Wind tower data accuracy judgment method and system, medium and electronic equipment
By analyzing the actual measured data and extreme weather conditions of the wind measurement tower in the wind power project and evaluating data deviation and correlation, the problem of inconsistent quality of wind measurement data in the wind power project is solved, and the accuracy of wind tower data and the evaluation accuracy of wind power project are improved.
Patent Information
- Application Number
- CN202411705094.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-26
- Publication Date
- 2025-04-15
AI Technical Summary
In wind power projects, the quality of wind measurement data is not unified, resulting in a deviation in the wind resource assessment conclusion. Especially in large-base onshore wind power projects, due to the location of the wind measurement tower, some wind measurement data are abnormal.
By obtaining the measured data of each wind measurement tower in the target wind farm area and the extreme weather period, processing the measured data to obtain invalid wind measurement data, analyzing the causes of the invalid data, comparing the measured data with the wind map and mesoscale data to judge the data deviation and correlation, and thus evaluating the accuracy of the wind tower data.
Effectively judge and correct the wind measurement tower data affected by extreme weather, improve the accuracy of the wind measurement tower data, and thus improve the accuracy of the power generation evaluation of wind power projects.
Smart Images
Figure CN119830025A_ABST
Abstract
Description
Technical Field
[0001] The present application belongs to the field of new energy wind power technology and relates to a method, system, medium and electronic equipment for determining the accuracy of wind tower data. Background Art
[0002] Since the 21st century, with the continuous breakthroughs in clean energy power generation technologies such as wind power, photovoltaics, and nuclear power, renewable energy has entered a stage of rapid development, providing a new direction for the development of future energy. In the development of wind power projects, the accuracy of wind resource assessment directly affects the scale and rate of return of project development. The means of power generation measurement are largely affected by the quality of existing wind measurement data, and the data quality causes deviations in the conclusions of wind resource assessment. Because the site of large-scale onshore wind power projects involves a large area, the location of wind towers varies in altitude, humidity, etc., which can easily lead to abnormalities in some wind measurement data in the site, resulting in a large number of wind towers on site but inconsistent data quality. Summary of the invention
[0003] The purpose of the present application is to provide a method, system, medium and electronic equipment for judging the accuracy of wind tower data, which are used to accurately judge the wind towers whose wind measurement data need to be corrected.
[0004] In a first aspect, the present application provides a method for determining the accuracy of wind tower data, the method comprising: obtaining measured data of each wind tower in a target wind farm area and a period of extreme weather occurrence corresponding to the same period as the wind measurement period; processing the measured data of each wind tower to obtain invalid wind measurement data; obtaining a cause determination result of the invalid wind measurement data based on the invalid wind measurement data and the period of extreme weather occurrence; obtaining wind measurement data of the target wind farm area in a wind map, and comparing the measured data with the wind measurement data in the wind map to obtain a data deviation result; obtaining mesoscale data at the location of each wind tower, and comparing the measured data of each wind tower with the mesoscale data to obtain a data correlation comparison result; and obtaining the accuracy of the wind tower data based on the cause determination result, the data deviation result, and the data correlation comparison result.
[0005] In an implementation of the first aspect, the process of processing the measured data of each wind tower to obtain invalid wind measurement data includes: judging and screening the measured data of each wind tower based on a test condition to obtain invalid wind measurement data, wherein the invalid wind measurement data is data that does not meet the test condition.
[0006] In an implementation of the first aspect, the test conditions include: the average wind speed range of the wind tower in the first time period; the absolute value of the average wind speed change in the wind speed channel in the second time period; whether the duration of continuous no change in wind speed is less than a third time period; wherein the first time period is less than the second time period, and the second time period is less than the third time period.
[0007] In an implementation of the first aspect, the process of obtaining a result of determining the cause of the invalid wind measurement data based on the invalid wind measurement data and the period of occurrence of extreme weather includes: obtaining a period of time when abnormal wind measurement data of each wind measurement tower occurs based on the invalid wind measurement data; and comparing the period of abnormal wind measurement data with the period of occurrence of extreme weather to obtain a result of determining the cause of the invalid wind measurement data.
[0008] In an implementation of the first aspect, the cause determination result of the invalid wind measurement data includes: if a time period when the wind measurement data is abnormal is consistent with a time period when the extreme weather occurs, then the abnormality of the wind tower data is caused by extreme weather.
[0009] In an implementation of the first aspect, the process of obtaining mesoscale data at the location of each wind tower, and comparing the measured data of each wind tower with the mesoscale data to obtain a data correlation comparison result includes: obtaining the correlation between the measured data of the wind towers and the mesoscale data in each wind direction sector; comparing the overall correlation of each wind tower to obtain a correlation comparison result of each wind tower; comparing the measured data of each wind tower with the change trend of the mesoscale monthly wind measurement data to obtain a monthly change trend comparison result; and obtaining the data correlation comparison result based on the correlation comparison result of each wind tower and the monthly change trend comparison result.
[0010] In an implementation of the first aspect, the process of obtaining the accuracy of the wind tower data based on the cause judgment result, the data deviation result and the data correlation comparison result includes: obtaining the degree to which each wind tower is affected by extreme weather according to the cause judgment result, the data deviation result and the data correlation comparison result; obtaining the accuracy of the wind tower data according to the degree to which each wind tower is affected by extreme weather.
[0011] In a second aspect, the present application provides a wind tower data accuracy judgment system, the wind tower data accuracy judgment system comprising: a data acquisition module, used to obtain the measured data of each wind measuring tower in the target wind field area and the extreme weather occurrence period corresponding to the same period of the wind measuring period; a data processing module, used to process the measured data of each wind measuring tower to obtain invalid wind measurement data; a time period comparison module, used to obtain the cause judgment result of the invalid wind measurement data based on the invalid wind measurement data and the extreme weather occurrence period; a wind measurement data comparison module, used to obtain the wind measurement data of the target wind field area in the wind map, and compare the measured data with the wind measurement data in the wind map to obtain a data deviation result; a mesoscale data comparison module, used to obtain the mesoscale data at the position of each wind measuring tower, and compare the measured data of each wind measuring tower with the mesoscale data to obtain a data correlation comparison result; a data accuracy acquisition module, used to obtain the accuracy of the wind tower data according to the cause judgment result, the data deviation result and the data correlation comparison result.
[0012] In a third aspect, the present application provides an electronic device, comprising: a memory on which a computer program is stored; and a processor, communicatively connected to the memory, for executing the computer program to implement the above-mentioned method for determining the accuracy of wind tower data.
[0013] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by an electronic device, implements the above-mentioned method for determining the accuracy of wind tower data.
[0014] As described above, the wind tower data accuracy judgment method, system, medium and electronic device described in this application have the following features:
[0015] Beneficial effects:
[0016] The wind tower data accuracy judgment method provided in the present application can obtain the cause judgment result of invalid wind measurement data based on the measured data of each wind tower in the target wind field area and the extreme weather occurrence period corresponding to the wind measurement period, obtain the deviation of the measured data of the wind tower according to the wind measurement data of the target wind field area in the wind map, and obtain the correlation comparison between the measured data of the wind tower and the mesoscale data. The wind tower with abnormal wind measurement data is effectively judged through the cause judgment result, data deviation result and data correlation comparison result. The accuracy of wind tower data can be improved by the wind tower data accuracy judgment method of the present application. BRIEF DESCRIPTION OF THE DRAWINGS
[0017] Figure 1 Shown is a schematic diagram of an application scenario of the method for determining the accuracy of wind tower data described in an embodiment of the present application.
[0018] Figure 2Shown is a process diagram of a method for determining the accuracy of wind tower data according to an embodiment of the present application.
[0019] Figure 3 Shown is a schematic diagram of the process of obtaining data correlation comparison results described in an embodiment of the present application.
[0020] Figure 4 Shown is a diagram of the wind speed of the T2 wind measurement tower affected by extreme weather conditions in an embodiment of the present application.
[0021] Figure 5 Shown is a schematic diagram of the results of the extreme weather summary for the target wind farm area described in the embodiments of the present application.
[0022] Figure 6 Shown is the measured data of the T2 wind tower in the embodiment of the present application and the monthly trend chart of the mesoscale wind speed.
[0023] Figure 7 Shown is the measured data of the T1 wind tower in the embodiment of the present application and the monthly trend chart of the mesoscale wind speed.
[0024] Figure 8 Shown is a structural schematic diagram of the wind tower data accuracy judgment system described in an embodiment of the present application.
[0025] Fig. 9 Shown is a schematic diagram of the structure of an electronic device described in an embodiment of the present application.
[0026] Component number description
[0027] 1 Wind tower data accuracy judgment system
[0028] 11 Data acquisition module
[0029] 12 Data Processing Module
[0030] 13. Time period comparison module
[0031] 14 Wind measurement data comparison module
[0032] 15 Mesoscale Data Comparison Module
[0033] 16 Data Accuracy Acquisition Module
[0034] 2 Electronic devices
[0035] 21 Memory
[0036] 22 Processor
[0037] Steps S11 to S16
[0038] Steps S21 to S24 DETAILED DESCRIPTION
[0039] The following describes the embodiments of the present application through specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. The present application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.
[0040] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present application, and thus the drawings only show components related to the present application rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed at will, and the component layout may also be more complicated.
[0041] Since the 21st century, with the continuous breakthroughs in clean energy power generation technologies such as wind power, photovoltaics, and nuclear power, renewable energy has entered a stage of rapid development, providing a new direction for the development of future energy. In the development of wind power projects, the accuracy of wind resource assessment directly affects the scale and rate of return of project development. The means of power generation measurement are largely affected by the quality of existing wind measurement data, and the data quality causes deviations in the conclusions of wind resource assessment. Because the site of large-scale onshore wind power projects involves a large area, the location of wind towers varies in altitude, humidity, etc., which can easily lead to abnormalities in some wind measurement data in the site, resulting in a large number of wind towers on site but inconsistent data quality.
[0042] At least to address the above problems, the following embodiments of the present application provide a method for determining the accuracy of wind tower data. The technical solution in the embodiments of the present application will be described in detail below in conjunction with the accompanying drawings in the embodiments of the present application.
[0043] Figure 1 The following is a schematic diagram of an application scenario of an embodiment of the present application. Figure 1 As shown in the figure, in a wind power project area in a hilly area at high latitudes, in order to fully understand the distribution of wind resources with complex terrain and significant changes in altitude, and to evaluate wind conditions at different altitudes, multiple wind towers of different heights were set up in the area. These wind towers are evenly distributed, covering wind conditions at different altitudes, and are equipped with meteorological sensors such as wind speed and wind direction to collect real-time data on wind conditions in the area.
[0044] Figure 2 The following is a schematic diagram of the process of determining the accuracy of wind tower data in one embodiment of the present application. Figure 2 As shown, the wind tower data accuracy judgment method includes:
[0045] S11, obtaining the measured data of each wind tower in the target wind field area and the extreme weather occurrence period corresponding to the wind measurement period.
[0046] For example, in order to measure the wind energy in the wind farm project area, a wind tower is installed in the target wind farm area and equipped with a variety of meteorological sensors and wind measuring devices to accurately measure key meteorological parameters such as wind speed, wind direction, temperature, humidity and air pressure. For example, an anemometer can capture the speed of air flow, while a wind vane indicates changes in wind direction. In addition, records of extreme weather events can be obtained by combining historical meteorological data from local meteorological stations, and satellite remote sensing technology can be used to monitor information such as surface temperature and vegetation coverage, which can also indirectly determine extreme weather events that may occur during the same period of the wind measurement period.
[0047] Among them, extreme weather includes, for example, low temperature freezing, high temperature heat waves, frost, thunderstorms, blizzards, sandstorms and the like.
[0048] S12, processing the measured data of each wind tower to obtain invalid wind measurement data. Invalid wind measurement data includes abnormal measured data such as missing data, abnormal data and data error.
[0049] S13, based on the invalid wind measurement data and the extreme weather occurrence period, obtaining a cause determination result of the invalid wind measurement data.
[0050] S14, obtaining the wind measurement data of the target wind field area in the wind atlas, and comparing the measured data with the wind measurement data in the wind atlas to obtain a data deviation result.
[0051] The wind map is a spatial distribution map that describes the wind conditions in a wind farm. The wind map can display wind energy parameters such as wind speed and wind direction at different heights and directions in the target wind farm area, and display the wind conditions in the entire target wind farm area. Wind maps are usually generated based on data sources such as ground meteorological stations, wind towers, and satellite remote sensing, using numerical weather forecast models or statistical analysis methods. The wind measurement data of the target wind farm area in the wind map is usually presented in the form of contour maps or color maps, showing information such as wind speed and wind direction in the area at different heights and directions. The wind map can further understand the law of change of wind speed with height, the trend of change of wind direction, etc. Comparing the measured data of the wind tower with the wind measurement data in the wind map can determine the actual wind conditions such as wind speed and wind resources of the wind tower in the target wind farm area.
[0052] S15, obtaining mesoscale data at the location of each wind tower, and comparing the measured data of each wind tower with the mesoscale data to obtain a data correlation comparison result.
[0053] Mesoscale data refers to meteorological data with a coverage range of tens to hundreds of kilometers, such as weather maps, numerical weather forecasts, meteorological satellite remote sensing data, etc. Mesoscale data can be used to obtain large-scale wind field characteristics in the target wind field area. Both the measured data of the wind tower and the mesoscale data are derived from atmospheric movement. There is a spatial and temporal correlation between the two. By comparing the correlation between the measured data of the wind tower and the mesoscale data, the degree of consistency between the two can be judged. If the correlation between the measured data and the mesoscale data is low, it indicates that the measured data may be abnormal.
[0054] S16, obtaining the accuracy of the wind tower data according to the cause determination result, the data deviation result and the data correlation comparison result.
[0055] The accuracy of the wind tower data is determined by determining the degree to which the wind tower is affected by extreme weather. If the wind tower is less affected by extreme weather, the accuracy of the wind tower data is relatively high. Conversely, if the wind tower is more affected by extreme weather, the accuracy of the wind tower data is low.
[0056] According to the above description, the wind tower data accuracy judgment method provided by the present application can obtain the cause judgment result of invalid wind measurement data based on the measured data of each wind tower in the target wind field area and the extreme weather occurrence period corresponding to the wind measurement period, obtain the deviation of the measured data of the wind tower according to the wind measurement data of the target wind field area in the wind map, and obtain the correlation comparison between the measured data of the wind tower and the mesoscale data. The wind tower with abnormal wind measurement data is effectively judged through the cause judgment result, data deviation result and data correlation comparison result. The accuracy of wind tower data can be improved by the wind tower data accuracy judgment method of the present application.
[0057] In one embodiment of the present application, the process of processing the measured data of each wind tower to obtain invalid wind measurement data includes: judging and screening the measured data of each wind tower based on the inspection conditions to obtain invalid wind measurement data, and the invalid wind measurement data is data that does not meet the inspection conditions. The invalid wind measurement data is screened to further determine whether the invalid data is caused by extreme weather. And the invalid wind measurement data is removed from the measured data of the wind tower.
[0058] Exemplarily, the measured data of the wind tower is judged according to the relevant inspection standards for wind energy resource measurement of the wind farm project, wherein the inspection conditions include: the average wind speed range of the wind tower in the first time period; the absolute value of the average wind speed change in the wind speed channel in the second time period; whether the duration of continuous no change in wind speed is less than a third time period; wherein the first time period is less than the second time period, and the second time period is less than the third time period.
[0059] For example, the test conditions may be: 1) the average wind speed of the wind tower within 10 minutes ranges from 0 to 40 m / s. 2) the absolute value of the average wind speed change in the wind speed channel within 1 hour ranges from 0 to 6 m / s. 3) the wind speed does not change continuously for less than 6 hours.
[0060] In one embodiment of the present application, the process of obtaining the accuracy of the wind tower data based on the cause judgment result, the data deviation result and the data correlation comparison result includes: obtaining the degree to which each wind tower is affected by extreme weather based on the cause judgment result, the data deviation result and the data correlation comparison result; obtaining the accuracy of the wind tower data based on the degree to which each wind tower is affected by extreme weather.
[0061] In one embodiment of the present application, the process of obtaining the cause judgment result of the invalid wind measurement data based on the invalid wind measurement data and the extreme weather occurrence period includes: obtaining the time period when the wind measurement data of each wind measurement tower is abnormal based on the invalid wind measurement data; comparing the time period when the wind measurement data is abnormal with the time period when the extreme weather occurs to obtain the cause judgment result of the invalid wind measurement data. The cause judgment result of the invalid wind measurement data includes: if the time period when the wind measurement data is abnormal is consistent with the time period when the extreme weather occurs, then the abnormality of the wind measurement tower data is caused by extreme weather.
[0062] For example, by verifying the measured data of the wind tower, the missing data and unreasonable data are sorted out to obtain invalid wind measurement data. The period of extreme weather occurrence is obtained based on the extreme weather summary in the area where the wind power project is located. The period when invalid wind measurement data appears is compared with the period when extreme weather occurs to determine whether the abnormal wind measurement data is caused by the influence of extreme weather on the wind tower.
[0063] In one embodiment of the present application, the process of obtaining the wind measurement data of the target wind field area in the wind atlas, and comparing the measured data with the wind measurement data in the wind atlas to obtain the data deviation result includes:
[0064] Compare the wind measurement data of each wind tower with the wind measurement data change trend of the corresponding target wind field area in the wind map to obtain the data deviation result. If the wind measurement data of each wind tower is consistent with the wind measurement data in the wind map, it means that there is no deviation in the data of each wind tower. On the contrary, if the wind measurement data of each wind tower is inconsistent with the wind measurement data in the wind map, it means that there is a deviation in the data of each wind tower. If the trend change of the difference between the wind tower data and the wind map data is consistent with the cause judgment result of the invalid wind measurement data, the degree of influence of the wind tower on extreme weather can be further obtained according to the size of the difference.
[0065] Figure 3The diagram shows a process of obtaining data correlation comparison results in one embodiment of the present application. Figure 3 As shown, the process of obtaining mesoscale data at the positions of each wind tower and comparing the measured data of each wind tower with the mesoscale data to obtain data correlation comparison results includes:
[0066] S21, obtaining the correlation between the measured data of the wind tower in each wind direction sector and the mesoscale data.
[0067] For example, the correlation values between the measured data of the wind tower and the mesoscale wind speed in the 16 wind direction sectors and the overall correlation value of each wind direction sector set are obtained according to the intercept and the slope. The higher the correlation, the higher the quality of the measured data.
[0068] S22, comparing the overall correlations of the wind towers to obtain a correlation comparison result of the wind towers.
[0069] Exemplarily, the overall correlation values of each wind tower are compared, and the degree to which each wind tower is affected by extreme weather is obtained according to the size of the overall correlation ratio.
[0070] S23, comparing the measured data of each wind tower with the change trend of the mesoscale monthly wind measurement data to obtain a comparison result of the monthly change trend.
[0071] For example, the measured data of the wind tower throughout the year are compared with the mesoscale data for monthly data change trends. If the annual change trends of the wind tower data and the mesoscale monthly wind data are basically consistent, it indicates that the wind tower is less affected by freezing; if the trend of the wind tower data changes significantly within a certain period of time, it indicates that the wind tower is more affected by extreme weather.
[0072] S24, obtaining the data correlation comparison result based on the correlation comparison result of each wind tower and the monthly trend comparison result.
[0073] Next, the method for judging the accuracy of wind tower data provided by the embodiment of the present application will be introduced in detail through a specific example. It should be noted that the content in this example is only used to explain and illustrate the method for judging the accuracy of wind tower data provided by the embodiment of the present application, and is not used to limit the scope of protection of the present application. In specific applications, corresponding steps can be added or deleted based on this example according to actual needs. Taking a wind power project at the junction of a desert and hilly area in Inner Mongolia as an example, 5 wind towers are installed on the project site. The wind towers are numbered T1 to T5. The locations of the wind towers are shown in the attached Figure 1 All towers have been measuring wind for a full year. The accuracy of wind tower data from each tower is determined as follows:
[0074] Step 1: Obtain the measured data of each wind tower in the target wind farm area and the extreme weather period corresponding to the wind measurement period. According to the extreme weather time recorded by the local meteorological bureau, ice and heavy snow in the target wind farm area often occur from December to February of the following year.
[0075] Step 2: Process the measured data of each wind tower to obtain invalid wind measurement data. Based on the invalid wind measurement data and the period of extreme weather, the cause of the invalid wind measurement data is determined. According to the wind energy resource measurement standard for wind farm projects, the measured data of the five wind towers are verified to obtain invalid wind measurement data that is unreasonable and missing. The verification results show that the three wind towers (T2, T3, and T5) located in the north have unreasonable wind speeds at the same time. According to Figure 4 It can be seen that the wind speed was zero for more than 24 hours from February 19 to February 20, while the two wind towers (T1 and T4) in the south did not find any abnormalities during this period. Figure 5 From the extreme weather summary information, we can see that there was an ice record on February 20, which means that the wind speed measurements of T2, T3, and T5 towers were distorted by ice during that period, but T1 and T4 towers were not affected. This situation often occurs from December to February of the following year, which is consistent with the period of frequent extreme weather.
[0076] Step 3: Obtain the wind measurement data of the target wind field area in the wind map, and compare the measured data with the wind measurement data in the wind map to obtain the data deviation result. Figure 1 As shown in Table 1, by comparing the full-year measured data of the five wind towers with the wind speed at the wind map, it can be seen that the wind speed difference between the wind towers T2, T3, and T5 and the wind map is close to 0.9 m / s, while the wind speed difference between the wind towers T1 and T4 and the wind map is about 0.55 m / s. The trend change of the difference is consistent with the impact of freezing analyzed in step 2, that is, the measured wind speed values of the wind towers T2, T3, and T5 are low, indicating that they are greatly affected by freezing. The wind measuring equipment fails due to icing, and the wind speed level during the affected period is seriously underestimated. The wind towers T1 and T4 are less affected.
[0077] Table 1 Comparison of wind speed measured by wind tower and wind spectrum
[0078] Wind tower number Wind speed (m / s) Measured wind speed (m / s) Difference(m / s) T1 7.964 7.406 0.558 T2 7.235 6.335 0.9 T3 7.765 6.823 0.942 T4 7.981 7.456 0.525 T5 7.608 6.761 0.847
[0079] Step 4: Obtain mesoscale data at the location of each wind tower, and compare the measured data of each wind tower with the mesoscale data to obtain a data correlation comparison result.
[0080] Among them, the correlation between the measured data of the wind tower and the mesoscale data in each wind direction sector is obtained, and the overall correlation of each wind tower is compared to obtain the correlation comparison results of each wind tower. The correlation between the mesoscale data at each wind tower point and the 16 wind direction sectors at each point is obtained. The two southernmost and northernmost wind towers T1 and T2 are used for comparison. The correlation table of T1 and T2 wind towers is shown in Table 2 and Table 3. Among them, the overall correlation R between T1 wind tower and mesoscale wind speed is 2 The overall correlation R between the T2 wind tower and the mesoscale wind speed is 0.691. 2 It is 0.605, which means that the T1 wind tower is much less affected by freezing than the T2 wind tower.
[0081] Table 2 Correlation between measured data of T2 wind tower and mesoscale wind speed
[0082] Sector (degrees) intercept Slope <![CDATA[R 2 ]]> 348.75-11.25 1.166 0.683 0.584 11.25-33.75 1.740 0.513 0.347 33.75-56.25 1.570 0.676 0.302 56.25-78.75 0.692 0.901 0.672 78.75-101.25 1.638 0.769 0.584 101.25-123.75 1.007 0.819 0.648 123.75-146.25 1.613 0.651 0.494 146.25-168.75 0.590 0.773 0.514 168.75-191.25 0.629 0.779 0.524 191.25-213.75 0.537 0.779 0.497 213.75-236.25 1.044 0.751 0.548 236.25-258.75 0.854 0.823 0.621 258.75-281.25 -0.560 0.962 0.702 281.25-303.75 -0.058 0.896 0.711 303.75-326.25 0.781 0.740 0.652 326.25-348.75 0.681 0.748 0.693 All 0.605
[0083] Table 3 Correlation between measured data of T1 wind tower and mesoscale wind speed
[0084]
[0085]
[0086] Compare the measured data of each wind tower with the change trend of the mesoscale monthly wind measurement data to obtain the comparison results of the monthly change trend. Figure 6 and Figure 7 By comparing the monthly wind speed trends of T1 and T2 wind towers with those of the mesoscale wind tower, it can be seen that the annual change trend of the wind speed of T1 wind tower is basically consistent with that of the mesoscale wind tower. The wind speed of T2 wind tower drops significantly from December to February of the following year, and its change trend is inconsistent with that of the other nine months of the year, further proving that the T2 wind tower is more affected by freezing than the T1 wind tower.
[0087] Step five, the accuracy of the wind tower data is obtained according to the cause judgment results, data deviation results and data correlation comparison results. The cause judgment results, data deviation results and data correlation comparison results effectively prove the degree to which the wind measurement data of different wind towers in the same area are affected by extreme weather, that is, the wind towers that need to correct the wind measurement data are obtained according to the wind speed conditions of the wind towers. The T2, T3, and T5 wind towers need to process the wind measurement data from December to February of the following year and correct the wind speed upward. After optimizing the processing of the wind tower wind measurement data, the accuracy of the wind tower data can be improved, and the accuracy of the subsequent power generation evaluation calculation is further improved.
[0088] In summary, the wind tower data accuracy judgment method of the present application provides a method for judging whether the accuracy of wind tower data is affected by extreme weather, by determining the period of occurrence of extreme weather in the target wind farm area, comparing and judging whether the invalid wind measurement data in the wind tower data is consistent with the extreme weather time, comparing and testing the wind tower measured data with the wind spectrum change trend, and comparing and judging the correlation between the wind tower measured data and the mesoscale and the month-to-month comparison, to comprehensively judge the accuracy of the wind tower measured data. The wind tower data accuracy judgment method of the present application is more suitable for large-scale wind farm sites under the large base model. It is aimed at the problem that there are many wind towers but the data quality varies due to the different degrees of influence of factors such as terrain and climate. It can effectively judge the specific wind towers that need to correct the wind measurement data, and also improve the accuracy of subsequent power generation assessment.
[0089] The protection scope of the method for determining the accuracy of wind tower data described in the embodiment of the present application is not limited to the execution order of the steps listed in the present embodiment. All solutions implemented by adding, reducing or replacing steps in the prior art based on the principles of the present application are included in the protection scope of the present application.
[0090] An embodiment of the present application also provides a wind tower data accuracy judgment system, which can implement the wind tower data accuracy judgment method described in the present application. However, the implementation device of the wind tower data accuracy judgment method described in the present application includes but is not limited to the structure of the wind tower data accuracy judgment system listed in the present embodiment. All structural deformations and replacements of the prior art made according to the principles of the present application are included in the protection scope of the present application.
[0091] Figure 8 The diagram shows a schematic diagram of the structure of a wind tower data accuracy judgment system in one embodiment of the present application. Figure 8As shown, the wind tower data accuracy judgment system includes: a data acquisition module, a data processing module, a time period comparison module, a wind measurement data comparison module, a mesoscale data comparison module, and a data accuracy acquisition module. Among them, the data acquisition module is used to obtain the measured data of each wind measurement tower in the target wind field area and the extreme weather occurrence period corresponding to the wind measurement period. The data processing module is used to process the measured data of each wind measurement tower to obtain invalid wind measurement data. The time period comparison module is used to obtain the cause judgment result of the invalid wind measurement data based on the invalid wind measurement data and the extreme weather occurrence period. The wind measurement data comparison module is used to obtain the wind measurement data of the target wind field area in the wind map, and compare the measured data with the wind measurement data in the wind map to obtain the data deviation result. The mesoscale data comparison module is used to obtain the mesoscale data at the location of each wind measurement tower, and compare the measured data of each wind measurement tower with the mesoscale data to obtain the data correlation comparison result. The data accuracy acquisition module is used to obtain the accuracy of the wind tower data according to the cause judgment result, the data deviation result and the data correlation comparison result.
[0092] It should be noted that Figure 8 The modules in the wind tower data accuracy judgment system 1 are shown in FIG. Figure 2 The steps in the method for determining the accuracy of wind tower data correspond to each other and will not be described in detail here.
[0093] In the several embodiments provided in the present application, it should be understood that the disclosed system, device or method can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of modules / units is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or units can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or modules or units, which can be electrical, mechanical or other forms.
[0094] The modules / units described as separate components may or may not be physically separated, and the components displayed as modules / units may or may not be physical modules, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules / units may be selected according to actual needs to achieve the purpose of the embodiments of the present application. For example, the functional modules / units in the various embodiments of the present application may be integrated into one processing module, or each module / unit may exist physically separately, or two or more modules / units may be integrated into one module / unit.
[0095] Those of ordinary skill in the art should further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.
[0096] The present application also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by the processor, the wind tower data accuracy judgment method provided by the present application is implemented. A person of ordinary skill in the art can understand that all or part of the steps in the method for implementing the above embodiment can be completed by instructing the processor through a program, and the program can be stored in a computer-readable storage medium, and the storage medium is a non-transitory medium, such as a random access memory, a read-only memory, a flash memory, a hard disk, a solid-state hard disk, a magnetic tape, a floppy disk, an optical disc, and any combination thereof. The above storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that includes one or more available media. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a digital video disc (DVD)), or a semiconductor medium (e.g., a solid-state disk (SSD)), etc.
[0097] The embodiment of the present application may also provide an electronic device. Fig. 9 The diagram shows a schematic diagram of the structure of an electronic device 2 in an embodiment of the present application. Fig. 9 As shown, in this embodiment, the electronic device 2 includes a memory 21 and a processor 22 .
[0098] The memory 21 is used to store computer programs. In some possible implementations, the memory 21 may include: ROM, RAM, a disk, a USB flash drive, a memory card, or an optical disk, etc., various media that can store program codes.
[0099] In the embodiment of the present application, the memory 21 may include a computer system readable medium in the form of a volatile memory, such as a RAM and / or a cache memory. The electronic device 2 may further include other removable / non-removable, volatile / non-volatile computer system storage media. The memory 21 may include at least one program product having a set (e.g., at least one) of program modules, which are configured to perform the functions of the various embodiments of the present application.
[0100] The processor 22 is connected to the memory 21 and is used to execute the computer program stored in the memory 21 so that the electronic device 2 executes the method for determining the accuracy of wind tower data.
[0101] Exemplarily, the processor 22 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc. In other embodiments, the processor 22 may also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.
[0102] The descriptions of the processes or structures corresponding to the above-mentioned figures have different emphases. For parts that are not described in detail in a certain process or structure, please refer to the relevant descriptions of other processes or structures.
[0103] The above embodiments are merely illustrative of the principles and effects of the present application and are not intended to limit the present application. Anyone familiar with the technology may modify or change the above embodiments without violating the spirit and scope of the present application. Therefore, all equivalent modifications or changes made by a person of ordinary skill in the art without departing from the spirit and technical ideas disclosed in the present application shall still be covered by the claims of the present application.
Claims
1. A method for determining the accuracy of wind tower data, characterized in that: The wind tower data accuracy judgment method comprises: Obtain the measured data of each wind tower in the target wind field area and the period of extreme weather occurrence corresponding to the wind measurement period; Processing the measured data of each wind tower to obtain invalid wind measurement data; Based on the invalid wind measurement data and the extreme weather occurrence period, obtaining a cause determination result of the invalid wind measurement data; Obtaining wind measurement data of the target wind field area in the wind atlas, and comparing the measured data with the wind measurement data in the wind atlas to obtain a data deviation result; Obtaining mesoscale data at the locations of each wind tower, and comparing the measured data of each wind tower with the mesoscale data to obtain a data correlation comparison result; The accuracy of the wind tower data is obtained according to the cause determination result, the data deviation result and the data correlation comparison result.
2. The method for determining the accuracy of wind tower data according to claim 1, characterized in that: The process of processing the measured data of each wind tower to obtain invalid wind measurement data includes: judging and screening the measured data of each wind tower based on the inspection conditions to obtain invalid wind measurement data, and the invalid wind measurement data is data that does not meet the inspection conditions.
3. The method for determining the accuracy of wind tower data according to claim 2, characterized in that: The inspection conditions include: The average wind speed range of the wind tower in the first time period; The absolute value of the average wind speed change in the wind speed channel during the second time period; Whether the duration of the continuous unchanged wind speed is less than the third time period; The first time period is shorter than the second time period, and the second time period is shorter than the third time period.
4. The method for determining the accuracy of wind tower data according to claim 1, characterized in that: The process of obtaining the cause determination result of the invalid wind measurement data based on the invalid wind measurement data and the extreme weather occurrence period includes: Based on the invalid wind measurement data, obtaining a time period when abnormal wind measurement data appears in each wind measurement tower; The time period when the wind measurement data is abnormal is compared with the time period when the extreme weather occurs to obtain a cause determination result of the invalid wind measurement data.
5. The method for determining the accuracy of wind tower data according to claim 4, characterized in that: The cause determination result of the invalid wind measurement data includes: if the time period of the abnormal wind measurement data coincides with the time period of the extreme weather, then the abnormal wind tower data is caused by extreme weather.
6. The method for determining the accuracy of wind tower data according to claim 1, characterized in that: The process of obtaining mesoscale data at the locations of each wind tower and comparing the measured data of each wind tower with the mesoscale data to obtain a data correlation comparison result includes: Obtaining the correlation between the measured data of the wind tower in each wind direction sector and the mesoscale data; Comparing the overall correlations of the wind towers to obtain a correlation comparison result of the wind towers; Compare the measured data of each wind tower with the change trend of the mesoscale monthly wind measurement data to obtain a comparison result of the monthly change trend; Based on the correlation comparison results of the wind towers and the monthly trend comparison results, the data correlation comparison results are obtained.
7. The method for determining the accuracy of wind tower data according to claim 1, characterized in that: The process of obtaining the accuracy of the wind tower data according to the cause determination result, the data deviation result and the data correlation comparison result includes: Obtaining the degree to which each wind tower is affected by extreme weather according to the cause determination result, the data deviation result and the data correlation comparison result; The accuracy of the wind tower data is obtained according to the degree to which each wind tower is affected by extreme weather.
8. A wind tower data accuracy judgment system, characterized in that: The wind tower data accuracy judgment system comprises: A data acquisition module is used to obtain the measured data of each wind tower in the target wind field area and the extreme weather occurrence period corresponding to the wind measurement period; A data processing module, used for processing the measured data of each wind tower to obtain invalid wind measurement data; A time period comparison module, used to obtain a cause determination result of the invalid wind measurement data based on the invalid wind measurement data and the extreme weather occurrence time period; A wind measurement data comparison module is used to obtain the wind measurement data of the target wind field area in the wind atlas, and compare the measured data with the wind measurement data in the wind atlas to obtain a data deviation result; A mesoscale data comparison module is used to obtain the mesoscale data at the location of each wind tower, and compare the measured data of each wind tower with the mesoscale data to obtain a data correlation comparison result; The data accuracy acquisition module is used to acquire the accuracy of the wind tower data according to the cause determination result, the data deviation result and the data correlation comparison result.
9. An electronic device, characterized in that: The electronic device comprises: a memory having a computer program stored thereon; A processor is communicatively connected to the memory, and is used to execute the computer program to implement the method for determining the accuracy of wind tower data according to any one of claims 1 to 7.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by an electronic device, the method for determining the accuracy of wind tower data according to any one of claims 1 to 7 is implemented.