CN112065668A - Wind turbine generator state abnormity assessment method and system - Google Patents
Wind turbine generator state abnormity assessment method and system Download PDFInfo
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Abstract
The invention relates to a wind turbine state abnormity evaluation method, which comprises the following steps that 1) when each wind turbine of a wind power plant normally operates, the wind speed and each variable of each wind turbine of the wind power plant under different wind speeds are collected; 2) carrying out normalization processing on the wind speed and each variable to obtain a normalized wind speed average value and a normalized variable average value, and generating a bivariate curve by the normalized wind speed average value and each normalized variable average value; 3) respectively summarizing the average values of all variables of other wind turbines except the test turbine to obtain the total variation curve of all the variables, respectively comparing the bivariate curves of the test turbine with the total variation curve of the variables to obtain the difference value, and judging whether the wind turbines are qualified. According to the wind power plant wind turbine calibration method, through mutual calibration of the turbines in the wind power plant, the power generation efficiency of the wind power plant can be improved, the operation risk of a fault turbine is reduced, the operation is convenient, the universality is good, and misjudgment caused by human can be avoided.
Description
Technical Field
The invention mainly relates to the technical field of wind power generation, in particular to a wind turbine generator state abnormity evaluation method and system based on a bivariate mutual difference value check method.
Background
Along with the continuous increase of the single machine capacity of the wind turbine generator, the wind energy utilization rate is obviously improved, and compared with the power generation of other clean energy sources, the wind power cost advantage is obvious. However, with the continuous increase of installed capacity and scale of wind power, the intermittence, instability and uncertainty of wind power itself bring many challenges to the safe and stable operation of the whole power grid after the wind power grid-connected operation, the wind power grid-connected time with fluctuation characteristics influences the tide distribution of the power grid, the wind power with high permeability is difficult to be absorbed in the power grid, and the active power supply and demand balance of the whole power grid is threatened seriously. The wind turbine generator set must be subjected to prediction analysis by using accurate and effective corresponding data of the wind turbine generator set, and the safety and stability of the construction and operation of key equipment and a power grid are ensured. During actual operation. Due to the influence of factors such as meter errors, sensor faults, wind abandoning and electricity limiting, environmental climate and the like, abnormal data are difficult to avoid, and if the abnormal data are directly used as the original input data of a wind power prediction model to calculate and analyze basic data of the influence of wind power on a system, the prediction accuracy and the reliability of an analysis result can be influenced. Due to the characteristics of intermittency, randomness, volatility and the like of wind power, the difficulty of planning and scheduling the power grid is increased. The method has important significance for accurately and effectively analyzing the related operation data of the wind power plant, evaluating the operation condition of the wind power plant, predicting the wind power speed and power and making a dispatching plan by a power dispatching department.
Disclosure of Invention
The purpose of the invention is as follows:
the invention provides a wind turbine state abnormity evaluation method and system based on a bivariate mutual difference value check method, and aims to solve the problems that abnormal data are directly used as original input data of a wind power prediction model to calculate and analyze basic data of wind power influence on a system, so that the accuracy of prediction and the reliability of an analysis result are influenced.
The technical scheme is as follows:
a wind turbine state abnormity assessment method comprises the following steps:
1) collecting the wind speed and each variable of each wind turbine of the wind power plant under different wind speeds when each wind turbine of the wind power plant normally operates;
2) carrying out normalization processing on the wind speed and each variable in the step 1) to obtain a normalized wind speed average value and a normalized variable average value, and generating a bivariate curve by the normalized wind speed average value and each normalized variable average value;
3) respectively summarizing the average values of all variables of other wind turbines except the test turbine to obtain the total variation curve of all the variables, respectively comparing the bivariate curves of the test turbine with the total variation curve of the variables to obtain the difference value, and judging whether the wind turbines are qualified.
The variable of the wind turbine generator comprises a variable pitch angle of the wind turbine generator, the rotating speed of a generator and the power of the generator.
The data acquisition process of a plurality of variables under different wind speeds in the step 1) comprises the following steps: different wind speeds are continuously taken by multiples of 0.5m/s, the data acquisition time is 30-60min under each wind speed, the acquisition frequency is 1Hz-5Hz, and the total effective acquisition time of all the wind speeds is 180-240 h.
In step 2), the normalized average wind speed is:
in the formula, ViRepresents the normalized wind speed mean value, V, at the ith wind speedn,i,jAnd the j group of collected array wind speeds under the ith wind speed is shown.
In step 2), the normalized variable average value comprises a normalized power average value, a normalized pitch angle average value and a normalized rotating speed average value, and the normalized power average value is as follows:
the normalized pitch angle average is:
the normalized average rotational speed is:
in the formula, PiRepresents the normalized power mean, P, at the ith wind speedn,i,jRepresenting the average power in the j group of acquisition arrays at the ith wind speed; beta is aiRepresents the normalized mean value of the pitch angle, beta, at the ith wind speedn,i,jRepresenting the average pitch angle in the j group of acquisition arrays at the ith wind speed; n isiRepresenting the normalized mean generator speed, n, at the ith wind speedn,i,jRepresents the average generator speed N in the j group of acquisition arrays under the ith wind speediIndicating the number of data groups contained in the collected data at the ith wind speed.
In the step 3), the step (c),
the calculation formula of the mutual difference value is as follows:
in the formula, the difference value errpowerAnd (3) representing a calculated comprehensive numerical error value, V representing a wind speed, f (V) representing probability density, cutin representing a cut-in wind speed, cutout representing a cut-out wind speed, P single measurement (V) representing a single variable curve, and P other measurement (V) of the whole field representing total variable curves of other wind generation sets of the wind power plant.
A wind turbine generator system calibration system comprises a variable acquisition module, a variable curve generation module and a variable evaluation module; the variable acquisition module inputs acquired data into the variable curve generation module to generate a variable curve, and the variable curve is input into the variable evaluation module to be evaluated and judged.
The variable acquisition module consists of a variable pitch angle acquisition unit of the wind turbine generator, a generator rotating speed acquisition unit and a generator power acquisition unit; the variable pitch angle acquisition unit is used for acquiring variable pitch angle data; the generator rotating speed acquisition unit is used for acquiring generator rotating speed data; the generator power acquisition unit is used for acquiring generator power data.
The variable curve generating module consists of a wind speed-power curve generating module, a wind speed-rotating speed curve generating module and a wind speed-variable pitch angle curve generating module; the wind speed-power curve generation module generates a wind speed-power curve; the wind speed-rotating speed curve generating module generates a wind speed-rotating speed curve; and the wind speed-variable pitch angle curve generating module generates a wind speed-variable pitch angle curve.
The variable evaluation module consists of a wind speed-power curve evaluation module, a wind speed-rotating speed curve evaluation module and a wind speed-variable pitch angle curve evaluation module; the wind speed-power curve evaluation module is used for carrying out comparative analysis on a wind speed-power curve; the wind speed-rotating speed curve evaluation module is used for carrying out comparative analysis on a wind speed-rotating speed curve; and the wind speed-variable pitch angle curve evaluation module is used for carrying out comparative analysis on the wind speed-variable pitch angle curve.
Compared with the prior art, the invention has the advantages that:
according to the wind power plant wind turbine calibration method, through mutual calibration of the turbines in the wind power plant, the power generation efficiency of the wind power plant can be improved, the operation risk of a fault turbine is reduced, the operation is convenient, the universality is good, and misjudgment caused by human can be avoided; and the method does not need to add extra hardware and can be suitable for different types of wind power plants. The wind power plant wind turbine generator verification system has the advantages of the method, and is simple in structure and easy to implement.
The invention provides a wind power plant wind turbine calibration method with convenient test and high efficiency, and correspondingly provides a wind power plant wind turbine calibration system which is simple in structure, simple and convenient to operate and easy to realize.
Drawings
FIG. 1 is a flow chart of a method of the present invention;
FIG. 2 is a graph of power at different wind speeds in the method of the present invention;
FIG. 3 is a graph of pitch angle at different wind speeds in the method of the present invention;
FIG. 4 is a graph of the generator speed at different wind speeds in the method of the present invention;
FIG. 5 is a block diagram of the verification system of the present invention;
FIG. 6 is a graph comparing power curves of two units with a total power curve in the method of the present invention;
FIG. 7 is a graph comparing two pitch angles with the total pitch angle in the method of the present invention;
FIG. 8 is a graph comparing the rotational speed of two generators of a cluster with the total rotational speed of the generators in the method of the present invention.
Detailed Description
The invention is further described below with reference to the figures and the specific embodiments of the description.
As shown in fig. 1, a wind turbine verification method for a wind farm includes the following steps:
1) the method comprises the steps of respectively carrying out data acquisition on a main control system of each machine position wind turbine generator of a wind power plant at different wind speeds, acquiring designated time at each wind speed to obtain multiple sets of acquired data, wherein each set of acquired data comprises wind data, generator data, a pitch angle, generating capacity and engine room vibration acceleration data, the wind data comprises wind speed and wind direction, and the generator data comprises power, rotating speed and torque of a generator.
2) Normalizing the plurality of variables collected in the step 1), namely averaging the collected variables to obtain a normalized variable average value of the plurality of variables at different wind speeds, and generating a plurality of bivariate curves at different wind speeds. Preprocessing wind speeds in all wind parameters to obtain an average value, a standard deviation, a maximum value and a minimum value of the wind speeds and obtain a normalized wind speed average value, wherein variables of the wind turbine generator comprise a variable pitch angle, a generator rotating speed and a generator power of the wind turbine generator, and double variable curves of the variables, namely a wind speed-power curve, a wind speed-variable pitch angle curve and a wind speed-rotating speed curve, are drawn according to multiple sets of collected data;
as shown in fig. 1, the wind data, the generator data, and the pitch angle data after the preprocessing are processed by the following method: the method is characterized in that when data acquisition is carried out on a main control system of a tested wind turbine generator under different wind speeds, specifically, the data acquisition time is 30-60min under each wind speed, the acquisition frequency is 1Hz-5Hz, and the total effective acquisition time of all the wind speeds is 180-240 h.
Therefore, the normalized wind speed average value, the normalized power average value, the normalized pitch angle average value and the normalized rotating speed average value under each wind speed are calculated according to the following formulas:
the normalized wind speed average is:
the normalized power average is:
the normalized pitch angle average is:
the normalized average rotational speed is:
in the formula, ViRepresents the normalized wind speed mean value, V, at the ith wind speedn,i,jRepresenting the j group of collected array wind speeds under the ith wind speed; represents PiNormalized power average, P, at the ith wind speedn,i,jRepresents the average power, beta, in the j-th collection array at the i-th wind speediRepresents the normalized mean value of the pitch angle, beta, at the ith wind speedn,i,jRepresenting the average pitch angle in the j group of acquisition arrays at the ith wind speed; n isiRepresenting the normalized mean generator speed, n, at the ith wind speedn,i,jRepresents the average generator speed N in the j group of acquisition arrays under the ith wind speediIndicating the number of data groups contained in the collected data at the ith wind speed.
Generating a wind speed-power curve (shown in FIG. 2) of the wind turbine generator according to the normalized wind speed average value and the normalized power average value at each wind speed; and generating a wind speed-pitch angle curve of the wind turbine generator set according to the normalized wind speed average value and the normalized pitch angle at each wind speed (as shown in fig. 3), and generating a wind speed-rotation speed curve of the wind turbine generator set according to the normalized wind speed average value and the normalized generator rotation speed at each wind speed (as shown in fig. 4).
3) And carrying out data acquisition on all variables of other wind turbines except the test turbine in the wind power plant, and respectively summarizing and averaging to obtain a total variation curve of each variable. And comparing each variable curve of the test unit (single unit) with the total variable curve of the variable respectively to obtain a mutual difference value, and judging whether the wind turbine generator is qualified.
Acquiring data of all variables of other wind turbines except the test turbine in the wind power plant, respectively summarizing and averaging to obtain a total variable curve of all variables, wherein the generation process of the total variable curve is the same as that of a single variable curve, namely, obtaining the normalized average values of the wind speeds and other variables of other wind turbines except the test turbine, and generating a wind speed-power total variable curve according to the normalized wind speed average value and the normalized power average value under each wind speed; and generating a wind speed-variable pitch angle total change curve according to the normalized wind speed average value and the normalized variable pitch angle under each wind speed, and generating a wind speed-rotating speed total change curve according to the normalized wind speed average value and the normalized generator rotating speed under each wind speed.
And comparing each variable curve of a single wind turbine generator with the total variable curve of the variable to obtain a mutual difference value, judging that the wind turbine generator is unqualified when the mutual difference value of the two variable curves of the single wind turbine generator is greater than a preset value, and judging that the wind turbine generator is qualified when the mutual difference value of the variable curves is within the preset value.
And carrying out data acquisition on all variables of other wind turbines except the test turbine in the wind power plant, and respectively summarizing and averaging to obtain a total variation curve of each variable. And comparing each variable curve of the single unit with the total variable curve of the variable. That is, when the variable is the generator power, comparing the wind speed-power curve of the testing unit with the total variation curve of the wind speed-power curve, wherein the total variation curve of the wind speed-power curve is obtained by summarizing all power data of other wind generation units (except the testing unit) and then averaging the summarized data to obtain the total variation curve of the wind speed-power curve; when the variable is the rotating speed of the generator, comparing a wind speed-rotating speed curve of the testing unit with a wind speed-rotating speed total change curve, wherein the wind speed-rotating speed total change curve is obtained by summarizing data of all rotating speeds of other wind generation units (except the testing unit) and then averaging the data to obtain a wind speed-rotating speed total change curve for comparison; when the variable is the pitch angle, comparing a wind speed-pitch angle curve of the test unit with a wind speed-pitch angle total change curve, wherein the wind speed-pitch angle total change curve is obtained by summarizing data of all pitch angles of other wind generation units (except the test unit) and then averaging the data to obtain a wind speed-pitch angle total change curve for comparison.
If the mutual difference value err of the above parameterspowerTwo or more than two are greater than the specified threshold value, and the tested megawatt wind generating set is judged not to be testedAnd if not, judging that the tested megawatt wind generating set passes the load and performance verification. If only one item is larger than the specified threshold value, the data acquisition is possible to have errors, and the unit operates in a normal working state; however, if both terms are larger than the specified threshold value, it can be said that the unit has a fault, so the difference value errpowerAnd judging that the unit has a fault if two or more than two items are larger than the specified threshold value, and failing to check. )
The calculation formula of the mutual difference value is as follows:
wherein the difference value errpowerAnd (3) representing a calculated comprehensive numerical error value, V representing a wind speed, f (V) representing probability density, cutin representing a cut-in wind speed, cutout representing a cut-out wind speed, P single measurement (V) representing a single variable curve, and P other measurement (V) of the whole field representing total variable curves of other wind generation sets of the wind power plant. The power curve, the pitch angle curve and the generator rotating speed curve are corresponded.
As shown in fig. 5, a wind turbine calibration system of a wind farm includes a variable acquisition module, a variable curve generation module, and a variable evaluation module; the variable acquisition module inputs acquired data into the variable curve generation module to generate a variable curve, and the variable curve is input into the variable evaluation module to be evaluated and judged.
The variable acquisition module comprises a variable pitch angle acquisition unit, a generator rotating speed acquisition unit and a generator power acquisition unit of the wind turbine; the variable pitch angle acquisition unit is used for acquiring variable pitch angle data and inputting the variable pitch angle data into the wind speed-variable pitch angle curve generation module; the generator rotating speed acquisition unit is used for acquiring generator rotating speed data and inputting the generator rotating speed data into the wind speed-rotating speed curve generation module; the generator power acquisition unit is used for acquiring generator power data and inputting the generator power data into the wind speed-power curve generation module.
The variable curve generating module comprises a wind speed-power curve generating module, a wind speed-rotating speed curve generating module and a wind speed-variable pitch angle curve generating module; the wind speed-power curve generation module generates a wind speed-power curve and a wind speed-power total change curve, and the wind speed-power curve and the wind speed-power total change curve are input into the wind speed-power curve evaluation module; the wind speed-rotating speed curve generation module generates a wind speed-rotating speed curve and a wind speed-rotating speed total change curve, and the wind speed-rotating speed curve and the wind speed-rotating speed total change curve are input into the wind speed-rotating speed curve evaluation module; the wind speed-variable pitch angle curve generation module generates a wind speed-variable pitch angle curve and a wind speed-variable pitch angle total change curve, and the wind speed-variable pitch angle curve and the wind speed-variable pitch angle total change curve are input into the wind speed-variable pitch angle curve evaluation module.
The variable evaluation module comprises a wind speed-power curve evaluation module, a wind speed-rotating speed curve evaluation module and a wind speed-variable pitch angle curve evaluation module; the wind speed-power curve evaluation module is used for carrying out comparative analysis on a wind speed-power curve; the wind speed-rotating speed curve evaluation module is used for carrying out comparative analysis on a wind speed-rotating speed curve; and the wind speed-variable pitch angle curve evaluation module is used for carrying out comparative analysis on the wind speed-variable pitch angle curve.
When each wind turbine of the wind power plant normally operates, the variable acquisition module acquires data of each variable of each wind turbine at different wind speeds, the variable curve generation module carries out standardization processing on the data of each variable of each wind turbine to obtain an average value of each variable at different wind speeds, a bivariate curve of each variable at different wind speeds is generated, the variable evaluation module compares each variable curve of a single wind turbine with each total variable curve corresponding to other wind turbines in the wind power plant to obtain a mutual difference value, when the mutual difference value of two or more bivariate curves is larger than a preset value, the wind turbine is judged to be unqualified, and otherwise, the wind turbine is qualified.
In this embodiment, the variable acquisition module includes a pitch angle acquisition unit of the wind turbine, a generator speed acquisition unit, and a generator power acquisition unit. The variable evaluation module comprises a unit performance evaluation module and a variable pitch system evaluation module, wherein the unit performance evaluation module is used for carrying out comparative analysis on the generator rotating speed and the generator power of the wind turbine; the pitch system evaluation module is used for carrying out comparative analysis on the pitch angle. When each wind turbine of a large wind power plant operates, wind speed and wind direction data are collected through cabin anemometers of all the wind turbines; the main control system collects the power, the rotating speed, the torque and the variable pitch angle of the generator of the unit. Then, inputting relevant variable information (mainly comprising wind speed and wind direction, generator power, generator rotating speed and variable pitch angle) into a wind turbine mutual-checking evaluation system by each wind turbine, extracting generator power and rotating speed signals, and identifying the performance of each wind turbine by combining the wind speed signals measured by each wind turbine; and extracting a pitch angle signal, and identifying a pitch system by combining the wind speed signals measured by the wind generation sets.
The testing process comprises the following steps: and carrying out data acquisition on all variables of other wind turbines except the test turbine in the wind power plant, and respectively summarizing and averaging to obtain a total variation curve of each variable. Each bivariate curve of a single unit is compared with the total variation curve of the variable, the performance evaluation compares and analyzes the numerical value and the rotating speed value of the dynamic power curve mathematical model of each unit, and the comprehensive numerical error is required to be smaller than the mutual difference value. The pitch system evaluation adopts pitch angle curves to carry out comparison analysis, and the numerical error is required to be integrated and is smaller than the mutual difference value. If one is judged not to meet the requirement, the next parameter (variable) is compared, and an alarm is given. If there are two errors, the test fails. Of course, in other embodiments, the parameters may be transformed as needed, i.e., the parameter acquisition requiring significant attention may be contrasted for analysis.
Example 1:
in this embodiment, two wind turbine generators are selected from thirty-three wind turbine generators, the system and the verification method are adopted to perform abnormal state evaluation on the wind turbine generators, the wind speeds are different and continuously valued by multiples of 0.5m/s, the data acquisition time at each wind speed is 30-60min, the data acquisition time in this embodiment is 45min, the data acquisition frequency is 1Hz-5Hz, the data acquisition time in this embodiment is 3Hz, and the total effective acquisition time of all the wind speeds is 180-240h, and the data acquisition time in this embodiment is 200 h. The preset value was set to 5%. As can be seen from fig. 6, the power curve mutual difference value of the unit 1 is 2% and is less than 5% of the preset value, and the power curve mutual difference value of the unit 2 is 8% and is greater than 5% of the preset value, and the system gives an alarm; as can be seen from fig. 7, the pitch angle curve mutual difference value of the No. 1 unit is 1% and is less than 5% of the preset value, and the pitch angle curve mutual difference value of the No. 2 unit is 4% and is less than 5% of the preset value; as can be seen from fig. 8, the mutual difference value of the speed curves of the generators of the unit 1 is 0.8% and is less than the preset value of 5%, and the mutual difference value of the speed curves of the generators of the unit 2 is 6% and is greater than the preset value of 5%; the power curve, the pitch angle curve and the generator rotating speed curve of the No. 1 unit are all smaller than a preset value by 5 percent, and the unit is qualified; and the difference value between the power curve of the No. 2 unit and the rotating speed curve of the generator is greater than the preset value, and the unit is unqualified. Therefore, the system and the method can accurately identify the abnormal state of the wind turbine generator set through mutual verification of the generator sets of the wind power plant, and can improve the power generation efficiency of the wind power plant.
Any multiple units can be selected, different wind speeds are continuously taken at multiples of 0.5m/s, the data acquisition time at each wind speed can be 30min, 40min, 50min, 55min, 60min and the like, the data acquisition frequency can be 1Hz, 2Hz, 4Hz, 5Hz and the like, the total effective acquisition time of all the wind speeds can be 180h, 190h, 210h, 220h, 230h, 240h and the like, and the preset value is set to be 5%. By adopting the conditions of the acquisition process and combining the system and the method, the abnormal state of the wind turbine generator can be accurately identified through mutual verification of the wind turbine generator, and the power generation efficiency of the wind turbine generator can be improved.
Claims (10)
1. A wind turbine state abnormity assessment method is characterized in that:
the method comprises the following steps:
1) collecting the wind speed and each variable of each wind turbine of the wind power plant under different wind speeds when each wind turbine of the wind power plant normally operates;
2) carrying out normalization processing on the wind speed and each variable in the step 1) to obtain a normalized wind speed average value and a normalized variable average value, and generating a bivariate curve by the normalized wind speed average value and each normalized variable average value;
3) respectively summarizing the average values of all variables of other wind turbines except the test turbine to obtain the total variation curve of all the variables, respectively comparing the bivariate curves of the test turbine with the total variation curve of the variables to obtain the difference value, and judging whether the wind turbines are qualified.
2. The wind turbine state anomaly evaluation method according to claim 1, characterized in that: the variable of the wind turbine generator comprises a variable pitch angle of the wind turbine generator, the rotating speed of a generator and the power of the generator.
3. The wind turbine state anomaly evaluation method according to claim 1, characterized in that: the data acquisition process of a plurality of variables under different wind speeds in the step 1) comprises the following steps: different wind speeds are continuously taken by multiples of 0.5m/s, the data acquisition time is 30-60min under each wind speed, the acquisition frequency is 1Hz-5Hz, and the total effective acquisition time of all the wind speeds is 180-240 h.
4. The wind turbine state anomaly evaluation method according to claim 1, characterized in that: in step 2), the normalized average wind speed is:
in the formula, ViRepresents the normalized wind speed mean value, V, at the ith wind speedn,i,jAnd the j group of collected array wind speeds under the ith wind speed is shown.
5. The wind turbine state anomaly evaluation method according to claim 1, characterized in that: in step 2), the normalized variable average value comprises a normalized power average value, a normalized pitch angle average value and a normalized rotating speed average value,
the normalized power average is:
the normalized pitch angle average is:
the normalized average rotational speed is:
in the formula, PiRepresents the normalized power mean, P, at the ith wind speedn,i,jRepresenting the average power in the j group of acquisition arrays at the ith wind speed; beta is aiRepresents the normalized mean value of the pitch angle, beta, at the ith wind speedn.i.jRepresenting the average pitch angle in the j group of acquisition arrays at the ith wind speed; n isiRepresenting the normalized mean generator speed, n, at the ith wind speedn,i,jRepresents the average generator speed N in the j group of acquisition arrays under the ith wind speediIndicating the number of data groups contained in the collected data at the ith wind speed.
6. The wind turbine state anomaly evaluation method according to claim 1, characterized in that: in the step 3), the step (c),
the calculation formula of the mutual difference value is as follows:
in the formula, the difference value errpowerAnd (3) representing a calculated comprehensive numerical error value, V representing a wind speed, f (V) representing probability density, cutin representing a cut-in wind speed, cutout representing a cut-out wind speed, P single measurement (V) representing a single variable curve, and P other measurement (V) of the whole field representing total variable curves of other wind generation sets of the wind power plant.
7. The wind turbine generator verification system according to claim 1, wherein: the checking system comprises a variable acquisition module, a variable curve generation module and a variable evaluation module; the variable acquisition module inputs acquired data into the variable curve generation module to generate a variable curve, and the variable curve is input into the variable evaluation module to be evaluated and judged.
8. The wind turbine verification system of claim 7, wherein: the variable acquisition module consists of a variable pitch angle acquisition unit of the wind turbine generator, a generator rotating speed acquisition unit and a generator power acquisition unit; the variable pitch angle acquisition unit is used for acquiring variable pitch angle data; the generator rotating speed acquisition unit is used for acquiring generator rotating speed data; the generator power acquisition unit is used for acquiring generator power data.
9. The wind turbine verification system of claim 7, wherein: the variable curve generating module consists of a wind speed-power curve generating module, a wind speed-rotating speed curve generating module and a wind speed-variable pitch angle curve generating module; the wind speed-power curve generation module generates a wind speed-power curve; the wind speed-rotating speed curve generating module generates a wind speed-rotating speed curve; and the wind speed-variable pitch angle curve generating module generates a wind speed-variable pitch angle curve.
10. The wind turbine verification system of claim 7, wherein: the variable evaluation module consists of a wind speed-power curve evaluation module, a wind speed-rotating speed curve evaluation module and a wind speed-variable pitch angle curve evaluation module; the wind speed-power curve evaluation module is used for carrying out comparative analysis on a wind speed-power curve; the wind speed-rotating speed curve evaluation module is used for carrying out comparative analysis on a wind speed-rotating speed curve; and the wind speed-variable pitch angle curve evaluation module is used for carrying out comparative analysis on the wind speed-variable pitch angle curve.
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| CN113033913A (en) * | 2021-04-16 | 2021-06-25 | 上海电气集团股份有限公司 | Air conditioner fault predictive maintenance method and system, electronic equipment and storage medium |
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