meteorological stations or new energy power stations within the target area. The observation data includes icing, air temperature at a height of 2m, humidity, dew point temperature, and wind speed at a height of 10m. The humidity-related parameter is the dew point temperature difference, which is used to measure humidity.

10. The regional wind power freezing weather identification system based on fuzzy probability according to claim 9, characterized in that, Dew point temperature refers to the temperature at which air cools to saturation under constant conditions of water vapor content and air pressure. In other words, it is the temperature at which water vapor condenses when atmospheric conditions remain unchanged. When using the dew point temperature difference to measure humidity, the smaller the dew point temperature difference, the greater the humidity. When the dew point temperature difference approaches 0°C, it indicates that the water vapor in the air has reached a state of near saturation.

11. The regional wind power freezing weather identification system based on fuzzy probability according to claim 8, characterized in that, The probability calculation module is specifically used for: The sample data of each meteorological element are divided into multiple level intervals; for each sample, the probability of fuzzy information allocation in each level interval is calculated using the following formula: in, Assign probabilities to fuzzy information in each level interval. For the first One sample, For the number of samples, For the first The median value of each grade range, Allocate the length of the information interval; Based on the cumulative probability distribution characteristics, the cumulative probability distribution is obtained. The probabilities of fuzzy information assigned to each sample in each level interval are accumulated to obtain the cumulative probability value of the meteorological element in each level: in, This represents the cumulative probability distribution.

12. The regional wind power freezing weather identification system based on fuzzy probability according to claim 8, characterized in that, The indicator determination module is specifically used for: The cumulative probability distributions of air temperature, dew point temperature, and dew point temperature difference are divided into intervals of 1 ℃, and wind speed is divided into intervals of 0.5 m / s. When a freezing disaster occurs, if the cumulative probability of air temperature exceeds the threshold, the corresponding air temperature value range is between -5 and 1 ℃; if the cumulative probability of dew point temperature exceeds the threshold, the corresponding dew point temperature value range is greater than -8 ℃; if the cumulative probability of dew point temperature difference exceeds the threshold, the corresponding dew point temperature difference value range is less than 5 ℃; and if the cumulative probability of wind speed reaches the threshold, the corresponding wind speed value range is less than 9 m / s.

13. The regional wind power freezing weather identification system based on fuzzy probability according to claim 12, characterized in that, The expression for the meteorological condition index for determining the freezing disaster weather in the target area is as follows: in, For temperature, Dew point temperature, This refers to wind speed.

14. The regional wind power freezing weather identification system based on fuzzy probability according to claim 13, characterized in that, The identification output module is specifically used for: Based on the satisfaction of the meteorological conditions indicators over time, the duration of freezing weather in the target area is assessed; when the temperature is between -5℃ and 1℃, the dew point temperature is less than -8℃, the dew point temperature difference is less than 5℃, and the wind speed is less than 9m / s, it is identified as the formation or development stage of freezing weather.

15. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the regional wind power freezing weather identification method based on fuzzy probability as described in any one of claims 1 to 7.

16. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the regional wind power freezing weather identification method based on fuzzy probability as described in any one of claims 1 to 7.

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