A fan operation characteristics analysis system based on MATLAB function modules

Abstract

The invention provides a wind turbine operation characteristic analysis system based on MATLAB function module, comprising: a data integration function module, which is used to integrate the wind speed-power of all days of the same wind turbine model into an array based on the software connection and data import function between MATLAB and Excel; a wind turbine scatter plot function module, which is used to generate a power scatter plot of the wind turbine based on the data in the integrated array and the regression analysis function in the fitting toolbox in the MATLAB function module; a power curve fitting function module, which is used to perform power curve fitting on the power scatter plot of the wind turbine generated by the wind turbine scatter plot function module by using a curve fitting method based on the data fitting function and the regression analysis function in the fitting toolbox in the MATLAB function module to obtain a fitted power curve; a wind energy utilization rate calculation function module, which calculates the wind energy utilization rate of the wind turbine based on the conversion efficiency formula of the wind turbine generator converting wind energy into electrical energy in the MATLAB function module; and a comprehensive evaluation of the operation characteristics of the wind turbine is realized.

Description

Technical Field

[0001] The present invention relates to the field of module design, and in particular to a MATLAB function module for analyzing fan operation characteristics based on big data. Background Art

[0002] In recent years, with the transformation of the national energy structure to green and low-carbon, the new energy power generation industry has ushered in a high-speed development cycle. On the one hand, as the wind turbines in the new energy stations increase in operating years, the wear and aging of the internal components of the equipment are becoming increasingly prominent, resulting in the gradual deterioration of the operating status of the wind turbines. Some units have exceeded the warranty period, which has caused the continuous increase in operation and maintenance costs and reduced the economic benefits of the wind farm. On the other hand, wind farms have accumulated a large amount of operating data, so it is necessary to analyze the operating characteristics of wind turbines through these big data, and at the same time, combine the equipment operating conditions, natural environment, equipment type, equipment manufacturer and other factors, and use reasonable and effective methods for comprehensive analysis, so as to provide a basis for the site selection and unit selection of the power plant, and can also combine the evaluation data to guide manufacturers to carry out corresponding optimization designs for different types of wind turbines. However, the analysis of wind turbine operation big data is quite time-consuming and the accuracy is not high if it is calculated by human power alone, so it is not realistic. Therefore, finding a multifunctional software processing method that can realize the analysis of the operating characteristics of multiple types of wind turbines is a reliable solution to this problem. Summary of the invention

[0003] In order to solve the problem that the analysis of wind turbine operation big data in the prior art is quite time-consuming and has low accuracy only by manual calculation, the present invention provides a wind turbine operation characteristic analysis system based on MATLAB function module, including:

[0004] Data integration function module, wind turbine scatter plot function module, power curve fitting function module and wind energy utilization rate calculation function module;

[0005] The data integration function module is used to integrate the wind speed-power of all days of the same wind turbine model into an array based on the software connection and data import function between MATLAB and Excel;

[0006] The fan scatter plot function module is used to generate a power scatter plot of the fan based on the data in the array integrated by the data integration function module and the regression analysis function in the fitting toolbox in the MATLAB function module;

[0007] The power curve fitting function module is used to fit the power scatter plot of the fan generated by the fan scatter plot function module with a curve fitting method based on the data fitting function and regression analysis function in the fitting toolbox in the MATLAB function module to obtain a fitted power curve;

[0008] The wind energy utilization rate calculation function module calculates the wind energy utilization rate of the wind turbine based on the conversion efficiency formula of the wind turbine converting wind energy into electrical energy in the MATLAB function module.

[0009] Preferably, the fan scatter plot function module includes a single fan scatter plot function module and a multiple fan scatter plot comparison function module;

[0010] The single wind turbine scatter plot function module is used to generate a power scatter plot of a single wind turbine based on the power values ​​in the array of the same wind turbine model integrated by the data integration function module and the regression analysis function in the fitting toolbox in the MATLAB function module;

[0011] The multiple wind turbine scatter plot comparison function module is used to generate a power scatter plot of each wind turbine model in the same coordinate based on the power values ​​in the array of multiple wind turbine models integrated by the data integration function module and the regression analysis function in the fitting toolbox in the MATLAB function module, thereby forming a scatter plot comparison diagram of multiple wind turbines.

[0012] Preferably, it also includes: a data batch import function module;

[0013] The data batch import function module is used to import the data of the fans in batches by using the MATLAB programming language of the MATLAB function module.

[0014] Preferably, it also includes: a fan theoretical power curve fitting function module;

[0015] The wind turbine theoretical power curve fitting function module fits the wind speed power curves at all wind speeds using the power values ​​corresponding to individual wind speeds of the same wind turbine model based on the data fitting function in the MATLAB fitting toolbox.

[0016] Preferably, it also includes: the proportion function module and the mean and standard deviation calculation function module, which are used to calculate the proportion of the actual power value and the theoretical value of the same fan model based on the proportion calculation formula in the MATLAB function module;

[0017] The mean and standard deviation obtaining function module obtains the data mean and standard deviation based on the actual operation data of the fan imported into the MATLAB function module and the theoretical value combined with the data in the MATLAB.

[0018] Preferably, it also includes: an abnormal power data value processing function module;

[0019] The abnormal power data value processing function module selects the accuracy value of the normal distribution function based on calling the E_S.dat file in the MATLAB function module, inversely deduce the confidence interval of the function, and eliminates data values ​​outside the confidence interval.

[0020] Preferably, it also includes: a power scatter point upper and lower limit marking function module;

[0021] The power scatter upper and lower limit marking function module is used to call the ChuLiHouShuJu.dat file in MATLAB after eliminating abnormal data based on the abnormal power data value processing function module, to obtain the maximum and minimum power values ​​under each wind speed, and to mark the upper and lower limits for the wind speed-power scatter diagram.

[0022] Preferably, it also includes: a wind speed power mean and variance obtaining function module;

[0023] The wind speed power mean and variance obtaining function module eliminates abnormal data through the abnormal power data value processing function module, sets the wind speed segment threshold, calls the ChuLiHouShuJu.dat file in the MATLAB function module to obtain the wind speed and power average values ​​in each wind speed segment, and finally fits the actual wind speed-power mean scatter point to the actual operation curve of the wind turbine, and obtains the variance value of the actual power relative to the theoretical power in each wind speed segment.

[0024] Preferably, it also includes: a power monthly mean and variance obtaining function module;

[0025] The power monthly mean and variance obtaining function module is used to calculate the monthly mean of actual power by calling the DaFengSu.dat file in the MATLAB function module in combination with the synthesized data of the wind turbine, and to calculate the monthly variance of the actual power using the theoretical power value as the expected value, and to compare the degree of deviation from the theoretical power by combining the monthly mean and the monthly variance.

[0026] Preferably, it also includes: a power statistics function module;

[0027] The power statistics function module calls the YuanShiShuJu.dat file in the MATLAB function module, screens out the power values ​​that meet the set wind speed adjustment, generates a new data array, and generates the DaFengSu.dat file for storage;

[0028] Preferably, the power statistics function module includes: a comparison between actual power generation and full theoretical power generation and wind power abandonment statistics function submodule and an actual wind power abandonment statistics function module

[0029] The actual power generation and full theoretical power generation comparison function submodule is used to call the DaFengSu.dat file to count the proportion of wind turbines that have reached or have not reached the full power generation state;

[0030] The actual wind power abandonment statistics function module is used to call the DaFengSu.dat file to count the wind power abandonment based on the actual power of the same model of wind turbines at different wind speeds every day and every month.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] The invention provides a fan operation characteristic analysis system based on MATLAB function module, comprising: a data integration function module, which is used for integrating the wind speed-power of all days of the same fan model into an array based on the software connection and data import function between MATLAB and Excel; a fan scatter plot function module, which is used for generating a power scatter plot of the fan based on the regression analysis function in a fitting toolbox in the MATLAB function module using the data in the integrated array; a power curve fitting function module, which is used for performing power curve fitting on the power scatter plot of the fan generated by the fan scatter plot function module using a curve fitting method based on the data fitting function and regression analysis function in the fitting toolbox in the MATLAB function module to obtain a fitted power curve; and a wind energy utilization rate calculation function module, which calculates the wind energy utilization rate of the fan based on the conversion efficiency formula of the wind turbine generator converting wind energy into electric energy in the MATLAB function module, thereby realizing a comprehensive evaluation of the operation characteristics of the fan. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a structural schematic diagram of the fan operation characteristic analysis system of the present invention;

[0034] Figure 2 A single fan scatter plot generated by a single fan scatter plot function module of the MATLAB function module of the present invention;

[0035] Figure 3 The scatter plots of multiple fans of the MATLAB function module of the present invention are compared with the scatter plots of multiple fans generated by the function module;

[0036] Figure 4 The actual wind speed power curve fitting diagram generated by the fitting power curve function module of the MATLAB function module of the present invention;

[0037] Figure 5 The actual power proportion diagram in different wind speed intervals generated by the proportion function module of the MATLAB function module of the present invention;

[0038] Figure 6The fitting curve is generated by the fan theoretical power curve fitting module function of the MATLAB function module of the present invention;

[0039] Figure 7 A normal distribution curve generated by a mean and standard deviation obtaining module of a MATLAB function module of the present invention;

[0040] Figure 8 It is a schematic diagram of the abnormal data processing effect generated by the abnormal data processing module function of the MATLAB function module of the present invention;

[0041] Fig. 9 It is a schematic diagram of power upper and lower limit marking generated by the power scatter point upper and lower limit marking module function of the MATLAB function module of the present invention;

[0042] Fig.10 It is a mean scatter point fitting power curve effect diagram generated by the mean and variance obtaining module of the MATLAB function module of the present invention;

[0043] Fig.11 The mean and variance effect diagrams generated by the mean and variance obtaining module of the MATLAB function module of the present invention;

[0044] Fig.12 It is a schematic diagram of wind energy utilization rate generated by the wind turbine wind energy utilization rate module function of the MATLAB function module of the present invention;

[0045] Fig.13 A schematic diagram of the monthly mean power value and variance generated by the monthly mean power value and variance obtaining module of the MATLAB function module of the present invention;

[0046] Fig.14 It is the actual wind power abandonment under the operating state generated by the actual wind power abandonment statistics module of the MATLAB function module of the present invention. DETAILED DESCRIPTION

[0047] In view of the problem that the analysis of fan operation big data in the prior art is quite time-consuming and has low accuracy if it relies solely on human calculation, the present invention designs a fan operation characteristic analysis system based on MATLAB function modules, which performs multi-faceted and in-depth analysis of fan operation big data based on MATLAB function modules to achieve a comprehensive evaluation of fan operation characteristics.

[0048] Embodiment 1: The present invention is achieved by the following technical solutions:

[0049] A fan operation characteristics analysis system based on MATLAB function modules, such as Figure 1 As shown, including:

[0050] Data integration function module, wind turbine scatter plot function module, power curve fitting function module and wind energy utilization rate calculation function module;

[0051] The data integration function module is used to integrate the wind speed-power of all days of the same wind turbine model into an array based on the software connection and data import function between MATLAB and Excel;

[0052] The fan scatter plot function module is used to generate a power scatter plot of the fan based on the data in the array integrated by the data integration function module and the regression analysis function in the fitting toolbox in the MATLAB function module;

[0053] The power curve fitting function module is used to fit the power scatter plot of the fan generated by the fan scatter plot function module with a curve fitting method based on the data fitting function and regression analysis function in the fitting toolbox in the MATLAB function module to obtain a fitted power curve;

[0054] The wind energy utilization rate calculation function module calculates the wind energy utilization rate of the wind turbine based on the conversion efficiency formula of the wind turbine converting wind energy into electrical energy in the MATLAB function module.

[0055] Preferably, the fan scatter plot function module includes a single fan scatter plot function module and a multiple fan scatter plot comparison function module;

[0056] The single fan scatter plot function module is used to generate a power scatter plot of a single fan based on the power values ​​in the array of the same fan model integrated by the data integration function module and the regression analysis function in the fitting toolbox in the MATLAB function module.

[0057] The multiple wind turbine scatter plot comparison function module is used to generate a power scatter plot of each wind turbine model in the same coordinate based on the power values ​​in the array of multiple wind turbine models integrated by the data integration function module and the regression analysis function in the fitting toolbox in the MATLAB function module, thereby forming a scatter plot comparison diagram of multiple wind turbines.

[0058] Preferably, it also includes: a data batch import function module;

[0059] The data batch import function module is used to import the data of the fans in batches by using the MATLAB programming language of the MATLAB function module.

[0060] Preferably, it also includes: a fan theoretical power curve fitting function module;

[0061] The wind turbine theoretical power curve fitting function module fits the wind speed power curves at all wind speeds using the power values ​​corresponding to individual wind speeds of the same wind turbine model based on the data fitting function in the MATLAB fitting toolbox.

[0062] Preferably, it also includes: the proportion function module and the mean and standard deviation calculation function module, which are used to calculate the proportion of the actual power value and the theoretical value of the same fan model based on the proportion calculation formula in the MATLAB function module;

[0063] The mean and standard deviation obtaining function module obtains the data mean and standard deviation based on the actual operation data of the fan imported into the MATLAB function module and the theoretical value combined with the data in the MATLAB.

[0064] Preferably, it also includes: an abnormal power data value processing function module;

[0065] The abnormal power data value processing function module selects the accuracy value of the normal distribution function based on calling the E_S.dat file in the MATLAB function module, inversely deduce the confidence interval of the function, and eliminates data values ​​outside the confidence interval.

[0066] Preferably, it also includes: a power scatter point upper and lower limit marking function module;

[0067] The power scatter upper and lower limit marking function module is used to call the ChuLiHouShuJu.dat file in MATLAB after eliminating abnormal data based on the abnormal power data value processing function module, to obtain the maximum and minimum power values ​​under each wind speed, and to mark the upper and lower limits for the wind speed-power scatter diagram.

[0068] Preferably, it also includes: a wind speed power mean and variance obtaining function module;

[0069] The wind speed power mean and variance obtaining function module eliminates abnormal data through the abnormal power data value processing function module, sets the wind speed segment threshold, calls the ChuLiHouShuJu.dat file in the MATLAB function module to obtain the wind speed and power average values ​​in each wind speed segment, and finally fits the actual wind speed-power mean scatter point to the actual operation curve of the wind turbine, and obtains the variance value of the actual power relative to the theoretical power in each wind speed segment.

[0070] Preferably, it also includes: a power monthly mean and variance obtaining function module;

[0071] The power monthly mean and variance obtaining function module is used to calculate the monthly mean of actual power by calling the DaFengSu.dat file in the MATLAB function module in combination with the synthesized data of the wind turbine, and to calculate the monthly variance of the actual power using the theoretical power value as the expected value, and to compare the degree of deviation from the theoretical power by combining the monthly mean and the monthly variance.

[0072] Preferably, it also includes: a power statistics function module;

[0073] The power statistics function module calls the YuanShiShuJu.dat file in the MATLAB function module, screens out the power values ​​that meet the set wind speed adjustment, generates a new data array, and generates the DaFengSu.dat file for storage;

[0074] Preferably, the power statistics function module includes: a comparison between actual power generation and full theoretical power generation and wind power abandonment statistics function submodule and an actual wind power abandonment statistics function module

[0075] The actual power generation and full theoretical power generation comparison function submodule is used to call the DaFengSu.dat file to count the proportion of wind turbines that have reached or have not reached the full power generation state;

[0076] The actual wind power abandonment statistics function module is used to call the DaFengSu.dat file to count the wind power abandonment based on the actual power of the same model of wind turbines at different wind speeds every day and every month.

[0077] Embodiment 2:

[0078] The patent of the present invention discloses a MATLAB functional module design for wind turbine operation characteristic analysis based on big data. The module consists of 15 functional modules, including batch data import, data integration, generation of single wind turbine scatter plot, multiple wind turbine scatter comparison plot, power curve fitting, statistical proportion of actual value and theoretical value at each wind speed, wind turbine theoretical power curve fitting, mean and standard deviation of power difference normal distribution function, abnormal power data value processing, power scatter upper and lower limit marking, wind speed power mean and variance calculation based on Bin method, wind energy utilization rate calculation, actual power generation and full theoretical power comparison and wind power abandonment statistics, power monthly mean and variance calculation, actual wind power abandonment statistics. The patent of the present invention has clear functional modules and is suitable for intelligent analysis of big data of wind turbine operation characteristics of various types.

[0079] A MATLAB function module design for fan operation characteristic analysis based on big data is characterized by: it consists of 15 basic function modules, each of which encapsulates a MATLAB data processing program, thereby realizing the corresponding fan data analysis and processing functions. When analyzing fan data, you only need to enter the necessary parameter settings in the function module to be analyzed, click Run to realize the function analysis, and give the final data results and graphical output.

[0080] A MATLAB function module design for fan operation characteristic analysis based on big data includes the following steps:

[0081] Step 1: Select MATLAB software and programming language as the development environment for functional module design, and select function as the model for functional module design;

[0082] Step 2: Set input and output parameters according to the MATLAB programming environment and the requirements of each functional module;

[0083] Step 3: According to the functions to be realized by each module, program development is realized through the relevant commands of the MATLAB programming environment;

[0084] Step 4: Based on the developed function M file, use encapsulation technology to perform standardized, generalized, extensible and callable encapsulation.

[0085] Each functional module is independent of each other, and data can be called from each other. In simple data analysis, it is only necessary to call the corresponding functional module. When multi-faceted and in-depth analysis of wind turbine big data is required, data from other modules can be called to avoid repeated operation of the same functional module.

[0086] A MATLAB function module for analyzing fan operation characteristics based on big data is as follows:

[0087] ① Design a data batch import function module, and then use the MATLAB programming language to process data in batches, saving time for manual operation; ② Design a data integration function module, and intelligently and quickly integrate the diverse and complex original data into the required data through simple function modules; ③ Design a function module for generating a scatter plot of a single fan, so as to intuitively observe the operating characteristics of the fan at different wind speeds; ④ Design a function module for scatter comparison of multiple fans, so as to observe and compare the operating advantages and disadvantages of fans of different models; ⑤ Design a function module for fitting power curves, and then compare the differences between the actual and theoretical power curves of fans at different wind speeds; ⑥ Design a function module for counting the proportion of actual values ​​to theoretical values ​​at each wind speed, by counting the proportion of actual power greater than or less than theoretical power in each interval , compare the operating characteristics of the wind turbine under different wind speeds; ⑦ Design the wind turbine theoretical power curve fitting function module, expand the power value corresponding to the individual wind speed provided by the manufacturer to the power value corresponding to all different wind speeds; ⑧ Design the mean and standard deviation of the power difference normal distribution function to obtain the function module, according to the difference between the actual power and the theoretical power of the wind turbine at each wind speed obeys the normal distribution law with a mean of 0, calculate the data mean and standard deviation, and thus obtain the normal distribution function curve expression; ⑨ Design the abnormal power data value processing function module, on the basis of ⑧, by setting a certain confidence interval to eliminate the abnormal data in the original data of the wind turbine; ⑩ Design the upper and lower limit marking function module of the power scatter point, so as to observe the data bandwidth under different wind speeds and compare which wind speed range the wind turbine has better operation effect; Design a function module for obtaining the mean and variance of wind speed power based on the Bin method. After the original data is processed by function module ⑨, set the wind speed segment threshold (taking 0.3 as an example, select the wind speed segment data of 0~0.3, 0.3~0.6...), obtain the average wind speed and power under each wind speed segment, and finally fit the actual operation curve of the wind turbine by the actual wind speed-power mean scatter points. At the same time, in order to facilitate the observation of the fluctuation of the actual power relative to the theoretical power under each wind speed segment, the variance value is obtained and marked on the graph; Design a wind energy utilization calculation function module to compare the actual operation and theoretical conversion efficiency of wind turbines in converting wind energy into electrical energy; Design a function module for comparing actual power generation with full-power theoretical power generation and statistically analyzing the amount of wind power abandoned. When the wind turbine is in full-power operation, count the number of actual power generation greater than, less than, and equal to the theoretical power at different wind speeds (not less than 12m / s) every day and every month, as well as the number of power generation not greater than 0 (used to calculate the amount of wind power abandoned in the shutdown state); Design the power monthly mean and variance calculation function module to comprehensively compare the deviation between actual power and theoretical power; Design an actual wind power abandonment statistics function module to count the wind power abandonment based on the actual power generated at different wind speeds every day and every month when the wind turbine is in normal operation.

[0088] In summary, the beneficial effects of the present invention are:

[0089] ① The functional modules of the present invention for wind turbine big data analysis are relatively comprehensive, which can better realize multi-faceted and in-depth analysis of wind turbine data, and then make a reasonable analysis of the wind turbine operating characteristics.

[0090] ② The functional modules of the present invention are clear, simple and easy to operate. The functions of each module are highly practical and can save manpower and data processing time.

[0091] Example 3

[0092] The data batch import function module of the present invention can realize the batch import function of fan data, and utilizes the software connection and data import function between MATLAB and Excel. It only needs to input the excel file name, worksheet name and selected data range in the function module, and click "Run" to import all the original data of the fan excel exported from the SCADA system into MATLAB. As shown in Table 1 of the original data of the fan, for example, input xlsread('file name.xlsx', 'worksheet name', 'B2:G28'), click "Run" to import all the data seen in Table 1 into MATLAB software, and then use MATLAB programming language to process the data in batches.

[0093]

[0094] Table 1

[0095] The data integration function module of the present invention can integrate the wind speed-power of all days of the same wind turbine model in the excel original data into one array. When analyzing the big data of the wind turbine, what needs to be analyzed is the data of one month or even one year, but the data exported from the SCADA system is multiple excel data tables of different days, so it is necessary to integrate the data of different days into one array for later data processing. This function module further integrates the data imported by the 'data batch import function module', connects the first positions of multiple arrays of the imported data by means of software programming, integrates them into one array, and generates YuanShiShuJu.dat file, thereby realizing the output, storage and call of the array. In this program, two variables, data length and number of days, are set, and the program is encapsulated in this function module. It only needs to input the two variable values ​​of length and number of days in the interface of this function module, and click to run to complete the data integration of multiple days of data of a single wind turbine, and multiple wind turbines can also be integrated at the same time. For example, 11 days of data from four wind turbines are selected, and the length of each day's data is 1440. After being processed and integrated by the MATLAB program, each wind turbine array is stored in the form of two columns of wind speed and wind power for 11 days as shown in Table 2.

[0096]

[0097] Table 2

[0098] The single fan scatter plot function module of the present invention can import the YuanShiShuJu.dat file integrated by the 'data integration function module' into this function submodule to generate a fan scatter plot, so as to visually observe the operating status of the fan at different wind speeds. The fan data integrated by the 'data integration function module' is an array of wind speed and power. The drawing program is encapsulated in the 'single fan scatter plot function module' to output Figure 2 The scatter plot shown.

[0099] The multi-fan scatter plot comparison function submodule of the present invention can import the YuanShiShuJu.dat file integrated by the 'data integration function module' into this function module to generate a multi-fan scatter plot, such as Figure 3 As shown, it is possible to observe and compare the operating advantages and disadvantages of different types of fans.

[0100] Figure 4 The embodiment shown shows the actual wind speed power curve fitting diagram of the power curve fitting function module. The principle is: the expression of wind turbine power is p = Aρυ 3 / 2, A is the blade swept area, ρ is the air density, both are constants, so the formula can be changed to p = a·υ 3 , you only need to calculate the a value to get the actual wind speed power curve. The encapsulated program function in this function module is: call the YuanShiShuJu.dat file, use the integrated wind speed and power as variables and dependent variables respectively, and use the program optimization method to obtain the a value that is most appropriate to the scatter plot, and bring this value into the function, and output the wind speed power curve and the wind speed power scatter plot in the same figure.

[0101] Figure 5 The illustrated embodiment shows a graph of the actual power ratio in different wind speed intervals, which is generated by a function module that counts the ratio of actual power value to theoretical value at each wind speed. In order to statistically compare the operating characteristics of the wind turbine at different wind speeds, the wind turbine is divided into three wind speed intervals of low wind speed, medium wind speed and high wind speed according to the size of the wind speed in actual operation, and the YuanShiShuJu.dat file, i.e. the integrated wind speed power array, is called to count the proportion of actual power greater than or less than theoretical power in each interval.

[0102] Figure 6 The embodiment shown shows the function of the wind turbine theoretical power curve fitting module. In the wind turbine theoretical wind speed-power curve, the wind turbine manufacturer only provides the power values ​​corresponding to the individual wind speeds shown in Table 3:

[0103]

[0104] Table 3

[0105] However, in the actual analysis of wind turbine operation status, it is necessary to compare the theoretical power generation of wind turbines under different wind speeds, so it is necessary to fit the wind speed power curve under all wind speeds through the data provided by the manufacturer. The MATLAB fitting toolbox (cftool) has a relatively complete data fitting function and regression analysis function. You only need to select the wind speed as vector x and the power as vector y in this toolbox, select the required fitting function or directly enter the function expression of the coefficient to be determined, and you can achieve scatter curve fitting, and generate the XiShu.dat file as an array of the independent variable coefficients after fitting, so as to facilitate the call of other functional modules. After comparative analysis, it was found that the power curve fitted by selecting the Gaussian function has a high degree of fit with the curve provided by the manufacturer.

[0106] Figure 7 The embodiment shown shows the function of the module for obtaining the mean and standard deviation of the normal distribution function of the power difference. When studying the operating status of the fan, the most direct method is to compare the deviation between the actual power and the theoretical power. The closer the deviation is to 0, the better the operation effect of the fan. According to this operating characteristic of the fan, it can be known that the difference between the actual power and the theoretical power of the fan at each wind speed obeys the normal distribution law with a mean of 0. By using the software connection and data import function between MATLAB and Excel, the actual operation data of the fan and the manufacturer's theoretical value are imported into MATLAB, and the program processing encapsulated in this module can be used to obtain Figure 7 The normal distribution curve is shown in Figure 1. Based on this rule, the mean and standard deviation of the data can be obtained to obtain the normal distribution function curve expression, and the mean and standard deviation values ​​can be used as arrays to generate the E_S.dat file for the convenience of calling other functional modules.

[0107] Figure 8 The illustrated embodiment shows the function of the abnormal data processing module. There are some abnormal data in the actual operating wind speed and power data of the fan derived from the SCADA system. These data affect the accuracy of the actual operating curve fitting of the fan. Therefore, abnormal data processing is required. Based on the fact that the difference between the actual power and the theoretical power at each wind speed of the fan obeys the normal distribution law with a mean of 0, abnormal data can be eliminated by setting a certain confidence interval. The encapsulation program function in this functional module is: call the E_S.dat file, select the appropriate normal distribution function accuracy value, invert the confidence interval of the function, eliminate the wind speed and power data points outside this interval, and retain the data within this interval, thereby realizing the processing of abnormal data. The processed array is generated into the ChuLiHouShuJu.dat file for storage so that other modules can call it for further analysis.

[0108] Fig. 9 The illustrated embodiment shows the function of the power scatter upper and lower limit marking module. This module calls the ChuLiHouShuJu.dat file (the array after removing the abnormal data of the wind turbine), obtains the maximum and minimum power values ​​at each wind speed, and then marks the upper and lower limits of the wind speed power scatter diagram, observes the data bandwidth at different wind speeds, and compares in which wind speed range the operation effect of this model of wind turbine is better.

[0109] Fig.10 , Fig.11 The illustrated embodiment shows the module function of obtaining the mean and variance of wind speed power based on the Bin method. To obtain the wind speed-power curve of the actual operation of the wind turbine more accurately, the ChuLiHouShuJu.dat file is called, the wind speed segment threshold is set (taking 0.3 as an example, the wind speed segment data of 0-0.3, 0.3-0.6... are selected), the wind speed and power average values ​​under each wind speed segment are obtained, and finally the actual wind speed-power mean scatter points are used to fit the actual operation curve of the wind turbine. In order to facilitate the observation of the fluctuation of the actual power relative to the theoretical power under each wind speed segment, the variance value is obtained and marked on the graph.

[0110] Fig.12 The embodiment shown shows the wind energy utilization module function of the wind turbine. The wind energy utilization is a key parameter for evaluating the operating characteristics of the wind turbine. It refers to the extent to which the wind turbine absorbs energy from natural wind energy, and represents the conversion efficiency of the wind turbine in converting wind energy into electrical energy. Its expression is: P =2P / (ρ·υ 3 A), A is the blade swept area, ρ is the air density. In the wind turbine wind energy utilization module, the fan blade swept area and air density are set as input variables. Only by giving the variable values, the theoretical and actual wind energy utilization can be obtained.

[0111] The illustrated embodiment shows the comparison between the actual power generation of the wind turbine and the full theoretical power generation and the function of the wind power abandonment statistics module. When the wind turbine is in strong wind (generally not less than 12m / s), the unit reaches the full power state, that is, when the wind speed of a 3MW wind turbine is not less than 12m / s, the theoretical power is 3000KW. In view of this characteristic, the full power operation state of the wind turbine is analyzed. Call the YuanShiShuJu.dat file, filter the power value under the condition of wind speed not less than 12m / s, generate a new data array, and generate the DaFengSu.dat file for storage, and count the number of actual power greater than, less than and equal to 3000 and the number of power not greater than 0 (used to calculate the wind power abandoned in the shutdown state) at different wind speeds (not less than 12m / s) every day and every month. Table 4 calls the DaFengSu.dat file to count the number of units at different wind speeds each month. Table 5 calls the DaFengSu.dat file to count the proportion of the number greater than, less than and equal to 3000 each month to the total number. Table 6 calls the DaFengSu.dat file to count the number not greater than 0 each month and the proportion of this number to the total power of shutdown and startup in that month.

[0112]

[0113] Table 4

[0114]

[0115] Table 5

[0116]

[0117] Table 6

[0118] Fig.13 The embodiment shown shows the function of the power monthly mean and variance calculation module. By calling the DaFengSu.dat file, on the one hand, the monthly mean of the actual power is calculated; on the other hand, the monthly variance of the actual power is calculated with the theoretical power value as the expected value. The monthly mean and variance data are combined to compare the degree of deviation from the theoretical power.

[0119] Fig.14 The illustrated embodiment shows the actual wind power abandonment statistics module function under the wind turbine operation state, which is used to count the wind power abandonment based on the actual power generation under different wind speeds (not less than 12m / s) every day and every month by calling the DaFengSu.dat file. Its significance lies in that the amount of wind power abandonment is an important parameter to measure the operation quality of the wind turbine. When the wind turbine is fully powered, the less wind is abandoned, the more revenue the station will get.

[0120] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0121] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0122] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0123] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0124] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are included in the scope of the claims of the present invention to be approved.

Claims

1. A fan operation characteristics analysis system based on MATLAB function modules, It is characterized in that include: Data integration function module, wind turbine scatter plot function module, power curve fitting function module and wind energy utilization rate calculation function module; The data integration function module is used to integrate the wind speed-power of all days of the same wind turbine model into an array based on the software connection and data import function between MATLAB and Excel; The fan scatter plot function module is used to generate a power scatter plot of the fan based on the data in the array integrated by the data integration function module and the regression analysis function in the fitting toolbox in the MATLAB function module; The power curve fitting function module is used to fit the power scatter plot of the fan generated by the fan scatter plot function module with a curve fitting method based on the data fitting function and regression analysis function in the fitting toolbox in the MATLAB function module to obtain a fitted power curve; The wind energy utilization rate calculation function module calculates the wind energy utilization rate of the wind turbine based on the conversion efficiency formula of the wind turbine converting wind energy into electrical energy in the MATLAB function module; The fan scatter plot function module includes a single fan scatter plot function module and a multiple fan scatter plot comparison function module; The single wind turbine scatter plot function module is used to generate a power scatter plot of a single wind turbine based on the power values ​​in the array of the same wind turbine model integrated by the data integration function module and the regression analysis function in the fitting toolbox in the MATLAB function module; The multiple wind turbine scatter plot comparison function module is used to generate a power scatter plot of each wind turbine model in the same coordinate based on the power values ​​in the array of multiple wind turbine models integrated by the data integration function module, based on the regression analysis function in the fitting toolbox in the MATLAB function module, to form a scatter plot comparison diagram of multiple wind turbines; It also includes: a proportion function module, which is used to calculate the proportion of the actual power value and the theoretical value of the same fan model based on the proportion calculation formula in the MATLAB function module; The mean and standard deviation calculation function module calculates the data mean and standard deviation based on the actual operation data of the fan imported into the MATLAB function module and the theoretical value combined with the data in the MATLAB; It also includes: an abnormal power data value processing function module; The abnormal power data value processing function module selects the accuracy value of the normal distribution function based on calling the E_S.dat file in the MATLAB function module, inverts the confidence interval of the function, and eliminates the data values ​​outside the confidence interval; It also includes: power scatter point upper and lower limit marking function module; The power scatter upper and lower limit marking function module is used to call the ChuLiHouShuJu.dat file in MATLAB after eliminating abnormal data based on the abnormal power data value processing function module, and obtain the maximum and minimum power values ​​under each wind speed to mark the upper and lower limits of the wind speed-power scatter diagram; It also includes: a wind speed power mean and variance calculation function module; The wind speed power mean and variance obtaining function module eliminates abnormal data through the abnormal power data value processing function module, sets the wind speed segment threshold, calls the ChuLiHouShuJu.dat file in the MATLAB function module to obtain the wind speed and power average values ​​in each wind speed segment, and finally fits the actual wind speed-power mean scatter point to the actual operation curve of the wind turbine, and obtains the variance value of the actual power relative to the theoretical power in each wind speed segment.

2. The fan operation characteristic analysis system according to claim 1, It is characterized in that Also includes: Data batch import function module; The data batch import function module is used to import the data of the fans in batches by using the MATLAB programming language of the MATLAB function module.

3. The fan operation characteristic analysis system according to claim 1, It is characterized in that It also includes: fan theoretical power curve fitting function module; The wind turbine theoretical power curve fitting function module fits the wind speed power curves at all wind speeds using the power values ​​corresponding to individual wind speeds of the same wind turbine model based on the data fitting function in the MATLAB fitting toolbox.

4. The fan operation characteristic analysis system according to claim 1, It is characterized in that Also includes: Functional module for obtaining monthly mean and variance of power; The power monthly mean and variance obtaining function module is used to calculate the monthly mean of actual power by calling the DaFengSu.dat file in the MATLAB function module in combination with the synthesized data of the wind turbine, and to calculate the monthly variance of the actual power using the theoretical power value as the expected value, and to compare the degree of deviation from the theoretical power by combining the monthly mean and the monthly variance.

5. The fan operation characteristic analysis system according to claim 1, It is characterized in that Also includes: Power statistics function module; The electricity statistics function module calls the YuanShiShuJu.dat file in the MATLAB function module, screens out the power values ​​that meet the set wind speed adjustment, generates a new data array, and generates the DaFengSu.dat file for storage.

6. The fan operation characteristic analysis system according to claim 5, It is characterized in that The electricity quantity statistics function module includes: a comparison between actual power generation and full theoretical power generation and wind power abandonment statistics function submodule and an actual wind power abandonment statistics function module The actual power generation and full theoretical power generation comparison function submodule is used to call the DaFengSu.dat file to count the proportion of wind turbines that have reached or have not reached the full power generation state; The actual wind power abandonment statistics function module is used to call the DaFengSu.dat file to count the wind power abandonment based on the actual power of the same model of wind turbines at different wind speeds every day and every month.

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