Wind power plant wind energy resource distribution prediction method and device
By obtaining the layer code of the wind tower, filtering and converting the wind direction code and power code data, grouping and accumulating the wind direction frequency and power ratio, the problem of inaccurate wind energy resource prediction in the existing technology is solved, and the economic benefits and stability of the wind farm are improved.
Patent Information
- Application Number
- CN202510711784.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-05-29
- Publication Date
- 2025-09-05
AI Technical Summary
Existing technologies lack in-depth statistics and display of wind direction and wind energy-related data, which makes it difficult to comprehensively and accurately predict wind energy resource conditions, limiting the improvement of wind power generation efficiency and benefits.
By obtaining the layer number code of the wind measurement tower, filtering and converting the wind direction code and power code data, grouping and accumulating them according to the wind direction angle, calculating the wind direction frequency and power ratio, the wind energy resource distribution prediction results of the wind farm are obtained.
It has achieved accurate prediction of wind energy resource distribution, provided a scientific basis, and supported the planning and layout of wind farms, equipment selection, and operation strategy formulation, thereby improving the efficiency of wind energy resource utilization and reducing power generation costs.
Smart Images
Figure CN120592816A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the field of new energy technology, and in particular relates to a method and device for predicting wind energy resource distribution in a wind farm. Background Art
[0002] In the field of new energy, wind power generation, as a clean energy technology that converts wind energy into electrical energy, has core equipment including wind rotors, generators, direction regulators, towers, etc. Among them, wind rotors, as key components for capturing wind energy, rotate under the drive of wind, thereby driving the generator to convert mechanical energy into electrical energy. Due to the high uncertainty and spatiotemporal variability of wind energy resources, there are significant differences in parameters such as wind speed and wind direction in different regions, seasons and even at different times. Therefore, accurate and comprehensive acquisition of wind energy resource data, as well as scientific analysis and effective utilization of data are crucial to improving the power generation efficiency of wind power generation systems, reducing operating costs and ensuring the safety and reliability of the system.
[0003] At present, in the field of wind power generation, wind towers are mainly used as the core carrier to obtain wind energy resource data. Through the various monitoring equipment installed on the wind towers, such as anemometers, wind vanes and auxiliary sensors, and through data recorders, the collected data is transmitted, stored and displayed, providing certain data support for the operation and management of wind power generation. However, the existing wind energy resource visualization process only presents wind speed, wind direction, power, temperature, pressure, humidity and other data related to the wind tower. There is a lack of in-depth statistics and display of wind direction and wind energy related data, making it difficult for technicians to comprehensively and accurately predict the actual situation of wind energy resources, which to a certain extent limits the improvement of wind power generation efficiency and benefits. Summary of the Invention
[0004] In response to the technical problems existing in the prior art, the present invention provides a method and device for predicting the distribution of wind energy resources in a wind farm to solve the technical problem that the prior art lacks in-depth statistics and display of wind direction and wind energy-related data, making it difficult for technicians to comprehensively and accurately predict the actual situation of wind energy resources, which to a certain extent limits the improvement of wind power generation efficiency and benefits.
[0005] In order to achieve the above object, the technical solution adopted by the present invention is: The present invention provides a method for predicting wind energy resource distribution in a wind farm, comprising: According to the number of layers of the wind tower, obtain the measurement point code of the corresponding layer in the wind tower; According to the measurement point codes of the corresponding floors in the wind measurement tower, the equipment statistical data of the corresponding floors in the wind measurement tower are obtained; Filter the wind direction code and power code data from the equipment statistical data of the corresponding floors of the wind measurement tower; perform data conversion on the wind direction code and power code data to obtain key-value pairs of wind direction angle and power value; According to the wind direction angle, the key-value pairs of wind direction angle and power value are grouped to obtain the wind direction grouping results; and based on the wind direction grouping results, the number of wind direction occurrences and power are accumulated to obtain the statistical results of each wind direction; Based on the statistical results of each wind direction, the frequency and power proportion of each wind direction are calculated to obtain the wind energy resource distribution prediction results of the wind farm.
[0006] Furthermore, the measurement point codes corresponding to the number of layers in the wind measurement tower are stored in a pre-constructed string array; wherein, in the pre-constructed string array, each element corresponds to a measurement point code corresponding to the number of layers in the wind measurement tower.
[0007] Furthermore, the process of obtaining the equipment statistical data of the corresponding number of layers in the wind measurement tower according to the measurement point codes of the corresponding number of layers in the wind measurement tower includes: Use the HttpUtils.postDevTagStatArchive method to send a POST request. The POST request contains the target URL, data type, device ID, layer code, wind direction code, power code, time range, and aggregation interval. Receive the return data of the POST request and obtain the equipment statistical information within the preset time period as the equipment statistical data of the corresponding number of layers in the wind measurement tower.
[0008] Furthermore, the equipment statistics of the corresponding floors in the wind tower are stored in the pre-built Maps list; The process of filtering wind direction-coded and power-coded data from the device statistics corresponding to the number of layers on the wind measurement tower includes: using the filter method of the Stream API to perform stream processing on the Maps list storing the device statistics corresponding to the number of layers on the wind measurement tower to filter out data containing wind direction-coded and power-coded data.
[0009] Furthermore, in the key-value pair of wind direction angle and power value, the key is the wind direction angle, and the value is the power value.
[0010] Furthermore, the key-value pairs of wind direction angle and power value are grouped according to the wind direction angle to obtain the wind direction grouping result, including: Use the Collectors.groupingBy method to group the key-value pairs of wind direction angle and power value according to the wind direction angle to obtain the wind direction grouping results. Call the WindDirection.fromAngle method to convert the wind direction angle into an enumeration direction. The enumeration directions include north, northeast, east, southeast, south, southwest, west, and northwest.
[0011] The present invention also provides a wind farm wind energy resource distribution prediction system, comprising: The measuring point acquisition module is used to obtain the measuring point code of the corresponding layer in the wind measurement tower according to the number of layers of the wind measurement tower; A data acquisition module is used to obtain equipment statistical data of corresponding layers in the wind measurement tower according to the measurement point codes of the corresponding layers in the wind measurement tower; The data screening module is used to filter the wind direction code and power code data from the statistical data of the equipment on the corresponding floors of the wind measurement tower; perform data conversion on the wind direction code and power code data to obtain the key-value pairs of wind direction angle and power value; The grouping and accumulation module is used to group the key-value pairs of wind direction angle and power value according to the wind direction angle to obtain the wind direction grouping results; and based on the wind direction grouping results, the number of wind direction occurrences and power are accumulated to obtain the statistical results of each wind direction; The proportion statistics module is used to calculate the frequency and power proportion of each wind direction based on the statistical results of each wind direction, and obtain the wind energy resource distribution prediction results of the wind farm.
[0012] The present invention also provides an electronic device, comprising: a processor suitable for executing a computer program; A computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, the method for predicting the distribution of wind energy resources in a wind farm is executed.
[0013] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for predicting the distribution of wind energy resources in a wind farm is implemented.
[0014] The present invention also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the method for predicting the distribution of wind energy resources in a wind farm is implemented.
[0015] Compared with the prior art, the present invention has the following beneficial effects: The wind energy resource distribution prediction method provided by the present invention obtains the measurement point code according to the number of wind measurement tower layers, and then obtains the equipment statistical data of the corresponding number of layers, which can accurately locate the wind energy monitoring information at different heights to comprehensively collect wind farm data; then, the wind direction code and power code data are screened from the equipment statistical data and converted into key-value pairs, and then grouped according to the wind direction angle and accumulated to calculate the statistical results; finally, the frequency and power share of each wind direction are calculated based on the statistical results, and the obtained wind energy resource distribution prediction results can realize the intuitive presentation of the wind energy resource conditions of different wind directions, provide a scientific and accurate basis for the planning and layout of wind farms, equipment selection and operation strategy formulation, help to improve the utilization efficiency of wind energy resources, reduce power generation costs, and enhance the economic benefits and stability of wind farms, which is of great significance to promoting the high-quality development of the wind power generation industry.
[0016] The wind farm wind energy resource distribution prediction system, electronic equipment, computer-readable storage medium and computer program product provided by the present invention have all the advantages of the above-mentioned wind farm wind energy resource distribution prediction method. BRIEF DESCRIPTION OF THE DRAWINGS
[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.
[0018] Figure 1 Flowchart of the method for predicting wind energy resource distribution in a wind farm provided in Example 1; Figure 2 This is a visualization display result diagram of the wind energy resource distribution prediction result of the wind farm in Example 1; Figure 3 This is a structural block diagram of the wind energy resource distribution prediction system for a wind farm provided in Example 2; Figure 4 This is a structural block diagram of the electronic device provided in Example 3. DETAILED DESCRIPTION
[0019] In order to make the technical problems, technical solutions, and beneficial effects solved by this application more clearly understood, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this application; it is obvious that the described embodiments are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of this application.
[0020] The present invention provides a method for predicting wind energy resource distribution in a wind farm, comprising the following steps: Step 100: According to the number of floors of the wind measurement tower, obtain the measurement point codes of the corresponding floors in the wind measurement tower.
[0021] Step 200: Obtain equipment statistical data corresponding to the number of floors in the wind measurement tower according to the measurement point codes corresponding to the number of floors in the wind measurement tower.
[0022] Step 300: Filter and obtain wind direction code and power code data from the equipment statistical data of the corresponding floors in the wind measurement tower; perform data conversion on the wind direction code and power code data to obtain key-value pairs of wind direction angle and power value.
[0023] Step 400: Group the key-value pairs of wind direction angle and power value according to the wind direction angle to obtain the wind direction grouping result; and based on the wind direction grouping result, accumulate the number of wind direction occurrences and power to obtain the statistical result of each wind direction.
[0024] Step 500: Calculate the frequency and power proportion of each wind direction based on the statistical results of each wind direction to obtain the wind energy resource distribution prediction result of the wind farm.
[0025] The wind energy resource distribution prediction method of the wind farm described in the present invention obtains the measurement point codes of the corresponding layers according to the number of layers of the wind measurement tower, and then obtains the equipment statistical data. By obtaining data in layers, it can accurately locate the data of different height layers, fully taking into account the differences in wind energy resources at different heights, ensuring the accuracy and pertinence of the obtained data, and providing a reliable basis for subsequent analysis and statistics; secondly, the wind direction code and power code data are screened out from the equipment statistical data and converted to obtain the key-value pairs of wind direction angle and power value, realizing the conversion of complex coded data into key-value pairs that are convenient for analysis and processing, thereby improving the data processing efficiency and preparing for subsequent grouping and statistical work; in addition, In addition, the key-value pairs are grouped according to the wind direction angle, and the number of wind direction occurrences and power are accumulated to obtain the statistical results of each wind direction, which can comprehensively and carefully analyze the wind energy resources under different wind directions. Not only the frequency of wind direction occurrence is considered, but also the power information is combined to make the analysis results more in-depth and comprehensive, providing strong support for the accurate prediction of wind energy resource distribution; based on the statistical results of each wind direction, the frequency of wind direction occurrence and power share are calculated to obtain the wind farm wind energy resource distribution prediction results, which can intuitively reflect the contribution of different wind directions to wind energy resources, help technical personnel accurately grasp the distribution characteristics of wind energy resources in wind farms, and provide a scientific basis for the planning, design and operation of wind farms.
[0026] The following further explains the wind energy resource distribution prediction method for a wind farm provided by the present invention with some specific embodiments: Example 1 As attached Figure 1 As shown, this embodiment 1 provides a method for predicting wind energy resource distribution in a wind farm, comprising the following steps: Step 1: Based on the number of floors of the wind tower, obtain the measurement point codes corresponding to the number of floors of the wind tower. The measurement point codes corresponding to the number of floors of the wind tower are stored in a pre-built string array; each element in the pre-built string array corresponds to a measurement point code corresponding to the number of floors of the wind tower.
[0027] Specifically, the steps are as follows: Step 11. Create a string array and obtain the pre-built string array STOREY_CODES. Each element in the pre-built string array STOREY_CODES corresponds to a measurement point code corresponding to the number of floors of the wind measurement tower. For example, STOREY_CODES={"1F","2F","3F",...}, where "1F" represents the first floor of the wind measurement tower, "2F" represents the second floor of the wind measurement tower, "3F" represents the third floor of the wind measurement tower, and so on. It should be noted that the length of the pre-built string array STOREY_CODES is expanded according to actual needs to support more floors.
[0028] Step 12. Get the number of floors (storeyNum) of the wind tower; determine whether the number of floors of the wind tower is within the valid index range of the pre-built string array STOREY_CODES; specifically, determine whether the number of floors (storeyNum) of the wind tower is less than 1 or greater than or equal to the length of the pre-built string array STOREY_CODES; if the number of floors (storeyNum) of the wind tower is less than 1 or greater than or equal to the length of the pre-built string array STOREY_CODES, the number of floors (storeyNum) of the wind tower is invalid, and the measurement point code corresponding to the first floor of the wind tower is returned to avoid feedback of null values or exceptions; if the number of floors (storeyNum) of the wind tower is greater than or equal to 1 or less than the length of the pre-built string array STOREY_CODES, the number of floors (storeyNum) of the wind tower is valid, and STOREY_CODES[storeyNum-1] is returned, which is the measurement point code of the corresponding floor in the wind tower.
[0029] Step 2: According to the measurement point codes of the corresponding floors in the wind tower, obtain the equipment statistical data of the corresponding floors in the wind tower. Specifically, the steps are as follows: Step 21. Use the HttpUtils.postDevTagStatArchive method to send a POST request. The POST request includes the target URL, data type, device ID, layer code, wind direction code, power code, time range, and aggregation interval. Specifically, the target URL is the target device address of the request. The data type is used to indicate the type of data to be queried. The time range is the time period of the data to be queried, including the start time and end time. The aggregation interval is the time interval for data aggregation, such as hourly, daily, or monthly.
[0030] Step 22: Receive the return data of the POST request, obtain the device statistics within the preset time period, and use them as the device statistics for the corresponding number of layers in the wind measurement tower; wherein the device statistics for the corresponding number of layers in the wind measurement tower are stored in a pre-built Maps list; after obtaining the data, check whether the Maps list is empty; if it is empty, return an empty object.
[0031] Step 3: Filter and obtain wind direction coded and power coded data from the statistical data of the equipment on the corresponding floors of the wind measurement tower; perform data conversion on the wind direction coded and power coded data to obtain key-value pairs of wind direction angle and power value.
[0032] Specifically, the steps are as follows: Step 21: Use the filter method of the Stream API to perform stream processing on the Maps list that stores the device statistical data of the corresponding layers of the wind measurement tower to filter out the data containing wind direction code and power code.
[0033] Step 22. Use the map method to convert the wind direction coded and power coded data into key-value pairs of wind direction angle and power value, and obtain the key-value pairs of wind direction angle and power value. For example, if the wind direction angle in the original data is stored in the field with the key "windAngle" and the power value is stored in the field with the key "power", use the map method to convert each data into a Map.Entry<Double,Double> object; the key is the wind direction angle and the value is the power value.
[0034] Step 4: Group the key-value pairs of wind direction angle and power value according to the wind direction angle to obtain the wind direction grouping result; and based on the wind direction grouping result, accumulate the number of wind direction occurrences and power to obtain the statistical results of each wind direction.
[0035] Specifically, the steps are as follows: Step 41: Use the Collectors.groupingBy method to group the key-value pairs of wind direction angle and power value according to the wind direction angle to obtain the wind direction grouping result; wherein, call the WindDirection.fromAngle method to convert the wind direction angle into an enumeration direction; wherein, the enumeration direction includes north, northeast, east, southeast, south, southwest, west, and northwest.
[0036] Step 42: Use the Collectors.reducing method to accumulate the number of wind direction occurrences and power according to the wind direction grouping results to obtain the statistical results of each wind direction, providing a basis for subsequent data analysis and visualization; wherein, the statistical results of each wind direction are stored in the pre-created WindStats class; the WindStats class is used to store the statistical information of each wind direction, including count and total power; wherein, count represents the number of times the corresponding wind direction appears; total power represents the sum of all power values under the corresponding wind direction.
[0037] Step 5: Based on the statistical results of each wind direction, calculate the frequency and power proportion of each wind direction to obtain the wind energy resource distribution forecast result of the wind farm. Specifically, the process is as follows: For each WindStats object, set the frequency and power percentage fields in the PlantWindPowerRoseDataRes object according to its corresponding wind direction; use the DecimalFormat class to format the frequency and power percentage as percentage strings; for example, create a DecimalFormat object df = new DecimalFormat("0%"); then, use the df.format(value) method to format the value as a percentage.
[0038] It should be noted that the statistical results windStats of each wind direction are traversed; for each WindStats object, the occurrence frequency and power proportion of the corresponding wind direction are calculated and set into the PlantWindPowerRoseDataRes object; the occurrence frequency of each wind direction is divided by the total occurrence number to obtain the occurrence frequency, and the power proportion is obtained by dividing the total power of each wind direction by the total power of all wind directions; the DecimalFormat class is used to format the value into a percentage string, and the calculated percentage string is set into the PlantWindPowerRoseDataRes object for subsequent use or return to the client.
[0039] This embodiment 1 is a method for predicting the distribution of wind energy resources in a wind farm. It uses a pre-built string array STOREY_CODES to store the measurement point codes of the corresponding layers in the wind tower. The STOREY_CODES array adopts independent management coding rules, so that adding a new layer only requires expanding the array without modifying the business logic; it uses filter(Objects::nonNull) to filter empty data, and combines Optional.ofNullable to handle field missing scenarios to avoid NullPointerException; example: when map.get("C6") takes a value, Optional is used to elegantly handle empty values and return 0.0 by default; stream().map().filter().collect() chain calls are used to achieve integrated processing of data cleaning, conversion, and grouping statistics, and the code is concise and efficient; example: Collectors.groupingBy groups by wind direction, and Collectors.reducing accumulates counts and power, replacing traditional nested loops, avoiding manual management of intermediate variables, and improving code readability and maintainability.
[0040] In this embodiment 1, WindDirection.fromAngle() is used to convert the angle value of the wind direction angle into an enumeration wind direction, avoiding hard-coded strings and improving code maintainability. Example: switch-case is directly based on enumeration branches to reduce the risk of logical errors. DecimalFormat("0%") is used to unify the percentage output format to ensure data standardization. Specifically, DecimalFormat df = new DecimalFormat("0%"); is used to initialize the object, and the format() method encapsulated by DecimalFormat is called for formatting. The parallel capability of stream processing is used to improve processing efficiency, especially for large data volumes. Secondly, the PlantWindPowerRoseDataRes object is used to dynamically fill in fields through the setter method. When supporting wind direction type expansion (such as adding a northwest by north direction), only the enumeration and switch-case branches need to be expanded.
[0041] It should also be noted that when displaying the wind energy resource distribution forecast results of wind farms, they are displayed according to wind direction and corresponding wind energy in the base coordinate system, as shown in the attached figure. Figure 2As shown; based on the polar coordinate system, the length of the line segments in each direction represents the frequency of occurrence of the wind direction. The longer the line segment, the more frequently the wind in that direction appears. By calculating the proportion of wind energy in that wind direction, the wind energy potential under different wind directions can be clearly displayed. Then, through the visual model data, high-frequency and high-wind speed areas can be identified, and site selection can be prioritized to improve power generation efficiency. It can also be combined with the wind energy density distribution to predict annual power generation and support project feasibility analysis.
[0042] The method for predicting the distribution of wind energy resources in a wind farm described in Example 1 ensures data positioning accuracy by clarifying the layer coding rules, uses HTTP requests to efficiently obtain equipment statistical data for a specific time period, and uses stream processing to group and count wind direction and power to generate intuitive statistical objects. Finally, the response object is encapsulated and the frequency of occurrence and power proportion of different wind directions are clearly presented in percentage form. The overall process logic is clear and data acquisition and processing are efficient, providing a reliable and easy-to-analyze data foundation for the establishment of a wind resource and wind direction relationship model.
[0043] Example 2 As attached Figure 3 As shown, this embodiment 2 provides a wind farm wind energy resource distribution prediction system, including a measurement point acquisition module, a data acquisition module, a data screening module, a grouping accumulation module and a proportion statistics module.
[0044] The measuring point acquisition module is used to obtain the measuring point codes of the corresponding floors of the wind measurement tower according to the number of floors of the wind measurement tower; the data acquisition module is used to obtain the equipment statistical data of the corresponding floors of the wind measurement tower according to the measuring point codes of the corresponding floors of the wind measurement tower; the data screening module is used to screen the wind direction code and power code data from the equipment statistical data of the corresponding floors of the wind measurement tower; the wind direction code and power code data are converted to obtain key-value pairs of wind direction angle and power value; the grouping accumulation module is used to group the key-value pairs of wind direction angle and power value according to the wind direction angle to obtain the wind direction grouping result; and according to the wind direction grouping result, the number of wind direction occurrences and power are accumulated to obtain the statistical result of each wind direction; the proportion statistics module is used to calculate the frequency of occurrence and power proportion of each wind direction according to the statistical result of each wind direction, and obtain the wind energy resource distribution prediction result of the wind farm.
[0045] Example 3 As attached Figure 4 As shown, this embodiment 3 provides an electronic device, including: a memory for storing a computer program; and a processor for implementing the steps of the method for predicting the distribution of wind energy resources in a wind farm when executing the computer program. Alternatively, the processor implements the functions of each module in the aforementioned system for predicting the distribution of wind energy resources in a wind farm when executing the computer program.
[0046] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing preset functions, and the instruction segments are used to describe the execution process of the computer program in the electronic device.
[0047] The electronic device may be a computing device such as a desktop computer, laptop, PDA, or cloud server. The electronic device may include, but is not limited to, a processor and memory. Those skilled in the art will appreciate that the above are examples of electronic devices and do not constitute a limitation on electronic devices. The electronic device may include more components than those described above, or a combination of certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.
[0048] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor. The processor is the control center of the electronic device and connects various parts of the entire electronic device using various interfaces and lines.
[0049] The memory may be used to store the computer programs and / or modules, and the processor implements various functions of the electronic device by running or executing the computer programs and / or modules stored in the memory and calling the data stored in the memory.
[0050] The memory may primarily include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function (such as sound playback or image playback); the data storage area may store data generated based on the use of the mobile phone (such as audio data and a phone book). Furthermore, the memory may include high-speed random access memory (RAM) and non-volatile memory, such as a hard disk, internal memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, at least one disk storage device, a flash memory device, or other volatile solid-state storage device.
[0051] Example 4 This embodiment 4 further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for predicting the distribution of wind energy resources in a wind farm are implemented.
[0052] If the modules / units integrated in the wind farm wind energy resource distribution prediction system are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium.
[0053] Based on this understanding, the present invention implements all or part of the process of the aforementioned wind farm wind energy resource distribution prediction method by using a computer program to instruct related hardware. The computer program may be stored in a computer-readable storage medium. When executed by a processor, the computer program may implement the steps of the aforementioned wind farm wind energy resource distribution prediction method. The computer program includes computer program code, which may be in source code form, object code form, executable file, or a pre-defined intermediate form.
[0054] The computer-readable storage medium may include: any entity or device that can carry the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium, etc.
[0055] Example 5 This embodiment 5 provides a computer product, which includes a computer program product, and the computer program is stored in a computer-readable storage medium; the processor of the electronic device reads the computer program from the computer-readable storage medium, and the processor executes the computer program, so that the electronic device can execute the wind farm wind energy resource distribution prediction method described in embodiment 1, which will not be repeated here.
[0056] It should be noted that a person skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods.
[0057] The above embodiment is only one of the implementation methods that can realize the technical solution of the present invention. The scope of protection claimed by the present invention is not limited only to this embodiment, but also includes changes, replacements and other implementation methods that can be easily thought of by any technician familiar with this technical field within the technical scope disclosed by the present invention.
Claims
1. A method for predicting wind energy resource distribution in a wind farm, characterized in that: include: According to the number of layers of the wind tower, obtain the measurement point code of the corresponding layer in the wind tower; According to the measurement point codes of the corresponding floors in the wind measurement tower, the equipment statistical data of the corresponding floors in the wind measurement tower are obtained; Filter the wind direction code and power code data from the equipment statistical data of the corresponding floors of the wind measurement tower; perform data conversion on the wind direction code and power code data to obtain key-value pairs of wind direction angle and power value; According to the wind direction angle, the key-value pairs of wind direction angle and power value are grouped to obtain the wind direction grouping results; According to the wind direction grouping results, the number of wind direction occurrences and power are accumulated to obtain the statistical results of each wind direction; Based on the statistical results of each wind direction, the frequency and power proportion of each wind direction are calculated to obtain the wind energy resource distribution prediction results of the wind farm.
2. A wind farm wind energy resource distribution prediction method according to claim 1, characterized in that: The measurement point codes corresponding to the number of layers in the wind measurement tower are stored in a pre-constructed string array; wherein, each element in the pre-constructed string array corresponds to a measurement point code corresponding to the number of layers in the wind measurement tower.
3. A method for predicting wind energy resource distribution in a wind farm according to claim 1, characterized in that: The process of obtaining the equipment statistical data of the corresponding layers of the wind measurement tower according to the measurement point codes of the corresponding layers of the wind measurement tower includes: Use the HttpUtils.postDevTagStatArchive method to send a POST request. The POST request contains the target URL, data type, device ID, layer code, wind direction code, power code, time range, and aggregation interval. Receive the return data of the POST request and obtain the equipment statistical information within the preset time period as the equipment statistical data of the corresponding number of layers in the wind measurement tower.
4. A method for predicting wind energy resource distribution in a wind farm according to claim 1, characterized in that: The equipment statistics of the corresponding layers in the wind tower are stored in the pre-built Maps list; The process of filtering wind direction-coded and power-coded data from the device statistics corresponding to the number of layers on the wind measurement tower includes: using the filter method of the Stream API to perform stream processing on the Maps list storing the device statistics corresponding to the number of layers on the wind measurement tower to filter out data containing wind direction-coded and power-coded data.
5. A method for predicting wind energy resource distribution in a wind farm according to claim 1, characterized in that: In the key-value pair of wind direction angle and power value, the key is the wind direction angle and the value is the power value.
6. A method for predicting wind energy resource distribution in a wind farm according to claim 1, characterized in that: The process of grouping the key-value pairs of wind direction angle and power value according to wind direction angle and obtaining the wind direction grouping results includes: Use the Collectors.groupingBy method to group the key-value pairs of wind direction angle and power value according to the wind direction angle to obtain the wind direction grouping results. Call the WindDirection.fromAngle method to convert the wind direction angle into an enumeration direction. The enumeration directions include north, northeast, east, southeast, south, southwest, west, and northwest.
7. A wind farm wind energy resource distribution prediction system, characterized in that: include: The measuring point acquisition module is used to obtain the measuring point code of the corresponding layer in the wind measurement tower according to the number of layers of the wind measurement tower; A data acquisition module is used to obtain equipment statistical data of corresponding layers in the wind measurement tower according to the measurement point codes of the corresponding layers in the wind measurement tower; The data screening module is used to filter the wind direction code and power code data from the statistical data of the equipment on the corresponding floors of the wind measurement tower; perform data conversion on the wind direction code and power code data to obtain the key-value pairs of wind direction angle and power value; The grouping accumulation module is used to group the key-value pairs of wind direction angle and power value according to the wind direction angle to obtain the wind direction grouping results; According to the wind direction grouping results, the number of wind direction occurrences and power are accumulated to obtain the statistical results of each wind direction; The proportion statistics module is used to calculate the frequency and power proportion of each wind direction based on the statistical results of each wind direction, and obtain the wind energy resource distribution prediction results of the wind farm.
8. An electronic device, characterized in that: include: a processor suitable for executing a computer program; A computer-readable storage medium having a computer program stored therein, wherein when the computer program is executed by the processor, the method for predicting the distribution of wind energy resources in a wind farm according to any one of claims 1 to 6 is executed.
9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for predicting wind energy resource distribution in a wind farm according to any one of claims 1 to 6 is implemented.
10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the method for predicting wind energy resource distribution in a wind farm according to any one of claims 1 to 6 is implemented.