CN115965293A - Wind power plant operation analysis method and system - Google Patents
Wind power plant operation analysis method and system Download PDFInfo
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- CN115965293A CN115965293A CN202310077943.3A CN202310077943A CN115965293A CN 115965293 A CN115965293 A CN 115965293A CN 202310077943 A CN202310077943 A CN 202310077943A CN 115965293 A CN115965293 A CN 115965293A
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Abstract
The utility model provides a wind-powered electricity generation field operation analysis method and system, set up first anemometer tower in the site scope of wind-powered electricity generation field, set up the second anemometer tower outside the site scope, arranged first meteorological collection equipment on the first anemometer tower, arranged second meteorological collection equipment on the second anemometer tower, the operation analysis includes: acquiring first anemometer tower data output by first meteorological acquisition equipment and second anemometer tower data output by second meteorological acquisition equipment; calculating to obtain wind resource characteristic parameters and representative annual wind energy elements of the wind power plant based on the first anemometer tower data and the second anemometer tower data; acquiring the actual generating capacity of the unit based on the wind resource characteristic parameters and the representative annual wind energy elements; and obtaining an operation analysis result of the wind power plant based on the comparison of the actual generating capacity of the unit and the theoretical generating capacity of the unit. According to the method disclosed by the invention, the accuracy of the operation analysis of the wind power plant can be improved.
Description
Technical Field
The disclosure relates to the technical field of wind power anemometry, in particular to a wind power plant operation analysis method and system.
Background
As is known, wind has strong uncertainty, which is a significant factor restricting the development of wind power projects, but wind is also a key of wind power technology and is a source of energy. Wind directly affects the design cost of the unit and threatens the operation safety of the unit, so that the accurate operation analysis of the wind power plant is very important. The traditional wind power plant operation analysis is generally based on observation data such as wind speed and the like acquired by a meteorological station or a wind measuring tower, and the accuracy of the traditional wind power plant operation analysis is low due to unreasonable observation data and few consideration factors in the analysis process. Therefore, a wind power plant operation analysis technology with higher accuracy is urgently needed.
Disclosure of Invention
The present disclosure is directed to solving, at least to some extent, one of the technical problems in the related art.
Therefore, a first purpose of the present disclosure is to provide a wind farm operation analysis method, which mainly aims to improve the accuracy of wind farm operation analysis.
A second object of the present disclosure is to provide a wind farm operation analysis system.
A third object of the present disclosure is to provide a wind farm operation analysis device.
In order to achieve the above object, an embodiment of a first aspect of the present disclosure provides a method for analyzing operation of a wind farm, where a first wind measurement tower is disposed within a site range of the wind farm, a second wind measurement tower is disposed outside the site range, a first meteorological acquisition device is disposed on the first wind measurement tower, a second meteorological acquisition device is disposed on the second wind measurement tower, and the operation analysis includes:
acquiring first meteorological measuring tower data output by the first meteorological acquisition equipment and second meteorological measuring tower data output by the second meteorological acquisition equipment;
calculating to obtain wind resource characteristic parameters and representative annual wind energy elements of the wind power plant based on the first anemometer tower data and the second anemometer tower data;
acquiring actual generating capacity of the unit based on the wind resource characteristic parameters and the representative year wind energy elements;
and obtaining an operation analysis result of the wind power plant based on the comparison between the actual generating capacity of the unit and the theoretical generating capacity of the unit.
In one embodiment of the present disclosure, the first anemometer tower data includes wind speed and wind direction, and the second anemometer tower data includes wind speed, wind direction, air temperature and air pressure; the wind resource characteristic parameters comprise air density, turbulence intensity, wind shear index, weibull distribution and 50-year-one-encounter maximum wind speed; the representative annual wind energy elements include a representative annual average wind speed, a representative annual wind power density, a representative annual hourly average wind speed, a representative annual hourly wind power density, a representative annual wind direction frequency, a representative annual wind energy density directional distribution, a representative annual wind speed, and a representative annual wind energy frequency distribution.
In an embodiment of the present disclosure, the calculating the wind resource characteristic parameter and the representative annual wind energy factor of the wind farm based on the first anemometer tower data and the second anemometer tower data includes: calculating an air density based on the second anemometer tower data; calculating turbulence intensity, wind shear index, weibull distribution, 50-year-first maximum wind speed, and representative year wind energy factors based on the first anemometer tower data and the second anemometer tower data.
In one embodiment of the present disclosure, the obtaining of the actual power generation of the unit based on the wind resource characteristic parameter and the representative annual wind energy element includes: obtaining a comprehensive correction coefficient by combining reduction coefficients of multiple energy loss factors; and obtaining the wind resource characteristic parameters and the representative year wind energy elements to obtain the actual generating capacity of the unit based on the comprehensive correction coefficient, the wind resource characteristic parameters and the representative year wind energy elements.
In an embodiment of the present disclosure, before the wind resource characteristic parameter and the representative annual wind energy element are calculated, the method further includes: and preprocessing the first anemometer tower data and the second anemometer tower data, wherein the preprocessing comprises integrity inspection and rationality inspection.
In an embodiment of the present disclosure, the preprocessing further includes statistically interpolating unreasonable and missing data in the first anemometer tower data and the second anemometer tower data screened after the integrity test and the rationality test.
In order to achieve the above object, an embodiment of a second aspect of the present disclosure provides an operation analysis system for a wind farm, in which a first wind measurement tower is disposed within a site range of the wind farm, a second wind measurement tower is disposed outside the site range, a first meteorological acquisition device is disposed on the first wind measurement tower, a second meteorological acquisition device is disposed on the second wind measurement tower, and the operation system includes:
the acquisition module is used for acquiring first meteorological station data output by the first meteorological acquisition equipment and second meteorological station data output by the second meteorological acquisition equipment;
the parameter operation module is used for calculating wind resource characteristic parameters and representative annual wind energy elements of the wind power plant based on the first anemometer tower data and the second anemometer tower data;
the generating capacity calculating module is used for obtaining the actual generating capacity of the unit based on the wind resource characteristic parameters and the representative year wind energy elements;
and the analysis module is used for obtaining an operation analysis result of the wind power plant based on comparison between the actual generating capacity of the unit and the theoretical generating capacity of the unit.
In one embodiment of the present disclosure, the first anemometer tower data includes wind speed and wind direction, and the second anemometer tower data includes wind speed, wind direction, air temperature and air pressure; the wind resource characteristic parameters comprise air density, turbulence intensity, wind shear index, weibull distribution and 50-year-one-encounter maximum wind speed; the representative year wind energy elements include a representative year average wind speed, a representative year wind power density, a representative year hourly average wind speed, a representative year hourly wind power density, a representative year wind direction frequency, a representative year wind power density direction distribution, a representative year wind speed, and a representative year wind power frequency distribution.
In an embodiment of the present disclosure, the parameter operation module is specifically configured to: calculating an air density based on the second anemometer tower data; calculating turbulence intensity, wind shear index, weibull distribution, 50-year-first-encounter maximum wind speed, and representative annual wind energy factor based on the first anemometer tower data and the second anemometer tower data.
In order to achieve the above object, an embodiment of the third aspect of the present disclosure provides a wind farm operation analysis device, including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the wind farm operation analysis method of the embodiment of the first aspect of the present disclosure.
In one or more embodiments of the present disclosure, a first wind measuring tower is disposed in a site range of a wind farm, a second wind measuring tower is disposed outside the site range, a first meteorological data collecting device is disposed on the first wind measuring tower, a second meteorological data collecting device is disposed on the second wind measuring tower, and the operation analysis includes: acquiring first anemometer tower data output by first meteorological acquisition equipment and second anemometer tower data output by second meteorological acquisition equipment; calculating to obtain wind resource characteristic parameters and representative annual wind energy elements of the wind power plant based on the first anemometer tower data and the second anemometer tower data; acquiring the actual generating capacity of the unit based on the wind resource characteristic parameters and the representative annual wind energy elements; and obtaining an operation analysis result of the wind power plant based on the comparison of the actual generating capacity of the unit and the theoretical generating capacity of the unit. Under the condition, wind measuring tower data corresponding to wind measuring towers in the site range and outside the site range of the wind power plant are integrated, so that wind resource characteristic parameters and representative annual wind energy elements are obtained, the actual generating capacity of the unit is further obtained, the actual generating capacity of the unit obtained by the method is more accurate, the reliability of a wind power plant operation analysis result obtained when the actual generating capacity of the unit is compared with the theoretical generating capacity of the unit is improved, and the accuracy of wind power plant operation analysis is improved.
Additional aspects and advantages of the disclosure 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 disclosure.
Drawings
In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present disclosure, and other drawings can be obtained by those skilled in the art without creative efforts. The above and/or additional aspects and advantages of the present disclosure will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a scene schematic diagram of a wind farm operation analysis method provided by an embodiment of the present disclosure;
FIG. 2 is a schematic flow chart diagram of a wind farm operation analysis method provided by the embodiment of the present disclosure;
FIG. 3 is a block diagram of a wind farm operation analysis system provided by an embodiment of the present disclosure;
FIG. 4 is a block diagram of a wind farm operation analysis device used to implement the wind farm operation analysis method of the disclosed embodiments.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the exemplary embodiments below are not intended to represent all implementations consistent with embodiments of the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the embodiments of the disclosure, as detailed in the claims that follow.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present disclosure. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present disclosure, "a plurality" means at least two, e.g., two, three, etc., unless explicitly specifically limited otherwise. It should also be understood that the term "and/or" as used in this disclosure refers to and encompasses any and all possible combinations of one or more of the associated listed items.
Reference will now be made in detail to the embodiments of the present disclosure, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the drawings are exemplary and intended to be illustrative of the present disclosure, and should not be construed as limiting the present disclosure.
The disclosure provides a wind power plant operation analysis method and system, and mainly aims to improve the accuracy of wind power plant operation analysis.
The wind power plant operation analysis method and system disclosed by the invention need two anemometer towers. The two anemometry towers are respectively a first anemometry tower arranged in the site range of the wind power plant and a second anemometry tower arranged outside the site range of the wind power plant. The first anemometer tower and the second anemometer tower have the same tower height. The two anemometer towers are similar to the wind field in terrain and have similar elevation. The first anemometer tower is located substantially in the center of the wind farm, and the second anemometer tower is located outside the range of the wind farm for auxiliary analysis. Wherein have arranged first meteorological collection equipment on the first anemometer tower, first meteorological collection equipment includes anemoscope and anemoscope, has arranged second meteorological collection equipment on the second anemometer tower, and second meteorological collection equipment includes anemoscope, air temperature sensor and baroceptor.
In an embodiment, fig. 1 is a scene schematic diagram of a wind farm operation analysis method provided in an embodiment of the present disclosure. As shown in fig. 1, a is a first anemometer tower, and B is a second anemometer tower. The second anemometer tower B is positioned in the northeast direction of the first anemometer tower A. The first anemometer tower a is located in the center of the site range S of the wind farm.
Fig. 2 is a schematic flow chart of a wind farm operation analysis method provided by the embodiment of the disclosure. As shown in fig. 2, the wind farm operation analysis method includes the following steps:
and S11, acquiring first meteorological mast data output by the first meteorological acquisition equipment and second meteorological mast data output by the second meteorological acquisition equipment.
In step S11, the first anemometer tower data includes wind speed and wind direction, and the first anemometer tower data is acquired by the first meteorological collecting device. The second anemometer tower data comprises wind speed, wind direction, air temperature and air pressure. And the second anemometer tower data is acquired by second meteorological acquisition equipment. In this case, because the old wind farm generally only comprises the anemometer and the anemoscope, the wind speed and the wind direction within the site range of the wind farm are obtained, and the subsequent operation analysis is performed by combining the wind speed, the wind direction, the air temperature and the air pressure outside the site range of the wind farm, so that the accuracy of the operation analysis of the wind farm is improved.
In some embodiments, the first meteorological collection device is in a plurality and is respectively arranged at different heights of the first anemometer tower. That is, anemometers and anemoscopes are respectively arranged at different heights of the first anemometer tower.
In some embodiments, the anemometers of the second meteorological collection device are plural and arranged at different heights of the second anemometer tower respectively. The anemoscope has a plurality ofly, and arranges respectively in the not co-altitude of second anemometer tower. The air temperature sensor and the air pressure sensor are respectively arranged at the corresponding preset heights. For example, when the tower is 70m high, 1 set of anemometers is installed at the height of 70m, 50m, 30m and 10m, 1 set of anemometers is installed at the height of 70m and 10m, 1 set of air temperature sensor is installed at the height of 10m, and 1 set of air pressure sensor is installed at the height of 7 m.
In step S11, the first anemometer tower data and the second anemometer tower data are data within a period of time, and the data periods of the first anemometer tower data and the second anemometer tower data are different.
In step S11, after the first anemometer tower data and the second anemometer tower data are acquired, the method further includes: and preprocessing the first anemometer tower data and the second anemometer tower data, wherein the preprocessing comprises integrity check and rationality check. And the preprocessing further comprises the step of carrying out statistical interpolation on unreasonable and lack-of-measurement data in the first anemometer tower data and the second anemometer tower data which are screened out after the integrity test and the rationality test. Wherein the rationality test comprises a range test, a correlation test, and a trend test.
Specifically, based on wind power plant engineering wind energy resource measurement and evaluation technical specification requirements, integrity inspection and rationality inspection are respectively carried out on first anemometer tower data and second anemometer tower data, reasonable data are reserved, and screened unreasonable data and lack-of-measurement data are processed. According to the unreasonable data, firstly, the unreasonable data is checked and judged from the original first anemometer tower data or the second anemometer tower data, the effective data which accords with the actual situation is reserved into the reasonable data (namely, the unreasonable data which removes the effective data) and the lack data, the correlation among all heights in the tower is established, then the interpolation processing is carried out through a correlation equation, and the remaining unreasonable data and the lack data which are subjected to the interpolation processing are reserved into the reasonable data, so that the first anemometer tower data and the second anemometer tower data which are subjected to the preprocessing are obtained. And the preprocessed first anemometer tower data and the preprocessed second anemometer tower data are respectively data of which the time period comprises at least one whole year.
In step S11, the preprocessed first anemometer tower data and second anemometer tower data are sent to subsequent steps to participate in operation analysis, so as to improve accuracy of operation analysis of the wind farm.
And S12, calculating to obtain wind resource characteristic parameters and representative annual wind energy elements of the wind power plant based on the first anemometer tower data and the second anemometer tower data.
Specifically, in step S12, a wind resource characteristic parameter of the wind farm is calculated based on the first anemometer tower data and the second anemometer tower data. Wind resource characteristic parameters include air density, turbulence intensity, wind shear index, weibull distribution, and 50-year-one-encounter maximum wind speed. Wherein the air density is calculated based on the second anemometer tower data; calculating turbulence intensity, wind shear index, weibull distribution, and 50-year-first maximum wind speed based on the first anemometer tower data and the second anemometer tower data.
For the air density, an air density of the second anemometer tower is calculated based on the second anemometer tower data. The formula for calculating the air density satisfies: ρ = P/(R × T), where ρ is the air density (kg/m) 3 ) (ii) a P is the annual average barometric pressure (Pa) calculated based on the barometric pressure in the second anemometer tower data; r is a gas constant (287J/kg.K); t is the annual average Kelvin absolute temperature (. Degree.C. + 273). In some embodiments, the annual average air density of different heights of the area where the second anemometer tower is located is calculated by adopting reanalysis temperature and air pressure data and utilizing a linear correlation method to interpolate the air temperature and the air pressure in the second anemometer tower data. The formula for calculating the annual average air density of other heights meets the following requirements: ρ is a unit of a gradient z ＝ρ h e -0.0001(z-h) Where ρ is z The air density at the height z of the hub of the anemometer tower is obtained; ρ is a unit of a gradient h The air density at height h is observed for the anemometer tower.
For the turbulence intensity, it is easy to understand that the turbulence intensity represents the degree of deviation of the instantaneous wind speed from the mean value, and is an index for evaluating the stability degree of the airflow, and the magnitude of the turbulence intensity is related to the quality of the wind power plant resources. Turbulence intensity is related to geographical location, terrain, surface roughness, and the type of weather system affected, among other factors. In the present embodiment, the turbulence intensity is calculated using the average wind speed and the standard deviation for the same period. And respectively calculating the turbulence intensity of the first anemometer tower and the turbulence intensity of the second anemometer tower based on the first anemometer tower data and the second anemometer tower data. Calculating turbulence intensityThe formula satisfies: i is T = σ/V, wherein I T Is the turbulence intensity; v is the average wind speed (m/s) in a certain wind speed interval; and sigma is the corresponding wind speed standard deviation (m/s).
For the wind shear index, it is easy to understand that the wind shear index represents an index of the variation of the near-ground wind speed with the height, and the variation of the wind speed with the height follows the prandtl empirical formula, but the variation of the wind speed with the height is different due to the difference of the roughness of the earth surface, and the wind shear index can be obtained by utilizing a power law wind profile formula, and the formula satisfies the following conditions:
wherein Vn and Vi are wind speeds (m/s) at the heights Zn and Zi respectively; α is the wind shear index, the value of which is related to the surface roughness. The larger the wind shear index, the faster the wind speed increases with height, the more energy gain is gained by increasing the tower height, and the more advantageous it is to use a tall tower. And respectively calculating the wind shear index of the first anemometer tower and the wind shear index of the second anemometer tower based on the first anemometer tower data and the second anemometer tower data.
For a Weibull distribution, it is well understood that a Weibull (Weibull) distribution is a probability function for describing a wind speed distribution. Two parameters may be applied to build a probabilistic model of the wind speed distribution. Weibull (Weibull) distribution is widely applied to understanding wind change rules and wind energy resource assessment, and is very important for development of wind power plants, design of wind generating sets and the like. Wherein, describing the wind speed distribution by using a Weibull (Weibull) distribution usually requires calculating shape parameters and scale parameters. The Weibull (Weibull) distribution probability density function for wind speed is expressed as:
f(v)＝(k/c)(v/c) k-1 exp[-(v/c) k ]
wherein the shape parameter k and the scale parameter c are estimated with an average wind speed and a standard deviation obtained based on the first anemometer tower data or the second anemometer tower data. Specifically, the shape parameter satisfies
Wherein the shape parameter k is a dimensionless quantity, when k =1For exponential distributions, the frequency curve slowly tends to be symmetrical as k increases from 1. When k =3.5, the Weibull (Weibull) distribution is actually very close to the normal distribution. The scale parameter satisfies->
The scale parameter c has the unit m/s, and Γ (1+1/k) is the gamma function. The average wind speed mu and the standard deviation sigma respectively satisfy:
in the formula: vi is the wind speed (m/s) observed in the first anemometer tower data or the second anemometer tower data; n is the number of wind speed sequences in the calculation time period. Thereby, a weibull distribution of the first anemometer tower and a weibull distribution of the second anemometer tower are calculated based on the first anemometer tower data and the second anemometer tower data, respectively.
For the 50-year-first maximum wind speed, in some embodiments, the 50-year-first maximum wind speed of each height of the wind farm is estimated by an extreme wind speed model (EWM) method and a quintupled average wind speed method. And respectively calculating the 50-year-first-encounter maximum wind speed of the first anemometer tower and the 50-year-first-encounter maximum wind speed of the second anemometer tower based on the first anemometer tower data and the second anemometer tower data. In the extreme wind speed model (EWM) method, the maximum wind speed is calculated according to the maximum wind speed value actually measured at each height in the first anemometer tower data and the second anemometer tower data and the maximum wind speed is calculated according to the extreme wind speed model (EWM) method at each height of the first anemometer tower and the second anemometer tower within 50 years. The calculation formula satisfies: v e1 max (Z)＝0.8V e50 max (Z) wherein V e1 max The maximum wind speed (m/s) of 1 year in one year; v e50 max The maximum wind speed (m/s) is 50 years; z is a reference wind speed height. In the quintupling average wind speed method, 5 times of annual average wind speed of each height in the first anemometer tower data and the second anemometer tower data are adopted to calculate 50 years of each height of each anemometer towerOnce the maximum wind speed is encountered.
In step S12, a representative annual wind energy element is calculated based on the first anemometer tower data and the second anemometer tower data. Specifically, representative year correction is carried out on the first anemometer tower data and the second anemometer tower data, and a representative year wind energy element is obtained through calculation based on the corrected first anemometer tower data and the corrected second anemometer tower data.
In step S12, the representative years include, as will be readily understood, the average wind speed year, the maximum value year, the minimum value year, and the like. Wherein the average wind speed year is a year in which the annual average wind speed is equal to or close to the 30 year average wind speed, the maximum year is a year in which the annual average wind speed is equal to or close to the maximum value of the 30 year average wind speed, and the minimum year is a year in which the annual average wind speed is equal to or close to the minimum value of the 30 year average wind speed.
In step S12, the representative year correction method for the first anemometer tower data includes: and establishing a correlation between the MERRA2 (namely 3Tier data) of the whole year period of wind measurement and the wind speed of different wind direction sectors of the first wind measurement tower. And (4) solving the algebraic difference between the MeRRA2 average wind speed in the last decade and the wind measuring synchronization average wind speed value on each quadrant correlation curve. The algebraic difference is the correction value of the corresponding sector. And performing representative year correction on the first anemometer tower data of the wind power plant by using the correction value. And 3, downloading data of the first anemometer tower by the Tier, wherein the data are the wind speed and the wind direction of the preset height at the lattice point of the tower point of the first anemometer tower. The second anemometer tower data representative yearly rectification method may be analogous to the representative yearly rectification method of the first anemometer tower data.
In step S12, the representative year wind energy elements include a representative year average wind speed, a representative year wind power density, a representative year hourly average wind speed, a representative year hourly wind power density, a representative year wind direction frequency, a representative year wind energy density direction distribution, a representative year wind speed, and a representative year wind energy frequency distribution. Therefore, the calculation of the representative year wind energy element based on the corrected first anemometer tower data and the corrected second anemometer tower data comprises the following steps: calculating representative annual average wind speed, representative annual wind power density, representative annual wind direction frequency, representative annual wind power density direction distribution, representative annual wind speed and representative annual wind power frequency distribution of the first anemometer tower based on the corrected first anemometer tower data; and calculating the representative annual average wind speed, the representative annual wind power density, the representative annual hourly average wind speed, the representative annual hourly wind power density, the representative annual wind direction frequency, the representative annual wind power density direction distribution, the representative annual wind speed and the representative annual wind power frequency distribution of the second anemometer tower based on the corrected second anemometer tower data.
In step S12, the representative hour-by-hour average wind speed and the representative hour-by-hour wind power density may be, for example, 1 hour unit. The primary wind energy direction and the secondary wind energy direction may be determined based on the representative year wind energy density direction distribution. The goodness of fit of the wind speed and wind energy frequency distribution of the two anemometer towers, the main wind speed frequency concentration intervals and the percentage of the wind speed frequency concentration intervals at different heights, and the main wind energy frequency concentration intervals and the percentage of the wind energy frequency concentration intervals can be determined based on the wind speed and the wind energy frequency distribution of the representative year.
And S13, acquiring the actual generating capacity of the unit based on the wind resource characteristic parameters and the representative annual wind energy elements.
In step S13, obtaining the actual power generation amount of the wind turbine generator set based on the wind resource characteristic parameter and the representative annual wind energy factor includes: obtaining a comprehensive correction coefficient by combining reduction coefficients of multiple energy loss factors; and obtaining the wind resource characteristic parameters and the representative annual wind energy elements based on the comprehensive correction coefficient, the wind resource characteristic parameters and the representative annual wind energy elements to obtain the actual generating capacity of the unit.
In step S13, the plurality of energy loss factors include, but are not limited to, wake flow reduction, air density correction reduction, power curve reduction, wind turbine utilization reduction, control and turbulence reduction, blade pollution reduction, energy losses such as field power and line loss, climate-influenced shutdown, grid fault and power limit reduction, software calculation error reduction, and wind power resource uncertainty reduction.
In step S13, the software used when the unit actual power generation amount calculation is performed may be Meteodyn-WT software, for example.
In step S13, the wind resource characteristic parameter and the representative annual wind energy element used when the actual power generation amount is calculated are the hub altitude wind resource characteristic parameter and the representative annual wind energy element. The wind resource characteristic parameters of the hub height and the representative annual wind energy elements are obtained by calculating the wind speed, the wind direction, the air temperature and the air pressure at the position, equal to the hub height of the wind driven generator, in the first anemometer tower data and the second anemometer tower data. This enables more accurate acquisition of the actual power generation amount.
In step S13, the wind energy resource distribution condition of the site area of the wind farm may also be comprehensively simulated and calculated by using Meteodyn WT software based on the wind resource characteristic parameters and the representative annual wind energy factors. At the moment, the actual generating capacity of the unit is calculated on the basis of wind energy resource analysis and calculation, and the obtained calculation result is more accurate.
And S14, obtaining an operation analysis result of the wind power plant based on comparison between the actual generating capacity of the unit and the theoretical generating capacity of the unit.
In step S14, the wind farm operation analysis result includes, but is not limited to, a power generation amount analysis result and a unit fault analysis result.
In step S14, since there are a plurality of wind turbine generator sets, the power generation analysis result and the unit fault analysis result of each wind turbine generator set are determined by comparing the unit actual power generation amount and the unit theoretical power generation amount of each wind turbine generator set and combining other relevant data.
For example, when the difference of the generating capacity of the units in the wind power plant is large, the average wind speed of the unit position points is possibly high, so that the generating capacity is high; the average wind speed of the machine position of the unit is low, so the generating capacity is low. Except for individual machine sites, the generating capacity of the whole set and the third power of annual average wind speed corresponding to the machine sites are basically in positive correlation, and the difference of the generating capacity of the whole set is considered to be mainly caused by the difference of wind resources. Wind resource differences are, for example, problems with unit location, resource level and wake losses. In addition, the actual generating capacity of the unit corresponding to the unit with lower theoretical generating capacity of the unit is also at a lower level, which shows that the main reason that the generating capacity of the subset after the operation of the wind power plant is lower is that the wind resource level of the position where the subset is located is poorer or is seriously influenced by wake flow, and the fault rate is high because part of the units are influenced by terrain due to improper micro-site selection.
In the operation analysis method for the wind farm of the embodiment of the present disclosure, a first anemometer tower is arranged in a site range of the wind farm, a second anemometer tower is arranged outside the site range, a first meteorological acquisition device is arranged on the first anemometer tower, a second meteorological acquisition device is arranged on the second anemometer tower, and the operation analysis includes: acquiring first anemometry tower data output by first meteorological acquisition equipment and second anemometry tower data output by second meteorological acquisition equipment; calculating to obtain wind resource characteristic parameters and representative annual wind energy factors of the wind power plant based on the first anemometer tower data and the second anemometer tower data; acquiring the actual generating capacity of the unit based on the wind resource characteristic parameters and the representative annual wind energy elements; and obtaining an operation analysis result of the wind power plant based on the comparison of the actual generating capacity of the unit and the theoretical generating capacity of the unit. Under the condition, wind measuring tower data corresponding to wind measuring towers in the site range and outside the site range of the wind power plant are integrated, so that wind resource characteristic parameters and representative annual wind energy elements are obtained, the actual generating capacity of the unit is further obtained, the actual generating capacity of the unit obtained by the method is more accurate, the reliability of a wind power plant operation analysis result obtained when the actual generating capacity of the unit is compared with the theoretical generating capacity of the unit is improved, and the accuracy of wind power plant operation analysis is improved. The wind measuring tower data corresponding to the wind measuring towers in the site range and outside the site range of the comprehensive wind power plant can well avoid measuring the higher or lower wind speed, so that the subsequent calculation is more accurate, and the accuracy of the operation analysis of the wind power plant is further improved.
The following are embodiments of the disclosed system that may be used to perform embodiments of the disclosed method. For details not disclosed in the embodiments of the system of the present disclosure, refer to the embodiments of the method of the present disclosure.
Referring to fig. 3, fig. 3 is a block diagram of a wind farm operation analysis system according to an embodiment of the present disclosure. The wind farm operation analysis system 10 comprises an acquisition module 11, a parameter operation module 12, a power generation amount calculation module 13 and an analysis module 14, wherein:
the acquiring module 11 is configured to acquire first meteorological data output by first meteorological acquisition equipment and second meteorological data output by second meteorological acquisition equipment;
the parameter operation module 12 is used for calculating wind resource characteristic parameters and representative annual wind energy factors of the wind power plant based on the first anemometer tower data and the second anemometer tower data;
the generating capacity calculating module 13 is used for obtaining the actual generating capacity of the unit based on the wind resource characteristic parameters and the representative annual wind energy elements;
the analysis module 14 is used for obtaining the operation analysis result of the wind power plant based on the comparison between the actual generating capacity of the unit and the theoretical generating capacity of the unit,
the first meteorological acquisition equipment is arranged on a first anemometer tower located within the site range of the wind power plant, and the second meteorological acquisition equipment is arranged on a second anemometer tower located outside the site range of the wind power plant.
Optionally, the first anemometer tower data includes wind speed and wind direction, and the second anemometer tower data includes wind speed, wind direction, air temperature and air pressure; the wind resource characteristic parameters comprise air density, turbulence intensity, wind shear index, weibull distribution and 50-year-one-encounter maximum wind speed; the representative annual wind energy elements include a representative annual average wind speed, a representative annual wind power density, a representative annual hourly average wind speed, a representative annual hourly wind power density, a representative annual wind direction frequency, a representative annual wind power density directional distribution, a representative annual wind speed, and a representative annual wind power frequency distribution.
Optionally, the parameter operation module 12 is specifically configured to: calculating an air density based on the second anemometer tower data; turbulence intensity, wind shear index, weibull distribution, 50-year-first maximum wind speed, and representative annual wind energy factors are calculated based on the first anemometer tower data and the second anemometer tower data.
Optionally, the wind farm operation analysis system 10 further comprises a preprocessing module. The preprocessing module is used for preprocessing the first anemometer tower data and the second anemometer tower data, and the preprocessing comprises integrity inspection and rationality inspection; and carrying out statistical interpolation on unreasonable and lack-of-measurement data in the first anemometer tower data and the second anemometer tower data screened after the integrity test and the rationality test.
Optionally, the power generation amount calculating module 13 is specifically configured to: obtaining a comprehensive correction coefficient by combining reduction coefficients of multiple energy loss factors; and obtaining the wind resource characteristic parameters and the representative annual wind energy elements based on the comprehensive correction coefficient, the wind resource characteristic parameters and the representative annual wind energy elements to obtain the actual generating capacity of the unit.
It should be noted that the explanation of the embodiment of the wind farm operation analysis method is also applicable to the wind farm operation analysis system of the embodiment, and is not repeated here.
In the wind farm operation analysis system of the embodiment of the disclosure, the acquisition module is used for acquiring first anemometer tower data output by first meteorological acquisition equipment and second anemometer tower data output by second meteorological acquisition equipment; the parameter operation module is used for calculating wind resource characteristic parameters and representative annual wind energy elements of the wind power plant based on the first anemometer tower data and the second anemometer tower data; the generating capacity calculating module is used for obtaining the actual generating capacity of the unit based on the wind resource characteristic parameters and the representative annual wind energy elements; the analysis module is used for obtaining an operation analysis result of the wind power plant based on comparison between the actual generating capacity of the unit and the theoretical generating capacity of the unit. Under the condition, wind measuring tower data corresponding to wind measuring towers in the site range and outside the site range of the wind power plant are integrated, so that wind resource characteristic parameters and representative annual wind energy elements are obtained, the actual generating capacity of the unit is further obtained, the actual generating capacity of the unit obtained by the method is more accurate, the reliability of a wind power plant operation analysis result obtained when the actual generating capacity of the unit is compared with the theoretical generating capacity of the unit is improved, and the accuracy of wind power plant operation analysis is improved. The wind measuring tower data corresponding to the wind measuring towers in the site range and outside the site range of the comprehensive wind power plant can well avoid measuring the higher or lower wind speed, so that the subsequent calculation is more accurate, and the accuracy of the operation analysis of the wind power plant is further improved.
According to an embodiment of the present disclosure, the present disclosure also provides a wind farm operation analysis device, a readable storage medium, and a computer program product.
FIG. 4 is a block diagram of a wind farm operation analysis device used to implement the wind farm operation analysis method of the disclosed embodiments. The wind farm operational analysis device is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. The wind farm operation analysis device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable wind farm operation analysis devices, and other similar computing devices. The components shown in the present disclosure, the connections and relationships of the components, and the functions of the components, are meant to be examples only, and are not meant to limit implementations of the present disclosure described and/or claimed in the present disclosure.
As shown in fig. 4, the wind farm operation analysis device 20 includes a calculation unit 21 that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM) 22 or a computer program loaded from a storage unit 28 into a Random Access Memory (RAM) 23. In the RAM 23, various programs and data required for the operation of the wind farm operation analysis device 20 can also be stored. The calculation unit 21, the ROM 22, and the RAM 23 are connected to each other via a bus 24. An input/output (I/O) interface 25 is also connected to bus 24.
A plurality of components in the wind farm operation analysis device 20 are connected to the I/O interface 25, including: an input unit 26 such as a keyboard, a mouse, etc.; an output unit 27 such as various types of displays, speakers, and the like; a storage unit 28, such as a magnetic disk, an optical disk, etc., the storage unit 28 being communicatively connected to the computing unit 21; and a communication unit 29 such as a network card, modem, wireless communication transceiver, etc. The communication unit 29 allows the wind farm operation analysis device 20 to exchange information/data with other wind farm operation analysis devices through a computer network such as the internet and/or various telecommunication networks.
The computing unit 21 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of the computing unit 21 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various dedicated Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and so forth. The computing unit 21 performs the various methods and processes described above, for example a wind farm operation analysis method. For example, in some embodiments, the wind farm operation analysis method may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 28. In some embodiments, part or all of the computer program may be loaded and/or installed on the wind farm operation analysis device 20 via the ROM 22 and/or the communication unit 29. When the computer program is loaded into RAM 23 and executed by the computing unit 21, one or more steps of the wind farm operation analysis method described above may be performed. Alternatively, in other embodiments, the calculation unit 21 may be configured to perform the wind farm operation analysis method in any other suitable way (e.g. by means of firmware).
Various implementations of the systems and techniques described above in this disclosure may be implemented in digital electronic circuitry, integrated circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic wind farm operational analysis devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program code, when executed by the processor or controller, causes the functions/acts specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In this disclosure, a machine-readable medium may be a tangible medium that may contain, or store a program for use by or in connection with an instruction execution system, apparatus, or wind farm operation analysis device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or wind farm operation analysis device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a compact disc read-only memory (CD-ROM), an optical storage wind farm operational analysis device, a magnetic storage wind farm operational analysis device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user may provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), the internet, and blockchain networks.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The Server may be a cloud Server, also called a cloud computing Server or a cloud host, and is a host product in a cloud computing service system, so as to solve the defects of high management difficulty and weak service extensibility in a traditional physical host and VPS service ("Virtual Private Server", or "VPS" for short). The server may also be a server of a distributed system, or a server incorporating a blockchain.
It should be understood that various forms of the flows shown above, reordering, adding or deleting steps, may be used. For example, the steps described in the present disclosure may be executed in parallel or sequentially or in different orders, and the present disclosure is not limited thereto as long as the desired results of the technical solutions of the present disclosure can be achieved.
The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made, depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the scope of protection of the present disclosure.
Claims (10)
1. A wind power plant operation analysis method is characterized in that a first anemometer tower is arranged in a site range of a wind power plant, a second anemometer tower is arranged outside the site range, first meteorological acquisition equipment is arranged on the first anemometer tower, second meteorological acquisition equipment is arranged on the second anemometer tower, and operation analysis comprises the following steps:
acquiring first meteorological measuring tower data output by the first meteorological acquisition equipment and second meteorological measuring tower data output by the second meteorological acquisition equipment;
calculating to obtain wind resource characteristic parameters and representative annual wind energy elements of the wind power plant based on the first anemometer tower data and the second anemometer tower data;
acquiring the actual generating capacity of the unit based on the wind resource characteristic parameters and the representative year wind energy elements;
and obtaining an operation analysis result of the wind power plant based on the comparison between the actual generating capacity of the unit and the theoretical generating capacity of the unit.
2. The wind farm operational analysis method of claim 1, wherein the first anemometer tower data comprises wind speed, wind direction, and the second anemometer tower data comprises wind speed, wind direction, air temperature, air pressure; the wind resource characteristic parameters comprise air density, turbulence intensity, wind shear index, weibull distribution and 50-year-one-encounter maximum wind speed; the representative year wind energy elements include a representative year average wind speed, a representative year wind power density, a representative year hourly average wind speed, a representative year hourly wind power density, a representative year wind direction frequency, a representative year wind power density direction distribution, a representative year wind speed, and a representative year wind power frequency distribution.
3. The wind farm operational analysis method of claim 2, wherein the calculating of the wind resource characteristic parameter and the representative year wind energy factor of the wind farm based on the first anemometer tower data and the second anemometer tower data comprises:
calculating an air density based on the second anemometer tower data;
calculating turbulence intensity, wind shear index, weibull distribution, 50-year-first maximum wind speed, and representative year wind energy factors based on the first anemometer tower data and the second anemometer tower data.
4. A wind farm operation analysis method according to claim 3, wherein said obtaining of the unit actual power generation based on said wind resource characteristic parameter and said representative annual wind energy element comprises:
obtaining a comprehensive correction coefficient by combining reduction coefficients of multiple energy loss factors;
and obtaining the wind resource characteristic parameters and the representative year wind energy elements to obtain the actual generating capacity of the unit based on the comprehensive correction coefficient, the wind resource characteristic parameters and the representative year wind energy elements.
5. The wind farm operation analysis method according to claim 1, further comprising, before calculating the wind resource characteristic parameter and the representative annual wind energy element:
and preprocessing the first anemometer tower data and the second anemometer tower data, wherein the preprocessing comprises integrity inspection and rationality inspection.
6. A wind farm operational analysis method according to claim 5, wherein the preprocessing further comprises statistical interpolation of unreasonable and missing data in the first and second anemometer tower data selected after integrity and rationality tests.
7. The utility model provides a fault analysis system for wind turbine, its characterized in that sets up first anemometer tower in the site scope of wind-powered electricity generation field set up the second anemometer tower outside the site scope, first meteorological collection equipment has been arranged on the first anemometer tower, second meteorological collection equipment has been arranged on the second anemometer tower, the operating system includes:
the acquiring module is used for acquiring first anemometer tower data output by the first meteorological collecting equipment and second anemometer tower data output by the second meteorological collecting equipment;
the parameter operation module is used for calculating wind resource characteristic parameters and representative annual wind energy elements of the wind power plant based on the first anemometer tower data and the second anemometer tower data;
the generating capacity calculating module is used for obtaining the actual generating capacity of the unit based on the wind resource characteristic parameters and the representative year wind energy elements;
and the analysis module is used for obtaining an operation analysis result of the wind power plant based on comparison between the actual generating capacity of the unit and the theoretical generating capacity of the unit.
8. The fault analysis system for a wind park according to claim 7, wherein the first anemometer tower data comprises wind speed, wind direction, the second anemometer tower data comprises wind speed, wind direction, air temperature, air pressure; the wind resource characteristic parameters comprise air density, turbulence intensity, wind shear index, weibull distribution and 50-year-one-encounter maximum wind speed; the representative annual wind energy elements include a representative annual average wind speed, a representative annual wind power density, a representative annual hourly average wind speed, a representative annual hourly wind power density, a representative annual wind direction frequency, a representative annual wind energy density directional distribution, a representative annual wind speed, and a representative annual wind energy frequency distribution.
9. The system according to claim 8, wherein the parameter calculation module is specifically configured to:
calculating an air density based on the second anemometer tower data;
calculating turbulence intensity, wind shear index, weibull distribution, 50-year-first-encounter maximum wind speed, and representative annual wind energy factor based on the first anemometer tower data and the second anemometer tower data.
10. A fault analysis device for a wind park, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a wind farm operation analysis method as defined in any one of claims 1 to 6.
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