CN114962173A - Method and device for detecting yawing abnormity of wind driven generator and electronic equipment - Google Patents
Method and device for detecting yawing abnormity of wind driven generator and electronic equipment Download PDFInfo
- Publication number
- CN114962173A CN114962173A CN202210557433.1A CN202210557433A CN114962173A CN 114962173 A CN114962173 A CN 114962173A CN 202210557433 A CN202210557433 A CN 202210557433A CN 114962173 A CN114962173 A CN 114962173A
- Authority
- CN
- China
- Prior art keywords
- wind
- yaw
- data
- determining
- abnormal
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- 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
- F03D—WIND MOTORS
- F03D17/00—Monitoring or testing of wind motors, e.g. diagnostics
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/70—Wind energy
- Y02E10/72—Wind turbines with rotation axis in wind direction
Landscapes
- Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Sustainable Development (AREA)
- Sustainable Energy (AREA)
- Chemical & Material Sciences (AREA)
- Combustion & Propulsion (AREA)
- Mechanical Engineering (AREA)
- General Engineering & Computer Science (AREA)
- Wind Motors (AREA)
Abstract
The application provides a method and a device for detecting yaw abnormity of a wind driven generator and electronic equipment. The method for detecting the yaw abnormality of the wind driven generator comprises the following steps: acquiring wind direction data, yaw frequency data and engine room position data of the wind driven generator; determining the wind deflection angle of the wind driven generator according to the cabin position data and the wind direction data; and determining whether the wind driven generator has abnormal yaw according to the wind deflection angle and the yaw frequency data. Therefore, the accuracy of detecting the yaw abnormity of the wind driven generator can be improved.
Description
Technical Field
The invention relates to the technical field of wind driven generator abnormity detection, in particular to a method and a device for detecting the yaw abnormity of a wind driven generator and electronic equipment.
Background
With the development of wind power generation technology, a wind driven generator is the same as a hydraulic machine, and is used as a power source to replace manpower and animal power, thereby playing an important role in the development of production capacity. The wind generator may convert wind energy into mechanical energy. The wind power generator includes a yaw system. The yaw system functions to align the wind direction quickly and smoothly when the direction of the wind velocity vector changes so that the wind rotor obtains maximum wind energy.
In actual operation, the yaw system is abnormal, so that the power generation stability of the wind driven generator is affected, and serious hardware failure or even safety accidents can be caused. Therefore, the related art can carry out statistical analysis on the yaw frequency of the wind driven generator of the wind turbine in advance, and identify the wind driven generator with frequent yaw.
However, since the operating system of the wind driven generator is complex, the yaw condition of the wind driven generator is difficult to reflect only by using the yaw frequency of the wind driven generator as the yaw data of the wind driven generator, and the accuracy of yaw detection and early warning of the wind driven generator is low.
Disclosure of Invention
The application provides a method and a device for detecting yaw abnormity of a wind driven generator and electronic equipment.
The application provides a method for detecting yaw abnormity of a wind driven generator, which comprises the following steps:
acquiring wind direction data, yaw frequency data and engine room position data of the wind driven generator;
determining the wind deflection angle of the wind driven generator according to the cabin position data and the wind direction data;
and determining whether the wind driven generator has abnormal yaw according to the wind deflection angle and the yaw frequency data.
Further, the determining whether the wind turbine generator is abnormal in yaw according to the yaw angle and the yaw frequency data includes:
determining a mean value of the yaw angle of the wind turbine at a plurality of moments within a predetermined time period;
determining an angle threshold according to an average value of the wind deflection angles of the plurality of wind driven generators;
determining yaw number data of the wind turbine during the predetermined time period;
determining a yaw frequency threshold value according to the yaw frequency data of the wind driven generators;
and when the condition that the mean value of the yaw angles of the wind driven generator exceeds the angle threshold value and the yaw frequency data of the wind driven generator exceeds at least one of the yaw frequency threshold values exists, determining that the wind driven generator has abnormal yaw.
Further, the determining a yaw number threshold value according to the yaw number data of the plurality of wind power generators includes:
determining an average of the yaw number data for a plurality of the wind turbines;
determining a standard deviation of the yaw times data for a plurality of the wind turbines;
and determining the yaw frequency threshold according to the average value of the yaw frequency data and the standard deviation of the yaw frequency data.
Further, the determining an angle threshold according to a mean value of the yaw angles of the plurality of wind power generators includes:
determining an average of the yaw angles of the plurality of wind turbines;
determining a standard deviation of a mean of the pair of wind yaw angles for a plurality of the wind turbines;
and determining the angle threshold according to the average value of the mean values of the wind deflection angles and the standard deviation of the mean values of the wind deflection angles.
Further, the predetermined time period comprises a plurality of sub-time periods, and the time lengths of the plurality of sub-time periods are the same;
determining whether the wind driven generator has abnormal yaw according to the yaw angle and the yaw frequency data comprises the following steps:
determining whether the plurality of wind driven generators in the sub-time period are abnormal in yaw according to the yaw angle and the yaw frequency data in the sub-time period;
after the determining whether the wind turbine is abnormally yawed according to the yaw angle and the yaw number data, the method further comprises:
marking the wind driven generator with abnormal yaw in the sub-time period as a fan with abnormal yaw;
counting the marking times of the same abnormal yawing fan in the preset time period;
and determining the fault level of the same abnormal yawing fan according to the marking times and the segment numbers of the sub time segments.
Further, the determining the fault level of the same abnormal yawing fan according to the marking times and the segment numbers of the plurality of sub-time segments includes:
determining the abnormal yawing fan with the marking times larger than half of the segment numbers of the plurality of sub-time segments as a high-risk abnormal yawing fan;
determining the yaw abnormal fan with the marking times equal to half of the segment number of the sub-time segment as a medium-risk yaw abnormal fan;
and determining the abnormal yawing fan with the marking times larger than zero and smaller than half of the segment number of the sub-time segments as a low-risk abnormal yawing fan.
Further, the method comprises:
acquiring working condition data of a plurality of wind driven generators in a wind field, wherein the working condition data comprises wind direction data and wind speed data;
classifying a plurality of wind driven generators in the wind field according to the wind direction data and the wind speed data to obtain a plurality of fan clusters;
determining the wind deflection angle of the wind driven generator according to the cabin position data and the wind direction data, and the method comprises the following steps:
determining the wind deflection angle of a plurality of wind driven generators in the fan cluster according to the cabin position data and the wind direction data of the plurality of wind driven generators in the fan cluster;
determining whether the wind driven generator has abnormal yaw according to the yaw angle and the yaw frequency data comprises the following steps:
and determining whether the wind driven generators in the fan cluster are abnormal in yaw according to the wind deflection angles and the yaw frequency data of the wind driven generators in the fan cluster.
Further, the classifying the plurality of wind power generators in the wind farm according to the wind direction data and the wind speed data to obtain a plurality of fan clusters includes:
determining the correlation degree of the working condition data of every two wind driven generators in the plurality of wind driven generators in the wind field;
and determining every two wind driven generators with the correlation degree larger than the correlation degree threshold value as belonging to the same wind driven generator cluster.
Further, the determining the correlation of the working condition data of every two wind power generators in the plurality of wind power generators in the wind farm includes:
determining the correlation degree of the wind speed data and the correlation degree of the wind direction data;
and determining the average value of the correlation degree of the wind speed data and the correlation degree of the wind direction data as the correlation degree of the working condition data.
Further, after determining the correlation of the operating condition data of every two wind power generators in the plurality of wind power generators in the wind farm, the method further includes:
and determining the wind driven generators with the correlation degrees which are not greater than the correlation degree threshold value with other wind driven generators in the wind field except the fan cluster as the fans with abnormal working conditions.
The application provides a aerogenerator driftage anomaly detection early warning device includes:
the acquisition module is used for acquiring wind direction data, yaw frequency data and engine room position data of the wind driven generator;
the first processing module is used for determining the wind deflection angle of the wind driven generator according to the cabin position data and the wind direction data;
and the second processing module is used for determining whether the wind driven generator is abnormal in yaw according to the yaw angle and the yaw frequency data.
An electronic device is provided that includes a processor and a memory;
a memory for storing a computer program;
a processor for implementing the method as claimed in any one of the above when executing a program stored in the memory.
The present application provides a computer readable storage medium having stored thereon a program which, when executed by a processor, implements a method as described in any one of the above.
In some embodiments, a method for detecting a yaw anomaly of a wind turbine generator according to the present application determines whether the wind turbine generator is yaw anomaly or not by using yaw frequency data that can reflect a yaw condition of the wind turbine generator and a yaw angle of the wind turbine generator.
Drawings
FIG. 1 is a schematic flow chart illustrating a method for detecting yaw anomaly of a wind turbine according to an embodiment of the present application;
FIG. 2 is a schematic flow chart illustrating step 130 of the method for detecting yaw anomaly of the wind turbine shown in FIG. 1;
FIG. 3 is a detailed flow chart of the method for detecting yaw anomaly of the wind turbine shown in FIG. 1;
FIG. 4 is a schematic flow chart illustrating a method for detecting yaw abnormality of a wind turbine generator shown in FIG. 1 when a plurality of wind turbine generators are present;
FIG. 5 is a thermodynamic diagram illustrating correlation between a plurality of wind turbines in the wind turbine yaw anomaly detection method shown in FIG. 1;
FIG. 6 is a schematic flowchart illustrating an application example of a method for detecting a yaw anomaly of a wind turbine according to an embodiment of the present application;
FIG. 7 is a schematic block diagram illustrating an apparatus for detecting yaw anomaly of a wind turbine according to an embodiment of the present application;
fig. 8 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 the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with one or more embodiments of the present specification. Rather, they are merely examples of apparatus and methods consistent with certain aspects of one or more embodiments of the specification, as detailed in the claims which follow.
It should be noted that: in other embodiments, the steps of the corresponding methods are not necessarily performed in the order shown and described herein. In some other embodiments, the method may include more or fewer steps than those described herein. Moreover, a single step described in this specification may be broken down into multiple steps in other embodiments; multiple steps described in this specification may be combined into a single step in other embodiments.
In order to solve the technical problem, embodiments of the present application provide a method for detecting a yaw anomaly of a wind turbine generator, which determines whether the wind turbine generator is in a yaw anomaly state or not by using yaw frequency data that can reflect a yaw condition of the wind turbine generator and a yaw angle of the wind turbine generator.
Fig. 1 is a schematic flow chart of a method for detecting a yaw anomaly of a wind turbine generator according to an embodiment of the present application. The method may include the following steps 110 to 130:
and step 110, acquiring wind direction data, yaw frequency data and cabin position data of the wind driven generator.
The nacelle position data, the wind direction data, and the yaw number data may reflect a yaw of the wind turbine.
Wherein, the wind driven generator can be a single wind driven generator. Therefore, whether the single wind driven generator is abnormal in yaw can be detected. The wind driven generator can also be a plurality of wind driven generators in a wind field. Therefore, whether the plurality of wind driven generators are in abnormal yaw can be detected. For a detailed description, see below.
And step 120, determining the wind deflection angle of the wind driven generator according to the cabin position data and the wind direction data.
The nacelle position data may comprise angle data of the nacelle. The yaw angle is the angle of the nacelle relative to the wind direction, i.e. the angle between the nacelle and the wind direction. The angle of the nacelle at the initial position is 0 degrees, i.e. the reference position. After the nacelle is yawed, the nacelle deviates from the initial position, and the nacelle position data is angle data of the current position of the nacelle relative to the initial position and is a yaw angle.
And step 130, determining whether the wind driven generator has abnormal yaw according to the yaw angle and the yaw frequency data.
In the embodiment of the application, the yaw frequency data and the wind deflection angle of the wind driven generator can reflect whether the wind driven generator is abnormal in yaw, compared with the method that only the yaw frequency of the wind driven generator is considered in the related technology, the method has the advantages that more factors related to the yaw of the wind driven generator are considered, and the accuracy of detecting the yaw abnormality of the wind driven generator can be improved. In addition, in the method for detecting and early warning the frequent yawing frequency of the wind driven generator in the related art, the frequent yawing frequency probability of the wind driven generator is calculated, so that the frequent yawing frequency detection and early warning of the wind driven generator are realized, the frequent yawing probability reflects the possibility of whether the wind driven generator is abnormal in yawing, and the yawing frequency data and the yawing angle to the wind of the wind driven generator are both more favorable for reflecting whether the actual wind driven generator is abnormal in yawing and improving the accuracy of detecting the abnormal yawing frequency of the wind driven generator.
FIG. 2 is a schematic flow chart illustrating step 130 of the method for detecting yaw abnormality of wind turbine shown in FIG. 1. As shown in fig. 2, in some embodiments of the present step 130, the step 130 further includes steps 131 to 135.
The preset time period may include a whole period of time. Thus, the user can determine whether the wind turbine is abnormally yawing throughout the period of time. The preset time period may include a plurality of sub-time periods. Therefore, a user can determine whether the wind driven generator in the sub-time periods is abnormal in yaw, the abnormal yaw fan can be determined more accurately by calculating the abnormal yaw for multiple times, and the data volume of each sub-time period calculated for multiple times is small, so that the calculation rate is high each time. For a detailed description, see below.
And 132, determining an angle threshold according to the average value of the wind deflection angles of the plurality of wind driven generators, wherein the angle threshold is used for determining whether the wind driven generators are abnormal in yaw.
Step 132 described above may be implemented by various embodiments. In some embodiments, the step 132 further includes a first step to a third step. First, in some embodiments, the windage yaw of a plurality of wind turbines is determinedAverage of the mean of the angles. The average value can reflect the variation trend of the whole data, and the variation trend of the whole data cannot be influenced by the individual abnormal data, so that the subsequent use of the average value is more favorable for determining a reasonable threshold value. The average value of the deviation angle of the wind may reflect the trend of the deviation angle of the wind. And step two, determining the standard deviation of the mean value of the wind deflection angles of the plurality of wind driven generators. The standard deviation may reflect fluctuations in the wind deflection angle, i.e. the standard deviation reflects the magnitude of the change in the data. The smaller the average value of the wind deflection angle and the standard deviation of the wind deflection angle are, the more stable the wind deflection angle is, which reflects the more stable the wind driven generator is. And a third step of determining an angle threshold according to the average value of the mean value of the windage yaw angle and the standard deviation of the mean value of the windage yaw angle. The angle threshold comprises an upper angle threshold and a lower angle threshold, wherein the upper angle threshold
Is the average value mu of the wind deflection angle 1 And 2 times the standard deviation sigma 1 To sum, i.e.
Lower threshold of angle
Is the average value mu of the wind deflection angle 1 And 2 times the standard deviation sigma 1 Difference of difference, e.g.
Therefore, the angle threshold is not a fixed threshold, and the accuracy of determining the angle threshold is improved along with the average value of the mean value of the windage yaw angle and the standard deviation of the mean value of the windage yaw angle.
The steps in the other embodiments of step 132 are similar to the steps in the above embodiments of step 132, except that in the second step of this other embodiment, the variance of the mean of the yaw angles of the plurality of wind turbines may be determined, and the variance may be further squared to obtain the standard deviation. And will not be described in detail herein.
And step 134, determining a yaw frequency threshold according to the yaw frequency data of the plurality of wind driven generators.
The above-described step 134 may be implemented by various embodiments. In some embodiments, the step 134 further includes first to third steps. In a first step, an average of yaw rate data for a plurality of wind turbines is determined. The average value of the yaw number data may reflect a variation trend of the yaw number data. And secondly, determining the standard deviation of the yaw frequency data of the wind driven generators. The standard deviation may reflect fluctuations in the yaw rate data. The smaller the average value of the yaw rate data and the standard deviation of the yaw rate data, the more stable the yaw rate data. And thirdly, determining a yaw frequency threshold according to the average value of the yaw frequency data and the standard deviation of the yaw frequency data. The threshold value of the yaw times comprises an upper threshold value of the yaw times
And lower threshold value of yaw frequency
Upper threshold value of yaw frequency
Is the mean value mu of the yaw number data 2 And 2 times the standard deviation sigma 2 To sum up, i.e.
Lower threshold of yaw frequency
Is the mean value mu of the yaw number data 2 And 2 times the standard deviation sigma 2 Difference of difference, e.g.
Using mean value of yaw rate data and yaw rate data in this wayAnd the standard deviation better reflects the fluctuation condition of the yaw frequency data compared with the average value, so that a more reasonable yaw frequency threshold value is determined.
The steps in the other embodiments of step 134 are similar to the steps in the above embodiment of step 134, except that in the second step of the other embodiment, the variance of the yaw rate data of the plurality of wind turbines can be determined, and the standard deviation can be obtained by re-developing the variance. And will not be described in detail herein.
And step 135, determining that the wind driven generator has abnormal yaw when at least one of the average value of the yaw angles of the wind driven generator exceeds the angle threshold value and the yaw frequency data of the wind driven generator exceeds the yaw frequency threshold value. Therefore, the wind deflection angle and the yaw frequency data are used, the angle threshold and the yaw frequency threshold which are respectively determined are not fixed thresholds, and the angle threshold and the yaw frequency threshold are respectively thresholds which change along with the wind deflection angle and the yaw frequency data, so that the running condition of the wind driven generator is met, and the accuracy of yaw abnormity detection is improved.
FIG. 3 is a detailed flowchart of the method for detecting yaw abnormality of the wind turbine shown in FIG. 1. As shown in fig. 3, the wind turbine yaw abnormality detection method may include steps 210 to 260.
And step 220, determining the wind deflection angles of the plurality of wind driven generators in the sub-time period according to the cabin position data and the wind direction data.
And step 230, determining whether the plurality of wind driven generators in the sub-time period are abnormal in yaw according to the wind deflection angle and the yaw frequency data in the sub-time period.
After determining whether the wind turbines in the sub-time period have abnormal yaw, if the wind turbines are not abnormal yaw, the wind turbines with normal yaw may record the numbers of the wind turbines, or may not record the numbers of the wind turbines with normal yaw. And when the yaw of the wind driven generator is abnormal, executing the following steps 240 to 260 so as to facilitate the following early warning for users and prevent protection.
And 240, marking the wind driven generator with abnormal yaw in the sub-time period as a wind driven generator with abnormal yaw.
In this step 240, counting the number of times that the same abnormal yawing fan is marked in the predetermined time period is to mark the same abnormal yawing fan according to the unique identifier of the abnormal yawing fan. Wherein, the unique identifier can be the number of the abnormal yaw fan. Therefore, the same abnormal yawing fan can be accurately marked, and omission or error is avoided.
And step 250, counting the marking times of the same abnormal yawing fan in a preset time period.
And step 260, determining the fault grade of the same abnormal yaw fan according to the marking times and the segment numbers of the sub-time segments. Following this step 260, the method further includes performing an abnormal yaw early warning for the fault level. Therefore, the user can be reminded conveniently and protected in time.
The above step 260 may be implemented by various embodiments. In some embodiments, step 260 includes identifying a yaw anomaly fan marked more than half of the number of segments of the plurality of sub-time segments as a high risk yaw anomaly fan. And determining the yaw abnormal fan with the marking times equal to half of the segment number of the sub-time segment as the medium-risk yaw abnormal fan. And determining the yaw abnormal fan with the marking frequency larger than zero and smaller than half of the segment number of the sub-time segment as the low-risk yaw abnormal fan. Therefore, the abnormal yawing fans can be marked, the abnormal degree of the abnormal yawing fans can be reflected more favorably, errors caused by single statistical data can be reduced, and the accuracy of determining different fault levels of the abnormal yawing fans can be improved. In other embodiments, step 260 includes determining the abnormal probability of the same abnormal yawing wind turbine by using the ratio of the number of marked times to the number of segments of the plurality of sub-periods. And determining the yaw abnormal fan with the abnormal probability of more than 1/2 as the high-risk yaw abnormal fan. And determining the yaw abnormal fan with the abnormal probability equal to 1/2 as the medium-risk yaw abnormal fan. And determining the yaw abnormal fan with the abnormal probability less than 1/2 as the low-risk yaw abnormal fan. Illustratively, the number of the segments of the plurality of sub-time segments is 20, the number of times of marking is 12, and the abnormal probability of the same yaw abnormal fan is equal to 1/2, and the yaw abnormal fan is a mid-risk yaw abnormal fan.
In this embodiment, when the abnormal yawing fans are counted for multiple times in multiple sub-time periods within the same predetermined time period, it is found that one or some abnormal yawing fans are continuously present, which indicates that the abnormal yawing fans are relatively serious in the abnormal yawing degree, and the deviation caused by single statistical data is avoided, so that the abnormal yawing fans are detected more accurately.
In the method for detecting and early warning frequent yawing frequency of the wind driven generator in the related technology, the analyzed object is the wind driven generator of a wind field. The wind driven generators are in different environmental conditions, and the working condition data have larger difference, such as a wind driven generator on the top of a mountain and a wind driven generator on the feet of the mountain. Therefore, the yaw times of the fans of the wind driven generators in the wind field are detected simultaneously, so that the yaw detection early warning accuracy of the wind driven generators is low.
Fig. 4 is a schematic flow chart illustrating a case where a plurality of wind turbines are provided in the wind turbine yaw abnormality detection method shown in fig. 1. When the wind driven generators are a plurality of wind driven generators in a wind field, the method for detecting the yaw abnormality of the wind driven generators comprises steps 310 to 360, and the following steps are described in detail:
referring to fig. 1 and 4, step 310 obtains operating condition data of a plurality of wind power generators in a wind farm, where the operating condition data includes wind direction data and wind speed data.
And the working condition data is used for reflecting the working operation condition of the environment where the wind driven generator is located. The wind driven generators are located in environments such as mountain tops or mountain feet, and the working operating conditions of the corresponding wind driven generators are greatly different. Illustratively, the working condition data comprises working condition data of the wind driven generator on the top of the mountain and working condition data of the wind driven generator under the feet of the mountain, and a plurality of fan clusters are obtained by classification according to different working condition data subsequently, so that the wind driven generators under different working conditions can be distinguished more easily, and whether the wind driven generator is abnormal in yaw is further determined more accurately.
And step 320, classifying the plurality of wind driven generators in the wind field according to the wind direction data and the wind speed data to obtain a plurality of fan clusters. Therefore, the wind driven generators with different working condition data can be distinguished, and the accuracy of the abnormal working condition of the wind driven generators can be improved.
The above step 320 may take various embodiments. In some embodiments, step 320 may include classifying the plurality of wind turbines in the wind farm using a relevancy algorithm according to the wind direction data and the wind speed data to obtain a plurality of wind turbine clusters. Therefore, the fans with similar working conditions can be determined through the correlation algorithm, and the determination of the wind driven generator with abnormal working conditions is facilitated.
Further, step 320 further includes step 1, determining a correlation of the operating condition data of every two wind turbines in the plurality of wind turbines in the wind farm. And 2, determining the two wind driven generators with the correlation degrees larger than the correlation threshold value as belonging to the same fan cluster. Therefore, the correlation is used, one-dimensional linear data is processed, the processed data volume is small, and the processing efficiency is improved. Wherein, the threshold value of the correlation degree can be determined according to the requirement of the user. The larger the correlation threshold value is, the closer the working condition data of every two wind driven generators are, the stronger the correlation is, and the more accurate the subsequent analysis is. The smaller the correlation threshold. The correlation threshold may be greater than 0.6 and less than 0.9. Illustratively, the correlation threshold is 0.7, so that two wind power generators with correlation greater than 0.7 can be divided into the same wind power generator cluster.
After the step 1, in some embodiments, the method may include a step 3, determining a wind power generator having a correlation not greater than a correlation threshold with other wind power generators in the wind field except for the wind power generator cluster as an abnormal-condition wind power generator. Therefore, the maintenance of the wind power generator of the whole wind field is considered, the abnormal condition of the wind power generator of the wind field is comprehensively analyzed, the maintenance efficiency of the wind power generator of the wind field is improved, and the wind power generator with the abnormal working condition can be conveniently and directly monitored in the later period. After the step of determining the fan with the abnormal working condition, the method further comprises the step of marking the fan with the abnormal working condition, and the fan with the abnormal working condition can be conveniently found through marking the fan with the abnormal working condition in the later period. For the fan with the abnormal working condition, the method can further comprise the step of carrying out abnormal working condition early warning on the fan with the abnormal working condition. Therefore, the user is convenient to remind, and the fan with abnormal working condition is protected in time. After the step 1, in other embodiments, the method further includes deleting the wind power generators, of which the correlation degrees with the other wind power generators except the wind power generator cluster in the wind field are not greater than the correlation degree threshold value, from the wind power generators in the wind field, so as to reduce the data of the subsequent processing.
Wherein, the step 1 further includes a step 1 of determining a correlation of the wind speed data and a correlation of the wind direction data. And 2, determining the mean value of the correlation degree of the wind speed data and the correlation degree of the wind direction data as the correlation degree of the working condition data.
The correlation degree of the wind speed data and the correlation degree of the wind direction data are determined by adopting the following Pearson correlation coefficient calculation formula:
where rho X，Y Representing the correlation coefficient between the two units, cov representing the covariance, X, Y representing the data sets (here, the two data, wind speed data and wind direction data) of the two wind turbines in a predetermined time period, respectively, mu X 、μ Y Respectively representing the mean of the data of two wind turbines over a predetermined period of time, E representing expectation, σ X 、σ Y Indicating the standard deviation, X, of the data for a predetermined period of time for two wind turbines i 、Y i Respectively representing the relevant ith data in the data of the two wind power generators,
representing the average of the data samples X over a predetermined period of time,
and the average value of the data samples Y in a preset time period is shown, the value range of i is (1, n), n represents the number of fans, and sigma represents summation.
Determining the mean value of the correlation degree of the wind speed data and the correlation degree of the wind direction data as the correlation degree of the working condition data by adopting the following mean value calculation formula:
in the formula (I), the compound is shown in the specification,
representing the correlation degree of the working condition data of the wind driven generator, wherein the correlation degree of the working condition data is the mean value of the correlation degree of the wind speed data and the correlation degree of the wind direction data,
the correlation degree of the wind speed data and the correlation degree of the wind direction data respectively representing the wind power generator are obtained by the Pearson correlation coefficient calculation formula described above.
In the embodiment of step 320, the average value of the correlation between the wind speed data and the wind direction data is used as the correlation between the operating condition data, so that the operating conditions of the plurality of wind power generators can be effectively reflected by the correlation between the operating condition data, and the accuracy of determining the operating conditions of the plurality of wind power generators can be improved.
In other embodiments of step 320, step 320 may include classifying the plurality of wind turbines in the wind farm by using a clustering algorithm according to the wind direction data and the wind speed data to obtain a plurality of wind turbine clusters. Therefore, the fans with similar working conditions can be determined through the clustering algorithm, the wind driven generator under the yawing working condition can be determined more easily, and the wind driven generator with abnormal yawing can be determined more easily. Wherein the clustering algorithm may comprise a k-meas algorithm. And will not be exemplified in detail herein.
And step 330, acquiring wind direction data, yaw frequency data and cabin position data of the wind driven generators in the fan clusters.
This step 330 may include acquiring wind direction data, yaw frequency data, and nacelle position data of a wind turbine generator of a single cluster from a plurality of fan clusters each time, and performing subsequent processing until the wind direction data, yaw frequency data, and nacelle position data of all the clusters are acquired for a plurality of times, and the number of the acquired times is the same as the number of the fan clusters, so that the amount of data processed each time is small.
Step 330 of FIG. 4 is similar to step 110 of FIG. 1, and in step 330 of FIG. 4, the wind generators in the plurality of wind turbine clusters are obtained as compared to step 110 of FIG. 1. Thus, whether the yaw of the plurality of wind driven generators is abnormal or not can be determined.
And 340, determining the wind deflection angles of the plurality of wind driven generators in the fan cluster according to the cabin position data and the wind direction data of the plurality of wind driven generators in the fan cluster.
And step 350, determining whether the wind driven generator has abnormal yaw according to the yaw angle and the yaw frequency data.
And step 360, determining whether the wind driven generators in the fan cluster are abnormal in yaw according to the wind deflection angles and the yaw frequency data of the wind driven generators in the fan cluster. Therefore, on the basis of distinguishing the wind driven generators, whether the wind driven generators in the fan clusters are abnormal in yaw can be determined, the accuracy of yaw abnormality detection can be improved, the whole process is executed according to the fan clusters, the data volume processed each time is small, and the processing efficiency is high.
Fig. 5 is a thermodynamic diagram illustrating correlation between a plurality of wind turbines in the wind turbine yaw abnormality detection method shown in fig. 1. Fig. 6 is a schematic flowchart illustrating an application example of the method for detecting a yaw anomaly of a wind turbine according to the embodiment of the present application. The degree of correlation is represented by the degree of lightness of the gradation in fig. 5, and the gradation is darker as the degree of correlation is higher.
The embodiment of the present application will be described in detail by taking 124 wind power generators of a 4MW offshore wind farm as an example.
And step 410, extracting wind direction data, yaw frequency data and wind speed data of 124 wind power generators of the wind field in a preset time period. The preset time period comprises a six hour sub-period of 2019-03-0300: 00: 00-2019-03-0306: 00: 00.
And 430, determining two wind driven generators with the correlation degree larger than 0.7 as the same fan cluster according to the correlation degree of the working condition data of every two wind driven generators in the plurality of wind driven generators in the wind field, and determining the wind driven generators which do not form the cluster as the fans with abnormal working conditions. Table 1 shows the wind turbine clusters of 124 wind turbines and wind turbines that do not form clusters. For example, based on the wind power generator 1 in the 1 st column in fig. 5, the wind power generators with the correlation degree of more than 0.7 with the wind power generator 1 in the 1 st column, such as the wind power generator 1, the wind power generator 2, the wind power generator 3, the wind power generator 4, the wind power generator 5, the wind power generator 33, the wind power generator 34, the wind power generator 35, and the wind power generator 36, in the 10 th row wind power generator in the 1 st column are determined as the same wind power generator cluster. For example, this fan cluster is referred to as fan cluster 1. The relevance of all rows and columns in fan cluster 1 may then be removed from FIG. 5. And then, continuing to determine the wind driven generator with the correlation degree of more than 0.7 with the wind driven generator 6 in the 1 st column from the 10-row wind driven generators in the 1 st column based on the wind driven generator 6 in the 1 st column. Please refer to the wind turbine cluster 2 in detail, and by analogy, the wind turbine cluster and the wind turbine without the cluster are obtained according to the correlation of 124 wind turbines. The data amount processed in this way is reduced, and the processing efficiency can be improved.
TABLE 1 wind turbine Cluster and wind turbine Generator not Cluster formed
And step 440, respectively determining the fan yaw frequency of each wind driven generator in the plurality of wind driven generators in each fan cluster within 6 hours and the wind deflection angle of each wind driven generator within 6 hours according to the plurality of fan clusters. The fan yawing times within 6 hours comprise recording the fan yawing times once per hour, and at the moment, the fan yawing times are the accumulated sum of the fan yawing times within 6 hours. The fan yaw frequency comprises data recorded once in 6 hours, and at this time, the fan yaw frequency is 6 hours.
TABLE 2 abnormal yaw fans in a fan cluster
And 480, marking the wind driven generator with abnormal yaw in the sub-time period as an abnormal yaw fan, and marking the abnormal working condition fan.
And 510, counting the marking times of the same abnormal yawing fan in the preset time period.
And step 520, determining the fault level of the same abnormal yawing fan according to the marking times and the segment numbers of the sub time periods. If the marked times of the abnormal yawing fan are more than or equal to 3, determining the abnormal yawing fan as a high-risk abnormal yawing fan; if the marked times of the abnormal yawing fan are equal to 2, determining the abnormal yawing fan as a medium-risk abnormal yawing fan; and if the marked times of the abnormal yawing fan are equal to 1, determining the abnormal yawing fan as a low-risk abnormal yawing fan. And the fault grades of the high-risk yaw abnormal fan, the medium-risk yaw abnormal fan and the low-risk yaw abnormal fan are given in the table 3.
TABLE 3 Fault class and numbering of abnormal yaw fans
Fig. 7 is a schematic block diagram illustrating a device for detecting a yaw abnormality of a wind turbine according to an embodiment of the present application.
As shown in fig. 7, the wind turbine yaw abnormality detection and early warning device includes:
the obtaining module 21 is configured to obtain wind direction data, yaw frequency data, and cabin position data of the wind turbine.
And the first processing module 22 is configured to determine a wind deflection angle of the wind turbine generator according to the cabin position data and the wind direction data.
And the second processing module 23 is configured to determine whether the wind turbine is abnormal in yaw according to the yaw angle and the yaw frequency data.
The implementation process of the functions and actions of each unit in the above device is specifically described in the implementation process of the corresponding step in the above method, and is not described herein again.
Fig. 8 is a schematic structural diagram of an electronic device 30 according to an embodiment of the present application. The electronic device 30 may include a processor 31, a memory 33 storing machine executable instructions, and a communication interface 32. The processor 31 and memory 33 may communicate via a system bus 34. Also, the processor 31 may perform the methods described above by reading and executing machine-executable instructions in the memory 33 corresponding to the data pull or data backhaul logic.
The memory 33 referred to herein may be any electronic, magnetic, optical, or other physical storage device that may contain or store information such as executable instructions, data, and the like. For example, the machine-readable storage medium may be: a RAM (random Access Memory), a volatile Memory, a non-volatile Memory, a flash Memory, a storage drive (e.g., a hard drive), a solid state drive, any type of storage disk (e.g., an optical disk, a dvd, etc.), or similar storage medium, or a combination thereof.
In some embodiments, there is also provided a machine-readable storage medium, such as the memory 33 in fig. 8, having stored therein machine-executable instructions that, when executed by a processor, implement the method described above. For example, the machine-readable storage medium may be a ROM, a RAM, a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and so forth.
Embodiments of the present application also provide a computer program, which is stored in a machine-readable storage medium, such as the memory 33 in fig. 8, and when executed by a processor, causes the processor 31 to perform the method described above.
The above description is only a preferred embodiment of the present disclosure, and should not be taken as limiting the present disclosure, and any modifications, equivalents, improvements, etc. made within the spirit and principle of the present disclosure should be included in the scope of the present disclosure.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
Claims (13)
1. A method for detecting abnormal yaw of a wind driven generator is characterized by comprising the following steps:
acquiring wind direction data, yaw frequency data and engine room position data of the wind driven generator;
determining the wind deflection angle of the wind driven generator according to the cabin position data and the wind direction data;
and determining whether the wind driven generator has abnormal yaw according to the wind deflection angle and the yaw frequency data.
2. The method for detecting yaw anomaly of a wind turbine generator according to claim 1, wherein the determining whether the wind turbine generator is yaw anomaly according to the yaw angle and the yaw frequency data comprises:
determining a mean value of the yaw angle of the wind turbine at a plurality of moments within a predetermined time period;
determining an angle threshold according to an average value of the wind deflection angles of the plurality of wind driven generators;
determining yaw frequency data of the wind driven generator in the preset time period;
determining a yaw frequency threshold value according to the yaw frequency data of the wind driven generators;
and when the condition that the mean value of the yaw angles of the wind driven generator exceeds the angle threshold value and the yaw frequency data of the wind driven generator exceeds at least one of the yaw frequency threshold values exists, determining that the wind driven generator has abnormal yaw.
3. The method for detecting yaw anomaly of a wind turbine generator according to claim 2, wherein determining a yaw threshold value according to yaw data of a plurality of wind turbines comprises:
determining an average of the yaw number data for a plurality of the wind turbines;
determining a standard deviation of the yaw times data for a plurality of the wind turbines;
and determining the yaw frequency threshold according to the average value of the yaw frequency data and the standard deviation of the yaw frequency data.
4. The method for detecting a yaw anomaly of a wind turbine according to claim 3, wherein said determining an angle threshold value from a mean value of said yaw angles of a plurality of said wind turbines comprises:
determining an average of the mean of the yaw angles of the plurality of wind turbines;
determining a standard deviation of a mean of the yaw angles of the plurality of wind turbines;
and determining the angle threshold according to the average value of the mean value of the wind deflection angle and the standard deviation of the mean value of the wind deflection angle.
5. The wind turbine yaw anomaly detection method according to claim 2, characterized in that said predetermined time period comprises a plurality of sub-time periods, said plurality of sub-time periods having the same time length;
determining whether the wind driven generator has abnormal yaw according to the yaw angle and the yaw frequency data comprises the following steps:
determining whether the plurality of wind driven generators in the sub-time period are abnormal in yaw according to the yaw angle and the yaw frequency data in the sub-time period;
after the determining whether the wind turbine is abnormally yawed according to the yaw angle and the yaw number data, the method further comprises:
marking the wind driven generator with abnormal yaw in the sub-time period as a fan with abnormal yaw;
counting the marking times of the same abnormal yawing fan in the preset time period;
and determining the fault grade of the same abnormal yaw fan according to the marking times and the segment numbers of the sub-time segments.
6. The method for detecting the yaw abnormality of the wind turbine according to claim 5, wherein the determining the fault level of the same yaw abnormality wind turbine according to the number of times of marking and the number of the segments of the plurality of sub-periods of time includes:
determining the abnormal yawing fan with the marking times larger than half of the segment numbers of the plurality of sub-time segments as a high-risk abnormal yawing fan;
determining the abnormal yawing fan with the marking times equal to half of the segment number of the sub-time segments as an abnormal medium-risk yawing fan;
and determining the abnormal yawing fan with the marking times larger than zero and smaller than half of the segment number of the sub-time segments as a low-risk abnormal yawing fan.
7. The method of detecting a yaw anomaly of a wind turbine as claimed in claim 1, characterized in that said method comprises:
acquiring working condition data of a plurality of wind driven generators in a wind field, wherein the working condition data comprises wind direction data and wind speed data;
classifying a plurality of wind driven generators in the wind field according to the wind direction data and the wind speed data to obtain a plurality of fan clusters;
determining the wind deflection angle of the wind driven generator according to the cabin position data and the wind direction data, and the method comprises the following steps:
determining the wind deflection angle of a plurality of wind driven generators in the fan cluster according to the cabin position data and the wind direction data of the plurality of wind driven generators in the fan cluster;
determining whether the wind driven generator has abnormal yaw according to the yaw angle and the yaw frequency data comprises the following steps:
and determining whether the wind driven generators in the fan cluster are abnormal in yaw according to the wind deflection angles and the yaw frequency data of the wind driven generators in the fan cluster.
8. The method for detecting yaw anomaly of a wind turbine generator according to claim 7, wherein the step of classifying the plurality of wind turbine generators in the wind field according to the wind direction data and the wind speed data to obtain a plurality of fan clusters comprises:
determining the correlation degree of the working condition data of every two wind driven generators in the plurality of wind driven generators in the wind field;
and determining every two wind driven generators with the correlation degree larger than the correlation degree threshold value as belonging to the same wind driven generator cluster.
9. The method of claim 8, wherein the determining the correlation of the operating condition data of each two wind turbines of the plurality of wind turbines in the wind farm comprises:
determining the correlation degree of the wind speed data and the correlation degree of the wind direction data;
and determining the average value of the correlation degree of the wind speed data and the correlation degree of the wind direction data as the correlation degree of the working condition data.
10. The wind turbine yaw anomaly detection method as recited in claim 8, said method further comprising, after said determining a correlation of operating condition data for each two of a plurality of wind turbines in said wind farm:
and determining the wind driven generators with the correlation degrees which are not greater than the correlation degree threshold value with other wind driven generators in the wind field except the fan cluster as the fans with abnormal working conditions.
11. The utility model provides a aerogenerator driftage anomaly detection early warning device which characterized in that includes:
the acquisition module is used for acquiring wind direction data, yaw frequency data and engine room position data of the wind driven generator;
the first processing module is used for determining the wind deflection angle of the wind driven generator according to the cabin position data and the wind direction data;
and the second processing module is used for determining whether the wind driven generator is abnormal in yaw according to the wind deflection angle and the yaw frequency data.
12. An electronic device comprising a processor and a memory;
a memory for storing a computer program;
a processor for implementing the method of any one of claims 1 to 10 when executing a program stored in the memory.
13. A computer-readable storage medium, having stored thereon a program which, when executed by a processor, carries out the method of any one of claims 1-10.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210557433.1A CN114962173A (en) | 2022-05-20 | 2022-05-20 | Method and device for detecting yawing abnormity of wind driven generator and electronic equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210557433.1A CN114962173A (en) | 2022-05-20 | 2022-05-20 | Method and device for detecting yawing abnormity of wind driven generator and electronic equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114962173A true CN114962173A (en) | 2022-08-30 |
Family
ID=82985351
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210557433.1A Pending CN114962173A (en) | 2022-05-20 | 2022-05-20 | Method and device for detecting yawing abnormity of wind driven generator and electronic equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114962173A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115434878A (en) * | 2022-11-09 | 2022-12-06 | 东方电气风电股份有限公司 | Wind generating set temperature cluster control method, device, equipment and medium |
-
2022
- 2022-05-20 CN CN202210557433.1A patent/CN114962173A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115434878A (en) * | 2022-11-09 | 2022-12-06 | 东方电气风电股份有限公司 | Wind generating set temperature cluster control method, device, equipment and medium |
| CN115434878B (en) * | 2022-11-09 | 2023-02-03 | 东方电气风电股份有限公司 | Wind generating set temperature cluster control method, device, equipment and medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109086793B (en) | Abnormity identification method for wind driven generator | |
| US9366235B2 (en) | Estimation of wind conditions at a wind turbine | |
| CN110362045B (en) | Marine doubly-fed wind turbine generator fault discrimination method considering marine meteorological factors | |
| CN115326147B (en) | Real-time monitoring method and system for desulfurizing tower for ship | |
| US20120209539A1 (en) | Turbine fault analysis | |
| CN103925155B (en) | The self-adapting detecting method that a kind of Wind turbines output is abnormal | |
| Seryasat et al. | Multi-fault diagnosis of ball bearing based on features extracted from time-domain and multi-class support vector machine (MSVM) | |
| CN114962173A (en) | Method and device for detecting yawing abnormity of wind driven generator and electronic equipment | |
| CN112418306A (en) | Gas turbine compressor fault early warning method based on LSTM-SVM | |
| CN108444713B (en) | Rolling bearing fault feature extraction method based on D's wavelet energy base | |
| GB2622708A (en) | SVM-based cold flow test detection method and system during diesel engine assembly | |
| CN110858072A (en) | Method and device for determining running state of equipment | |
| CN103559414B (en) | A kind of envirment factor the point estimation method based on Arrhenius relationship | |
| Seryasat et al. | Fault diagnosis of ball-bearings using principal component analysis and support-vector machine | |
| CN112855458B (en) | Anemometer fault diagnosis method, system and medium for wind generating set | |
| CN113297291A (en) | Monitoring method, monitoring system, readable storage medium and wind driven generator | |
| CN112576454A (en) | Wind turbine generator main shaft temperature early warning method and device based on multi-dimensional early warning strategy | |
| CN112836424A (en) | Early icing fault prediction method for fan blade | |
| CN111192163A (en) | Generator reliability medium-short term prediction method based on wind turbine generator operating data | |
| CN114637791A (en) | Power plant equipment early warning method based on similarity | |
| CN114925731A (en) | Method for detecting abnormal value of monitoring data of flexible inclinometer | |
| CN113281003A (en) | Fixed marine structure real-time damage monitoring method and load level monitoring method | |
| CN113723634B (en) | Fault variable determining method, system and readable storage medium | |
| JP7125518B2 (en) | Abnormality diagnosis support method and abnormality diagnosis support device for multivariate data | |
| Avendano-Valencia et al. | On multiple-model linear-parameter-varying based SHM for time-dependent structures under uncertainty |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |