CN117807379A - Wind speed long sequence interpolation method, device and storage medium - Google Patents
Wind speed long sequence interpolation method, device and storage medium Download PDFInfo
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Abstract
The invention relates to the technical field of wind data prediction, in particular to a wind speed long sequence interpolation method, a device, equipment and a computer storage medium. According to the wind speed long sequence interpolation method, the situation that the wind direction distribution of the reference station is identical to that of the wind measuring tower is not simply assumed, and under the condition that the wind direction is considered, the wind speed is predicted after being decomposed according to the wind direction, and for the situation that the terrain is complex and the local terrain of the wind direction is severely distorted, the method has better prediction accuracy.
Description
Technical Field
The invention relates to the technical field of wind data prediction, in particular to a wind speed long sequence interpolation method, a device, equipment and a computer storage medium.
Background
Wind energy is one of the most abundant renewable clean energy sources, and compared with other energy sources, wind power has the advantages of short construction period, convenient layout, easy distributed internet surfing and the like. In recent years, the wind power industry develops rapidly, and plays an important role in realizing green development and carbon neutralization targets. In the process of carrying out actual measurement of wind resources, wind measurement data may be affected by factors from many aspects such as human beings, environment and the like to cause the problem of missing or distortion. According to technical Specification for measuring and evaluating wind energy resources of wind farm engineering (NB/T31147-2018), the effective data integrity rate of a time period selected by a wind measuring tower is more than 90%, and continuous at least one complete year is met during wind measurement, so that self-interpolation or interpolation methods are needed for wind measurement data which are not measured and are invalid so as to improve the data integrity rate and the wind measurement duration. In addition, since the wind measuring work occupies a large amount of time in the current stage of the wind power project, if a reasonable and rapid data supplementing method can be provided under the condition that the measured data measuring time is short, the data of the whole year can be obtained quickly, and accordingly preliminary evaluation of wind power project resources can be carried out, and the development efficiency of the wind power project can be improved. The interpolation method is particularly important because a certain degree of error is caused when the reference data is used for interpolation of the missing data.
The purpose of the long term correction of wind data is to estimate the long term average power production of the wind farm for 20 years from the short term (typically one year) wind data. Annual fluctuations in wind speed can be significant, and therefore this work is extremely important.
The wind data is typically interpolated by MCP (Measure Correlate Predict). MCP is a mathematical method for predicting after correlation analysis of two groups of test data, namely, predicting long-term average wind speed and wind direction distribution of a wind measuring tower point by using wind data of a long-term reference site.
The source of the long-term reference wind data may be a weather station located closer to the wind farm and in the same mesoscale climate zone, or long-term wind data simulated by a weather model, such as a mesoscale climate model or NCEP/NCAR database, or the like. The long-term wind data must have a sufficiently long coincidence period with the anemometer tower wind data, and the correlation between the two is stable.
MCP is multipurpose to regression analysis, and is a statistical analysis method for searching the quantitative relation of interdependence between two or more variables. According to the number of independent variables, the analysis can be divided into unitary regression analysis and multiple regression analysis; the relationship between independent and dependent variables can be classified into linear regression analysis and nonlinear regression analysis. Some nonlinear regression can be converted into linear regression, which is convenient for calculation.
The y represents the measured average wind speed of the anemometer tower, x represents the synchronous average wind speed of the reference station, and the regression equation of the wind data is
y＝f(x)
Wherein f (x) -regression analysis model.
Regression values are denoted by Y, then:
Y＝f(x)
the above formula is a mathematical expression of the relationship between measured wind data of the anemometer tower and long-term reference station wind data. Long-term reference wind data (such as weather stations) are acquired from stations near wind farms and in the same mesoscale climate zone, and the wind speed at any moment is different from that of the synchronous anemometer tower data, but a certain relation exists. The process of establishing this connection, i.e. the process of finding the regression analysis model f (x), i.e. the regression analysis process of the wind data.
The most common in the current engineering is a linear regression model, and a linear relation is assumed between a reference station and the synchronous wind speed of a anemometer tower. A one-dimensional linear model recommended by a wind power plant wind energy resource assessment method (GB/T18710-2002) is the most common method in engineering, and the calculation formula is as follows
Y＝ax+b
For wind resource prediction, accurate wind speed prediction is essential, and in most cases wind direction may also be important. As noted above, the MCP model is typically built by linear regression over a plurality of wind direction sectors of the reference station. However, if the reference station and the anemometer tower are far apart, the difference in atmospheric stability is large, or the terrain at the location is complex, there is a possibility that a considerable direction change occurs between the reference station and the anemometer tower, which causes a great error in the conventional advanced MCP method.
Disclosure of Invention
Therefore, the invention aims to solve the technical problem of large wind speed prediction error in the prior art.
In order to solve the technical problems, the invention provides a wind speed long sequence interpolation method, which comprises the following steps:
acquiring wind speed data of a reference station;
taking wind directions into consideration, decomposing the wind speed data of the reference station along an X axis and a Y axis on a Cartesian coordinate system to obtain two wind speed components of the reference station;
inputting the two wind speed components of the reference station into a pre-trained wind speed prediction model of a wind measuring tower to obtain two wind speed prediction components of the wind measuring tower on an X axis and a Y axis of a Cartesian coordinate system;
and calculating a target wind speed predicted value according to the two wind speed predicted components.
Preferably, the construction process of the wind speed prediction model of the anemometer tower comprises the following steps:
and linearly regressing the wind speed data of the reference station along the X axis and the Y axis in a Cartesian coordinate system to obtain the wind speed prediction model of the anemometer tower.
Preferably, the formula of the anemometer tower wind speed prediction model is expressed as:
wherein x is 1 、x 2 Two components of the reference station length sequence wind vector in a Cartesian coordinate system, y 1 、y 2 Respectively two components of wind vector of predicted value of anemometer tower in Cartesian coordinate system, a 1 、a 2 、b 11 、b 12 、b 21 、b 22 Is a model parameter.
Preferably, the solving process of the model parameters is as follows:
fitting the formula expression of the wind speed prediction model of the wind measuring tower by using a least square method to obtain a solving formula of model parameters.
Preferably, the solving formula for obtaining the model parameters further includes:
obtaining measured data of the wind speed of a historical anemometer tower and corresponding wind speed data of a reference station, and decomposing the measured data along an X axis and a Y axis on a Cartesian coordinate system to obtain a training set;
and solving by utilizing the training set based on the solving formula to obtain the model parameters.
Preferably, the solution formula is:
wherein N is the number of contained elements.
Preferably, the calculation formula of the target wind speed predicted value is:
wherein y is 1 、y 2 The wind speed prediction components of the anemometer tower on the X axis and the Y axis of the Cartesian coordinate system are respectively.
The invention also provides a wind speed long sequence interpolation device, which comprises:
the data acquisition module is used for acquiring wind speed data of the reference station;
the data processing module is used for decomposing the wind speed data of the reference station along the X axis and the Y axis on a Cartesian coordinate system in consideration of wind direction to obtain two wind speed components of the reference station;
the wind speed prediction module is used for inputting the two wind speed components of the reference station into a pre-trained wind speed prediction model of the wind measuring tower to obtain two wind speed prediction components of the wind measuring tower on an X axis and a Y axis of a Cartesian coordinate system, and calculating to obtain a target wind speed prediction value according to the two wind speed prediction components.
The invention also provides a wind speed long sequence interpolation device, which comprises:
a memory for storing a computer program;
and the processor is used for realizing the steps of the wind speed long sequence interpolation method when executing the computer program.
The invention also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor implements the steps of a wind speed long sequence interpolation method as described above.
Compared with the prior art, the technical scheme of the invention has the following advantages:
according to the wind speed long sequence interpolation method, the situation that the wind direction distribution of the reference station is identical to that of the wind measuring tower is not simply assumed, and under the condition that the wind direction is considered, the wind speed is predicted after being decomposed according to the wind direction, and for the situation that the terrain is complex and the local terrain of the wind direction is severely distorted, the method has better prediction accuracy.
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In order that the invention may be more readily understood, a more particular description of the invention will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings, in which:
FIG. 1 is a flow chart of an implementation of a wind speed long sequence interpolation method provided by the invention;
FIG. 2 is a graph of measured wind direction rose of analytical data;
FIG. 3 is a rose diagram of measured wind direction of a anemometer tower;
FIG. 4 is an analysis chart of the overall correlation of anemometer towers with analytical data.
Detailed Description
The core of the invention is to provide a wind speed long sequence interpolation method, a device, equipment and a computer storage medium, so that the prediction accuracy is effectively improved.
In order to better understand the aspects of the present invention, the present invention will be described in further detail with reference to the accompanying drawings and detailed description. It will be apparent that the described embodiments are only some, but not all, embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, fig. 1 is a flowchart illustrating an implementation of a wind speed long sequence interpolation method according to the present invention; the specific operation steps are as follows:
s101, acquiring wind speed data of a reference station;
s102, considering wind direction, decomposing the wind speed data of the reference station along an X axis and a Y axis on a Cartesian coordinate system to obtain two wind speed components of the reference station;
s103, inputting the two wind speed components of the reference station into a pre-trained wind speed prediction model of a wind measuring tower to obtain two wind speed prediction components of the wind measuring tower on an X axis and a Y axis of a Cartesian coordinate system;
s104, calculating to obtain a target wind speed predicted value according to the two wind speed predicted components.
Based on the above embodiment, the process for constructing the wind speed prediction model of the anemometer tower includes:
linearly regressing the wind speed data of the reference station along the X axis and the Y axis in a Cartesian coordinate system to obtain the wind speed prediction model of the anemometer tower:
wherein x is 1 、x 2 Two components of the reference station length sequence wind vector in a Cartesian coordinate system, y 1 、y 2 Respectively two components of wind vector of predicted value of anemometer tower in Cartesian coordinate system, a 1 、a 2 、b 11 、b 12 、b 21 、b 22 Is a model parameter.
Based on the above embodiment, the solving process of the model parameters is as follows:
fitting the formula expression of the wind speed prediction model of the wind measuring tower by using a least square method to obtain a solving formula of model parameters;
obtaining measured data of the wind speed of a historical anemometer tower and corresponding wind speed data of a reference station, and decomposing the measured data along an X axis and a Y axis on a Cartesian coordinate system to obtain a training set;
and solving by utilizing the training set based on the solving formula to obtain the model parameters.
Based on the above embodiment, the solution formula is:
wherein N is the number of contained elements.
Based on the above embodiment, the calculation formula of the target wind speed predicted value is:
wherein y is 1 、y 2 The wind speed prediction components of the anemometer tower on the X axis and the Y axis of the Cartesian coordinate system are respectively.
According to the wind speed long sequence interpolation method, the situation that the wind direction distribution of the reference station is identical to that of the wind measuring tower is not simply assumed, and under the condition that the wind direction is considered, the wind speed is predicted after being decomposed according to the wind direction, and for the situation that the terrain is complex and the local terrain of the wind direction is severely distorted, the method has better prediction accuracy.
Based on the embodiment, the actual measurement data of the height of a certain wind measuring tower for 90m in the whole year is obtained, and the reference data adopts ERA5 to analyze the data. The wind direction distribution of the measured data and the analysis data can be found to be different by observing the figures 2 and 3. The correlation of the two is analyzed, the calculation result is shown in figure 4, and the analysis data wind speed and the wind tower wind speed correlation can be found to have a general correlation coefficient of only 0.53
The measured data (January, march, july, september and October) of the anemometer tower for half a year and corresponding synchronous reference data are used as learning data, and are respectively applied to the traditional one-dimensional linear model and the method of the invention to obtain respective model parameters. The two methods were then used to calculate predictions based on other half year reference data (february, april, june, august, october, december) and compared to contemporaneous measured data, with the following results:
| measured data y | Analyzing data x | One-dimensional model | The invention is that | |
| Average value of | 7.90 | 5.28 | 7.22 | 7.65 |
It can be seen that when the correlation between the anemometer tower and long-term data is poor and the wind direction distribution has certain difference, the calculation accuracy of the wind speed long sequence interpolation can be improved by the method.
The embodiment of the invention also provides a wind speed long sequence interpolation device; the specific apparatus may include:
the data acquisition module is used for acquiring wind speed data of the reference station;
the data processing module is used for decomposing the wind speed data of the reference station along the X axis and the Y axis on a Cartesian coordinate system in consideration of wind direction to obtain two wind speed components of the reference station;
the wind speed prediction module is used for inputting the two wind speed components of the reference station into a pre-trained wind speed prediction model of the wind measuring tower to obtain two wind speed prediction components of the wind measuring tower on an X axis and a Y axis of a Cartesian coordinate system, and calculating to obtain a target wind speed prediction value according to the two wind speed prediction components.
The wind speed long sequence interpolation device of the present embodiment is configured to implement the foregoing wind speed long sequence interpolation method, so that a specific implementation of the wind speed long sequence interpolation device may be an example portion of the foregoing wind speed long sequence interpolation method, for example, the data acquisition module, the data processing module, and the wind speed prediction module are respectively configured to implement steps S101 to S104 in the foregoing wind speed long sequence interpolation method, so that a specific implementation thereof may refer to descriptions of examples of respective portions and will not be repeated herein.
The specific embodiment of the invention also provides a wind speed long sequence interpolation device, which comprises: a memory for storing a computer program; and the processor is used for realizing the step of the wind speed long sequence interpolation method when executing the computer program.
The specific embodiment of the invention also provides a computer readable storage medium, wherein the computer readable storage medium is stored with a computer program, and the computer program realizes the steps of the wind speed long sequence interpolation method when being executed by a processor.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It is apparent that the above examples are given by way of illustration only and are not limiting of the embodiments. Other variations and modifications of the present invention will be apparent to those of ordinary skill in the art in light of the foregoing description. It is not necessary here nor is it exhaustive of all embodiments. While still being apparent from variations or modifications that may be made by those skilled in the art are within the scope of the invention.
Claims (10)
1. A wind speed long sequence interpolation method, characterized by comprising:
acquiring wind speed data of a reference station;
taking wind directions into consideration, decomposing the wind speed data of the reference station along an X axis and a Y axis on a Cartesian coordinate system to obtain two wind speed components of the reference station;
inputting the two wind speed components of the reference station into a pre-trained wind speed prediction model of a wind measuring tower to obtain two wind speed prediction components of the wind measuring tower on an X axis and a Y axis of a Cartesian coordinate system;
and calculating a target wind speed predicted value according to the two wind speed predicted components.
2. The method for interpolating a long sequence of wind speeds according to claim 1, wherein the process of constructing the anemometer tower wind speed prediction model includes:
and linearly regressing the wind speed data of the reference station along the X axis and the Y axis in a Cartesian coordinate system to obtain the wind speed prediction model of the anemometer tower.
3. The method according to claim 2, wherein the formula of the anemometer tower wind speed prediction model is expressed as:
wherein x is 1 、x 2 Two components of the reference station length sequence wind vector in a Cartesian coordinate system, y 1 、y 2 Respectively two components of wind vector of predicted value of anemometer tower in Cartesian coordinate system, a 1 、a 2 、b 11 、b 12 、b 21 、b 22 Is a model parameter.
4. A method according to claim 3, wherein the solving process of the model parameters is:
fitting the formula expression of the wind speed prediction model of the wind measuring tower by using a least square method to obtain a solving formula of model parameters.
5. The method of long wind speed sequence interpolation according to claim 4, wherein the solving formula of the obtained model parameters further comprises:
obtaining measured data of the wind speed of a historical anemometer tower and corresponding wind speed data of a reference station, and decomposing the measured data along an X axis and a Y axis on a Cartesian coordinate system to obtain a training set;
and solving by utilizing the training set based on the solving formula to obtain the model parameters.
6. The method of claim 5, wherein the solving formula is:
wherein N is the number of contained elements.
7. The method according to claim 1, wherein the calculation formula of the target wind speed predicted value is:
wherein y is 1 、y 2 The wind speed prediction components of the anemometer tower on the X axis and the Y axis of the Cartesian coordinate system are respectively.
8. An apparatus for interpolating a long sequence of wind speeds, comprising:
the data acquisition module is used for acquiring wind speed data of the reference station;
the data processing module is used for decomposing the wind speed data of the reference station along the X axis and the Y axis on a Cartesian coordinate system in consideration of wind direction to obtain two wind speed components of the reference station;
the wind speed prediction module is used for inputting the two wind speed components of the reference station into a pre-trained wind speed prediction model of the wind measuring tower to obtain two wind speed prediction components of the wind measuring tower on an X axis and a Y axis of a Cartesian coordinate system, and calculating to obtain a target wind speed prediction value according to the two wind speed prediction components.
9. An apparatus for wind speed long sequence interpolation, comprising:
a memory for storing a computer program;
a processor for implementing the steps of a method according to any one of claims 1 to 7 when said computer program is executed.
10. A computer readable storage medium, characterized in that the computer readable storage medium has stored thereon a computer program which, when executed by a processor, implements the steps of a wind speed long sequence interpolation method according to any of claims 1 to 7.
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