CN113931808A - Method and device for diagnosing yaw error of wind turbine generator - Google Patents
Method and device for diagnosing yaw error of wind turbine generator Download PDFInfo
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
- F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
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Abstract
The application discloses a method and a device for diagnosing yaw errors of a wind turbine generator. The method comprises the following steps: collecting the operating parameters of the wind turbine generator, carrying out smooth filtering processing on the operating parameters, and determining a yaw error angle according to the operating parameters after the smooth filtering processing. Therefore, smooth filtering processing can be carried out on the basis of the operation parameters acquired by the wind turbine generator, the yaw error angle of the wind turbine generator is determined according to the processing result, and yaw error diagnosis of the wind turbine generator is carried out on the basis of taking second-level data as a data source, so that the risk that the yaw error diagnosis result is large in volatility when the second-level data are directly adopted is reduced, the inaccuracy problem of yaw error diagnosis results when minute-level average data are adopted is solved, the diagnosis precision of the wind yaw error of the wind turbine generator is effectively improved, and the power generation efficiency of the wind turbine generator is improved.
Description
Technical Field
The application relates to the technical field of energy, in particular to a method and a device for diagnosing yaw error of a wind turbine generator, the wind turbine generator, electronic equipment and a storage medium.
Background
At present, with the aggravation of the problem of energy shortage, people are urgently required to develop new energy to meet the energy demand of people. Wind power generation has the advantages of being renewable, environment-friendly and the like, and is widely applied, and a wind turbine generator is an important part of wind power generation and can convert wind energy into alternating current energy. At present, in order to improve the power generation performance of the wind turbine, the yaw error of the wind turbine needs to be diagnosed.
Disclosure of Invention
The present application aims to solve at least to some extent one of the technical problems in the above-mentioned technology.
Therefore, a first objective of the present application is to provide a method for diagnosing a yaw error of a wind turbine generator, which can perform smoothing filtering processing based on an operation parameter acquired by the wind turbine generator, determine a yaw error angle of the wind turbine generator according to a processing result, and perform yaw error diagnosis of the wind turbine generator on the basis of using second-level data as a data source, thereby reducing a risk of a large fluctuation of a yaw error diagnosis result directly performed by using the second-level data, overcoming an inaccurate problem of performing the yaw error diagnosis result by using minute-level average data, effectively improving a diagnosis precision of the wind turbine generator on the yaw error, and improving a power generation efficiency of the wind turbine generator.
The second purpose of the present application is to provide a wind turbine yaw error diagnosis device.
A third object of the present application is to provide a wind turbine.
A fourth object of the present application is to provide an electronic device.
A fifth object of the present application is to propose a computer-readable storage medium.
An embodiment of a first aspect of the present application provides a method for diagnosing a yaw error of a wind turbine generator, including: collecting operating parameters of a wind turbine generator; carrying out smooth filtering processing on the operation parameters; and determining a yaw error angle according to the running parameters after the smoothing filtering processing.
According to the method for diagnosing the yaw error of the wind turbine generator, smooth filtering processing is carried out on the basis of the operation parameters acquired by the wind turbine generator, the yaw error angle of the wind turbine generator is determined according to the processing result, and the yaw error diagnosis of the wind turbine generator is carried out on the basis of taking second-level data as a data source, so that the risk that the fluctuation of yaw error diagnosis results is high when the second-level data are directly adopted is reduced, the problem that the yaw error diagnosis results are inaccurate when minute-level average data are adopted is solved, the diagnosis precision of the wind turbine generator on the yaw error is effectively improved, and the power generation efficiency of the wind turbine generator is improved.
In addition, the method for diagnosing the yaw error of the wind turbine generator set provided by the embodiment of the application can also have the following additional technical characteristics:
in an embodiment of the present application, the performing a smoothing filtering process on the operating parameter includes: determining the average value of the operation parameters in a time window with a first preset length before the current time as the operation parameters at the current time; and determining the next current time according to the current time and a preset first sliding step length.
In an embodiment of the present application, the performing a smoothing filtering process on the operating parameter includes: determining the average value of the operation parameters in a time window with a second preset length before the current time and in a time window with a third preset length after the current time as the operation parameters at the current time; and determining the next current time according to the current time and a preset second sliding step length.
In an embodiment of the present application, the determining a yaw error angle according to the operating parameter after the smoothing filtering process includes: removing abnormal data of the running parameters after the smooth filtering processing; performing binomial fitting on the included angle between the engine room and the wind direction and the power data in the operation parameters after the abnormal data are removed to obtain a binomial relation between the engine room and the wind direction and the power data; and determining the included angle between the engine room corresponding to the maximum power point and the wind direction as the yaw error angle according to the binomial relation.
In an embodiment of the present application, the performing abnormal data elimination on the operation parameter after the smoothing filtering processing includes at least one of: removing the operation parameters after smooth filtering processing in grids lower than a preset first quantity threshold value by adopting a grid division method; performing abnormal data elimination on the operating parameters subjected to the smoothing filtering by adopting a clustering algorithm; and adopting an integrated learning algorithm to remove abnormal data of the operating parameters after the smooth filtering processing.
In one embodiment of the present application, the method further comprises: and replacing the operating parameters after the smooth filtering processing in the grids with the average value of the operating parameters after the smooth filtering processing in the grids.
In an embodiment of the present application, the determining a yaw error angle according to the operating parameter after the smoothing filtering process includes: determining candidate yaw error angles corresponding to different wind speed intervals according to the running parameters after the smoothing filtering processing; and determining the yaw error angle according to the candidate yaw error angles corresponding to different wind speed intervals.
In an embodiment of the application, the determining the yaw error angle according to the candidate yaw error angles corresponding to different wind speed intervals includes: abnormal data elimination is carried out on the candidate yaw error angles corresponding to different wind speed intervals; and determining the yaw error angle according to the candidate yaw error angle after the abnormal data are removed.
In an embodiment of the present application, the performing abnormal data elimination on the candidate yaw error angles corresponding to different wind speed intervals includes at least one of: removing the candidate yaw error angles corresponding to the wind speed intervals with the quantity of the effective data lower than a preset second quantity threshold value during the binomial fitting; and eliminating abnormal data according to the difference between the candidate yaw error angles corresponding to different wind speed intervals.
In an embodiment of the application, the determining the yaw error angle according to the candidate yaw error angles corresponding to different wind speed intervals includes: determining weights corresponding to different wind speed intervals; determining the yaw error angle from the weights and the candidate yaw error angle.
An embodiment of a second aspect of the present application provides a wind turbine generator yaw error diagnosis device, including: the acquisition module is used for acquiring the operating parameters of the wind turbine generator; the processing module is used for carrying out smooth filtering processing on the operation parameters; and the determining module is used for determining a yaw error angle according to the running parameters after the smoothing filtering processing.
The diagnosis device of wind turbine generator system driftage error of embodiment of this application, the operating parameter based on wind turbine generator system gathers carries out smooth filtering process, the driftage error angle of wind turbine generator system is confirmed according to the processing result, on the basis of using second level data as the data source, carry out the driftage error diagnosis of wind turbine generator system, both reduced the great risk of volatility that directly adopts second level data to carry out driftage error diagnosis result, the inaccurate problem of adopting minute level average data to carry out driftage error diagnosis result has been overcome again, the diagnostic accuracy of unit to wind driftage error has effectively been improved, the generating efficiency of unit has been improved.
An embodiment of a third aspect of the present application provides a wind turbine generator, including: the wind turbine yaw error diagnosis device according to the embodiment of the second aspect of the present application.
The wind turbine generator set provided by the embodiment of the invention can be used for performing smoothing filtering processing on the basis of the operation parameters acquired by the wind turbine generator set, determining the yaw error angle of the wind turbine generator set according to the processing result, and performing yaw error diagnosis on the wind turbine generator set on the basis of taking second-level data as a data source, so that the risk of high fluctuation of the yaw error diagnosis result directly performed by adopting the second-level data is reduced, the problem of inaccuracy of the yaw error diagnosis result performed by adopting minute-level average data is solved, the diagnosis precision of the wind yaw error by the wind turbine generator set is effectively improved, and the power generation efficiency of the wind turbine generator set is improved.
An embodiment of a fourth aspect of the present application provides an electronic device, including: the device comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein when the processor executes the program, the method for diagnosing the yaw error of the wind turbine generator is realized according to the embodiment of the first aspect of the application.
The electronic equipment of the embodiment of the invention executes the computer program stored on the memory through the processor, can perform smooth filtering processing based on the operation parameters acquired by the wind turbine, determines the yaw error angle of the wind turbine according to the processing result, and performs yaw error diagnosis on the wind turbine on the basis of taking second-level data as a data source, thereby not only reducing the risk of high volatility of the yaw error diagnosis result directly performed by adopting the second-level data, but also overcoming the inaccuracy problem of performing the yaw error diagnosis result by adopting minute-level average data, effectively improving the diagnosis precision of the wind turbine on the yaw error, and improving the generating efficiency of the wind turbine.
An embodiment of a fifth aspect of the present application provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the method for diagnosing yaw error of a wind turbine generator according to the embodiment of the first aspect of the present application.
The computer readable storage medium of the embodiment of the invention stores a computer program and is executed by a processor, can perform smooth filtering processing based on the operation parameters acquired by the wind turbine, determines the yaw error angle of the wind turbine according to the processing result, and performs yaw error diagnosis on the wind turbine on the basis of taking second-level data as a data source, thereby reducing the risk of high fluctuation of the yaw error diagnosis result directly performed by adopting the second-level data, overcoming the inaccuracy problem of performing the yaw error diagnosis result by adopting minute-level average data, effectively improving the diagnosis precision of the wind yaw error by the wind turbine, and improving the generating efficiency of the wind turbine.
Additional aspects and advantages of the present application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the present application.
Drawings
The foregoing and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a schematic flow chart of a method for diagnosing a yaw error of a wind turbine generator according to an embodiment of the present application;
FIG. 2 is a schematic flow chart of a method for diagnosing a yaw error of a wind turbine generator according to another embodiment of the present application;
FIG. 3 is a schematic flow chart of a method for diagnosing a yaw error of a wind turbine generator according to another embodiment of the present application;
FIG. 4 is a schematic flow chart of a method for diagnosing a yaw error of a wind turbine generator according to another embodiment of the present application;
FIG. 5 is a schematic flow chart of a method for diagnosing a yaw error of a wind turbine generator according to another embodiment of the present application;
FIG. 6 is a schematic flow chart of a method for diagnosing a yaw error of a wind turbine generator according to a specific example of the present application;
FIG. 7 is a schematic structural diagram of a wind turbine yaw error diagnosis device according to an embodiment of the present application;
FIG. 8 is a schematic structural diagram of a wind turbine generator according to another embodiment of the present application; and
fig. 9 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary and intended to be used for explaining the present application and should not be construed as limiting the present application.
The method and the device for diagnosing the yaw error of the wind turbine generator, the electronic device and the storage medium according to the embodiments of the present application are described below with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart of a method for diagnosing a yaw error of a wind turbine generator according to an embodiment of the present application.
As shown in fig. 1, a method for diagnosing a yaw error of a wind turbine generator according to an embodiment of the present application includes:
and S101, collecting the operating parameters of the wind turbine generator.
In the embodiment of the application, the operating parameters of the wind turbine generator can be acquired through a Data Acquisition And monitoring Control (Scada for short) system. It should be noted that the acquisition regions and types of the operation parameters can be set according to actual situations, and are not limited too much here.
In one embodiment, the collection area of the operation parameter may specifically include, but is not limited to, at least one of a wind turbine and a generator in a wind turbine.
In one embodiment, the operating parameters may specifically include, but are not limited to, power, speed, wind speed, pitch angle, nacelle to wind angle, unit status flags, and ambient temperature.
In one embodiment, the Scada system may collect the operating parameters of the wind turbine through a plurality of collecting devices. It should be noted that the collecting device may be configured according to actual situations, and is not limited herein, for example, the collecting device may include a sensor, and the sensor includes, but is not limited to, a temperature sensor, a current sensor, a voltage sensor, a wind speed sensor, and the like.
And S102, performing smooth filtering processing on the operation parameters.
In the embodiment of the application, the smooth filtering processing can be performed on the operation parameters of the wind turbine generator collected in the step S101, so as to obtain the operation parameters after the smooth filtering processing.
In one embodiment, the data cleaning and preprocessing of the operating parameters is performed prior to the smoothing filtering of the operating parameters. It should be noted that the data cleaning and preprocessing operation may specifically include, but is not limited to, an out-of-range data cleaning, a unit shutdown data cleaning, a limited power data cleaning, and a data cleaning that remains unchanged for a long time, and in addition, if the acquired operation parameters include a unit state flag bit, valid data may be selected from the flag bits for analysis.
It can be understood that because the second-level data is used as a data source for diagnosing the wind yaw error of the unit, smooth filtering processing needs to be carried out on the operation parameters in consideration of the variability of the wind speed, the fluctuation of the unit operation and the rotation inertia characteristics in the operation process.
In one embodiment, the step S102 "performing the smoothing filtering process on the operation parameter" may include the following two possible embodiments:
in the mode 1, the average value of the operation parameters in the time window with the first preset length before the current time is determined as the operation parameter of the current time, and the next current time is determined according to the current time and the preset first sliding step length.
In the embodiment of the application, a time window with a certain length is selected, that is, the length of the preset time window is a first preset length and a sliding step length, that is, a first sliding step length. The average value of the operation parameters in the time window before the current time is calculated, and the average value is used as the operation parameters of the current time, so that the operation parameters of the current time are considered to be related to the operation parameters of a historical period of time, and the average value of the operation parameters of the historical period of time is used as the operation parameters of the current time. For example, setting the length of the time window to 10 seconds, setting the sliding step length to 1 second, calculating an average value of the running parameters with indexes from 1 to 10 in the original running parameters, and taking the average value as the running parameter after the 10 th index filtering, and calculating the average value calculated by the running parameters with indexes from 2 to 11 as the running parameter after the 11 th index filtering, and then sequentially calculating, thereby completing the process of running parameter smoothing filtering processing. It should be noted that the length of the time window and the length of the first sliding step can be set according to practical situations, and is not limited herein.
Therefore, the method can determine the average value of the operation parameters in the time window with the first preset length before the current time as the operation parameter of the current time, and determine the next current time according to the preset first sliding step length, thereby reducing the fluctuation of the operation parameters, improving the accuracy of the operation parameters and further improving the diagnosis precision of the yaw error of the unit.
And 2, determining the average value of the operation parameters in the time window with the second preset length before the current time and in the time window with the third preset length after the current time as the operation parameter of the current time, and determining the next current time according to the current time and the preset second sliding step length.
In the embodiment of the application, two time windows with a certain length before and after the current moment are selected, namely, the length of the time window before the current moment is preset to be a second preset length, the length of the time window after the current moment is preset to be a third preset length, and the sliding step length is selected to be a second sliding step length. The method takes the influence of the operation parameters of the current moment on the operation parameters of the historical moment into consideration, and simultaneously the operation parameters of the current moment also influence the operation parameters of the future moment, so that the average value of the operation parameters of a historical period of time and a future period of time is taken as the operation parameters of the current moment as the center. It should be noted that the lengths of the time window and the second sliding step can be set according to practical situations, and are not limited herein.
Therefore, the method can determine the average value of the operation parameters in the time window of the second preset length before the current time and the third preset length after the current time as the operation parameter of the current time by respectively calculating the average value of the operation parameters in the time window of the second preset length before the current time and the average value of the operation parameters in the time window of the third preset length after the current time, and determine the next current time according to the preset second sliding step length, thereby reducing the volatility of data, improving the accuracy of the operation parameters and further improving the diagnosis precision of the yaw error of the unit.
And S103, determining a yaw error angle according to the running parameters after the smoothing filtering processing.
In the embodiment of the present application, the yaw error angle may be determined according to the operation parameters obtained in step S102 after the smoothing filtering process.
In one embodiment, as shown in fig. 2, the step S103 "determining the yaw error angle according to the operation parameters after the smoothing filtering process" may include:
s201, removing abnormal data of the operation parameters after the smoothing filtering processing.
S202, performing binomial fitting on the included angle between the engine room and the wind direction and the power data in the operation parameters after the abnormal data are removed to obtain the binomial relation between the engine room and the included angle between the wind direction and the power data.
And S203, determining an included angle between the cabin corresponding to the maximum power point and the wind direction as a yaw error angle according to the binomial relation.
In an embodiment, the step S201 "performing abnormal data elimination on the operation parameters after the smoothing filtering processing" may specifically include, but is not limited to, at least one of the following three possible embodiments:
mode 1, a grid division method is adopted, and the operation parameters after smooth filtering processing in the grids which are lower than a preset first quantity threshold value are removed.
In the embodiment of the application, the number of samples in the grid, that is, the number of the operation parameters after the filtering process is calculated by adopting a grid division method, and the operation parameters after the filtering process in the grid, which are lower than a certain sample number, for example, a first number threshold, are deleted. For example, a two-dimensional grid is formed by two variables, namely an included angle between a cabin of a wind turbine generator and a wind direction and power, sample distribution under different grid divisions is inevitably different due to fluctuation of operation of the wind turbine generator, a grid with a small sample amount is an area with low occurrence frequency of unit operation data, operation parameters after filtering processing in the grid are taken as abnormal points to be removed, and for a grid meeting a preset first quantity threshold, the operation parameters after filtering processing in the grid are taken as normal operation data to be reserved. It should be noted that the preset first number threshold may be set according to practical situations, and is not limited herein.
Therefore, the method can eliminate the operation parameters in the grids which are lower than the preset first quantity threshold value by carrying out grid division on the operation parameters of the unit, can improve the accuracy of the operation parameters, and further improves the diagnosis precision of the yaw error of the unit.
And 2, performing abnormal data elimination on the operation parameters subjected to the smoothing filtering by adopting a clustering algorithm.
In the embodiment of the application, the clustering algorithm can be adopted to remove abnormal data of the operating parameters after the smooth filtering processing. The Clustering algorithm may specifically include, but is not limited to, a DBSCAN (Density-Based Clustering of Applications with Noise) algorithm, a LOF (Local Outlier Factor) algorithm, and the like. It should be noted that the clustering algorithm in the embodiment of the present application may be set according to actual situations, and is not limited herein.
Therefore, the method can remove abnormal data of the running parameters after the smooth filtering processing through a clustering algorithm, can improve the accuracy of the running parameters, and further improves the diagnosis precision of the yaw error of the unit.
And 3, eliminating abnormal data of the operation parameters subjected to the smoothing filtering by adopting an integrated learning algorithm.
In the embodiment of the application, an ensemble learning algorithm can be adopted to remove abnormal data of the operating parameters after the smoothing filtering processing. For example, the abnormal point elimination can be performed on the operation parameters after the smoothing filtering processing through an isolated forest algorithm. It should be noted that the ensemble learning algorithm may be set according to actual situations, and is not limited herein.
Therefore, the method can remove abnormal data of the running parameters after the smoothing filtering processing through the integrated learning algorithm, can improve the accuracy of the running parameters, and further improves the diagnosis precision of the yaw error of the unit.
In an implementation manner, the method for diagnosing the yaw error of the wind turbine generator according to the embodiment of the present application may further include the following steps: after the grid division method is adopted, the operation parameters after smooth filtering processing in the grids which are lower than the preset first quantity threshold value are removed, the operation parameters after smooth filtering processing in the rest grids can be replaced by the average value of the operation parameters after smooth filtering processing in the grids, namely the average value of the operation parameters in each grid can be calculated, the average value is used as the operation parameters of the unit in the grid, namely the average value operation parameter is only used for replacing the operation parameters under one grid, the accuracy of the operation parameters is improved, and the influence of the unit power fluctuation on the diagnosis result is further reduced.
In an embodiment, the step S202 "performing binomial fitting on the included angle between the nacelle and the wind direction and the power data in the operation parameters after the abnormal data is removed to obtain a binomial relationship between the nacelle and the wind direction and the power data" may specifically include the following two possible embodiments:
mode 1, a fitting process of data is achieved by median regression.
In the embodiment of the application, if the data obtained after the abnormal points are removed is not perfect, the fitting process of the data can be realized through median regression, so that the binomial relation between the cabin and the wind direction included angle and the power data is obtained. It should be noted that, the specific manner of the median regression can be set according to the actual situation, and is not limited herein.
Therefore, the method can realize the fitting process of the data by adopting the median regression, thereby obtaining the binomial relation between the cabin and the wind direction included angle and the power data.
Mode 2, a common binomial fit is used.
In the embodiment of the application, if the data obtained after the abnormal points are removed is perfect, the fitting process of the data can be realized through common binomial fitting, so that the binomial relation between the cabin and the wind direction included angle and the power data is obtained. It should be noted that the specific manner of the ordinary binomial fitting can be set according to actual situations, and is not limited herein.
Therefore, the method can adopt a common binomial to realize the fitting process of the data, thereby obtaining the binomial relation between the included angle between the engine room and the wind direction and the power data and improving the stability of the fitting result.
In one embodiment, in step S203, "the included angle between the nacelle and the wind direction corresponding to the maximum power point is determined as the yaw error angle according to the binomial relationship", that is, the included angle between the nacelle and the wind direction corresponding to the maximum power point is found in the binomial relationship, and this angle is the optimal deviation angle, that is, the yaw error angle of the unit.
It can be understood that, in a normal state, the unit operates within a certain deviation angle range, the power is increased when the yaw angle is closer to 0 degrees, that is, the maximum power is reached when the yaw angle is 0 degrees, at this time, the unit has no yaw static error, that is, the yaw static error is 0 degrees, when the yaw static error exists, the power maximization is necessarily reached at the yaw static error position, and the yaw error angle is obtained by using the power maximization idea.
In summary, according to the method for diagnosing the yaw error of the wind turbine generator, the yaw error angle can be determined according to the operation parameters after the smoothing filtering processing by performing the smoothing filtering processing on the acquired operation parameters of the wind turbine generator. Therefore, smooth filtering processing can be carried out on the basis of the operation parameters acquired by the wind turbine generator, the yaw error angle of the wind turbine generator is determined according to the processing result, and yaw error diagnosis of the wind turbine generator is carried out on the basis of taking second-level data as a data source, so that the risk that the yaw error diagnosis result is large in volatility when the second-level data are directly adopted is reduced, the inaccuracy problem of yaw error diagnosis results when minute-level average data are adopted is solved, the diagnosis precision of the wind yaw error of the wind turbine generator is effectively improved, and the power generation efficiency of the wind turbine generator is improved. Meanwhile, abnormal data are cleaned in a grid division mode, so that the influence of unit power fluctuation on a diagnosis result is reduced, and the diagnosis precision and the unit power generation efficiency are further improved.
On the basis of any of the above embodiments, as shown in fig. 3, the step S103 "determining a yaw error angle according to the operation parameters after the smoothing filtering process" may further include the following steps S301 and S302:
s301, determining candidate yaw error angles corresponding to different wind speed intervals according to the running parameters after the smoothing filtering processing.
In the embodiment of the application, after the operation parameters after the smoothing filtering processing are received, the wind speed binning processing can be performed on the operation parameters. For example, with 0.5 meter per second (m/s) as a wind speed interval, the operation parameter division is performed by wind speed division, for example, the central wind speeds of the wind speed intervals are respectively selected to be 3.5m/s, 4m/s, 4.5m/s, 5m/s, 5.5m/s, 6m/s, 6.5m/s, 7m/s, 7.5m/s, and the like, and each wind speed interval is decreased and increased by 0.25m/s as a specific wind speed interval based on the central wind speed, for example, for a central wind speed of 3.5m/s, the corresponding wind speed interval is [3.25m/s, 3.75m/s ]. By analogy with the method, the wind speed intervals of a plurality of central wind speeds can be obtained.
The candidate yaw error angles corresponding to different wind speed intervals can be determined according to the operation parameters after the smoothing filtering processing, namely, the diagnosis of the wind yaw error is carried out on the different wind speed intervals based on the operation parameters in each wind speed interval according to a plurality of wind speed intervals obtained after the wind speed binning processing, so that the candidate yaw error angles corresponding to the different wind speed intervals are obtained.
S302, determining a yaw error angle according to candidate yaw error angles corresponding to different wind speed intervals.
In the embodiment of the application, the final yaw error angle can be determined according to a plurality of candidate yaw error angles corresponding to different wind speed intervals.
In an embodiment, as shown in fig. 4, the step S302 "determining a yaw error angle according to candidate yaw error angles corresponding to different wind speed intervals" may specifically include:
s401, abnormal data elimination is conducted on the candidate yaw error angles corresponding to different wind speed intervals.
S402, determining a yaw error angle according to the candidate yaw error angle after the abnormal data are removed.
In an embodiment, the step S401 of performing abnormal data elimination on the candidate yaw error angles corresponding to different wind speed intervals may specifically include at least one of the following manners:
mode 1: and eliminating candidate yaw error angles corresponding to the wind speed intervals with the quantity of the effective data lower than a preset second quantity threshold value during the binomial fitting.
Specifically, candidate yaw error angles corresponding to wind speed intervals with the quantity of effective data lower than a preset second quantity threshold value during binomial fitting can be eliminated through consistency check, that is, the data quantity of each wind speed interval is detected, if the effective data of a certain wind speed interval during quadratic fitting is less than the preset quantity threshold value, the yaw error angle of the wind speed interval is not calculated, and the yaw error angle is calculated in the wind speed interval with the data quantity meeting requirements.
It should be noted that the second number threshold may be set according to practical situations, and is not limited herein.
Mode 2: and eliminating abnormal data according to the difference between the candidate yaw error angles corresponding to different wind speed intervals.
Specifically, whether the yaw error angles calculated in different wind speed intervals have obvious differences is analyzed, for example, an abnormal diagnosis result is deleted through a box diagram method, and most of normal results are used as deviation angle diagnosis results of different wind speed intervals.
It can be understood that when a yaw error exists in a unit, the yaw error angle of different wind speed intervals should be a constant, however, due to great fluctuation in the actual operation of the unit, the actual diagnosis result still has a phenomenon that the diagnosis results of different wind speed intervals are inconsistent, for example, the deviation diagnosis result of most wind speed intervals is 5deg (Degree, angle number), and the diagnosis result of a certain interval is 10deg, and the consistency check is to solve the problem that conflicts exist in the diagnosis results, so as to improve the stability and accuracy of the diagnosis results.
In an embodiment, as shown in fig. 5, the step S302 "determining a yaw error angle according to candidate yaw error angles corresponding to different wind speed intervals" may specifically include:
s501, determining weights corresponding to different wind speed intervals.
And S502, determining a yaw error angle according to the weight and the candidate yaw error angle.
Specifically, the weights corresponding to different wind speed intervals may be determined through normalization processing, that is, the sample size of each wind speed interval is obtained in consideration of the data distribution of different wind speed intervals, the sample sizes of the wind speed intervals are normalized, and the normalized value is used as the weight corresponding to the wind speed interval. It should be noted that the normalization process may be set according to actual situations, and is not limited herein.
In one embodiment, the step S502 "determining the yaw error angle according to the weight and the candidate yaw error angle" may specifically include: and multiplying the yaw error angle of each wind speed interval by the weight obtained after the wind speed interval normalization processing, and then performing addition processing on multiple wind speed intervals to obtain a final yaw error diagnosis result, namely the yaw error angle.
Therefore, the method can determine candidate yaw error angles and weights corresponding to different wind speed intervals according to the operating parameters after the smoothing filtering processing, eliminates abnormal data of the candidate yaw error angles, determines the yaw error angles according to the weights and the candidate yaw error angles, reduces the volatility of the operating parameters, improves the diagnosis precision of the unit on the wind yaw errors, and improves the generating efficiency of the unit.
To make the present application more clear to those skilled in the art, fig. 6 is a schematic flow chart of a method for diagnosing a yaw error of a wind turbine generator according to a specific example of the present application, and as shown in fig. 6, the method for diagnosing a yaw error of a wind turbine generator may include the following steps:
s601, collecting the operation parameters of the wind turbine generator. The execution continues with steps S602-S603 or S604-S605.
And S602, determining the average value of the operation parameters in the time window with the first preset length before the current time as the operation parameters at the current time.
S603, determining the next current time according to the current time and a preset first sliding step length. The execution continues with steps S606-S607 or S608 or S609.
S604, determining the average value of the operation parameters in the time window with the second preset length before the current time and the time window with the third preset length after the current time as the operation parameter of the current time
And S605, determining the next current time according to the current time and a preset second sliding step length. Steps S606-S607 or S608 or S609 are performed.
And S606, removing the operation parameters after the smooth filtering processing in the grids which are lower than the preset first quantity threshold value by adopting a grid division method.
S607, the operation parameters after the smoothing filtering in the grid are replaced with the average value of the operation parameters after the smoothing filtering in the grid.
And S608, performing abnormal data elimination on the operation parameters subjected to the smoothing filtering by adopting a clustering algorithm.
And S609, performing abnormal data elimination on the operation parameters subjected to the smoothing filtering by adopting an integrated learning algorithm.
S610, performing binomial fitting on the included angle between the engine room and the wind direction and the power data in the operation parameters after the abnormal data are removed to obtain the binomial relation between the engine room and the included angle between the wind direction and the power data.
And S611, determining the included angle between the engine room corresponding to the maximum power point and the wind direction as a yaw error angle according to the binomial relation.
For specific descriptions of steps S601 to S611, refer to the descriptions of relevant contents in the above embodiments, which are not described herein again.
In order to implement the above embodiment, the present application further provides a wind turbine yaw error diagnosis device.
Fig. 7 is a schematic structural diagram of a wind turbine yaw error diagnosis device according to an embodiment of the present application.
As shown in fig. 7, the wind turbine yaw error diagnosis apparatus 100 according to the embodiment of the present application includes: an acquisition module 110, a processing module 120, and an identification module 130.
The acquisition module 110 is used for acquiring the operating parameters of the wind turbine;
the processing module 120 is configured to perform a smoothing filtering process on the operation parameter;
the determining module 130 is configured to determine a yaw error angle according to the smooth filtered operation parameter.
In an embodiment of the present application, the processing module 120 is specifically configured to: determining the average value of the operation parameters in a time window with a first preset length before the current time as the operation parameters at the current time; and determining the next current time according to the current time and a preset first sliding step length.
In an embodiment of the present application, the processing module 120 is specifically configured to: determining the average value of the operation parameters in a time window with a second preset length before the current time and in a time window with a third preset length after the current time as the operation parameters at the current time; and determining the next current time according to the current time and a preset second sliding step length.
In an embodiment of the application, the determining module 130 is specifically configured to: removing abnormal data of the running parameters after the smooth filtering processing; performing binomial fitting on the included angle between the engine room and the wind direction and the power data in the operation parameters after the abnormal data are removed to obtain a binomial relation between the engine room and the wind direction and the power data; and determining the included angle between the engine room corresponding to the maximum power point and the wind direction as the yaw error angle according to the binomial relation.
In an embodiment of the present application, the determining module 130 is specifically configured to at least one of: removing the operation parameters after smooth filtering processing in grids lower than a preset first quantity threshold value by adopting a grid division method; performing abnormal data elimination on the operating parameters subjected to the smoothing filtering by adopting a clustering algorithm; and adopting an integrated learning algorithm to remove abnormal data of the operating parameters after the smooth filtering processing.
In an embodiment of the present application, the determining module 130 is further configured to: and replacing the operating parameters after the smooth filtering processing in the grids with the average value of the operating parameters after the smooth filtering processing in the grids.
In an embodiment of the application, the determining module 130 is specifically configured to: determining candidate yaw error angles corresponding to different wind speed intervals according to the running parameters after the smoothing filtering processing; and determining the yaw error angle according to the candidate yaw error angles corresponding to different wind speed intervals.
In an embodiment of the application, the determining module 130 is specifically configured to: abnormal data elimination is carried out on the candidate yaw error angles corresponding to different wind speed intervals; and determining the yaw error angle according to the candidate yaw error angle after the abnormal data are removed.
In an embodiment of the present application, the determining module 130 is specifically configured to at least one of: removing the candidate yaw error angles corresponding to the wind speed intervals with the quantity of the effective data lower than a preset second quantity threshold value during the binomial fitting; and eliminating abnormal data according to the difference between the candidate yaw error angles corresponding to different wind speed intervals.
In an embodiment of the application, the determining module 130 is specifically configured to: determining weights corresponding to different wind speed intervals; determining the yaw error angle from the weights and the candidate yaw error angle.
It should be noted that details that are not disclosed in the wind turbine yaw error diagnosis device according to the embodiment of the present application refer to details that are disclosed in the wind turbine yaw error diagnosis method according to the embodiment of the present application, and are not described herein again.
To sum up, the diagnosis device of wind turbine generator system yaw error of this application embodiment, can carry out smooth filtering processing based on the operating parameter that the wind turbine generator system gathered, according to the yaw error angle of processing result determination wind turbine generator system, on the basis of using second level data as the data source, carry out the yaw error diagnosis of wind turbine generator system, both reduced the great risk of volatility that directly adopts second level data to carry out the yaw error diagnosis result, overcome the inaccurate problem that adopts minute level average data to carry out the yaw error diagnosis result again, effectively improved the diagnostic accuracy of unit to wind yaw error, the generating efficiency of unit has been improved.
In order to realize the embodiment, the application further provides a wind turbine generator.
FIG. 8 is a schematic structural diagram of a wind turbine generator according to an embodiment of the present application.
As shown in fig. 8, the wind turbine 200 according to the embodiment of the present application includes the wind turbine yaw error diagnosis apparatus 100.
The wind turbine generator system of the embodiment of the application, can carry out smooth filtering processing based on the operating parameter that the wind turbine generator system gathered, according to the driftage error angle of handling the result determination wind turbine generator system, on the basis of using second level data as the data source, carry out the driftage error diagnosis of wind turbine generator system, both reduced the great risk of volatility that directly adopts second level data to carry out driftage error diagnosis result, the inaccurate problem of adopting minute level average data to carry out driftage error diagnosis result has been overcome again, the diagnostic accuracy of unit to wind driftage error has effectively been improved, the generating efficiency of unit has been improved.
In order to implement the above embodiments, as shown in fig. 9, an embodiment of the present application provides an electronic device 300, including: the wind turbine yaw error diagnosis system comprises a memory 310, a processor 320 and a computer program stored on the memory 310 and capable of running on the processor 320, wherein the processor 320 executes the program to realize the wind turbine yaw error diagnosis method.
The electronic equipment of the embodiment of the application executes the computer program stored on the memory through the processor, smooth filtering processing can be carried out on the basis of the operation parameters acquired by the wind turbine generator, the yaw error angle of the wind turbine generator is determined according to the processing result, yaw error diagnosis of the wind turbine generator is carried out on the basis of taking second-level data as a data source, the risk that the volatility of yaw error diagnosis results is high due to the fact that second-level data are directly adopted is reduced, the problem that inaccuracy of yaw error diagnosis results is carried out due to the fact that minute-level average data are adopted is solved, the diagnosis precision of the wind turbine generator to the yaw errors is effectively improved, and the power generation efficiency of the wind turbine generator is improved.
In order to implement the above embodiments, the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the method for diagnosing the yaw error of the wind turbine generator is implemented.
The computer-readable storage medium of the embodiment of the application stores a computer program and is executed by a processor, smooth filtering processing can be carried out on the basis of operating parameters acquired by a wind turbine generator, the yaw error angle of the wind turbine generator is determined according to a processing result, and yaw error diagnosis of the wind turbine generator is carried out on the basis of taking second-level data as a data source, so that the risk that the volatility of yaw error diagnosis results is high due to the fact that the second-level data are directly adopted is reduced, the problem that the yaw error diagnosis results are inaccurate due to the fact that minute-level average data are adopted is solved, the diagnosis precision of the wind turbine generator on the yaw errors is effectively improved, and the power generation efficiency of the wind turbine generator is improved.
In the description of the present application, it is to be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," "circumferential," and the like are used in the orientations and positional relationships indicated in the drawings for convenience in describing the present application and for simplicity in description, and are not intended to indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and are therefore not to be considered limiting of the present application.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present application, "a plurality" means two or more unless specifically limited otherwise.
In this application, unless expressly stated or limited otherwise, the terms "mounted," "connected," "secured," and the like are to be construed broadly and can include, for example, fixed connections, removable connections, or integral parts; can be mechanically or electrically connected; either directly or indirectly through intervening media, either internally or in any other relationship. The specific meaning of the above terms in the present application can be understood by those of ordinary skill in the art as appropriate.
In this application, unless expressly stated or limited otherwise, the first feature "on" or "under" the second feature may be directly contacting the first and second features or indirectly contacting the first and second features through intervening media. Also, a first feature "on," "over," and "above" a second feature may be directly or diagonally above the second feature, or may simply indicate that the first feature is at a higher level than the second feature. A first feature being "under," "below," and "beneath" a second feature may be directly under or obliquely under the first feature, or may simply mean that the first feature is at a lesser elevation than the second feature.
In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Although embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application, and that variations, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present application.
Claims (14)
1. A method for diagnosing yaw error of a wind turbine generator is characterized by comprising the following steps:
collecting operating parameters of a wind turbine generator;
carrying out smooth filtering processing on the operation parameters;
and determining a yaw error angle according to the running parameters after the smoothing filtering processing.
2. The diagnostic method of claim 1, wherein said smoothing the operating parameter comprises:
determining the average value of the operation parameters in a time window with a first preset length before the current time as the operation parameters at the current time;
and determining the next current time according to the current time and a preset first sliding step length.
3. The diagnostic method of claim 1, wherein said smoothing the operating parameter comprises:
determining the average value of the operation parameters in a time window with a second preset length before the current time and in a time window with a third preset length after the current time as the operation parameters at the current time;
and determining the next current time according to the current time and a preset second sliding step length.
4. The diagnostic method of claim 1, wherein determining a yaw error angle from the smoothed filter processed operating parameter comprises:
removing abnormal data of the running parameters after the smooth filtering processing;
performing binomial fitting on the included angle between the engine room and the wind direction and the power data in the operation parameters after the abnormal data are removed to obtain a binomial relation between the engine room and the wind direction and the power data;
and determining the included angle between the engine room corresponding to the maximum power point and the wind direction as the yaw error angle according to the binomial relation.
5. The diagnostic method of claim 4, wherein the performing abnormal data culling on the smooth filtered operating parameters comprises at least one of:
removing the operation parameters after smooth filtering processing in grids lower than a preset first quantity threshold value by adopting a grid division method;
performing abnormal data elimination on the operating parameters subjected to the smoothing filtering by adopting a clustering algorithm;
and adopting an integrated learning algorithm to remove abnormal data of the operating parameters after the smooth filtering processing.
6. The diagnostic method of claim 5, further comprising:
and replacing the operating parameters after the smooth filtering processing in the grids with the average value of the operating parameters after the smooth filtering processing in the grids.
7. The diagnostic method of claim 1, wherein determining a yaw error angle from the smoothed filter processed operating parameter comprises:
determining candidate yaw error angles corresponding to different wind speed intervals according to the running parameters after the smoothing filtering processing;
and determining the yaw error angle according to the candidate yaw error angles corresponding to different wind speed intervals.
8. The diagnostic method of claim 7, wherein determining the yaw error angle from the candidate yaw error angles for different wind speed intervals comprises:
abnormal data elimination is carried out on the candidate yaw error angles corresponding to different wind speed intervals;
and determining the yaw error angle according to the candidate yaw error angle after the abnormal data are removed.
9. The diagnostic method of claim 8, wherein the abnormal data culling of the candidate yaw error angles corresponding to different wind speed intervals comprises at least one of:
removing the candidate yaw error angles corresponding to the wind speed intervals with the quantity of the effective data lower than a preset second quantity threshold value during the binomial fitting;
and eliminating abnormal data according to the difference between the candidate yaw error angles corresponding to different wind speed intervals.
10. The diagnostic method of claim 7, wherein determining the yaw error angle from the candidate yaw error angles for different wind speed intervals comprises:
determining weights corresponding to different wind speed intervals;
determining the yaw error angle from the weights and the candidate yaw error angle.
11. A wind turbine yaw error diagnosis device is characterized by comprising:
the acquisition module is used for acquiring the operating parameters of the wind turbine generator;
the processing module is used for carrying out smooth filtering processing on the operation parameters;
and the determining module is used for determining a yaw error angle according to the running parameters after the smoothing filtering processing.
12. A wind turbine, comprising: the wind turbine yaw error diagnostic apparatus according to claim 11.
13. An electronic device, comprising: memory, processor and computer program stored on the memory and executable on the processor, the processor implementing the method for diagnosing a yaw error of a wind turbine as claimed in any one of claims 1 to 10 when executing the program.
14. A computer-readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method for diagnosing a yaw error of a wind turbine according to any one of claims 1 to 10.
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