CN114879279A - Wind power plant representative year wind speed determination method and system - Google Patents
Wind power plant representative year wind speed determination method and system Download PDFInfo
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Abstract
The invention discloses a method and a system for determining wind speed of a wind power plant in a representative year, wherein the method comprises the following steps: acquiring the annual wind speed of the wind measuring tower and the wind measuring speed of a weather station, wherein the wind measuring speed of the weather station comprises the historical annual wind speed and the annual wind speed of the weather station; respectively carrying out edge distribution curve fitting on the annual wind speed of the wind measuring tower and the wind measuring speed of the meteorological station; obtaining a rank correlation coefficient according to the annual wind speed of the wind measuring tower and the wind measuring speed of the meteorological station; constructing a wind measuring 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 a wind speed distribution diagram 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 between the wind measuring year and the representative year of the wind measuring tower, and correcting the wind speed of the representative year. The representative year wind speed is determined by analyzing the relevance between the long-term weather station and the anemometer tower, and the problem of determining the representative year wind speed when the relevance between the weather station and the anemometer tower is poor is solved.
Description
Technical Field
The invention relates to the technical field of wind power generation, in particular to a method and a system for determining wind speed of a wind power plant in a representative year.
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 building the wind power plant is that wind energy resources with rich energy and stable wind direction are required, and places with rich wind energy resources are selected as much as possible when the wind power plant is selected. Therefore, the evaluation of the wind energy resources of the wind power plant is an important link for the construction and operation of the whole wind power plant.
In wind resource assessment, the purpose of calculating the representative year is to analyze and calculate a set of wind speed series of the representative year capable of reflecting the average level of the wind power plant in a plurality of years of running period in the future, calculate the average generated energy and the grid electricity price of the wind power plant in the plurality of years of running in the future by the change rule of the annual average wind speed of the meteorological station near the wind field and the correlation between the wind field and the actually measured wind speed of the meteorological station, wherein two indexes of the wind speed and the wind power density are important indexes in feasibility analysis of the wind power plant.
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 a anemometer tower;
(2) after the wind to be measured expires one year, verifying the data integrity rate, and collecting synchronous meteorological data of a nearby meteorological station and annual average wind speed of more than 30 years;
(3) correcting short-term wind measuring data of a wind field into representative annual wind speed data; the method comprises the following steps:
1) and (5) making a wind speed related curve of each wind direction quadrant of the wind measuring tower and the corresponding annual meteorological station.
The specific method of the wind speed correlation curve in a certain wind direction quadrant is as follows: and establishing a rectangular coordinate system, wherein the abscissa axis is the wind speed of the meteorological station, and the ordinate axis is the wind speed of the anemometer tower. Taking a certain wind speed value (a plurality of wind speed values generally appear in a wind direction quadrant at different moments) of the wind measuring tower in the quadrant as a vertical coordinate, finding out the wind speed values (the wind speed values are not necessarily the same, and the wind direction is not necessarily corresponding to the wind direction of the wind measuring tower of the wind power plant) of the corresponding moments of the meteorological station, and calculating the average value of the wind speed values as a horizontal coordinate to determine a point of the relevant curve. Repeating the above process for each remaining wind speed of the anemometer tower in the quadrant, so as to obtain a wind speed correlation curve in the quadrant. The process is repeated for the rest quadrants, and the wind speed correlation curves of the 16 anemometers and the meteorological station can be obtained.
2) For each wind speed correlation curve, the annual average wind speed of the meteorological station and the annual average wind speed of the meteorological station in the same period of observation of the anemometer tower are indicated on the axis of abscissa, then two corresponding wind speed values of the wind power plant anemometer tower are found on the axis of ordinate, and the two wind speed values are calculated to obtain an algebraic difference (total 16 algebraic differences).
3) And adding the corresponding wind speed algebraic difference to each wind speed in each wind direction quadrant of the anemometer tower data to obtain corrected anemometer tower wind speed data.
(4) And substituting the corrected wind speed data into WASP software, and 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 power plants, but some wind power plants are far away from a meteorological station or are influenced by terrain, the correlation of the data of the anemometer tower and the meteorological station is not good, and the method in the specification can not be applied to correction at the moment; in addition, if the anemometry year is a strong wind year, the wind speed may be negative when the above method is used for booking.
Disclosure of Invention
In order to solve the problems, the invention provides a method and a system for determining the wind speed of a wind power plant in a representative year.
In order to achieve the purpose, the invention adopts the following technical scheme:
in a first aspect, the invention provides a wind speed representative year determination method for a wind power plant, which comprises the following steps:
acquiring the annual wind speed of the wind measuring tower and the wind measuring speed of a weather station, wherein the wind measuring speed of the weather station comprises the historical annual wind speed and the annual wind speed of the wind measuring;
the historical annual wind speed is the same year wind speed as the annual average wind speed of the meteorological station, and the anemometric annual wind speed is the same period wind speed as the anemometric annual wind speed;
respectively carrying out edge distribution curve fitting on the annual wind speed of the wind measuring tower, the historical annual wind speed of the meteorological station and the annual wind speed of the meteorological station;
obtaining a rank correlation coefficient according to the annual wind speed of the anemometer tower, the historical annual wind speed of the meteorological station and the annual wind speed of the meteorological station;
constructing a wind measuring 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 meteorological station so as to respectively construct a wind speed distribution diagram 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 between the wind measuring year and the representative year of the wind measuring tower, and correcting the wind speed of the representative year.
As an alternative embodiment, the rank correlation coefficient τ is:
wherein sign () is a sign functionWhen (x) i -x j )(y i -y j )>When 0, sign is 1; (x) i -x j )(y i -y j )<At 0, sign is-1; (x) i -x j )(y i -y j ) When 0, sign is 0; x is the number of i 、x j The annual hourly wind speed and the y time of wind measurement of the wind measuring tower at the ith moment and the jth moment i 、y j The historical annual hourly wind speed or the anemometric annual hourly wind speed of the meteorological station at the ith moment and the jth moment.
As an alternative embodiment, the process of constructing the connection function of the anemometer tower and the meteorological station comprises the following steps: obtaining a correlation parameter theta of the Copula function according to the rank correlation coefficient tau, specifically:
as an alternative embodiment, the process of constructing the connection function of the anemometer tower and the meteorological station comprises the following steps: calculating a Copula function of the anemometry year according to the correlation parameters, the edge distribution of the anemometry annual wind speed of the anemometry tower and the edge distribution of the anemometry annual wind speed of the meteorological station; and calculating a Copula function of the historical years according to the correlation parameters, the edge distribution of the annual wind speed measured by the wind measuring tower and the edge distribution of the historical annual wind speed of the meteorological station.
As an alternative embodiment, the anemometric annual linking function and the historical annual linking 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 marginal distribution of annual wind speed of anemometer tower 2 ……u n And theta is a correlation parameter for the marginal distribution of the measured annual wind speed or the historical annual wind speed of each meteorological station.
As an alternative embodiment, the process of constructing the wind measuring tower in the wind speed distribution diagram representing the year and the anemometry year comprises the following steps: the method comprises the steps of obtaining historical annual average wind speed and annual wind measuring average wind speed of a weather station according to historical annual wind speed and annual wind measuring wind speed of the weather station, constructing a wind speed distribution diagram of a wind measuring tower in a representative year according to the historical annual average wind speed of the weather station by adopting a historical annual connection function, and constructing the wind speed distribution diagram of the wind measuring tower in the wind measuring year according to the annual average wind speed of the weather station by adopting the annual wind measuring connection function.
As an alternative embodiment, the wind speed distribution of the anemometer tower in the anemometer year is:
wherein u is 1 For marginal distribution of annual wind speed of anemometer tower 2 ……u n For each meteorological station measuring the marginal distribution of annual or historical wind speeds, U 2 ……U n Representing the marginal probability of the historical annual average wind speed of each meteorological station or the annual average wind speed measured by the meteorological station.
In a second aspect, the present invention provides a wind farm representative year wind speed determination system, comprising:
a wind speed acquisition module configured to acquire an annual wind speed of a wind measuring tower and an annual wind speed of a weather station, the annual wind speed of the weather station including historical annual wind speed and annual wind speed of the weather station;
the edge distribution module is configured to perform edge distribution curve fitting on the annual wind speed measured by the wind measuring tower, the historical annual wind speed measured by the meteorological station and the annual wind speed measured by the meteorological station respectively;
the correlation calculation module is configured to obtain a rank correlation coefficient according to the annual wind speed of the wind measuring tower, the historical annual wind speed of the meteorological station and the annual wind speed of the meteorological station;
the wind speed distribution module is configured to construct a wind measuring 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 meteorological station so as to construct a wind speed distribution diagram of the wind measuring tower in a wind measuring year and a representative year respectively;
and the wind speed revision module is configured to obtain a wind speed revision value according to the wind speed difference of the anemometer tower between the anemometer year and the representative year so as to revise the wind speed of the representative year.
In a third aspect, the present invention provides an electronic device comprising a memory and a processor, and computer instructions stored on the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the method of the first aspect is performed.
In a fourth aspect, the present invention provides a computer readable storage medium for storing computer instructions which, when executed by a processor, perform the method of the first aspect.
Compared with the prior art, the invention has the beneficial effects that:
the conventional method only adopts correlation coefficients to represent the relation between the meteorological station and the wind speed of the anemometer tower, a single coefficient value cannot really reflect the correlation, and the meteorological station only can select the meteorological station closest to the wind power plant. The invention provides a method and a system for determining wind speed of a wind power plant in a representative year.A Copula function is adopted to evaluate the correlation between a meteorological station and the wind speed of a wind measuring tower, so that the wind speed of the wind power plant in the representative year is calculated; the meteorological station is not limited to one meteorological station any more, the conditional distribution of the wind speed of the wind measuring tower can be calculated under the condition of the wind speed of any meteorological station, and the wind speed of the wind power plant representative year can be determined according to the data of a plurality of meteorological stations.
The invention provides a method and a system for determining wind speed of a wind power plant in a representative year.
Advantages of additional aspects of the invention 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 invention.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
FIG. 1 is a flow chart of a wind farm representative year wind speed determination method provided in embodiment 1 of the present invention;
fig. 2 is an edge distribution diagram of annual wind speed of a wind tower provided in embodiment 1 of the present invention;
fig. 3 is a wind speed condition distribution diagram of a representative year and a wind year of a wind measuring tower provided in embodiment 1 of the present invention;
FIG. 4 is a time-by-time wind speed scatter plot of a meteorological station and a wind tower for wind measurement provided in embodiment 1 of the present invention;
FIGS. 5(a) -5(b) are graphs comparing the results of the process of example 1 of the present invention with those of the prior art.
Detailed Description
The invention is further described with reference to the following figures and examples.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. 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 invention 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 invention. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise, and it should be understood that the terms "comprises" and "comprising", and any variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The embodiments and features of the embodiments of the present invention 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 correlation between a long-term weather station and an anemometer tower, solves the problem of obtaining and revising the representative annual wind speed when the correlation between the weather station and the anemometer tower is poor, and provides a scientific decision basis for the development of the wind farm, and includes the following steps:
acquiring the annual wind speed of the anemometer tower and the wind speed of a weather station, wherein the wind speed of the weather station comprises the historical annual wind speed and the annual wind speed of the weather station;
respectively carrying out edge distribution curve fitting on the annual wind speed of the wind measuring tower and the wind measuring speed of the meteorological station;
obtaining a rank correlation coefficient according to the annual wind speed of the wind measuring tower and the wind measuring speed of the meteorological station;
constructing a wind measuring 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 meteorological station so as to respectively construct a wind speed distribution diagram 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 between the wind measuring year and the representative year of the wind measuring tower, and correcting the wind speed of the representative year.
The wind speed representative year determination method of the wind farm of the present embodiment is explained in detail below.
S1: setting a wind measuring tower in a wind power plant area, and acquiring the wind measuring annual wind speed of the wind measuring tower;
in the embodiment, a representative place is selected in a wind power plant to set up a wind measuring tower, the wind measuring tower comprises equipment such as an anemoscope, a temperature sensor and a pressure sensor, wind measurement is carried out in a wind measuring year, and wind speed in the wind measuring year is obtained.
S2: acquiring the wind measuring speed of a peripheral or mesoscale meteorological station, wherein the wind measuring speed of the meteorological station comprises historical annual wind speed and annual wind measuring wind speed;
s3: judging the correlation between the wind measuring speed of the weather station and the wind measuring annual speed of the wind measuring tower, and if the correlation between the wind measuring speed of the weather station and the wind measuring annual speed of the wind measuring tower exceeds a threshold value, determining the representative annual wind speed according to the correlation; it can be understood that the process is only required to adopt the existing method, and the embodiment is not described in detail.
S4: if the correlation between the wind measuring speed of the meteorological station and the wind measuring annual speed of the wind measuring tower is lower than the threshold value or the meteorological stations are multiple, the representative annual wind speed is calculated by adopting the following method.
S401: calculating the annual wind speed of the anemometer tower, the historical annual wind speed of the meteorological station and the edge distribution of the meteorological station annual wind speed, and performing curve fitting, wherein the curve fitting is shown in FIG. 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 standard deviation MSE, wherein the smaller the standard deviation MSE value is, the better the fitting of the edge distribution is.
The standard deviation MSE is expressed as:
wherein E is an expected value; n is the total number of samples; x c (i) The ith calculated value of the edge distribution; x 0 (i) For the ith observation, the observation is represented by empirical probability, the experience of the random variable does not exceed probability evaluation, and the Grinforn formula is adopted:
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 annual wind speed of the anemometer tower and the historical annual wind speed of the meteorological station or the annual wind speed of the anemometer tower;
wherein the anemometry year is 8760 hours, x i ，x j Representing annual hourly wind speed, y, measured by the anemometer tower i ，y j Representing the historical year of the meteorological station or the hourly wind speed of the anemometry year, and i and j represent different times; sign () is a sign function when (x) i -x j )(y i -y j )>When 0, sign is 1; (x) i -x j )(y i -y j )<At 0, sign is-1; (x) i -x j )(y i -y j ) When 0, sign is 0.
S403: calculating Copula functions of the anemometer tower and the meteorological station in the anemometer 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 a joint distribution. Has the advantages 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 an elliptic type, a quadratic type, an Archimedean type, etc., and the present embodiment adopts a symmetric Gumbel-Hougaard Copula function in the Archimedean type.
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 a wind measuring year according to the correlation parameter theta, the edge distribution of the wind measuring tower wind measuring year wind speed and the edge distribution of the weather station wind measuring year wind speed; calculating a Copula function of a historical year according to the correlation parameter theta, the edge distribution of the annual wind speed of the anemometer tower and the edge distribution of the annual 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 marginal distribution of annual wind speed of anemometer tower 2 ……u n And theta is a correlation parameter for the marginal distribution of the measured annual wind speed or the historical annual wind speed of each meteorological station.
Given u 2 ＝U 2 ,…,u n ＝U n Situation(s)U is 1 The conditional probability distribution of (a) is:
in the formula, F (u) 1 |u 2 ＝U 2 ,…,u n ＝U n ) For wind velocity condition distribution of anemometer tower, u 2 ……u n For each meteorological station measuring the marginal distribution of annual or historical wind speeds, U 2 ……U n And the marginal probability represents a specific value given by each meteorological station, namely the marginal probability of the historical annual average wind speed or the annual average wind speed measured by the meteorological station.
Respectively obtaining the historical annual average wind speed of the weather station and the anemometry annual average wind speed of the weather station according to the historical annual wind speed of the weather station and the anemometry annual wind speed of the weather station; according to the historical annual average wind speed and the annual average wind speed of the wind measuring station, two conditional probability curves are obtained by adopting a formula (6), as shown in fig. 3, namely a wind speed distribution diagram of the wind measuring tower in the representative year and the wind measuring year.
S404: and obtaining a representative annual wind speed revision value 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 annual wind speed, the generated energy is calculated, the calculation process of the representative annual wind speed when the correlation between the meteorological station and the anemometer tower is poor is solved, the method can be used for correcting multiple meteorological stations, and the obtained data is accurate.
The steps S1-S3 in this embodiment may be performed by a conventional method, and the step S4 is described in detail below, but the scope of the present embodiment is not limited to this embodiment.
The wind power plant adopts a wind measuring tower with the height of 70m to measure wind, wind speed meters are respectively arranged at the heights of 70m, 50m and 10m, wind direction meters are respectively arranged at the heights of 70m and 10m, a temperature meter and a pressure meter are arranged at the height of 10m, and the effective integrity rate of field measured data is 92.6% through inspection, so that the standard requirement is met.
Acquiring annual average wind speed of a meteorological station nearest to the wind power plant in the last 30 years, wherein the statistical values of the wind speed of the meteorological station and the wind measuring tower are shown in a table 1;
TABLE 1 Meteorological station and anemometer tower wind speed statistical table
It can be seen that the average wind speed of the meteorological station in nearly 30 years is 2.62m/s, the wind speed of the meteorological station in the wind measurement year is 2.93m/s, so that the wind measurement year is a strong wind year, and the wind energy elements of the wind power plant in the representative year are reasonably calculated according to the relevant analysis of the wind speed and wind direction hour records of the field wind measurement data and the meteorological station in the same period. According to the method in the wind power plant wind energy resource assessment method, the correlation between the wind speed at 70m of the wind power plant and each sector of the wind speed of the meteorological station is obtained through calculation and is shown in table 2;
TABLE 2 table of analysis results relating to each sector
It can be seen that the correlation coefficients of the four sectors of the NNE, the NE, the SSE and the NW are all better and are all above 0.8, the correlation equations can be directly adopted for correction, the correlation coefficients of the three sectors of the SE, the WSW and the W are poorer and are all below 0.5, and if the method is used for calculation, the error of the corrected wind speed is larger; and because the anemometry year is a big wind year, which is higher than the average wind year by about 0.3m/s, the first term coefficients of 16 sectors are all larger than 1, especially the first term coefficient of the SSW sector is 3.26, which is equivalent to that the wind speed of each SSW wind direction is subtracted by 3.26 x 0.3 ≈ 1m/s, that is, if the anemometry year wind speed is below 0.9m/s, the obtained representative year wind speed is negative, which is obviously irregular, but the technical method in the embodiment can avoid the situation.
The annual average wind speed at the meteorological station and the edge distribution of the hourly wind speed at 70m of the anemometer tower are evaluated by the MSE standard as shown in Table 3, and the results show that both can be described by a Weibull distribution.
TABLE 3 MSE values for different distribution types
Obtaining a correlation parameter theta which is 1.40 according to the rank correlation coefficient tau; according to the maximum likelihood method, the correlation parameter θ is obtained as 1.51, which is not very different from each other, and the result of calculating the rank correlation coefficient τ is used in this embodiment.
The historical annual average wind speed of the meteorological station is 2.6m/s, the annual average wind speed of wind measurement is 2.9m/s, the edge distribution of the meteorological station is 0.482 when the average wind speed is 2.6m/s and 0.694 when the wind speed is 2.9m/s, which are obtained according to Weibull distribution parameters; the condition Copula function calculation is to calculate the wind speed distribution of the anemometer tower when the wind speed is 2.6m/s and 2.9m/s respectively.
The historical annual average wind speed of the meteorological station is the representative annual wind speed, namely the wind speed when the annual average wind speed is 2.6m/s is the representative annual wind speed, the wind speed when the annual average wind speed is 2.9m/s is the anemometry annual wind speed, and the difference under the same probability is the corrected value of the representative annual wind speed.
The average wind speed and wind power density of the wind field are shown in FIGS. 5(a) -5(b), and the wind power density of the wind field is 276W/m by the wind power resource assessment method of the wind farm 2 The wind power density calculated by the Copula method is 251W/m 2 And the difference between the two is not great, thereby indicating the applicability of the method.
In order to analyze the calculation errors of the two methods, a certain wind power plant is taken as an example for illustration, and data of two meteorological stations around a wind measuring tower are collected together. Due to the lack of long series of observations, correction was made for each month's data. The monthly average wind speeds of the anemometer tower and the two meteorological stations are shown in the table 4;
table 4 monthly 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 (ii) a The 1-year average wind speed of the meteorological station is 2.44 m/s; the 2-year average wind speed of the meteorological station is 2.61 m/s. If the wind speed is in an ideal state, the calculated average wind speed per month is about 6.62m/s, and therefore the calculated average wind speed per month is used as a true value to be compared and analyzed with the calculated values obtained by the two methods. Respectively calculating the rank correlation coefficients of the wind measuring towers and the meteorological stations in each month as shown in a table 5;
TABLE 5 values of Kendall τ rank correlation coefficients for each month
It can be known that the rank correlation coefficients of each month are different, wherein the correlation coefficients of two meteorological stations in april are basically equal, but the difference between the correlation coefficients of the meteorological stations in april and the correlation coefficients of the meteorological stations in 8 months is larger, and the meteorological station 1 is only 0.0191, namely the correlation between the meteorological stations and the anemometer tower is extremely poor.
When the edge distribution of the two meteorological stations is solved, two conditions of hourly wind speed distribution and monthly wind speed distribution are comprehensively considered, and the edge distribution is obtained as shown in table 6 by taking the rank correlation coefficient as a distribution coefficient.
TABLE 6 average wind speed and frequency table of meteorological station in each month
Also, the weibull parameters for anemometers in different months are not consistent, as in table 7.
TABLE 7 Weibull parameter values of anemometry towers in each month
The results of the five case calculations, namely, the weather station 1 alone, the weather station 2 alone, both weather stations, the normative weather station 1, and the normative weather station 2, are compared with the ideal values, as shown in tables 8 and 9.
Table 8 the results of the five methods are compared with the ideal wind speed and analyzed in units: m/s
TABLE 9W/m analysis by comparing the calculation results of the five methods with the ideal value of wind power density 2
The RMSE values for the five calculation methods relative to the ideal values are calculated as in tables 10 and 11.
TABLE 10 analysis of the calculation results of the five methods and the ideal wind speed RMSE
TABLE 11 analysis of the results of the five methods and the ideal wind power density RMSE
As can be seen from tables 10 and 11, in the five methods, the RMSE, whether the wind speed or the wind power density, is the minimum value of the RMSE, i.e., the closest value to the true value, when both meteorological stations of the Copula method are considered, while the RMSE calculated by the normative method meteorological station 1 is the maximum value, i.e., the most deviated from the true value.
Aiming at the defects that the correlation between a meteorological station and a wind measuring tower is poor and negative wind speed is possible, the method for determining the wind speed of the wind power plant in the representative year is provided in the embodiment, the method is suitable for one or more meteorological stations, the correlation between the wind measuring tower and the meteorological station is not considered, and all the wind speeds are guaranteed to be positive values. As can be seen from example calculation, the method has the advantages of reliable result, simple solving process and good practicability.
Example 2
The embodiment provides a wind speed determination system for a representative year of a wind farm, which comprises:
a wind speed acquisition module configured to acquire an annual wind speed of a wind measuring tower and an annual wind speed of a weather station, the annual wind speed of the weather station including historical annual wind speed and annual wind speed of the weather station;
the edge distribution module is configured to perform edge distribution curve fitting on the annual wind speed measured by the wind measuring tower, the historical annual wind speed measured by the meteorological station and the annual wind speed measured by the meteorological station respectively;
the correlation calculation module is configured to obtain a rank correlation coefficient according to the annual wind speed of the wind measuring tower, the historical annual wind speed of the meteorological station and the annual wind speed of the meteorological station;
the wind speed distribution module is configured to construct a wind measuring 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 meteorological station so as to construct a wind speed distribution diagram of the wind measuring tower in a wind measuring year and a representative year respectively;
and the wind speed revision module is configured to obtain a wind speed revision value according to the wind speed difference of the anemometer tower between the anemometer year and the representative year so as to revise the wind speed of the representative year.
It should be noted that the modules correspond to the steps described in embodiment 1, and the modules are the same as the corresponding steps in the implementation examples and application scenarios, but are not limited to the disclosure in embodiment 1. It should be noted that the modules described above as part of a system may be implemented 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 executed on the processor, the computer instructions when executed by the processor performing the method of embodiment 1. For brevity, no further description is provided herein.
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 processor, a digital signal processor DSP, an application specific integrated circuit ASIC, an off-the-shelf programmable gate array FPGA or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, 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 both read-only memory and random access memory, and may 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 device type information.
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 implemented by a hardware processor, or implemented by a combination of hardware and software modules in the processor. The software modules may be located in ram, flash, rom, prom, or eprom, registers, among other storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor. To avoid repetition, it is not described in detail here.
Those of ordinary skill in the art will appreciate that the various illustrative elements, i.e., algorithm steps, described in connection with the embodiments disclosed herein may 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 implementation. 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.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.
Claims (10)
1. A wind power plant representative year wind speed determination method is characterized by comprising the following steps:
acquiring the annual wind speed of the anemometer 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 annual wind speed of the meteorological station;
respectively carrying out edge distribution curve fitting on the annual wind speed of the anemometer tower, the historical annual wind speed of the meteorological station and the annual wind speed of the meteorological station;
obtaining a rank correlation coefficient according to the annual wind speed of the anemometer tower, the historical annual wind speed of the meteorological station and the annual wind speed of the meteorological station;
constructing a wind measuring 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 meteorological station so as to respectively construct a wind speed distribution diagram 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 between the wind measuring year and the representative year of the wind measuring tower, and correcting the wind speed of the representative year.
2. The wind farm representative year wind speed determination method according to claim 1, wherein the rank correlation coefficient τ is:
in the formula, x i 、x j The annual hourly wind speed and the y time of wind measurement of the wind measuring tower at the ith moment and the jth moment i 、y j Historical annual hourly wind speed or anemometric annual hourly wind speed of the meteorological station at the ith moment and the jth moment; sign () is a sign function when (x) i -x j )(y i -y j )>When 0, sign is 1; (x) i -x j )(y i -y j )<At 0, sign is-1; (x) i -x j )(y i -y j ) When 0, sign is 0.
3. The method for determining the representative annual wind speed of the wind farm according to claim 1, wherein the process of constructing the anemometric annual linking function and the historical annual linking function comprises the following steps: obtaining a correlation parameter theta of the Copula function according to the rank correlation coefficient tau, specifically:
4. the method for determining the representative annual wind speed of the wind farm according to claim 3, wherein the process of constructing the anemometric annual linking function and the historical annual linking function comprises the following steps: calculating a Copula function of the anemometry year according to the correlation parameters, the edge distribution of the anemometry annual wind speed of the anemometry tower and the edge distribution of the anemometry annual wind speed of the meteorological station; and calculating a Copula function of the historical years according to the correlation parameters, the edge distribution of the annual wind speed measured by the wind measuring tower and the edge distribution of the historical annual wind speed of the meteorological station.
5. The method for determining representative annual wind speed of a wind farm according to claim 1, wherein the anemometric annual linking function and the historical annual linking 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 marginal distribution of annual wind speed of anemometer tower 2 ……u n And theta is a correlation parameter for the marginal distribution of the measured annual wind speed or the historical annual wind speed of each meteorological station.
6. The wind speed determination method for the representative year of the wind farm according to claim 1, wherein the step of constructing a wind speed distribution graph of the wind measuring tower in the representative year and the wind measuring year comprises the following steps: the method comprises the steps of obtaining historical annual average wind speed and anemometry annual average wind speed of a weather station according to historical annual wind speed and anemometry annual wind speed of the weather station, constructing a wind speed distribution diagram of a wind measuring tower in a representative year according to the historical annual average wind speed of the weather station by adopting a historical annual connection function, and constructing the wind speed distribution diagram of the wind measuring tower in the wind measuring year according to the anemometry annual average wind speed of the weather station by adopting the anemometry annual connection function.
7. The wind speed determination method for the representative year of the wind farm according to claim 6, wherein the wind speed distribution of the anemometer tower in the anemometer year is as follows:
wherein u is 1 For marginal distribution of annual wind speed of anemometer tower 2 ……u n For each meteorological station measuring the marginal distribution of annual or historical wind speeds, U 2 ……U n Representing the marginal probability of the historical annual average wind speed of each meteorological station or the annual average wind speed measured by the meteorological station.
8. A wind farm representative year wind speed determination system, comprising:
a wind speed acquisition module configured to acquire an annual wind speed of a wind measuring tower and an annual wind speed of a weather station, the annual wind speed of the weather station including historical annual wind speed and annual wind speed of the weather station;
the edge distribution module is configured to perform edge distribution curve fitting on the annual wind speed measured by the wind measuring tower, the historical annual wind speed measured by the meteorological station and the annual wind speed measured by the meteorological station respectively;
the correlation calculation module is configured to obtain a rank correlation coefficient according to the annual wind speed of the wind measuring tower, the historical annual wind speed of the meteorological station and the annual wind speed of the meteorological station;
the wind speed distribution module is configured to construct a wind measuring 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 meteorological station so as to construct a wind speed distribution diagram of the wind measuring tower in a wind measuring year and a representative year respectively;
and the wind speed revision module is configured to obtain a wind speed revision value according to the wind speed difference of the anemometer tower between the anemometer year and the representative year so as to revise the wind speed of the representative year.
9. An electronic device comprising a memory and a processor and computer instructions stored on the memory and executed on the processor, the computer instructions when executed by the processor performing the method of any of claims 1-7.
10. A computer-readable storage medium storing computer instructions which, when executed by a processor, perform the method of any one of claims 1 to 7.
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