CN114879279B - Wind farm representative year wind speed determining method and system - Google Patents
Wind farm representative year wind speed determining method and system Download PDFInfo
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Abstract
The application discloses a method and a system for determining the annual wind speed of a wind farm, comprising the following steps: acquiring the wind year wind speed of a wind tower and the wind speed of a meteorological station, wherein the wind speed of the meteorological station comprises the historical year wind speed and the wind year wind speed of the meteorological station; respectively carrying out edge distribution curve fitting on the wind year wind speed of the wind measuring tower and the wind measuring wind speed of the meteorological station; obtaining a rank correlation coefficient according to the wind year wind speed of the wind measuring tower and the wind measuring wind speed of the meteorological station; constructing a wind year connection function and a historical year connection function according to the rank correlation coefficient, the fitting result of the wind measuring tower and the fitting result of the weather station so as to respectively construct wind speed distribution diagrams of the wind measuring tower in the wind measuring year and the representative year; and obtaining a wind speed correction value according to the wind speed difference of the anemometer tower between the anemometer year and the representative year, and revising the representative year wind speed. The correlation between the long-term weather station and the anemometer tower is analyzed to determine the representative annual wind speed, so that the problem of determining the representative annual wind speed when the correlation between the weather station and the anemometer tower is poor is solved.
Description
Technical Field
The application relates to the technical field of wind power generation, in particular to a method and a system for determining the annual wind speed of a wind farm.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
The most basic condition for constructing the wind power plant is to have wind energy resources with rich energy and stable wind direction, and the site with rich wind energy resources should be selected as much as possible when the wind power plant is selected. Therefore, the assessment of wind energy resources of the wind power plant is an important link of the construction and operation of the whole wind power plant.
In wind resource evaluation, the purpose of calculating a representative year is to analyze and calculate a set of wind speed series capable of reflecting the average level of a wind farm in the next several years of running period and calculate the average power generation amount and the internet electricity price of the wind farm in the next several years of running period through the change rule of the annual average wind speed of a weather station near the wind farm and the correlation relation between the wind farm and the measured wind speed of the weather station, wherein two indexes of the wind speed and the wind power density are important indexes in the feasibility analysis of the wind farm.
Wind farm wind resource assessment generally follows the following steps:
(1) Selecting a point with good representativeness to the wind power plant from the wind power plant and setting up a wind measuring tower;
(2) After the wind to be measured expires for one year, verifying the data integrity rate, and collecting the synchronous meteorological data of the nearby meteorological stations and the average wind speed over 30 years;
(3) Correcting short-term wind measurement data of a wind field to represent annual wind speed data; the method comprises the following steps:
1) And (5) making a wind speed correlation curve of each wind direction quadrant of the anemometer tower and the weather station in the corresponding year.
The specific method of the wind speed correlation curve in a certain wind direction quadrant is as follows: a right-angle coordinate system is built, the abscissa axis is the wind speed of the weather station, and the ordinate axis is the wind speed of the anemometer tower. Taking a certain wind speed value of the wind measuring tower in the quadrant (a plurality of wind speed values are generally in a wind direction quadrant and respectively appear at different moments) as an ordinate, finding out the wind speed value of the weather station at the corresponding moment (the wind speed values are not necessarily the same, the wind direction is not necessarily corresponding to the wind direction of the wind measuring tower of the wind power plant), and obtaining the average value of the wind speed values as an abscissa to obtain a point of a relevant curve. And repeating the process for each other wind speed of the anemometer tower in the quadrant, so as to obtain a wind speed correlation curve in the quadrant. And repeating the process for the other quadrants to obtain the wind speed correlation curves of the 16 anemometer towers and the weather station.
2) For each wind speed related curve, the annual average wind speed of the weather station and the annual average wind speed of the weather station at the same period as the observation of the wind measuring tower are indicated on the abscissa axis, then two wind speed values of the wind measuring tower of the corresponding wind power plant are found on the ordinate axis, and algebraic difference values (16 algebraic difference values in total) of the two wind speed values are obtained.
3) And adding a corresponding wind speed algebraic difference value to each wind speed in each wind direction quadrant of the wind measuring tower data to obtain corrected wind speed data of the wind measuring tower.
(4) Substituting the corrected wind speed data into WASP software, calculating the generated energy of the WASP software, thereby evaluating the economic benefit of the wind power plant and verifying the feasibility of the wind power plant.
The method in the step (3) is suitable for most wind farms, but some wind farms are far away from a weather station or affected by terrain, the correlation between the wind tower and the weather station data is not good, and at the moment, correction cannot be performed by using the method in the specification; in addition, if the wind year is a high wind year, the wind speed may be negative when the above method is used for the calibration.
Disclosure of Invention
In order to solve the problems, the application provides a method and a system for determining the representative annual wind speed of a wind farm, which are used for determining the representative annual wind speed by analyzing the relevance of a long-term weather station and a wind tower and solving the problem of determining the representative annual wind speed when the relevance of the weather station and the wind tower is poor.
In order to achieve the above purpose, the present application adopts the following technical scheme:
in a first aspect, the present application provides a method for determining a wind speed of a wind farm representative year, comprising:
acquiring the wind annual wind speed of a wind tower and the wind speed of a meteorological station, wherein the wind speed of the meteorological station comprises the historical annual wind speed and the wind annual wind speed;
the historical annual wind speed is the same year wind speed as the annual average wind speed of a meteorological station, and the anemometry annual wind speed is the same year wind speed as the anemometry annual wind speed;
respectively performing edge distribution curve fitting on the wind year wind speed of the wind tower and the historical year wind speed of the weather station and the wind year wind speed of the weather station;
obtaining a rank correlation coefficient according to the wind year wind speed of the wind measuring tower, the historical year wind speed of the weather station and the wind year wind speed of the weather station;
constructing a wind year connection function and a historical year connection function according to the rank correlation coefficient, the fitting result of the wind measuring tower and the fitting result of the weather station so as to respectively construct wind speed distribution diagrams of the wind measuring tower in the wind measuring year and the representative year;
and obtaining a wind speed correction value according to the wind speed difference of the anemometer tower between the anemometer year and the representative year, and revising the representative year wind speed.
As an alternative embodiment, the rank correlation coefficient τ is:
wherein sign () is a sign function, when (x i -x j )(y i -y j )>At 0 sign=1; (x) i -x j )(y i -y j )<At 0 sign= -1; (x) i -x j )(y i -y j ) When=0, sign=0; x is x i 、x j For the wind-year time-by-time wind speed of the wind measuring tower at the ith moment and the jth moment, y i 、y j Weather calendar for ith and jth timeShi Nian wind speed from time to time or wind speed from time to time in the wind year.
In an alternative embodiment, the process of constructing the connection function of the anemometer tower and the weather station includes: the relevance parameter theta of the Copula function is obtained according to the rank relevance coefficient tau, and specifically comprises the following steps:
in an alternative embodiment, the process of constructing the connection function of the anemometer tower and the weather station includes: calculating Copula functions of the wind year according to the correlation parameters, the edge distribution of the wind year wind speed of the wind tower and the edge distribution of the wind year wind speed of the meteorological station; and calculating a Copula function of the historical year according to the correlation parameters and the edge distribution of the wind year wind speed of the wind measuring tower and the edge distribution of the historical year wind speed of the meteorological station.
As an alternative embodiment, the anemometry year connection function and the historical year connection function are:
C(u 1 ,u 2 ,…,u n )＝exp(-((-lnu 1 ) θ +(-lnu 2 ) θ +…+(-lnu n ) θ ) 1/θ ),θ≥1
wherein u is 1 For the edge distribution of wind year wind speed of wind measuring tower, u 2 ……u n And measuring the edge distribution of the wind annual wind speed or the historical annual wind speed for each meteorological station, wherein θ is a correlation parameter.
In an alternative embodiment, the wind tower is constructed by the following steps in the process of representing the wind speed distribution diagram of the year and the wind year: the method comprises the steps of respectively obtaining the historical annual average wind speed and the wind year average wind speed of a weather station according to the historical annual wind speed and the wind year average wind speed of the weather station, constructing a wind speed distribution diagram of a wind measuring tower in a representative year by adopting a historical annual connection function according to the historical annual average wind speed of the weather station, and constructing a wind speed distribution diagram of the wind measuring tower in a wind year by adopting a wind year connection function according to the wind year average wind speed of the weather station.
As an alternative embodiment, the wind speed distribution of the anemometer tower in the anemometry year is:
wherein u is 1 For the edge distribution of wind year wind speed of wind measuring tower, u 2 ……u n Measuring the edge distribution of wind annual or historical annual wind speeds for each meteorological station, U 2 ……U n Representing the edge probability of the historical annual average wind speed of each meteorological station or the measured annual average wind speed of each meteorological station.
In a second aspect, the present application provides a wind farm representative annual wind speed determination system comprising:
a wind speed acquisition module configured to acquire a wind year wind speed of a wind tower and a wind speed of a weather station, the wind speed of the weather station including a historical year wind speed and a wind year wind speed of the weather station;
the edge distribution module is configured to perform edge distribution curve fitting on the wind year wind speed of the wind tower, the historical year wind speed of the weather station and the wind year wind speed of the weather station respectively;
the correlation calculation module is configured to obtain a rank correlation coefficient according to the wind year wind speed of the wind tower, the historical year wind speed of the weather station and the wind year wind speed of the weather station;
the wind speed distribution module is configured to construct a wind year connection function and a historical year connection function according to the rank correlation coefficient, the fitting result of the wind measuring tower and the fitting result of the weather station so as to respectively construct wind speed distribution diagrams of the wind measuring tower in the wind measuring year and the representative year;
and the wind speed revising module is configured to obtain a wind speed revising value according to the wind speed difference of the anemometer tower between the anemometer year and the representative year, so as to revise the representative year wind speed.
In a third aspect, the application provides an electronic device comprising a memory and a processor and computer instructions stored on the memory and running on the processor, which when executed by the processor, perform the method of the first aspect.
In a fourth aspect, the present application provides a computer readable storage medium storing computer instructions which, when executed by a processor, perform the method of the first aspect.
Compared with the prior art, the application has the beneficial effects that:
the conventional method only adopts the correlation coefficient to represent the relation between the weather station and the wind speed of the wind measuring tower, the single coefficient value can not truly reflect the correlation, and the weather station can only select the weather station closest to the wind power plant. The application provides a method and a system for determining the annual wind speed of a wind farm, wherein a Copula function is adopted to evaluate the relevance between a meteorological station and the wind speed of a anemometer tower, so as to calculate the annual wind speed of the wind farm; the weather station is not limited to one weather station any more, the condition distribution of the wind speed of the wind measuring tower can be calculated under the condition of the wind speed of any weather station, and the wind speed of the wind farm representing the year can be determined together according to the data of a plurality of weather stations.
The application provides a method and a system for determining the wind power plant representative annual wind speed, which are characterized in that the relevance of a meteorological station and a wind tower wind speed is analyzed more deeply through rank correlation coefficient, the joint distribution of the meteorological station and the wind tower wind speed and the conditional distribution of the wind tower wind speed under the condition of any meteorological station wind speed, and the result is true and reliable and has more effectiveness in practical application.
Additional aspects of the application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the application.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application.
FIG. 1 is a flowchart of a method for determining wind speed of a wind farm representative year according to embodiment 1 of the present application;
FIG. 2 is a graph showing the edge distribution of the annual wind speed of the wind measuring tower according to embodiment 1 of the present application;
FIG. 3 is a graph showing wind speed conditions of representative years and anemometry years for the anemometer tower according to example 1 of the present application;
FIG. 4 is a time-by-time wind speed scatter plot of the anemometer annual weather station and the anemometer tower provided in embodiment 1 of the present application;
FIGS. 5 (a) -5 (b) are graphs comparing the results of the method of example 1 of the present application with those of the prior art.
Detailed Description
The application is further described below with reference to the drawings and examples.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the application. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular forms also are intended to include the plural forms, and furthermore, it is to be understood that the terms "comprises" and "comprising" and any variations thereof are intended to cover non-exclusive inclusions, such as, for example, processes, methods, systems, products or devices that comprise a series of steps or units, are not necessarily limited to those steps or units that are expressly listed, but may include other steps or units that are not expressly listed or inherent to such processes, methods, products or devices.
Embodiments of the application and features of the embodiments may be combined with each other without conflict.
Example 1
As shown in fig. 1, the embodiment provides a method for determining a representative annual wind speed of a wind farm, which determines the representative annual wind speed by analyzing the relevance between a long-term weather station and a wind tower, solves the problem that the weather station and the wind tower are poor in relevance, obtains and revises the representative annual wind speed, and provides scientific decision basis for development of the wind farm, and the method comprises the following steps:
acquiring the wind year wind speed of a wind tower and the wind speed of a meteorological station, wherein the wind speed of the meteorological station comprises the historical year wind speed and the wind year wind speed of the meteorological station;
respectively carrying out edge distribution curve fitting on the wind year wind speed of the wind measuring tower and the wind measuring wind speed of the meteorological station;
obtaining a rank correlation coefficient according to the wind year wind speed of the wind measuring tower and the wind measuring wind speed of the meteorological station;
constructing a wind year connection function and a historical year connection function according to the rank correlation coefficient, the fitting result of the wind measuring tower and the fitting result of the weather station so as to respectively construct wind speed distribution diagrams of the wind measuring tower in the wind measuring year and the representative year;
and obtaining a wind speed correction value according to the wind speed difference of the anemometer tower between the anemometer year and the representative year, and revising the representative year wind speed.
The wind farm representative annual wind speed determination method of the present embodiment is explained in detail below.
S1: setting up a wind measuring tower in a wind power field area, and acquiring the wind year wind speed of the wind measuring tower;
in this embodiment, a wind tower is set up by selecting a representative place in the wind farm, where the wind tower includes devices such as an anemometer, a anemoscope, a temperature sensor, and a pressure sensor, and performs wind measurement in a year of wind measurement and obtains a year of wind speed of the wind measurement.
S2: acquiring the wind measuring wind speed of a peripheral or mesoscale weather station, wherein the wind measuring wind speed of the weather station comprises a historical annual wind speed and a wind measuring annual wind speed;
s3: judging the correlation between the wind measurement wind speed of the meteorological station and the wind measurement annual wind speed of the wind measurement tower, and if the correlation between the wind measurement wind speed of the meteorological station and the wind measurement annual wind speed of the wind measurement tower exceeds a threshold value, determining the representative annual wind speed according to the correlation; it will be appreciated that the process may be performed by existing methods, and this embodiment is not described with particular emphasis.
S4: if the correlation between the wind measuring speed of the meteorological station and the wind measuring annual wind speed of the wind measuring tower is lower than a threshold value, or if a plurality of meteorological stations are provided, calculating the representative annual wind speed by adopting the following method.
S401: calculating the wind year wind speed of the wind tower, the historical year wind speed of the meteorological station and the edge distribution of the wind year of the meteorological station, and performing curve fitting, as shown in figure 2;
the edge distribution comprises normal distribution, gamma distribution, exponential distribution, weibull distribution, lognormal distribution, logistic distribution and the like; and evaluating the fitting degree of the edge distribution by adopting a standard deviation MSE, wherein the smaller the standard deviation MSE value is, the better the edge distribution fitting is.
The standard deviation MSE is expressed as:
wherein E is an expected value; n is the total number of samples; x is X c (i) An ith calculated value for the edge distribution; x is X 0 (i) For the ith observation, the observation is represented by an empirical probability, the experience of the random variable does not exceed the probability assessment, using the Gringenten formula:
where k is the kth observation in ascending order and N is the sample size.
S402: calculating a rank correlation coefficient tau according to the wind year wind speed of the wind measuring tower and the historical year or wind year wind speed of the meteorological station;
wherein, the wind year is 8760 hours, x i ，x j Represents the wind speed of the wind measuring tower from time to time in wind year, y i ，y j The wind speed of a meteorological station in historical years or wind measuring years from time to time is represented, and i and j represent different times; sign () is a sign function, when (x i -x j )(y i -y j )>At 0 sign=1; (x) i -x j )(y i -y j )<At 0 sign= -1; (x) i -x j )(y i -y j ) When=0, sign=0.
S403: calculating Copula functions of the anemometer tower and the meteorological station in the anemometry year and the historical year according to the edge distribution obtained in the step S401 and the rank correlation coefficient tau obtained in the step S402;
the Copula function is a multidimensional joint distribution function with a definition domain of [0,1] uniform distribution, and can connect marginal distributions of a plurality of random variables to construct joint distribution. The advantages are that: the correlation between random variables can be characterized by Kendall's; the joint distribution between variables can be established without assuming a random variable edge distribution type. The Copula function includes elliptic type, quadratic type, archimedean type, etc., and the symmetrical gummel-Hougaard Copula function in Archimedean type is adopted in this embodiment.
In this embodiment, the rank correlation coefficient τ has a functional relationship with the correlation parameter θ of the Copula function:
calculating a correlation parameter theta according to the rank correlation coefficient tau obtained in the step S402, and calculating a Copula function of the wind year according to the correlation parameter theta, the edge distribution of the wind year wind speed of the wind tower and the edge distribution of the wind year wind speed of the meteorological station; calculating a Copula function of the historical year according to the correlation parameter theta, the edge distribution of the wind year wind speed of the wind measuring tower and the edge distribution of the historical year wind speed of the meteorological station;
C(u 1 ,u 2 ,…,u n )＝exp(-((-lnu 1 ) θ +(-lnu 2 ) θ +…+(-lnu n ) θ ) 1/θ ),θ≥1 (5)
wherein u is 1 For the edge distribution of wind year wind speed of wind measuring tower, u 2 ……u n And measuring the edge distribution of the wind annual wind speed or the historical annual wind speed for each meteorological station, wherein θ is a correlation parameter.
Given u 2 ＝U 2 ,…,u n ＝U n In the case of u 1 The conditional probability distribution of (2) is:
wherein F (u) 1 |u 2 ＝U 2 ,…,u n ＝U n ) For measuring wind speed condition distribution of wind tower, u 2 ……u n For each meteorological station measuring the edge distribution of wind speed of wind years or history years, U 2 ……U n Representing the edge probability of a given specific value for each weather station, i.e. the historical annual average wind speed or the weather station's annual average wind speed.
Respectively obtaining the historical annual average wind speed of the weather station and the measured annual average wind speed of the weather station according to the historical annual wind speed of the weather station and the measured annual wind speed of the weather station; according to the historical annual average wind speed and the measured annual average wind speed of the weather station, two conditional probability curves are obtained by adopting a formula (6), and the two conditional probability curves are shown in figure 3, namely wind speed distribution diagrams of the measured wind tower in representative years and measured wind years.
S404: and obtaining a correction value of the representative annual wind speed according to the difference value of the two conditional probability curves, and revising the representative annual wind speed.
In the embodiment, the wind resource condition of the wind power plant can be calculated according to the revised representative year wind speed, the generated energy is calculated, the representative year wind speed calculation process when the correlation of the meteorological station and the anemometer tower data is poor is solved, the method can be used for correcting multiple meteorological stations, and the obtained data is accurate.
The steps S1 to S3 in this embodiment may be all conventional methods, and the following details the step S4, but the scope of protection of this embodiment is not limited to the embodiment.
A wind measuring tower with the height of 70m is adopted for wind measuring work in a wind power plant, an anemometer is respectively arranged at the heights of 70m, 50m and 10m, a anemoscope is respectively arranged at the heights of 70m and 10m, a thermometer and a manometer are arranged at the heights of 10m, and the effective integrity rate of field measured data is 92.6% after inspection, so that the standard requirement is met.
Acquiring the average annual wind speed of a weather station closest to a wind power plant for the last 30 years, wherein the statistical values of the wind speeds of the weather station and a anemometer tower are shown in table 1;
TABLE 1 Meteorological station and anemometer
It can be seen that the average wind speed of the meteorological station is 2.62m/s in nearly 30 years, and the wind year wind speed of the meteorological station is 2.93m/s, so that the wind year is a big wind year, and the wind energy elements of the wind power plant representing years are reasonably calculated according to the correlation analysis between the on-site wind measurement data and the synchronous wind speed and wind direction hour record of the meteorological station. According to the method in wind power plant wind energy resource evaluation method, calculating to obtain the correlation of each sector of the wind speed at 70m of the wind power plant and the wind speed of the weather station as shown in table 2;
table 2 table of analysis results for each sector
It can be seen that the correlation coefficients of the four NNE, NE, SSE, NW sectors are better and are above 0.8, the correction can be directly carried out by adopting a correlation equation, the correlation coefficients of the three SE, WSW and W sectors are worse and are below 0.5, and if the correction wind speed error obtained by the calculation of the method is larger; because the wind year is the big wind year, the primary term coefficient of 16 sectors is more than 1 and is higher than the flat wind year, especially the primary term coefficient of the SSW sector is 3.26, which is equivalent to the wind speed of each SSW wind direction minus 3.26 x 0.3 is about 1m/s, that is, if the wind year wind speed is less than 0.9m/s, the obtained representative year wind speed is negative, which is obviously not regular, and the technical method in the embodiment can avoid the occurrence of the situation.
The annual average wind speed at the meteorological station and the edge distribution of the time-to-time wind speed at the anemometer tower 70m are evaluated using the MSE standard as shown in Table 3, which shows that both can be described by the Weibull distribution.
TABLE 3 MSE values for different distribution types
Obtaining a correlation parameter theta=1.40 according to the rank correlation coefficient tau; according to the maximum likelihood method, a correlation parameter θ=1.51 is obtained, and the correlation parameter θ=1.51 is not different from the correlation parameter θ.
The historical annual average wind speed of the weather station is 2.6m/s, the measured annual average wind speed is 2.9m/s, the edge distribution of the weather station is 0.482 when the average wind speed is 2.6m/s, and the edge distribution of the weather station is 0.694 when the wind speed is 2.9 m/s; the condition Copula function calculation is to calculate the wind speed distribution of the wind measuring tower when the wind speed is 2.6m/s and 2.9m/s respectively.
The historical annual average wind speed of the weather station is representative annual wind speed, namely the wind speed when the annual average wind speed is 2.6m/s is representative annual wind speed, the wind speed when the annual average wind speed is 2.9m/s is measured annual wind speed, and the difference under the same probability is representative annual wind speed plus value.
The average annual and monthly wind speed and wind power density calculated by the two methods are shown in the figures 5 (a) -5 (b), and the wind power density of the wind field calculated by the wind power resource evaluation method of the wind power field is 276W/m 2 The wind power density calculated by the Copula method was 251W/m 2 The two are not very different, thus indicating the applicability of the method.
In order to analyze the calculation errors of the two methods, a certain wind farm is taken as an example for explanation, and the data of two meteorological stations around the anemometer tower are collected together. Due to the lack of long series of observations, corrections are made to the month data. The average monthly wind speeds of the anemometer tower and the two meteorological stations are shown in table 4;
table 4 month average anemometer units: m/s
The annual average wind speed of the anemometer tower is 6.62m/s, and the wind power density is 340.83W/m 2 The method comprises the steps of carrying out a first treatment on the surface of the The average wind speed of the weather station in 1 year is 2.44m/s; the average wind speed of the weather station for 2 years is 2.61m/s. In the ideal state, the calculated average wind speed per month is about 6.62m/s, and therefore, it is regarded asAnd comparing and analyzing the true value with the calculated value obtained by the two methods. The rank correlation coefficients of the wind towers and the weather stations of each month are calculated respectively as shown in table 5;
table 5 Kendall tau rank correlation coefficient values for each month
It can be seen that the rank correlation coefficients of each month are not consistent, wherein the correlation coefficients of two weather stations in the month of four are basically equal, but the difference between the weather stations in the month of 8 is larger, and the correlation between the weather stations and the anemometer tower is extremely poor, wherein the weather station 1 is only 0.0191.
When the edge distribution of two weather stations is solved, two conditions of time-by-time wind speed distribution and month-by-month wind speed distribution are comprehensively considered, and the rank correlation coefficient is used as a distribution coefficient to obtain the edge distribution of the weather stations as shown in Table 6.
TABLE 6 average wind speed frequency meter for weather station of each month
Likewise, the weibull parameters for the different month anemometer towers were also inconsistent, as shown in table 7.
TABLE 7 measuring the values of the Taweibull parameters for each month
The calculation results of five cases, namely, only weather station 1, only weather station 2, both weather stations, standard weather station 1 and standard weather station 2, are compared with the ideal values, as shown in tables 8 and 9.
Table 8 comparing the calculated results of the five methods with the ideal value wind speed: m/s
Table 9W/m is analyzed by comparing the calculated results of the five methods with the ideal wind power density 2
RMSE values were calculated for the five calculation methods relative to the ideal values, as shown in tables 10 and 11.
Table 10 five methods calculation results and ideal value wind speed RMSE analysis
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Table 11 five methods calculation results and ideal value wind power density RMSE analysis
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As can be seen from tables 10 and 11, the RMSE is the smallest in both wind speed and wind power density in the five methods, i.e. it is closest to the true value when both weather stations of the Copula method consider, while the RMSE is the largest in the calculation result of weather station 1 of the canonical method, i.e. it is most deviated from the true value.
Aiming at the defects that the correlation between a meteorological station and a anemometer tower is poor and negative wind speed can occur, the embodiment provides a method for determining the wind speed of a wind farm in a representative year, which is suitable for one or more meteorological stations, does not consider the correlation between the anemometer tower and the meteorological stations, and ensures that all wind speeds are positive values. As can be seen from example calculation, the method of the embodiment has reliable results, simpler solving process and good practicability.
Example 2
The present embodiment provides a wind farm representative annual wind speed determination system, comprising:
a wind speed acquisition module configured to acquire a wind year wind speed of a wind tower and a wind speed of a weather station, the wind speed of the weather station including a historical year wind speed and a wind year wind speed of the weather station;
the edge distribution module is configured to perform edge distribution curve fitting on the wind year wind speed of the wind tower, the historical year wind speed of the weather station and the wind year wind speed of the weather station respectively;
the correlation calculation module is configured to obtain a rank correlation coefficient according to the wind year wind speed of the wind tower, the historical year wind speed of the weather station and the wind year wind speed of the weather station;
the wind speed distribution module is configured to construct a wind year connection function and a historical year connection function according to the rank correlation coefficient, the fitting result of the wind measuring tower and the fitting result of the weather station so as to respectively construct wind speed distribution diagrams of the wind measuring tower in the wind measuring year and the representative year;
and the wind speed revising module is configured to obtain a wind speed revising value according to the wind speed difference of the anemometer tower between the anemometer year and the representative year, so as to revise the representative year wind speed.
It should be noted that the above modules correspond to the steps described in embodiment 1, and the above modules are the same as examples and application scenarios implemented by the corresponding steps, but are not limited to those disclosed in embodiment 1. It should be noted that the modules described above may be implemented as part of a system in a computer system, such as a set of computer-executable instructions.
In further embodiments, there is also provided:
an electronic device comprising a memory and a processor and computer instructions stored on the memory and running on the processor, which when executed by the processor, perform the method described in embodiment 1. For brevity, the description is omitted here.
It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate array FPGA or other programmable logic device, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may include read only memory and random access memory and provide instructions and data to the processor, and a portion of the memory may also include non-volatile random access memory. For example, the memory may also store information of the device type.
A computer readable storage medium storing computer instructions which, when executed by a processor, perform the method described in embodiment 1.
The method in embodiment 1 may be directly embodied as a hardware processor executing or executed with a combination of hardware and software modules in the processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in a memory, and the processor reads the information in the memory and, in combination with its hardware, performs the steps of the above method. To avoid repetition, a detailed description is not provided herein.
Those of ordinary skill in the art will appreciate that the elements of the various examples described in connection with the present embodiments, i.e., the algorithm steps, can be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
While the foregoing description of the embodiments of the present application has been presented in conjunction with the drawings, it should be understood that it is not intended to limit the scope of the application, but rather, it is intended to cover all modifications or variations within the scope of the application as defined by the claims of the present application.
Claims (10)
1. A method for determining a representative annual wind speed of a wind farm, comprising:
acquiring the wind annual wind speed of a wind tower and the wind speed of a meteorological station, wherein the wind speed of the meteorological station comprises the historical annual wind speed and the wind annual wind speed;
respectively performing edge distribution curve fitting on the wind year wind speed of the wind tower and the historical year wind speed of the weather station and the wind year wind speed of the weather station;
obtaining a rank correlation coefficient according to the wind year wind speed of the wind measuring tower, the historical year wind speed of the weather station and the wind year wind speed of the weather station;
constructing a wind year connection function and a historical year connection function according to the rank correlation coefficient, the fitting result of the wind measuring tower and the fitting result of the weather station so as to respectively construct wind speed distribution diagrams of the wind measuring tower in the wind measuring year and the representative year;
and obtaining a wind speed correction value according to the wind speed difference of the anemometer tower between the anemometer year and the representative year, and revising the representative year wind speed.
2. A method for determining the annual wind speed of a wind farm according to claim 1, wherein the rank correlation coefficient τ is:
wherein x is i 、x j For the wind-year time-by-time wind speed of the wind measuring tower at the ith moment and the jth moment, y i 、y j The historical year time-by-time wind speed or the wind-measuring year time-by-time wind speed of the weather station at the ith moment and the jth moment; sign () is a sign function, when (x i -x j )(y i -y j )>At 0 sign=1; (x) i -x j )(y i -y j )<At 0 sign= -1; (x) i -x j )(y i -y j ) When=0, sign=0.
3. A method for determining the annual wind speed of a wind farm according to claim 1, wherein the constructing of the yearly connecting function and the historic yearly connecting function comprises: the relevance parameter theta of the Copula function is obtained according to the rank relevance coefficient tau, and specifically comprises the following steps:
4. a method for determining the annual wind speed of a wind farm according to claim 3, wherein the constructing of the yearly connecting function and the historic yearly connecting function comprises: calculating Copula functions of the wind year according to the correlation parameters, the edge distribution of the wind year wind speed of the wind tower and the edge distribution of the wind year wind speed of the meteorological station; and calculating a Copula function of the historical year according to the correlation parameters and the edge distribution of the wind year wind speed of the wind measuring tower and the edge distribution of the historical year wind speed of the meteorological station.
5. A method for determining the annual wind speed of a wind farm according to claim 1, wherein the yearly connecting function and the historic yearly connecting function are:
C(u 1 ,u 2 ,…,u n )＝exp(-((-lnu 1 ) θ +(-lnu 2 ) θ +…+(-lnu n ) θ ) 1/θ ),θ≥1
wherein u is 1 For the edge distribution of wind year wind speed of wind measuring tower, u 2 ……u n For each weather phaseAnd measuring the edge distribution of the wind annual wind speed or the historical annual wind speed by the station, wherein θ is a correlation parameter.
6. A method for determining the annual wind speed of a wind farm according to claim 5, wherein constructing the wind tower during the course of the annual wind speed profile of the representative year and the anemometric year comprises: the method comprises the steps of respectively obtaining the historical annual average wind speed and the wind year average wind speed of a weather station according to the historical annual wind speed and the wind year average wind speed of the weather station, constructing a wind speed distribution diagram of a wind measuring tower in a representative year by adopting a historical annual connection function according to the historical annual average wind speed of the weather station, and constructing a wind speed distribution diagram of the wind measuring tower in a wind year by adopting a wind year connection function according to the wind year average wind speed of the weather station.
7. A method for determining the annual wind speed of a wind farm according to claim 6, wherein the wind tower wind speed profile is:
wherein u is 1 Edge distribution of wind annual wind speed for wind measuring tower; when calculating the annual wind speed distribution of the anemometry, u 2 ……u n For each meteorological station measuring the edge distribution of wind annual velocity, U 2 ……U n The edge probability of the average wind speed of each meteorological station in the wind year is represented; when calculating the wind speed distribution of the history year, u 2 ……u n For the edge distribution of the historical annual wind speed of each meteorological station, U 2…… U n Represents the edge probability of the historical annual average wind speed for each meteorological station.
8. A wind farm representative annual wind speed determination system, comprising:
a wind speed acquisition module configured to acquire a wind year wind speed of a wind tower and a wind speed of a weather station, the wind speed of the weather station including a historical year wind speed and a wind year wind speed of the weather station;
the edge distribution module is configured to perform edge distribution curve fitting on the wind year wind speed of the wind tower, the historical year wind speed of the weather station and the wind year wind speed of the weather station respectively;
the correlation calculation module is configured to obtain a rank correlation coefficient according to the wind year wind speed of the wind tower, the historical year wind speed of the weather station and the wind year wind speed of the weather station;
the wind speed distribution module is configured to construct a wind year connection function and a historical year connection function according to the rank correlation coefficient, the fitting result of the wind measuring tower and the fitting result of the weather station so as to respectively construct wind speed distribution diagrams of the wind measuring tower in the wind measuring year and the representative year;
and the wind speed revising module is configured to obtain a wind speed revising value according to the wind speed difference of the anemometer tower between the anemometer year and the representative year, so as to revise the representative year wind speed.
9. An electronic device comprising a memory and a processor and computer instructions stored on the memory and running on the processor, which when executed by the processor, perform the method of any one of claims 1-7.
10. A computer readable storage medium storing computer instructions which, when executed by a processor, perform the method of any of claims 1-7.
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