Clutter detection device, weather observation system, clutter detection method, and program

Abstract

To provide a clutter detector capable of automatically detecting position of a wind mill clutter.SOLUTION: A clutter detector of an embodiment includes: a first discrimination part 32 which receives observation data in which weather condition is observed and determines a plurality of pieces of clear weather data observed during clear weather based on the observation data; and a second discrimination part 33 which calculates clutter statistic value associated with Doppler velocity for each range bin of a radar coverage area based on the plurality of pieces of clear weather data, and determines wind mill clutter, which is an echo caused by wind mills, on the basis of the variance of the statistic value.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] An embodiment of the present invention relates to a clutter detection device, a weather observation system, a clutter detection method, and a program. [Background technology]

[0002] In recent years, with the increasing demand for renewable energy, wind power generators (also called wind turbines) are being installed in various places. In order to expand the observation range of weather radars and increase the amount of power generated by wind turbines, they are often installed in places with good visibility and ventilation. In other words, weather radars and wind turbines are often installed in close proximity to each other.

[0003] In conventional weather radars, in order to remove non-precipitation echoes from ground structures and the like, a process for removing echoes whose Doppler velocity is zero, so-called MTI (Moving Target Indicator) processing, is used.

[0004] Because wind turbines have a speed component due to their rotation, conventional MTI processing cannot remove echoes from wind turbines (called wind turbine clutter). Non-Patent Document 1 proposes a technique for removing wind turbine clutter. To apply this method, position information of the wind turbine clutter is required.

[0005] Conventionally, in order to detect the location information of wind turbine clutter, it is necessary to manually register it using a map or the like. This work is laborious and time-consuming. In addition, when a new wind turbine is constructed, the wind turbine is not reflected on the map, and the location information of the wind turbine clutter cannot be registered. In this case, it becomes difficult to distinguish the wind turbine clutter, and the weather conditions cannot be observed more accurately. [Prior art documents] [Non-patent literature]

[0006] [Non-Patent Document 1] Kong, F., Y. Zhang, R. Palmer, "Wind Turbine Clutter Mitigation for Weather Radar by Adaptive Spectrum Processing," 2012 IEEE Radar Conference, Atlanta, GA, 2012. Summary of the Invention [Problem to be solved by the invention]

[0007] An object of the present invention is to provide a clutter detection device, a weather observation system, a clutter detection method, and a program that are capable of automatically detecting the position of wind turbine clutter. [Means for solving the problem]

[0008] The clutter detection device according to the embodiment includes a first discrimination unit that receives observation data of weather conditions and discriminates between a plurality of clear weather data, which are observation data for clear weather conditions, based on the observation data, and a second discrimination unit that calculates clutter statistics related to Doppler speed for each range bin in the radar coverage area based on the plurality of clear weather data, and discriminates wind turbine clutter, which is an echo caused by wind turbines, based on the variance of the statistical values. [Brief description of the drawings]

[0009] [Figure 1] FIG. 1 is a block diagram of a weather observation system according to the first embodiment. [Diagram 2] FIG. 2 is a block diagram of the clutter detection device shown in FIG. [Diagram 3] FIG. 3 is a block diagram showing an example of a hardware configuration of the weather analysis device and the clutter detection device shown in FIG. [Figure 4] FIG. 4 is a flowchart illustrating the overall flow of the operation of the clutter detection device to detect wind turbine clutter. [Diagram 5]FIG. 5 is a flowchart illustrating the clear weather data discrimination operation performed by the clear weather data discriminating section. [Figure 6] FIG. 6 is a graph showing an example of the relationship between the number of echoes and the occurrence frequency in the clear weather data discrimination operation. [Figure 7] FIG. 7 is a schematic diagram illustrating observation data on a clear day. [Figure 8] FIG. 8 is a schematic diagram illustrating observation data during precipitation. [Figure 9] FIG. 9 is a flowchart illustrating the wind turbine clutter discrimination operation by the wind turbine clutter discriminating unit. [Figure 10] FIG. 10 is a diagram illustrating sectors and range bins in a radar coverage area. [Figure 11] FIG. 11 is a graph illustrating the statistics of clutter with velocity components. [Figure 12] FIG. 12 is a graph illustrating the statistics of clutter that has no velocity component. [Figure 13] FIG. 13 is a diagram for explaining an example of a wind turbine clutter map. [Figure 14] FIG. 14 is a graph illustrating statistics of clutter having a velocity component according to the second embodiment. [Figure 15] FIG. 15 is a graph illustrating statistics of clutter having no velocity component according to the second embodiment. [Figure 16] FIG. 16 is a graph illustrating statistics of clutter having a velocity component according to the third embodiment. [Figure 17] FIG. 17 is a graph illustrating statistics of clutter having no velocity component according to the third embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] Hereinafter, the embodiments will be described with reference to the drawings. Some of the embodiments shown below are examples of devices and methods for embodying the technical idea of ​​the present invention, and the technical idea of ​​the present invention is not specified by the shape, structure, arrangement, etc. of the components. Each functional block can be realized as either hardware or software, or a combination of both. It is not essential that each functional block is distinguished as in the following example. For example, some functions may be executed by a functional block other than the functional block shown in the example. Furthermore, the functional block shown in the example may be divided into smaller functional sub-blocks. In the following description, elements having the same functions and configurations are given the same reference numerals, and duplicated descriptions are omitted.

[0011] [1] First embodiment [1-1] Configuration of meteorological observation system 1 1 is a block diagram of a weather observation system 1 according to the first embodiment. The weather observation system 1 includes a weather radar 10, a weather analysis device 20, and a clutter detection device 30.

[0012] The weather radar 10 is a radar device installed on the ground. The weather radar 10 observes weather conditions (rain, snow, rain clouds, rain area, wind direction, wind speed, etc.) in a predetermined range (radar coverage area) centered on the installation location, and generates meteorological observation data related to the weather conditions. The weather radar 10 observes raindrops by transmitting and receiving radio waves. In addition, the weather radar 10 scans for raindrops at predetermined time intervals.

[0013] The weather radar 10 includes an antenna unit 11 and a radar signal processing unit 12. The antenna unit 11 transmits radio waves and receives the reflected waves (echoes). The radar signal processing unit 12 performs general signal processing such as modulation, signal strength amplification, and frequency conversion on the echoes received by the antenna unit 11.

[0014] The weather radar 10 is, for example, a phased array weather radar (PAWR). The phased array weather radar electronically varies the directional angle by controlling the phase of a signal input to an array of antenna elements that constitute a phased array antenna. The weather radar 10 transmits and receives radio waves while varying the directional angle of the antenna. For example, the weather radar 10 varies the directional angle in the elevation direction (vertical direction) within a certain angle range (e.g., 90 degrees) by electrical phase control. The weather radar 10 also varies the directional angle in the azimuth direction (horizontal direction) mechanically by a drive mechanism.

[0015] The weather radar 10 may be a multi-parameter phased array weather radar (MP (multi parameter)-PAWR). MP-PAWR is also called dual-polarization phased array weather radar (dual-polarization PAWR). MP-PAWR is a method of simultaneously transmitting a horizontally polarized pulse signal and a vertically polarized pulse signal by a fan beam. By using MP-PAWR, the three-dimensional structure of rain clouds can be observed more accurately, and the precipitation intensity can be estimated more accurately. The weather radar 10 may be a parabolic weather radar.

[0016] The weather analysis device 20 analyzes the weather observation data obtained by the weather radar 10. The weather analysis device 20 includes a communication interface (communication I / F) 21, a RAW data processing unit 22, a RAW data storage unit 23, a data analysis unit 24, and an analysis data storage unit 26.

[0017] The communication interface 21 is an interface for communicating with the weather radar 10 etc. The communication interface 21 repeatedly receives meteorological observation data from the weather radar 10. The meteorological observation data received by the communication interface 21 is sent to a RAW data processing unit 22.

[0018] The RAW data processing unit 22 generates RAW data including three-dimensional data using the meteorological observation data sent from the communication interface 21. The three-dimensional data includes a plurality of two-dimensional polar coordinate data. The polar coordinate data includes data in the elevation angle direction. The RAW data generated by the RAW data processing unit 22 is stored in the RAW data storage unit 23.

[0019] The data analysis unit 24 sequentially acquires one cycle's worth of RAW data from the RAW data storage unit 23. The data analysis unit 24 uses the RAW data to analyze and quantify the weather conditions in mesh units. The data analysis unit 24 analyzes, for example, precipitation intensity, types of precipitation particles (rain, snow, hail), movements of rain clouds, wind direction, and wind speed. The analysis data generated by the data analysis unit 24 is stored in the analysis data storage unit 26. In addition, the analysis data generated by the data analysis unit 24 is appropriately transmitted to the outside.

[0020] The data analysis unit 24 also includes a wind turbine clutter map storage unit 25 that stores a wind turbine clutter map. Clutter is echoes caused by objects other than targets. The wind turbine clutter map includes position information of clutter caused by wind turbines installed on the ground (called wind turbine clutter). The data analysis unit 24 removes clutter reflected by the wind turbines and analyzes the weather conditions. This allows for more accurate analysis of the weather conditions.

[0021] [1-1-1] Clutter detection device 30 Fig. 2 is a block diagram of the clutter detection device 30 shown in Fig. 1. The clutter detection device 30 comprises an analysis data acquisition section 31, a clear weather data discrimination section 32, and a wind turbine clutter discrimination section 33.

[0022] The analysis data acquisition unit 31 acquires observation data from the weather analysis device 20. Specifically, the analysis data acquisition unit 31 sequentially acquires the observation data for a predetermined period stored in the analysis data storage unit .

[0023] The clear weather data discrimination unit 32 discriminates observation data for clear weather based on the observation data acquired by the analysis data acquisition unit 31. That is, the clear weather data discrimination unit 32 extracts observation data for clear weather from the observation data acquired by the analysis data acquisition unit 31.

[0024] The wind turbine clutter discriminator 33 detects and discriminates the position of wind turbine clutter based on the observation data for clear skies discriminated by the clear skies data discriminator 32. Then, the wind turbine clutter discriminator 33 generates a discrimination result including the position of the wind turbine clutter.

[0025] [1-1-2] Hardware configuration Fig. 3 is a block diagram showing an example of a hardware configuration of the weather analysis device 20 and the clutter detection device 30 shown in Fig. 1. The weather analysis device 20 and the clutter detection device 30 each include a processor 40, a ROM (Read Only Memory) 41, a RAM (Random Access Memory) 42, an input / output interface (input / output I / F) 43, an input device 44, a display device 45, an auxiliary storage device 46, and a communication interface 21. The processor 40, the ROM 41, the RAM 42, the input / output interface 43, and the communication interface 21 are connected via a bus 47.

[0026] The processor 40 is a CPU (Central Processing Unit) that comprehensively controls the operations of the weather analysis device 20 and the clutter detection device 30. The processor 40 executes various types of arithmetic processing using programs and data stored in the ROM 41, the RAM 42, and the auxiliary storage device 46.

[0027] A specific program for realizing the functions of this embodiment is stored in the ROM 41 and / or the auxiliary storage device 46. The functions of this embodiment are realized by the processor 40 executing the specific program stored in the ROM 41 and / or the auxiliary storage device 46.

[0028] The ROM 41 is a read-only storage device that stores basic programs for operating the computer, environment files, etc. The RAM 42 is a storage device that stores programs executed by the processor 40 and data required for executing the programs, and is capable of high-speed reading and writing.

[0029] The input / output interface 43 is a device that mediates connections between various hardware and the bus 47. Hardware such as an input device 44, a display device 45, and an auxiliary storage device 46 are connected to the input / output interface 43. The input device 44 is a device that processes input from a user, such as a keyboard and a mouse. The display device 45 is a device that displays calculation results and images to the user, such as a liquid crystal display device or an organic EL display device. The auxiliary storage device 46 is a large-capacity storage device that stores programs and data, such as a hard disk drive (HDD) or a solid state drive (SSD).

[0030] The communication interface 21 transmits and receives data to and from external devices via wired and wireless communication.

[0031] [1-2] Operation Next, the operation of the weather observation system 1 configured as above will be described.

[0032] The data analysis unit 24 continuously analyzes the weather conditions for each cycle. One cycle is a period during which the radar coverage area centered on the weather radar 10 is observed for one revolution (360 degrees in azimuth). The observation data for one cycle is three-dimensional data. The analysis data storage unit 26 stores the analysis data as the analysis result by the data analysis unit 24 for a long period such as on a monthly or yearly basis.

[0033] The clutter detection device 30 performs wind turbine clutter detection operation using the analysis data stored in the analysis data storage unit 26.

[0034] FIG. 4 is a flowchart illustrating the overall flow of the operation of the clutter detection device 30 to detect wind turbine clutter.

[0035] The analysis data acquisition unit 31 sequentially acquires analysis data (also called observation data) for a predetermined period stored in the analysis data storage unit 26 (step S100). The predetermined period in step S100 is in units of weeks, months, or years, such as one week, one month, six months, or one year. The predetermined period can be set appropriately.

[0036] Next, the clear weather data discrimination unit 32 discriminates observation data during clear weather (also called clear weather data) based on the observation data acquired by the analysis data acquisition unit 31 (step S101). The clear weather data discriminated by the clear weather data discrimination unit 32 is sequentially sent to the wind turbine clutter discrimination unit 33.

[0037] Next, the wind turbine clutter discriminator 33 discriminates the position of the wind turbine clutter based on the multiple pieces of clear weather data (step S102). The position of the wind turbine clutter is the same as the position of the wind turbine.

[0038] Next, the wind turbine clutter discrimination unit 33 transmits the wind turbine clutter discrimination result including the position information of the wind turbine clutter to the data analysis unit 24 (step S103). The data analysis unit 24 uses the received wind turbine clutter discrimination result to register the wind turbine clutter position in the wind turbine clutter map.

[0039] [1-2-1] Clear weather data discrimination operation The Doppler speed of precipitation echoes observed during rainfall varies with time. This characteristic is similar to that of wind turbine clutter. If wind turbine clutter detection is performed using observation data during rainfall, there is a possibility that a point with precipitation echoes will be erroneously determined to be wind turbine clutter. In this embodiment, the observation data used for wind turbine clutter detection is observation data obtained on a clear day (no precipitation echo). The clear weather data discrimination unit 32 plays a role in passing observation data determined to be from a clear weather day among the observation data acquired from the analysis data storage unit 26 to the wind turbine clutter discrimination unit 33 at the subsequent stage.

[0040] 5 is a flowchart illustrating the clear weather data discrimination operation by the clear weather data discriminating unit 32. The process in FIG. 5 corresponds to the process in step S101 in FIG.

[0041] The observation data acquired by the analysis data acquisition unit 31 includes reception power data for each of a plurality of echoes.

[0042] The clear weather data discriminator 32 receives, for example, a plurality of echo information for one period corresponding to the radar coverage area from the analysis data acquirer 31 (step S200). The echo information includes received power data of the echoes.

[0043] Next, the clear weather data discriminator 32 counts the number of echoes having reception power equal to or greater than a threshold T1 based on one cycle of echo information (step S201). The threshold T1 is set based on a statistical value of the reception power of windturbine clutter, and is set lower than the reception power of windturbine clutter. The threshold T1 is also set lower than the reception power of echoes due to rainclouds. In other words, echoes having reception power equal to or greater than the threshold T1 include echoes due to windturbine clutter and rainclouds.

[0044] Next, the clear weather data discriminator 32 discriminates observation data in which the number of echoes is equal to or greater than the threshold E1 and equal to or less than the threshold E2 as clear weather observation data (referred to as clear weather data) (step S202).

[0045] Figure 6 is a graph showing an example of the relationship between the number of echoes and occurrence frequency in clear weather data discrimination operation. The curve in Figure 6 is obtained by connecting the peaks of multiple histograms. For simplicity, multiple histograms are not shown. The same applies to other figures related to frequency distribution.

[0046] The clear sky data discriminator 32 determines that observation data in which the number of echoes is less than a threshold value E1 is abnormal data. The threshold value E1 is set to a value that makes it possible to discriminate data in which the number of echoes is extremely small and which is not used in meteorological analysis.

[0047] FIG. 7 is a schematic diagram illustrating observation data on a clear day. The outer periphery of FIG. 7 indicates the radar coverage area of ​​the weather radar 10. On a clear day, there are relatively few echoes because there are no echoes from rain clouds or the like. On a clear day, clutter with a received power equal to or greater than a threshold T1 is observed. In the case of FIG. 7, it is determined that the day is clear, that is, that it is not raining. The clear-day data discrimination unit 32 determines that observation data in which the number of echoes is equal to or greater than a threshold E1 and equal to or less than a threshold E2 is clear-day data.

[0048] Figure 8 is a schematic diagram explaining observation data during precipitation. The dot-hatched areas in Figure 8 represent rain clouds. When it is raining, there are relatively more echoes from rain clouds, etc. In the case of Figure 8, it is determined that it is raining.

[0049] The clear weather data discriminator 32 determines that observation data in which the number of echoes is greater than a threshold value E2 is precipitation observation data (also called rainfall data). The threshold value E2 is set to a value that allows discrimination of the presence of rainclouds.

[0050] Next, the clear weather data discriminator 32 outputs the clear weather data determined in step S201 (step S203). After that, the clear weather data discriminator 32 repeats the above operation over multiple periods.

[0051] The processing unit of the clear weather data discriminator 32 is not limited to one period corresponding to the radar coverage area, but may be a partial period obtained by dividing one period.

[0052] [1-2-2] Wind turbine clutter discrimination operation 9 is a flowchart illustrating the wind turbine clutter discrimination operation by the wind turbine clutter discriminator 33. The process in FIG. 9 corresponds to the process in step S102 in FIG.

[0053] The wind turbine clutter discriminator 33 receives a plurality of pieces of clear weather data from the clear weather data discriminator 32 (step S300). The wind turbine clutter discriminator 33 collects clear weather data at a plurality of times over a predetermined period for each range bin to be observed.

[0054] FIG. 10 is a diagram for explaining sectors and range bins in radar coverage. FIG. 10 shows the radar coverage, where N is north and E is east. The weather analysis device 20 processes received signals in a 360-degree radar coverage centered on the weather radar 10. A sector is a processing unit in the azimuth direction, and is a processing unit obtained by dividing the radar coverage into multiple parts in the azimuth direction. A range bin is a unit of data obtained by sampling a received signal in the range direction (distance direction) at a predetermined time (i.e., a predetermined distance), and is a unit cell when the radar coverage is divided in the distance direction at intervals equivalent to the sampling period of the received signal. The sizes of the sector and range bin can be set arbitrarily.

[0055] Next, the windturbine clutter discriminator 33 calculates clutter statistics related to the Doppler speed for each range bin using multiple clear weather data (including clutter information) corresponding to multiple periods (step S301). The Doppler speed is a parameter that indicates the moving direction and moving speed of an observed object, and is calculated based on the difference between the transmission frequency when the weather radar 10 transmits radio waves and the reception frequency when the weather radar 10 receives the radio waves. The observation data received by the windturbine clutter discriminator 33 includes Doppler speed information of the echo. If the observation data received by the windturbine clutter discriminator 33 does not include Doppler speed information, the windturbine clutter discriminator 33 calculates the Doppler speed of the echo.

[0056] Next, the wind turbine clutter discrimination unit 33 calculates the variance of the statistical value calculated in step S301 (step S302). Variance is the square of the standard deviation, and is the average of the squares of the differences between the average value and each piece of data in a certain group of numerical data. To simplify the process, the variance may be calculated by sampling a portion of the target data.

[0057] Next, the wind turbine clutter discriminator 33 determines whether the variance calculated in step S302 is equal to or greater than a threshold value T2 (step S303).

[0058] Next, the wind turbine clutter discrimination unit 33 determines that the range bins whose variance is equal to or greater than the threshold T2 are the positions of wind turbine clutter (referred to as wind turbine clutter positions) (step S304). The wind turbine clutter discrimination unit 33 also determines that the range bins whose variance is smaller than the threshold T2 are the positions of clutter other than wind turbines. The positions referred to here are the positions where the echoes are generated.

[0059] Figure 11 is a graph that explains the statistics of clutter with a velocity component. The horizontal axis of Figure 11 is the Doppler velocity of the clutter, and the vertical axis is the occurrence frequency. The velocity component is the moving speed of the target.

[0060] Figure 11 corresponds to the statistics of wind turbine clutter. Wind turbine clutter has Doppler velocity caused by the rotation of wind turbines. Wind turbine clutter has a relatively large variance of Doppler velocity.

[0061] Figure 12 is a graph explaining the statistics of clutter that has no velocity component. Figure 12 corresponds to the statistics of clutter reflected by buildings, etc. Clutter that has no velocity component has a peak in occurrence frequency near a Doppler velocity of 0 (m / s), and the variance of the Doppler velocity is relatively small.

[0062] In this embodiment, a threshold value T2 is set that can distinguish the variance between Figure 11 and Figure 12. This makes it possible to distinguish wind turbine clutter by comparing the variance with the threshold value T2.

[0063] Next, the wind turbine clutter discrimination unit 33 generates a wind turbine clutter discrimination result (step S305). The wind turbine clutter discrimination result includes wind turbine clutter position information and position information of clutter other than wind turbines.

[0064] Thereafter, the wind turbine clutter discriminator 33 transmits the wind turbine clutter discrimination result to the weather analysis device 20.

[0065] The data analysis section 24 of the meteorological analysis device 20 updates the wind turbine clutter map stored in the wind turbine clutter map storage section 25 based on the wind turbine clutter discrimination result transmitted from the wind turbine clutter discrimination section 33 .

[0066] Fig. 13 is a diagram for explaining an example of a wind turbine clutter map. The outer periphery of Fig. 13 indicates the radar coverage area of ​​the weather radar 10. In the wind turbine clutter map, wind turbine clutter positions (wind turbine clutter in the figure) and positions of structures other than wind turbines (clutter other than wind turbines in the figure) are registered.

[0067] The data analysis unit 24 removes the wind turbine clutter using the wind turbine clutter map. Then, the data analysis unit 24 analyzes the weather conditions using the observation data from which the wind turbine clutter has been removed. This allows the weather conditions to be analyzed with greater accuracy.

[0068] [1-3] Effects of the first embodiment In the first embodiment, the clear weather data discriminator 32 discriminates observation data for clear weather conditions from multiple observation data for clear weather conditions. The wind turbine clutter discriminator 33 calculates statistics of clutter related to Doppler speed using the observation data for clear weather conditions. The wind turbine clutter discriminator 33 discriminates wind turbine clutter, which is an echo caused by a wind turbine, based on the variance of the statistics.

[0069] Therefore, according to the first embodiment, the position of the wind turbine clutter within the radar coverage area can be automatically detected, thereby reducing the work and labor required to identify the position of the wind turbine clutter.

[0070] In addition, the wind turbine clutter positions can be detected using observation data from the constantly observing weather radar 10. This makes it possible to identify the positions of newly constructed wind turbines without waiting for the map to be updated. In addition, the positions of newly constructed wind turbines can be registered in the wind turbine clutter map without using a map.

[0071] Furthermore, when analyzing weather conditions, it is possible to remove wind turbine clutter using more accurate wind turbine clutter position information, which allows for more accurate analysis of weather conditions.

[0072] [2] Second embodiment The second embodiment is a modified example of the wind turbine clutter discrimination operation, in which the variance is calculated using the received power of the clutter.

[0073] The wind turbine clutter discriminator 33 calculates statistical values ​​of clutter related to reception power for each range bin using multiple clear weather data corresponding to multiple periods. The observation data received by the wind turbine clutter discriminator 33 includes information on reception power of echoes.

[0074] Next, the wind turbine clutter discriminator 33 determines that the range bins whose variance is equal to or greater than the threshold T2 are wind turbine clutter positions.

[0075] Fig. 14 is a graph explaining the statistics of clutter having a velocity component. The horizontal axis of Fig. 14 is the received power of clutter, and the vertical axis is the occurrence frequency.

[0076] Figure 14 shows the statistics of wind turbine clutter. The received power of wind turbine clutter fluctuates due to the rotation of the wind turbine. In other words, the variance of the received power of wind turbine clutter is relatively large.

[0077] Fig. 15 is a graph explaining the statistics of clutter that does not have a velocity component. Fig. 15 corresponds to the statistics of clutter reflected by buildings, etc. Clutter that does not have a velocity component has a relatively small variance in the received power.

[0078] In this embodiment, a threshold value T2 is set that can distinguish the variance between Figure 14 and Figure 15. This makes it possible to distinguish wind turbine clutter by comparing the variance with the threshold value T2.

[0079] Other operations are the same as those in the first embodiment. In the second embodiment, the same effects as those in the first embodiment can be obtained.

[0080] The second embodiment may be used in combination with the first embodiment.

[0081] [3] Third embodiment The third embodiment is another modified example of the wind turbine clutter discrimination operation, in which the variance is calculated using the radar reflection factor of the clutter.

[0082] The wind turbine clutter discriminator 33 calculates a statistical value of clutter related to the radar reflection factor for each range bin using multiple clear weather data corresponding to multiple periods. The observation data received by the wind turbine clutter discriminator 33 includes radar reflection factor information of the echo. If the observation data does not include radar reflection factor information, the wind turbine clutter discriminator 33 calculates the radar reflection factor of the echo.

[0083] The radar reflectivity factor is a parameter that varies depending on the particle size of the particles that reflect radio waves. If the rainfall intensity is R (mm / h), the radar reflectivity factor Z (mm 6 / m 3 ) is expressed by the following formula: Z=a×R b a and b are constants.

[0084] Next, the wind turbine clutter discriminator 33 determines that the range bins whose variance is equal to or greater than the threshold T2 are wind turbine clutter positions.

[0085] Figure 16 is a graph explaining the statistics of clutter with a velocity component. The horizontal axis of Figure 16 is the radar reflectivity factor of the clutter, and the vertical axis is the occurrence frequency.

[0086] Figure 16 shows the statistics of wind turbine clutter. The radar reflectivity of wind turbine clutter fluctuates due to the rotation of the wind turbines. In other words, the variance of the radar reflectivity of wind turbine clutter is relatively large.

[0087] Figure 17 is a graph explaining the statistics of clutter that does not have a velocity component. Figure 17 corresponds to the statistics of clutter reflected by buildings, etc. Clutter that does not have a velocity component has a relatively small variance in radar reflectivity factors.

[0088] In this embodiment, a threshold value T2 is set that can distinguish the variance between Figure 16 and Figure 17. This makes it possible to distinguish wind turbine clutter by comparing the variance with the threshold value T2.

[0089] Other operations are the same as those in the first embodiment. In the third embodiment, the same effects as those in the first embodiment can be obtained.

[0090] The third embodiment may be used in combination with the first embodiment.

[0091] Although some embodiments of the present invention have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. These novel embodiments can be implemented in various other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their modifications are included in the scope and spirit of the invention, and are included in the scope of the invention and its equivalents described in the claims. [Explanation of symbols]

[0092] 1...weather observation system, 10...weather radar, 11...antenna section, 12...radar signal processing section, 20...weather analysis device, 21...communication interface, 22...RAW data processing section, 23...RAW data storage section, 24...data analysis section, 25...wind turbine clutter map storage section, 26...analysis data storage section, 30...clutter detection device, 31...analysis data acquisition section, 32...clear weather data discrimination section, 33...wind turbine clutter discrimination section, 40...processor, 41...ROM, 42...RAM, 43...input / output interface, 44...input device, 45...display section, 46...auxiliary storage device, 47...bus.

Claims

1. a first discrimination unit that receives observation data of weather conditions and discriminates a plurality of clear weather data that are observation data for clear weather conditions based on the observation data; a second discrimination unit that calculates a statistical value of clutter related to Doppler speed based on the plurality of clear weather data for each range bin of the radar coverage area, and discriminates wind turbine clutter, which is an echo caused by a wind turbine, based on a variance of the statistical value; A clutter detection device comprising:

2. a first discrimination unit that receives observation data of weather conditions and discriminates a plurality of clear weather data that are observation data for clear weather conditions based on the observation data; a second discrimination unit that calculates a statistical value of clutter related to reception strength for each range bin of the radar coverage area based on the plurality of clear weather data, and discriminates wind turbine clutter, which is an echo caused by a wind turbine, based on a variance of the statistical value; A clutter detection device comprising:

3. a first discrimination unit that receives observation data of weather conditions and discriminates a plurality of clear weather data that are observation data for clear weather conditions based on the observation data; a second discrimination unit that calculates a statistical value of clutter related to a radar reflectivity factor for each range bin of the radar coverage area based on the plurality of clear weather data, and discriminates wind turbine clutter, which is an echo caused by a wind turbine, based on a variance of the statistical value; A clutter detection device comprising:

4. The first determination unit counts the number of echoes whose received power is equal to or greater than a first threshold value for each cycle of the weather radar; The observation data in which the measured number of echoes is equal to or greater than the second threshold and equal to or less than the third threshold is determined to be the clear weather data.

4. A clutter detection device according to claim 1.

5. The second discrimination unit determines that a corresponding range bin is a wind turbine clutter position when the variance is equal to or greater than a fourth threshold value.

5. A clutter detection device according to any one of claims 1 to 4.

6. The second discrimination unit generates a wind turbine clutter discrimination result including wind turbine clutter position information.

6. A clutter detection apparatus according to claim 5.

7. A clutter detection device according to claim 6; an analysis unit that analyzes weather conditions by removing wind turbine clutter using a wind turbine clutter map in which wind turbine clutter positions are registered; Equipped with The analysis unit updates the wind turbine clutter map using the wind turbine clutter discrimination result. Weather observation system.

8. receiving observation data of weather conditions, and determining a plurality of clear weather data that are observation data for clear weather conditions based on the observation data; calculating a clutter statistic relating to Doppler velocity for each range bin of the radar coverage based on the plurality of clear weather data; Based on the variance of the statistical values, wind turbine clutter, which is an echo caused by a wind turbine, is discriminated. Clutter detection methods.

9. On the computer, receiving observation data of weather conditions, and determining a plurality of clear weather data that are observation data for clear weather conditions based on the observation data; calculating a clutter statistic related to Doppler velocity for each range bin of the radar coverage based on the plurality of clear sky data; A process of determining wind turbine clutter, which is an echo caused by a wind turbine, based on the variance of the statistical values; A program that executes the following.

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