CN110083896B - Standard reaching conformity degree evaluation method for wind turbine generator operating power curve - Google Patents
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Abstract
The standard-reaching conformity evaluation method of the wind turbine generator operating power curve comprises the following steps: the first step is as follows: establishing a standard power curve function aiming at a wind speed and power data table of the type of the wind turbine generator to be evaluated; the second step is that: cleaning the historical second-level wind speed and power data of the type of the wind turbine generator to be evaluated, and establishing an operation power curve function; the third step: and (4) performing coincidence coefficient calculation on the standard power curve function model in the first step and the operating power curve function model in the second step. The main structure of the whole power curve is divided into four areas, namely a fan starting stage, a power coefficient constant stage, a power constant stage and a rotating speed constant stage, on the premise that the relation of each area of the wind turbine generator is definite, design and evaluation methods of the areas are respectively provided, and meanwhile, a design and calculation flow chart of the whole wind turbine generator is designed, so that the preliminary evaluation design of the wind turbine generator is realized, and a basis is provided for subsequent numerical simulation and experimental verification.
Description
Technical Field
The invention relates to a method for evaluating the standard-reaching conformity of an operating power curve of a wind turbine generator, which can realize the integral preliminary design evaluation of the wind turbine generator and can be used for evaluating the performance of a wind power system.
Background
The good power generation performance of the wind turbine generator is a determining factor for ensuring the production operation management and development strategy planning of wind power generation enterprises. The power curve is an important index for describing the operation performance of the wind turbine generator, but due to the influence of factors such as temperature, air pressure, blade pollution and the characteristics of the wind turbine generator, the power curves formed by different wind fields, different wind turbine generators and different time periods have differences. Therefore, the difference between the actual operation power curve and the standard power curve is analyzed, factors influencing the power characteristics of the wind turbine generator are known, the operation state of the wind turbine generator can be mastered, and early work is performed for fault diagnosis. At present, no report is found.
Disclosure of Invention
The purpose of the invention is as follows:
the invention provides a method for evaluating the conformity of an operating power curve of a wind turbine generator to a standard degree, and aims to solve the problem that the difference between an actual operating power curve and a standard power curve cannot be better analyzed in the past.
The technical scheme is as follows:
the method for evaluating the standard reaching conformity of the operating power curve of the wind turbine generator is characterized by comprising the following steps of:
the method comprises the following steps:
the first step is as follows: establishing a standard power curve function aiming at a wind speed and power data table of the type of the wind turbine generator to be evaluated;
the second step: cleaning the historical second-level wind speed and power data of the type of the wind turbine generator to be evaluated, and establishing an operation power curve function;
the third step: and (4) performing coincidence coefficient calculation on the standard power curve function model in the first step and the operating power curve function model in the second step to obtain a final evaluation result.
And the standard power curve mathematical model established in the first step and the power curve mathematical model to be evaluated established in the second step both adopt a least square method.
The cleaning process in the second step includes a historical data standardization process method and a data cleaning process method.
The historical data standardization processing method comprises the following steps:
the conversion is returned to the reference air density, the gas constant R is 287.05J/(kg.K), and the measured average absolute air temperature in 10min is T 10min And the measured 10min average air pressure is B 10min The measured 10min average wind speed value is V 10min Standard air density is rho 0 Then the converted wind speed V n Comprises the following steps:
the data cleaning processing method comprises the following steps:
dividing the power and the standardized wind speed data into segments with a wind speed interval of 0.5m/s, and forming a plurality of micro-clusters by each data segment respectively by adopting a data cleaning algorithm based on Spark optimization K-means clustering algorithm, wherein the brief structure of the data of the micro-clusters is defined as: c = [ N, L = s ,S s ,C s ,B s ,p,p 1 ]Wherein N is the number of data points contained in the micro-cluster, L s For data element attribute linear sum, S s As a sum of squares of data elements, C s For data elements cubic sum, B s For the data element to the fourth power sum, p is the initial position for micro-cluster generation, p 1 Updating the position for the micro-cluster at last; along with the change of the position, the number N of the micro-clusters is more and more, and regular maintenance is needed, namely, the distance D between every two micro-clusters is calculated according to a formula (2), if the distance D is smaller than a set threshold value, the micro-clusters are combined according to a formula (3), and otherwise, data elimination is carried out;
C 1 +C 2 ＝[N 1 +N 2 ,L s1 +L s2 ,S s1 +S s2 ,C s1 +C s2 ,B s1 +B s2 ] (3)
wherein C is 1 ,C 2 Two micro-clusters; x is a radical of a fluorine atom i 、x i Are respectively the ith and jth data values in the two micro-clusters；N 1 ,N 2 The number of data points contained in the two micro-clusters, L s1 ,L s2 For the linear sum of the data element attributes in two micro-clusters, S s1 ,S s1 Is the sum of squares of data elements in two micro-clusters, C s1 ,C s2 For a cubic sum of data elements within two micro-clusters, B s1 ,B s2 The sum of squares of data elements in the two micro-clusters is obtained;
and data cleaning can be realized according to the distance D between the micro-clusters and the size of a set threshold, wherein the set threshold is generally mu +/-3 sigma, wherein mu is a mean value, and sigma is a standard deviation.
The coincidence coefficient calculation method in the third step is as follows:
at the same wind speed, the corresponding two power sequences of the standard power curve function a and the operating power curve function b are x respectively a And x b A, b, two curve power sequence x a And x b Cross correlation coefficient R of ab The calculation formula of (2) is formula (4), and the deviation coefficient formula d ab Is formula (5); the calculation process of the coincidence coefficient based on the correlation analysis comprises the following steps:
where N is the number of data, x a (n) is the power value corresponding to the nth data in the a curve power sequence, x b (n) is a power value corresponding to the nth data in the b curve power sequence;
if the number of the wind turbine generators is M and the number of the wind turbine generators is i, the coincidence coefficient Con is expressed as a formula 6);
wherein
Is the cross-correlation coefficient of the operating power curve of the No. i wind turbine generator and the standard power curve, and is based on the comparison result>
And the deviation coefficient of the operating power curve of the No. i wind turbine generator and the standard power curve is shown.
And after the coincidence coefficients are calculated, ranking evaluation is carried out on the coincidence coefficients of the wind driven generators.
Ranking evaluation adopts an integral operation power curve conformity coefficient ranking and a partition operation power curve conformity coefficient ranking, and one of the two is selected or used simultaneously;
the ranking of the integral operation power curve conformity coefficient is that when a plurality of wind driven generators exist, the ranking is carried out on the integral operation power curve conformity coefficient of each wind driven generator;
the partition operation power curve accords with the coefficient ranking, namely when a plurality of wind driven generators exist, the operation power curve of each wind driven generator is divided into regions, and then the corresponding region operation power curve of each wind driven generator accords with the coefficient ranking.
The area division mode includes the following two modes, either one or both of which are adopted:
dividing an operating power curve into a cut-in section wind speed curve, a full-power state section wind speed curve and a cut-out section wind speed curve according to wind speed and power data to be evaluated of a fan type provided by a manufacturer;
and dividing the operation power curve into a fan starting area curve, a power coefficient constant area curve, a power constant area curve and a rotating speed constant area curve according to the operation state of the fan. ( Explanation: when the conformity degree of the sub-areas is calculated, the standard power curve is divided into corresponding areas, the conformity degree calculation is carried out on the corresponding area of the operating power curve and the corresponding area on the standard power curve, and then the calculation results are ranked )
The system comprises a standard power curve function building module, an operating power curve function building module and a coincidence coefficient calculating module;
the standard power curve function building module is used for building a standard power curve function aiming at a wind speed and power data table of the model of the wind turbine generator to be evaluated;
the operating power curve function building module is used for cleaning historical operating second-level data of the type of the wind turbine generator to be evaluated and building an operating power curve function;
and the coincidence coefficient calculation module performs coincidence coefficient calculation on the standard power curve function model constructed in the standard power curve function construction module and the operating power curve function model constructed by the operating power curve function construction module to obtain a final evaluation result.
The advantages and effects are as follows:
the invention provides a method for evaluating the compliance of an operating power curve of a wind turbine generator, which divides the main structure of the whole power curve into four areas, namely a fan starting stage, a power coefficient constant stage, a power constant stage and a rotating speed constant stage.
Drawings
FIG. 1: standard power characteristic curve diagram
FIG. 2: spark optimization K-means clustering algorithm-based data cleaning algorithm framework
FIG. 3: coincidence coefficient calculation flow chart based on correlation analysis
FIG. 4: power curve conformity coefficient calculation flow chart of wind turbine generator
FIG. 5: ranking condition of 33 units of specified model of certain windpark
The specific implementation mode is as follows:
the power curve can directly reflect the running state of the wind turbine generator. According to the cut-in wind speed, the full-power state wind speed and the cut-out wind speed in the wind speed and power data to be evaluated of a certain fan type provided by a manufacturer, a power curve can be roughly divided into three parts. The power curve is divided according to the running state of the fan, and mainly comprises four sub-areas, namely a fan starting area, a power coefficient constant area, a power constant area and a rotating speed constant area, as shown in fig. 1. The curve shape of the different zones is influenced by the respective main control assembly. Therefore, partitioning and overall computation should be performed on the compliance of the power curve.
1) Historical data standardization calculation method
In the invention, the design calculation of the power curve is mainly the calculation of wind speed-power, and in the drawing of the power curve, because the operating environment of the fan is changeable, in order to ensure that the whole power curve in the operating state can be directly compared and analyzed with a standard power curve provided by a manufacturer, the wind speed in the data needs to be calculated in a standardized way.
According to the power generation formula of the wind turbine generator, the main parameter influencing the power curve is the air density. It is therefore necessary to convert back to the reference air density. According to GBT 18451.2-2003, if the wind turbine is power-controlled automatically, the gas constant R is 287.05J/(kg. K), and the measured average absolute temperature in 10min is T 10min And the measured 10min average air pressure is B 10min The measured 10min average wind speed value is V 10min Standard air density is rho 0 Then the converted wind speed V n Comprises the following steps:
2) Data cleaning calculation method
The wind speed and the power in the power curve drawing are taken as main bodies of research, and the quantity and the quality of wind speed-power data reflect the performance quality of the wind turbine generator, the stability of the running state and the like. Therefore, the invention adopts Spark technology to optimize data distribution and applies K-means clustering algorithm to carry out data cleaning.
Dividing the power and the standardized wind speed data into segments with a wind speed interval of 0.5m/s, and forming a plurality of micro-clusters by each data segment respectively through K-means, wherein the brief data structure of the micro-clusters is defined as: c = [ N, L = s ,S s ,C s ,B s ,p,p 1 ]Wherein N is the number of data points contained in the micro-cluster, L s For data element attribute linear sum, S s As a sum of squares of data elements, C s For data elements cubic sum, B s For the data element to the fourth power sum, p is the initial position for micro-cluster generation, p 1 The location is updated last for the micro-cluster. Along with the change of the position, the number N of the micro-clusters is more and more, and regular maintenance is needed, namely, the distance D between every two micro-clusters is calculated according to a formula (2), if the distance D is smaller than a set threshold value, the micro-clusters are combined according to a formula (3), and otherwise, data elimination is carried out. A data cleaning algorithm framework based on Spark optimization K-means clustering algorithm is shown in figure 2.
C 1 +C 2 ＝[N 1 +N 2 ,L s1 +L s2 ,S s1 +S s2 ,C s1 +C s2 ,B s1 +B s2 ] (3)
Wherein C is 1 ,C 2 Two micro-clusters; n is a radical of 1 ,N 2 The number of data points contained in the two micro-clusters, L s1 ,L s2 Linear sum of attributes of data elements in two micro-clusters, S s1 ,S s1 Is the sum of squares of data elements in two micro-clusters, C s1 ,C s2 For a cubic sum of data elements within two micro-clusters, B s1 ,B s2 Is the sum of the squares of the data elements in the two micro-clusters.
And data cleaning can be realized according to the distance D between the micro-clusters and the size of a set threshold, wherein the set threshold is generally mu +/-3 sigma, wherein mu is a mean value, and sigma is a standard deviation. And finally, performing curvilinear analysis on scattered points of the power curve by using the cleaned wind speed-power data.
3) Drawing of Power curves
The wind speed and power data to be evaluated according to a certain fan model provided by a manufacturer are shown in a table 1.
Table 1: standard power curve chart for fan
As can be seen from the table, the cut-in wind speed set by the manufacturer was 3m/s, and when the wind speed reached 12m/s, the fully developed state was entered, the cut-out wind speed was 25m/s. When the wind speed interval is 3 m/s-12 m/s, the corresponding wind speed shows a non-linear growth trend. A mathematical model 1 of the power curve can be created using a least squares method, and a standard power curve graph is plotted, as shown in fig. 1. A mathematical model 2, hereinafter referred to as an operating power curve, may be created using a least squares method based on historical data. Other methods of power curve mapping are possible, but this patent claims that the power curve can be expressed as a function, so the power curve is constructed using the least squares method.
4) Standard conformity calculation method
The correlation analysis is a basic method for comparing the similarity degree of two functions, and the basic idea is to estimate the power offset by using two correlation functions. Compared with other algorithms, the cross-correlation matching algorithm is simple to implement, low in calculation complexity and strong in robustness, and therefore is applied to many occasions. And dividing the standard reaching conformity calculation of the power curve of the wind turbine into 4 sub calculation analysis areas according to the subareas of the power curve. The invention uses cross-correlation unbiased estimation to calculate the standard conformity of the unit power curve and the standard curve and completes the unit performance ranking.
At the same wind speed, the corresponding two power sequences of the standard power curve function and the operating power curve function are x respectively a And x b Cross correlation coefficient R of power sequence of a, b two curves ab The calculation formula of (2) is formula (4), and the deviation coefficient formula d ab Is formula (5); the calculation process of the coincidence coefficient based on the correlation analysis comprises the following steps: the flow of calculating the coincidence coefficient based on the correlation analysis is shown in fig. 3.
Wherein N is the data number, and x (N) is the power value corresponding to the nth data.
If the number of the wind turbine generator is M, and the number of the wind turbine generator is i, the coincidence coefficient Con is expressed as formula (6).
Wherein
Is the cross-correlation coefficient of the No. i wind turbine generator and the standard power curve, is evaluated>
Is the deviation coefficient of No. i wind turbine generator and the standard power curve
The conformity coefficient of the power curve and the standard power curve of a plurality of units of the same type can be ranked in a mode of first partitioning and then integrating (partitioning is carried out by taking wind speed as reference). The partition ranking reflects the state of each subassembly, and the overall ranking reflects the complex condition of the whole machine, so that the method is more suitable for standard reaching judgment. The subareas and the whole are complementary, and the standard reaching conformity evaluation can be more accurately carried out. The power curve of a single fan is integrally in accordance with a coefficient calculation flow chart as shown in FIG. 4.
5) Ranking
Ranking the plurality of wind driven generators according to the calculated values of the conformity degree, facilitating understanding and analysis, normalizing the values conforming to the coefficients, wherein the values all belong to intervals (0,1), the values conforming to the coefficients are represented by fan numbers and are presented with ranking conditions in a broken line graph mode, the larger the value conforming to the coefficient is, the higher the ranking of the unit is, the more 33 wind turbine units of a certain wind field specified type are taken as an example, the whole ranking of the unit is carried out, and the ranking conditions are shown in fig. 5.
Wind turbine generator system operating power curve conformity degree of reaching standard evaluation system, its characterized in that: the system comprises a standard power curve function building module, an operating power curve function building module and a coincidence coefficient calculating module;
the standard power curve function building module is used for building a standard power curve function aiming at a wind speed and power data table of the model of the wind turbine generator to be evaluated;
the operating power curve function building module is used for cleaning historical operating second-level data of the type of the wind turbine generator to be evaluated and building an operating power curve function;
and the coincidence coefficient calculation module performs coincidence coefficient calculation on the standard power curve function model constructed in the standard power curve function construction module and the operating power curve function model constructed by the operating power curve function construction module to obtain a final evaluation result.
Embodiments of the present application may be provided as methods, systems, or computer program products. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Claims (9)
1. The method for evaluating the standard reaching conformity of the operating power curve of the wind turbine generator is characterized by comprising the following steps of:
the method comprises the following steps:
the first step is as follows: establishing a standard power curve function aiming at a wind speed and power data table of the model of the wind turbine generator to be evaluated;
the second step is that: cleaning the historical second-level wind speed and power data of the type of the wind turbine generator to be evaluated, and establishing an operation power curve function;
the third step: performing coincidence coefficient calculation on the standard power curve function model in the first step and the operating power curve function model in the second step to obtain a final evaluation result;
the coincidence coefficient calculation method in the third step is as follows:
at the same wind speed, the corresponding two power sequences of the standard power curve function a and the operating power curve function b are x respectively a And x b A, b, two curve power sequence x a And x b Cross correlation coefficient of
The calculation formula of (2) is formula (4), and the deviation coefficient formula d ab Is formula (5); the calculation process of the coincidence coefficient based on the correlation analysis comprises the following steps:
where N is the number of data, x a (n) is the power value corresponding to the nth data in the a curve power sequence, x b (n) is a power value corresponding to the nth data in the b curve power sequence;
if the number of the wind turbine generator is M, and the number of the wind turbine generator is i, the coincidence coefficient Con is expressed as formula 6);
wherein
Is the cross correlation coefficient of the operating power curve of the No. i wind turbine generator and the standard power curve,
the deviation coefficient of the operating power curve of the No. i wind turbine generator and the standard power curve is obtained.
2. The method for evaluating the compliance of the operating power curve of a wind turbine generator according to claim 1, wherein the method comprises the following steps: and the standard power curve mathematical model established in the first step and the power curve mathematical model to be evaluated established in the second step both adopt a least square method.
3. The method for evaluating the compliance of the operating power curve of a wind turbine generator according to claim 1, wherein the method comprises the following steps: the cleaning process in the second step includes a historical data standardization process method and a data cleaning process method.
4. The wind turbine generator operating power curve conformity degree after standard evaluation method of claim 3, characterized in that:
the historical data standardization processing method comprises the following steps:
the conversion is returned to the reference air density, the gas constant R is 287.05J/(kg.K), and the measured average absolute air temperature in 10min is T 10min And the measured 10min average air pressure is B 10min The measured 10min average wind speed value is V 10min Standard air density is rho 0 Then the converted wind speed V n Comprises the following steps:
5. the wind turbine generator operating power curve conformity degree after standard evaluation method of claim 4, characterized in that:
the data cleaning processing method comprises the following steps:
dividing the power and the standardized wind speed data into segments with a wind speed interval of 0.5m/s, and forming a plurality of micro-clusters by each data segment respectively by adopting a data cleaning algorithm based on Spark optimization K-means clustering algorithm, wherein the brief structure of the data of the micro-clusters is defined as: c = [ N, L = s ,S s ,C s ,B s ,p,p 1 ]Wherein N is the number of data points contained in the micro-cluster, L s For data element attribute linear sum, S s As a sum of squares of data elements, C s As a sum of data elements, B s For the data element to the fourth power sum, p is the initial position for micro-cluster generation, p 1 Updating the position for the micro-cluster at last; along with the change of the position, the number N of the micro-clusters is more and more, and regular maintenance is needed, namely, the distance D between every two micro-clusters is calculated according to a formula (2), and if the distance D is smaller than a set threshold value, the micro-clusters are combined according to a formula (3)Otherwise, data elimination is carried out;
C 1 +C 2 ＝[N 1 +N 2 ,L s1 +L s2 ,S s1 +S s2 ,C s1 +C s2 ,B s1 +B s2 ] (3)
wherein C is 1 ,C 2 Two micro-clusters; x is the number of i 、x i The ith and jth data values in the two micro-clusters respectively; n is a radical of 1 ,N 2 The number of data points contained in the two micro-clusters, L s1 ,L s2 Linear sum of attributes of data elements in two micro-clusters, S s1 ,S s1 Is the sum of squares of data elements in two micro-clusters, C s1 ,C s2 For a cubic sum of data elements within two micro-clusters, B s1 ,B s2 The sum of squares of data elements in the two micro-clusters is obtained;
and (3) realizing data cleaning according to the distance D between the micro clusters and the size of a set threshold, wherein the set threshold selects mu +/-3 sigma, mu is a mean value, and sigma is a standard deviation.
6. The method for evaluating the compliance of the operating power curve of a wind turbine generator according to claim 1, wherein the method comprises the following steps:
and after the coincidence coefficients are calculated, ranking evaluation is carried out on the coincidence coefficients of the wind driven generators.
7. The method for evaluating the compliance of the operating power curve of the wind turbine generator according to claim 6, wherein the method comprises the following steps: ranking evaluation adopts an integral operation power curve conformity coefficient ranking and a partition operation power curve conformity coefficient ranking, and one of the two is selected or used simultaneously;
the ranking of the integral operation power curve conformity coefficient is that when a plurality of wind driven generators exist, the ranking is carried out on the integral operation power curve conformity coefficient of each wind driven generator;
the partition operation power curve accords with the coefficient ranking, namely when a plurality of wind driven generators exist, the operation power curve of each wind driven generator is divided into regions, and then the corresponding region operation power curve of each wind driven generator accords with the coefficient ranking.
8. The method for evaluating the compliance of the operating power curve of a wind turbine generator according to claim 7, wherein the method comprises the following steps: the area division mode includes the following two modes, either one or both of which are adopted:
dividing an operating power curve into a cut-in section wind speed curve, a full-transmission state section wind speed curve and a cut-out section wind speed curve according to wind speed and power data to be evaluated of a fan type provided by a manufacturer;
and dividing the operation power curve into a fan starting area curve, a power coefficient constant area curve, a power constant area curve and a rotating speed constant area curve according to the operation state of the fan.
9. Wind turbine generator system operating power curve conformity degree evaluation system up to standard, its characterized in that: the system comprises a standard power curve function building module, an operating power curve function building module and a coincidence coefficient calculating module;
the standard power curve function building module is used for building a standard power curve function aiming at a wind speed and power data table of the model of the wind turbine generator to be evaluated;
the operating power curve function building module is used for cleaning historical operating second-level data of the type of the wind turbine generator to be evaluated and building an operating power curve function;
and the coincidence coefficient calculation module performs coincidence coefficient calculation on the standard power curve function model constructed in the standard power curve function construction module and the operating power curve function model constructed by the operating power curve function construction module to obtain a final evaluation result.
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| CN112598207A (en) * | 2020-06-12 | 2021-04-02 | 河北新天科创新能源技术有限公司 | Wind turbine generator operating power curve evaluation method based on multiple cleaning |
| CN111666458B (en) * | 2020-06-22 | 2023-04-18 | 中国船级社质量认证有限公司 | Fitting method for power curve of wind turbine generator |
| CN112632749B (en) * | 2020-11-24 | 2023-08-15 | 华能国际电力股份有限公司 | Method and device for evaluating power generation performance of wind driven generator |
| CN113431800A (en) * | 2021-07-16 | 2021-09-24 | 西安热工研究院有限公司 | Method for distinguishing running state of fan in real time |
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