KR20230082459A - Method, computer-readable storage medium, computer program and apparatus for wind condition measurement using 3d lidar and system including the same - Google Patents

Abstract

The present invention collects first wind condition data from a meteorological tower, collects a point cloud from lidar to generate second wind condition data, performs regression analysis on the first wind condition data and the second wind condition data, and performs a regression analysis on the Provided is a wind condition measurement method for correcting the second wind condition data based on the correction equation according to the present invention.

Description

Wind condition measuring method using 3D lidar, computer readable recording medium, computer program, device, and wind condition measuring system including the same SAME}

The present invention relates to a wind condition measuring method using a 3D lidar, a computer readable recording medium, a computer program, an apparatus, and a wind condition measuring system including the same, and more particularly, to a wind condition measuring method using a point cloud measured by a 3D lidar. The present invention relates to a wind condition measuring method capable of measuring a wind condition, a computer readable recording medium, a computer program, an apparatus, and a wind condition measuring system including the same.

In general, a method of measuring wind conditions at sea by installing a marine meteorological tower or a floating wind lidar on the sea is used.

In this regard, according to the detailed standards for power generation business permits, if the location of a candidate site for an offshore wind farm exceeds an effective distance of 5 km from the land, a wind condition instrument must be installed on the sea to measure wind condition resources.

However, in the case of a maritime weather tower, since the maritime weather tower must be erected after installing substructures such as jackets, installation costs are required to be 60 to 90 times higher than those of land-based weather towers.

Therefore, it is virtually impossible to perform wind condition resource evaluation by installing an expensive offshore meteorological tower at the stage of obtaining a power generation business permit where economic feasibility is not secured. Therefore, a plan that can reduce costs and accurately measure the offshore wind condition is required. am.

Domestic Patent No. 10-1853122 (2018.04.23.)

The technical problem to be solved by the present invention is a wind condition measurement method capable of correcting the wind condition data measured by the 3D lidar by measuring wind condition data using a weather tower and 3D lidar and performing regression analysis on the measured wind condition data, It is to provide a computer readable recording medium, a computer program, a device, and a wind condition measurement system including the same.

One aspect of the present invention comprises the steps of collecting first wind condition data from a weather tower; generating second wind condition data by collecting point clouds from LiDAR; performing a regression analysis on the first wind condition data and the second wind condition data; and correcting the second wind condition data based on a correction equation according to the regression analysis.

The generating of the second wind condition data may include generating land wind condition data at a point on the land where the distance to the sea is equal to the distance from the point where the lidar is installed to the sea; and generating sea wind condition data at a sea point within a preset distance from the lidar.

The correcting of the second wind condition data may include classifying suitable data and false missing data from the sea wind condition data; correcting the erroneous missing data from the fit data based on the correction equation; and adding the corrected false missing data to an area of the false missing data classified from the sea wind condition data.

The performing of the regression analysis may include setting one of the first wind condition data and the land wind condition data as an independent variable; setting another one of the first wind condition data and the land wind condition data as a dependent variable; and performing the regression analysis on the independent variable and the dependent variable.

In addition, the lidar may be installed at the same location as the location where the weather tower is installed.

Another aspect of the present invention is a collection unit for collecting first wind condition data from a weather tower and collecting a point cloud from LIDAR; an analysis unit generating second wind condition data using the point cloud and performing a regression analysis on the first wind condition data and the second wind condition data; and a correction unit correcting the second wind condition data based on a correction equation according to the regression analysis.

In addition, the analyzer generates land wind condition data at a point on land where the distance to the sea is equal to the distance from the point where the lidar is installed to the sea, and at a point on the sea that falls within a preset distance from the lidar. of sea wind condition data can be generated.

Further, the correction unit classifies suitable data and false missing data from the sea wind condition data, corrects the false missing data from the suitable data based on the correction equation, and classifies the false missing data from the sea wind condition data. The corrected false missing data may be added to the area of .

In addition, the analysis unit sets one of the first wind condition data and the land wind condition data as an independent variable, sets the other of the first wind condition data and the land wind condition data as a dependent variable, and sets the independent variable and The regression analysis for the dependent variable may be performed.

In addition, the lidar may be installed at the same location as the location where the weather tower is installed.

Another aspect of the present invention, the weather tower for measuring the first wind condition data; lidar to measure point clouds; and collecting the first wind condition data and the point cloud, generating second wind condition data using the point cloud, performing regression analysis on the first wind condition data and the second wind condition data, and performing the regression analysis on the first wind condition data and the second wind condition data. and a wind condition measuring device for correcting the second wind condition data based on a correction equation according to the above.

Another aspect of the present invention is a computer readable recording medium storing a computer program, the computer program, when executed by a processor, comprising the steps of: collecting first wind condition data from a meteorological tower; generating second wind condition data by collecting point clouds from LiDAR; performing a regression analysis on the first wind condition data and the second wind condition data; and correcting the second wind condition data based on a correction equation according to the regression analysis.

Another aspect of the present invention is a computer program stored on a computer readable recording medium, wherein the computer program, when executed by a processor, includes: collecting first wind condition data from a weather tower; generating second wind condition data by collecting point clouds from LiDAR; performing a regression analysis on the first wind condition data and the second wind condition data; and correcting the second wind condition data based on a correction equation according to the regression analysis.

According to one aspect of the present invention described above, by providing a wind condition measurement method using a 3D lidar, a computer readable recording medium, a computer program, an apparatus, and a wind condition measurement system including the same, wind condition data using a weather tower and 3D lidar It is possible to correct the wind condition data measured by 3D lidar by measuring and performing regression analysis on the measured wind condition data.

1 is a schematic diagram of a wind condition measuring system according to an embodiment of the present invention.
2 is a block diagram of a wind condition measuring device according to an embodiment of the present invention.
3 is a graph showing an example of a correction equation calculated by the analysis unit of FIG. 2 .
4 is a flowchart of a wind condition measurement method according to an embodiment of the present invention.

Advantages and features of the present invention, and methods of achieving them, will become clear with reference to the detailed description of the following embodiments taken in conjunction with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but may be implemented in various different forms, and only these embodiments make the disclosure of the present invention complete, and common knowledge in the art to which the present invention belongs. It is provided to completely inform the person who has the scope of the invention, and the present invention is only defined by the scope of the claims.

In describing the embodiments of the present invention, if it is determined that a detailed description of a known function or configuration may unnecessarily obscure the subject matter of the present invention, the detailed description will be omitted. In addition, terms to be described later are terms defined in consideration of functions in the embodiment of the present invention, which may vary according to the intention or custom of a user or operator. Therefore, the definition should be made based on the contents throughout this specification.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings.

1 is a schematic diagram of a wind condition measuring system according to an embodiment of the present invention.

Referring to FIG. 1 , the wind condition measuring system 1 may include a weather tower 10, a lidar 20, and a wind condition measuring device 30. Accordingly, the wind condition measurement system 1 may analyze wind condition data measured by the weather tower 10 and the lidar 20 to calculate a correction equation, and use the calculated correction formula to calculate the lidar 20 Wind condition data measured by can be corrected.

In this regard, the weather tower 10 may mean a land weather tower installed on land. Accordingly, the weather tower 10 may measure wind speed, wind direction, and the like using sensors such as an anemometer and a wind vane provided in the weather tower 10 .

Meanwhile, the weather tower 10 may measure first wind condition data. Here, the first wind condition data may include at least one of wind speed and wind direction measured by the weather tower 10 .

At this time, the weather tower 10 may measure the first wind condition data for each preset time period, and may record the time when the first wind condition data is measured.

The lidar 20 emits a laser beam and observes a beam reflected or scattered from particles in the air. Alternatively, the lidar 20 may observe light scattered by fine particles in the air.

Through this, the lidar 20 may generate a point cloud by analyzing the observed beam waveform. In this case, the point cloud may include a plurality of points, and each point may include information such as a distance to the LIDAR 20, a height, and a direction.

To this end, the lidar 20 may generate points based on a time interval from a time point of emitting a beam to a time point of receiving a reflected beam, and a frequency or phase difference between the emitted beam and the reflected beam.

At this time, the lidar 20 may measure the point cloud at each preset time period, and accordingly, the lidar 20 may record the point cloud measurement point.

In one embodiment, lidar 20 may be a 3D scanning lidar. Here, the 3D scanning lidar may measure a point cloud in a 3D space by radiating a beam with a plurality of layers.

Depending on the embodiment, the lidar 20 may be installed at the same location as the location where the weather tower 10 is installed, or installed adjacent to the weather tower 10 depending on the surrounding environment of the location where the weather tower 10 is installed. there is.

Accordingly, the wind condition measuring device 30 may receive and store the first wind condition data from the meteorological tower 10 and receive and store the point cloud from the LIDAR 20 .

The wind condition measuring device 30 may generate second wind condition data using a point cloud. That is, the wind condition measuring device 30 may generate second wind condition data based on changes in points included in the point cloud measured according to a preset time period.

In this case, the second wind condition data may include at least one of wind speed and wind direction at a point measured by the LIDAR 20 . For example, the wind condition measuring device 30 uses a change in position of points appearing from a point cloud observed at one point in time and a point cloud observed at another point in time, a changed angle, a time interval at which the point cloud is observed, etc. Wind condition data can be created.

In this regard, the wind condition measuring device 30 may generate second wind condition data from the point cloud for each preset time period. In this case, the wind condition measuring device 30 may combine the point cloud observation point with the second wind condition data generated by the corresponding point cloud.

The wind condition measuring device 30 may perform a regression analysis on the first wind condition data and the second wind condition data, and may correct the second wind condition data based on a correction equation according to the regression analysis.

Hereinafter, the wind condition measuring device 30 according to an embodiment of the present invention will be described in detail.

2 is a block diagram of a wind condition measuring device according to an embodiment of the present invention.

Referring to FIG. 2 , the wind condition measuring device 30 may include a collecting unit 31 , an analyzing unit 33 and a correcting unit 35 .

The collecting unit 31 may collect first wind condition data from the weather tower 10 and collect point clouds from the LIDAR 20 .

At this time, the collection unit 31 may be set so that the receiver receives the first wind condition data or point cloud from the meteorological tower 10 or lidar 20 through a wireless or wired network, and stores the first wind condition data and Can be set to store point clouds.

The analyzer 33 may generate second wind condition data using the point cloud and perform regression analysis on the first wind condition data and the second wind condition data.

In this regard, the second wind condition data may include land wind condition data and sea wind condition data. In this case, the land wind condition data may indicate at least one of wind speed and wind direction on land, and the sea wind condition data may indicate at least one of wind speed and wind direction on the sea.

That is, when the direction in which the lidar 20 collects points is the land direction, the analyzer 33 may generate land wind condition data using the points received from the lidar 20 . Alternatively, the analyzer 33 may generate land wind condition data using a point on the land where the distance to the sea is equal to the distance from the point where the lidar 20 is installed to the sea.

At this time, the land wind condition data may mean wind condition data at a land point (eg, 1.6 Km) away from the point where the lidar 20 is installed by a preset distance interval.

Meanwhile, when the direction in which the lidar 20 collects points is the sea direction, the analyzer 33 may generate sea wind condition data using the points received from the lidar 20 . Alternatively, the analyzer 33 may generate sea wind condition data by using a point at a sea point belonging to a preset distance (eg, 10 Km) from a point where the lidar 20 is installed.

In one embodiment, the analyzer 33 receives at least one of land range and sea range from the user in advance, or selects between a land point to generate land wind condition data and a sea point to generate sea wind condition data from the user. At least one can be entered.

The analysis unit 33 may perform a regression analysis on the first wind condition data and land wind condition data.

To this end, the analyzer 33 may set one of the first wind condition data and the land wind condition data as a dependent variable, and the analyzer 33 may set the other one of the first wind condition data and the land wind condition data as an independent variable. there is. Accordingly, the analysis unit 33 may calculate a correction equation by performing regression analysis on the dependent variable and the independent variable.

Here, regression analysis may be a mathematical technique for determining a relational expression between an independent variable and a dependent variable so that a change in a dependent variable according to a change in an independent variable appears.

In one embodiment, the analyzer 33 may perform a regression analysis on the first wind condition data and the land wind condition data to determine a linear equation representing a relationship between the first wind condition data and the land wind condition data.

In this case, the analyzer 33 may perform a regression analysis using the first wind condition data and land wind condition data generated at the same time point or the closest time point to each other.

Through this, it is possible to check the value of the coefficient of determination in the correction equation calculated according to the regression analysis. At this time, it can be understood that the closer the value of the coefficient of determination is to 1, the higher the correlation between the first wind condition data and the land wind condition data.

Here, the correction equation is a linear equation according to the regression analysis result, and may be used in a process of correcting false missing data of the second wind condition data.

For example, FIG. 3 is a graph showing an example of a correction equation calculated by the analyzer of FIG. 2 .

Referring to FIG. 3 , the distribution of the first wind condition data measured by the weather tower 10 and the second wind condition data generated based on the LIDAR 20 can be confirmed with a plurality of points.

In addition, a solid line representing a graph of a correction equation, which is a result of regression analysis on the first wind condition data and the second wind condition data, can be confirmed.

At this time, it can be understood that the closer the coefficient of determination of the correction equation is to 1, the higher the correlation between the first wind condition data and the second wind condition data.

Referring back to FIG. 2 , the correction unit 35 may correct the second wind condition data based on a correction equation according to regression analysis.

Specifically, the correction unit 35 classifies fit data and false missing data from the sea wind condition data, corrects the false missing data from the fit data based on a correction equation, and places the incorrectly missing data classified from the sea wind condition data. Corrected false missing data can be added.

Here, suitable data may mean data having a value belonging to a normal range among sea wind condition data. In addition, false missing data may mean data that is not measured or has a value out of a normal range among sea wind condition data.

In this case, the normal range may mean a range within a preset threshold value from an average value of wind condition data.

In one embodiment, the normal range may be set based on the first wind direction data or based on the second wind direction data.

In another embodiment, the normal range is the difference between the value of the dependent variable calculated by inputting any one of the first wind condition data and the second wind condition data as the independent variable of the correction equation and the input independent variable value to a preset threshold. It may be a range set so as not to deviate.

Meanwhile, the false missing data may include false data and missing data. In this case, the false data may be sea wind condition data having a value outside the normal range, and the missing data may be empty or set to indicate non-missing data. It may be sea wind condition data having a value.

In this regard, the analyzer 33 may set sea wind condition data according to a corresponding point as missing data when a point of a certain point is not detected from the point cloud measured at a certain point in time.

Meanwhile, the correction unit 35 may check false-missing data based on a condition preset by a user.

The correction unit 35 may correct erroneous missing data from suitable data based on a correction equation. To this end, the correction unit 35 may correct the false-missing data by using fitting data measured at a time point adjacent to the measurement point at which the false-missing data is measured.

Specifically, the correction unit 35 may substitute the first fit data and the second fit data, which are fit data measured at a time point adjacent to the measurement time point at which the false missing data is measured, into independent variables of the correction equation. In this case, the correction unit 35 may calculate the first correction data by inputting the first suitable data into the correction equation, and calculate the second correction data by inputting the second appropriate data into the correction equation.

Accordingly, the correction unit 35 may correct the false missing data based on the first correction data and the second correction data.

In an embodiment, if fit data exists at a time point immediately before and after a time point of measuring false-missing data, the first fitted data is fitted data measured at a time point immediately before the measuring time point of false-missing data, and the second fitted data is The fitted data may be fitted data measured at a point in time immediately after the measuring point of the false missing data.

In this case, the correction unit 35 may correct the value at the time of measuring the false-missing data based on the average value of the first correction data and the second correction data.

In another embodiment, when false-missing data exists immediately after the measurement point of false-missing data, and fitted data continuously exists immediately before the measurement point-in-time of false-missing data, the first fitted data is mismatched. The fit data may be fit data measured at a time point immediately before the measurement time point of the side data, and the second fit data may be fit data measured at a time point immediately before the measurement time point of the first fit data.

In this case, the correction unit 35 is configured to measure false-missing data based on the amount of change between the first correction data and the second correction data and the amount of change between the measurement time of the first fit data and the measurement time of the second fit data. value can be determined.

In another embodiment, when false-missing data exists immediately before the measurement point of false-missing data, and fitted data continuously exists immediately after the measurement point-in-time of false-missing data, the first fitted data is The fitted data may be fitted data measured immediately after the measuring time of the false missing data, and the second fitted data may be fitted data measured immediately after the measuring time of the first fitted data.

In this case, the correction unit 35 is configured to measure false-missing data based on the amount of change between the second correction data and the first correction data and the amount of change between the measurement time of the second fit data and the measurement time of the first fit data. value can be corrected.

Through this, the correction unit 35 may correct erroneous data among sea wind condition data accumulated for a long period of time as suitable data. This may generate an effect of increasing the accuracy of sea wind condition data accumulated for a long period of time.

4 is a flowchart of a wind condition measurement method according to an embodiment of the present invention.

Referring to FIGS. 2 and 4 , the wind condition measurement method includes collecting first wind condition data (S100), generating second wind condition data (S200), and performing regression analysis on the first wind condition data and the second wind condition data. It may include a step of performing (S300) and a step of correcting the second wind condition data (S400).

The wind condition measuring device 30 may collect first wind condition data from the meteorological tower 10 (S100).

In addition, the wind condition measuring device 30 may generate second wind condition data by collecting a point cloud from the LIDAR 20 (S200).

Accordingly, the wind condition measuring device 30 may perform regression analysis on the first wind condition data and the second wind condition data (S300).

In addition, the wind condition measuring device 30 may correct the second wind condition data based on the correction equation according to the regression analysis (S400).

Various embodiments of the present document are software (eg, machine-readable storage media) (eg, memory (internal memory or external memory)) including instructions stored in a storage medium readable by a machine (eg, a computer). : program). A device is a device capable of calling a stored command from a storage medium and operating according to the called command, and may include an electronic device (eg, a device) according to the disclosed embodiments. When the command is executed by a processor (eg, a processor), the processor may perform a function corresponding to the command directly or by using other components under the control of the processor. An instruction may include code generated or executed by a compiler or interpreter. The device-readable storage medium may be provided in the form of a non-transitory storage medium. Here, 'non-temporary' only means that the storage medium does not contain a signal and is tangible, but does not distinguish whether data is stored semi-permanently or temporarily in the storage medium.

According to one embodiment, the method according to various embodiments disclosed in this document may be provided by being included in a computer program product.

The above description is merely an example of the technical idea of the present invention, and various modifications and variations can be made to those skilled in the art without departing from the essential qualities of the present invention. Therefore, the embodiments disclosed in the present invention are not intended to limit the technical idea of the present invention, but to explain, and the scope of the technical idea of the present invention is not limited by these embodiments. The protection scope of the present invention should be construed according to the claims below, and all technical ideas within the scope equivalent thereto should be construed as being included in the scope of the present invention.

1: wind condition measurement system
10: weather tower
20: Lida
30: Wind condition measuring device

Claims (13)

Collecting first wind condition data from a weather tower;
generating second wind condition data by collecting point clouds from LiDAR;
performing a regression analysis on the first wind condition data and the second wind condition data; and
and correcting the second wind condition data based on a correction equation according to the regression analysis.
The method of claim 1, wherein generating the second wind condition data comprises:
generating land wind condition data at a point on land where the distance to the sea is equal to the distance from the point where the lidar is installed to the sea; and
A wind condition measuring method comprising: generating sea wind condition data at a point on the sea that falls within a preset distance from the lidar.
The method of claim 2, wherein the correcting of the second wind condition data comprises:
classifying suitable data and incorrectly missing data from the sea wind condition data;
correcting the erroneous missing data from the fit data based on the correction equation; and
and adding the corrected false missing data to an area of the false missing data classified from the sea wind condition data.
The method of claim 2, wherein performing the regression analysis comprises:
setting one of the first wind condition data and the land wind condition data as an independent variable;
setting another one of the first wind condition data and the land wind condition data as a dependent variable; and
A wind condition measurement method comprising: performing the regression analysis on the independent variable and the dependent variable.
The method of claim 1, wherein the lidar,
A method for measuring wind conditions, which is installed at the same point as the point at which the weather tower is installed.
a collecting unit that collects first wind condition data from a weather tower and collects a point cloud from LIDAR;
an analysis unit generating second wind condition data using the point cloud and performing a regression analysis on the first wind condition data and the second wind condition data; and
and a correction unit correcting the second wind condition data based on a correction equation according to the regression analysis.
The method of claim 6, wherein the analysis unit,
Generates land wind condition data at a point on land where the distance to the sea is equal to the distance from the point where the lidar is installed to the sea, and generates sea wind condition data at a point on the sea that falls within a preset distance from the lidar. Wind condition measuring device to do.
The method of claim 7, wherein the correction unit,
Classifying fit data and false missing data from the sea wind condition data, correcting the false missing data from the fit data based on the correction equation, and correcting the false missing data classified from the sea wind condition data. A wind condition measuring device that adds false missing data.
The method of claim 7, wherein the analysis unit,
One of the first wind condition data and the land wind condition data is set as an independent variable, the other one of the first wind condition data and the land wind condition data is set as a dependent variable, and the A wind condition instrument that performs regression analysis.
The method of claim 6, wherein the lidar,
A wind condition measuring device installed at the same point as the point at which the weather tower is installed.
a meteorological tower for measuring first wind condition data;
lidar to measure point clouds; and
The first wind condition data and the point cloud are collected, second wind condition data is generated using the point cloud, regression analysis is performed on the first wind condition data and the second wind condition data, and the regression analysis A wind condition measuring system comprising: a wind condition measuring device for correcting the second wind condition data based on a correction equation according to the present invention.
A computer-readable recording medium storing a computer program,
When the computer program is executed by a processor,
Collecting first wind condition data from a weather tower;
generating second wind condition data by collecting point clouds from LiDAR;
performing a regression analysis on the first wind condition data and the second wind condition data; and
Correcting the second wind condition data based on a correction equation according to the regression analysis; comprising instructions for causing the processor to perform a method comprising:
As a computer program stored on a computer-readable recording medium,
When the computer program is executed by a processor,
Collecting first wind condition data from a weather tower;
generating second wind condition data by collecting point clouds from LiDAR;
performing a regression analysis on the first wind condition data and the second wind condition data; and
A computer program comprising instructions for causing the processor to perform a method including correcting the second wind condition data based on a correction equation according to the regression analysis.

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