CN118462505A - Intelligent early warning method, system, equipment and medium for yaw diagonal angle abnormality of wind turbine generator - Google Patents
Intelligent early warning method, system, equipment and medium for yaw diagonal angle abnormality 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
- F03D—WIND MOTORS
- F03D17/00—Monitoring or testing of wind motors, e.g. diagnostics
- F03D17/005—Monitoring or testing of wind motors, e.g. diagnostics using computation methods, e.g. neural networks
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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
- F03D—WIND MOTORS
- F03D17/00—Monitoring or testing of wind motors, e.g. diagnostics
- F03D17/027—Monitoring or testing of wind motors, e.g. diagnostics characterised by the component being monitored or tested
- F03D17/029—Blade pitch or yaw drive systems, e.g. pitch or yaw angle
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- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
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Abstract
The invention provides an intelligent early warning method, system, equipment and medium for yaw diagonal angle abnormality of a wind turbine, which comprise the following steps: step 1, preprocessing the obtained historical data of the target unit to obtain a matrix, wherein a first column of the matrix is wind speed, a second column of the matrix is opposite wind angle and a third column of the matrix is active power; step 2, dividing active power into bins by utilizing wind speed and opposite wind angle to obtain a plurality of wind speed-angle bins; step 3, respectively constructing a two-dimensional matrix and a three-dimensional matrix according to the obtained wind speed-angle bins, and step 4, judging whether the dynamic deviation on the wind angle is normal or not by utilizing the obtained two-dimensional matrix; judging whether the target unit operates normally or not by using the three-dimensional matrix, wherein if the unit operates abnormally, constructing a four-dimensional matrix by using the three-dimensional matrix, and judging whether the static deviation of the wind angle is normal or not by using the four-dimensional matrix; the invention can more comprehensively and accurately judge the problem of abnormal yaw vs angle of the unit.
Description
Technical Field
The invention belongs to the technical field of fault early warning of a variable pitch motor of a wind turbine, and particularly relates to an intelligent early warning method, system, equipment and medium for yaw opposite wind angle abnormality of a wind turbine.
Background
In recent years, intelligent operation and maintenance of a wind farm have become development hot spots in the industry, but in the intelligent operation and maintenance process of the wind farm, early warning of faults of various devices through various intelligent algorithms is particularly important. Whether the yaw of the unit is abnormal to wind is related to the absorption of wind energy of the unit, the generating capacity of the unit is directly affected, moreover, the dynamic deviation of the angle of the unit to wind is abnormal, the load of the unit can be increased, and the service life of the unit is reduced due to long-time running with diseases.
At present, the industry provides various different methods for early warning the wind angle abnormality of the wind turbine generator, but most of the methods are applied offline to a small number of sets, the abnormal fault tolerance of data acquisition and reading of the large number of sets is poor, the online application can influence the accuracy of the calculation result of the algorithm model, and the model is seriously blocked and cannot run due to disconnection; the current method for judging the abnormal wind angle of the unit not only pays little attention to the dynamic deviation angle, but also the calculation method of the static deviation angle is usually to check the power of different wind angles at the specified wind speed or check the power curve of different wind angles, and analyze the curve above.
Disclosure of Invention
The invention aims to provide an intelligent early warning method, system, equipment and medium for yaw wind angle abnormality of a wind turbine generator, which solve the defect that the existing calculation of the wind angle has larger deviation, so that the judgment result of the wind angle abnormality is inaccurate.
In order to achieve the above purpose, the invention adopts the following technical scheme:
The invention provides an intelligent early warning method for yaw diagonal angle abnormality of a wind turbine generator, which comprises the following steps:
Step 1, preprocessing the obtained historical data of the target unit to obtain a matrix, wherein a first column of the matrix is wind speed, a second column of the matrix is opposite wind angle and a third column of the matrix is active power;
Step 2, dividing active power into bins by utilizing wind speed and opposite wind angle to obtain a plurality of wind speed-angle bins;
step 3, respectively constructing a two-dimensional matrix and a three-dimensional matrix according to the obtained wind speed-angle bins;
step 4, judging whether the dynamic deviation of the wind angle is normal or not by using the obtained two-dimensional matrix; judging whether the target unit operates normally or not by using the three-dimensional matrix, wherein if the unit operates abnormally, constructing a four-dimensional matrix by using the three-dimensional matrix, and judging whether the static deviation of the wind angle is normal or not by using the four-dimensional matrix.
Preferably, in step1, the obtained historical data of the target unit is preprocessed to obtain a matrix, and the specific method is as follows:
s11, the historical data comprise a unit state, wind speed, active power, a pitch angle and a wind angle; acquiring configuration parameters of a target unit, wherein the configuration parameters comprise cut-in wind speed, rated wind speed and rated power;
S12, screening data of wind speed larger than cut-in wind speed smaller than rated wind speed, active power with power larger than zero, normal power generation of a unit state and pitch angle smaller than 2 from historical data to form an obtained matrix.
Preferably, in step 2, active power is divided into bins with the width of 1 by using wind speed and wind angle, so as to obtain a plurality of wind speed-angle bins.
Preferably, in step 3, a two-dimensional matrix is constructed according to the obtained plurality of wind speed-angle bins, and the specific method is as follows:
And counting the data quantity of the active power corresponding to each opposite wind angle in each wind speed-angle bin, and combining the opposite wind angle and the active power data quantity to form a two-dimensional matrix.
Preferably, in step 3, whether the dynamic deviation of the wind angle is normal is judged by using the obtained two-dimensional matrix, and the specific method is as follows:
acquiring a wind angle corresponding to the maximum data volume from all the data volumes in the two-dimensional matrix;
If the absolute value of the pair of opposite wind angles is larger than or equal to a set threshold value, the dynamic deviation of the opposite wind angles of the target unit is abnormal; otherwise, the dynamic deviation of the opposite wind angle of the target unit is normal.
Preferably, in step 3, a three-dimensional matrix is constructed according to the obtained multiple wind speed-angle bins, and the specific method is as follows:
counting the data quantity of active power in each wind speed-angle bin;
Deleting the wind speed-angle bin corresponding to the data volume smaller than the set value to obtain a residual wind speed-angle bin;
calculating the power average value among all the active powers corresponding to each wind speed-angle bin;
Combining the wind speeds, wind angles and power average values in all the rest wind speed-angle bins to form a three-dimensional matrix;
In the step 3, a four-dimensional matrix is constructed by utilizing a three-dimensional matrix, and the specific method is as follows:
obtaining a maximum power average value corresponding to each wind speed from the three-dimensional matrix;
Obtaining a wind angle corresponding to the maximum power average value;
Acquiring all power average values when the wind angle corresponding to each wind speed is zero from the three-dimensional matrix, and calculating the average value corresponding to all power average values;
calculating the power difference between each maximum power average value and each average value;
acquiring the sum of data amounts of power mean values corresponding to each wind speed;
And combining the wind speed and the wind angle in the three-dimensional matrix and the sum and the power difference of the calculated data quantity to form a four-dimensional matrix.
Preferably, in step 3, whether the static deviation to the wind angle is normal is judged by using a four-dimensional matrix, and the specific method is as follows:
Calculating the average value of the power differences corresponding to all the power differences;
Comparing the obtained power difference average value with a set threshold value, wherein if the power difference average value is larger than the set threshold value, calculating the static deviation of the wind angle by using a four-dimensional matrix; otherwise, the target unit operates normally;
comparing the obtained absolute value of the static deviation of the opposite wind angle with a threshold value, wherein if the absolute value of the static deviation of the opposite wind angle is larger than the threshold value, the static deviation of the opposite wind angle is abnormal; otherwise, the static deviation of the wind angle is normal.
An intelligent early warning system for yaw versus wind angle abnormality of a wind turbine generator comprises:
The data acquisition unit is used for preprocessing the obtained historical data of the target unit to obtain a matrix, wherein the first column of the matrix is wind speed, the second column of the matrix is opposite wind angle and the third column of the matrix is active power;
The bin dividing unit is used for dividing the active power into bins by utilizing wind speed and opposite wind angle to obtain a plurality of wind speed-angle bins;
a matrix construction unit for constructing a two-dimensional matrix and a three-dimensional matrix according to the obtained wind speed-angle bins,
The anomaly judging unit is used for judging whether the dynamic deviation of the wind angle is normal or not by utilizing the obtained two-dimensional matrix; judging whether the target unit operates normally or not by using the three-dimensional matrix, wherein if the unit operates abnormally, constructing a four-dimensional matrix by using the three-dimensional matrix, and judging whether the static deviation of the wind angle is normal or not by using the four-dimensional matrix.
A processing device comprising at least a processor and a memory, the memory having stored thereon a computer program which, when executed by the processor, is executed to implement the steps of the method.
A computer storage medium having stored thereon computer readable instructions executable by a processor to implement steps according to the method
Compared with the prior art, the invention has the beneficial effects that:
According to the intelligent early warning method for yaw versus wind angle abnormality of the wind turbine generator, provided by the invention, the static deviation and the dynamic deviation of the versus wind angle are calculated respectively, the problem of yaw versus wind angle abnormality of the wind turbine generator can be more comprehensively and accurately judged, in the process of judging the static deviation of the versus wind angle, the four-dimensional matrix constructed by the wind speed, the versus wind angle, the sum of data quantity and the power difference is utilized, and then the static deviation of the versus wind angle is calculated by utilizing the four-dimensional matrix, and the process combines the operation mechanism of the wind turbine generator, such as the condition that the length of a blade, the air density and a main control program of the wind turbine generator are unchanged in the power climbing stage, the output power of the wind turbine generator is mainly related to the wind speed and the versus wind angle; by using the multi-parameter automatic weighting method, not only is the static deviation of the wind angle calculated effectively, but also a large amount of parameter adjustment in the later stage of the calculation of the static deviation of the wind angle is avoided, a large amount of manpower and time are saved, and the accuracy of the wind angle abnormity judgment result is further improved.
Furthermore, the method is used for preprocessing the obtained historical data of the target unit, so that the method has a certain fault tolerance function when applied to the early warning of a large-batch unit line, and the problem that the algorithm model calculation result is abnormal due to the abnormal data acquisition and reading problems in the large-batch unit application is avoided to a great extent.
The intelligent early warning system for yaw diagonal angle abnormality of the wind turbine provided by the invention calculates the static deviation and the dynamic deviation of the diagonal angle respectively, and can judge the problem of yaw diagonal angle abnormality of the wind turbine more comprehensively and accurately.
Drawings
FIG. 1 is a schematic flow chart of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth such as the particular system architecture, techniques, etc., in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
It should be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It should also be understood that the term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
As used in the present description and the appended claims, the term "if" may be interpreted as "when..once" or "in response to a determination" or "in response to detection" depending on the context. Similarly, the phrase "if a determination" or "if a [ described condition or event ] is detected" may be interpreted in the context of meaning "upon determination" or "in response to determination" or "upon detection of a [ described condition or event ]" or "in response to detection of a [ described condition or event ]".
Furthermore, the terms "first," "second," "third," and the like in the description of the present specification and in the appended claims, are used for distinguishing between descriptions and not necessarily for indicating or implying a relative importance.
Reference in the specification to "one embodiment" or "some embodiments" or the like means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," and the like in the specification are not necessarily all referring to the same embodiment, but mean "one or more but not all embodiments" unless expressly specified otherwise. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless expressly specified otherwise.
Example 1
As shown in fig. 1, the intelligent early warning method for yaw versus wind angle abnormality of a wind turbine generator provided by the embodiment includes the following steps:
Step 1, preprocessing the obtained historical data of the target unit to obtain a matrix, wherein a first column of the matrix is wind speed, a second column of the matrix is opposite wind angle and a third column of the matrix is active power;
Step 2, dividing active power into bins by utilizing wind speed and opposite wind angle to obtain a plurality of wind speed-angle bins;
step3, respectively constructing a two-dimensional matrix and a three-dimensional matrix according to the obtained wind speed-angle bins,
Step 4, judging whether the dynamic deviation of the wind angle is normal or not by using the obtained two-dimensional matrix; judging whether the target unit operates normally or not by using the three-dimensional matrix, wherein if the unit operates abnormally, constructing a four-dimensional matrix by using the three-dimensional matrix, and judging whether the static deviation of the wind angle is normal or not by using the four-dimensional matrix.
According to the embodiment, the static deviation and the dynamic deviation of the yaw angle of the unit are calculated respectively, and the problem that the yaw angle of the unit is abnormal can be comprehensively and accurately judged.
Example 2
Step 1, SCADA data of three months and 1min level of a unit are read, wherein the SCADA data comprise unit states, wind speeds, active power, pitch angles and opposite wind angles; and meanwhile, the cut-in wind speed, the rated wind speed and the rated power of the unit are obtained in the unit configuration.
And step 2, calculating the data quantity of the read SCADA data, and when the data quantity is more than or equal to 116640, screening normal data before full sending, otherwise, reporting that the original data is insufficient, and finishing calculation.
Step 3, screening out data of wind speed larger than cut-in wind speed and smaller than rated wind speed, power larger than 0, normal power generation of a unit state and a pitch angle smaller than 2, and forming a three-dimensional matrix Q1 containing wind speed, opposite wind angle and active power;
and 4, calculating the data quantity of the three-dimensional matrix Q1, and when the data quantity is larger than or equal to 51840, carrying out bin division calculation on the data of the three-dimensional matrix Q1, otherwise, reporting that the useful data is insufficient, and ending the calculation.
Step 5, calculating the data in the three-dimensional matrix Q1:
S51, carrying out bin division on active power by utilizing wind speed and opposite wind angle, wherein the widths are 1, so as to obtain a plurality of wind speed-angle bins, and the wind speed and opposite wind angle in each wind speed-angle bin are V i and alpha j respectively;
s52, calculating the data quantity of active power in each wind speed-angle bin;
S53, deleting a wind speed-angle bin corresponding to the data quantity less than 50 to obtain a residual wind speed-angle bin, wherein the wind speed and the opposite wind angle in the residual wind speed-angle bin are V ii and alpha jj respectively;
s54, calculating a power average value W ii-jj of the active power corresponding to each wind speed-angle bin;
S55, combining the wind speeds V ii corresponding to all the rest wind speed-angle bins, the wind angle alpha jj and the power average W ii-jj to form a three-dimensional matrix Q2.
Step 6, counting the data quantity n j of the active power corresponding to each opposite wind angle alpha j in each wind speed-angle bin, and combining the opposite wind angle and the active power data quantity to form a two-dimensional matrix which is marked as Q4;
Obtaining a diagonal angle alpha n corresponding to the maximum data volume from all data volumes n j in the matrix Q4;
step 7, if the absolute value of the opposite wind angle alpha n is more than or equal to 5, the dynamic deviation of the opposite wind angle of the unit is abnormal, otherwise, the dynamic deviation is normal;
Step8, calculating a three-dimensional matrix Q2:
S81, obtaining a maximum power average value W MAX ii corresponding to each wind speed V ii from the three-dimensional matrix Q2; and obtaining a diagonal angle alpha ii corresponding to the maximum power W MAX ii;
S82, acquiring the average value W O ii of all power average values W ii-jj when the corresponding wind opposite angle of each wind speed V ii is 0 from the three-dimensional matrix Q2;
S83, calculating a power difference W ii between the maximum power W MAX ii corresponding to each wind speed V ii and the average value W O ii;
S84, calculating a power difference average value W μ among all power differences W ii in the three-dimensional matrix Q2;
s85, calculating a sum N ii of data amounts corresponding to each wind speed V ii;
S86, combining the obtained wind speed V ii, the wind angle alpha ii, the sum of data amounts N ii and the power difference W ii to form a four-dimensional matrix Q3.
Step 9, if the power average value W μ is larger than 5% of rated power, entering step 10 to calculate static angle deviation, otherwise, enabling the power deviation small unit to be normal;
Step 10, calculating static angle deviation beta by using data of a four-dimensional matrix Q3, wherein the calculation formula is as follows:
Wherein N ii is the sum of the data amounts; w ii is the power difference; beta ii is the static angle deviation corresponding to the ith row of the four-dimensional matrix; ii is (1, 2,3,4,) n; n is the number of rows of the four-dimensional matrix.
And step 11, if the absolute value of the static angle deviation beta is larger than an empirical value (the empirical value is 8), reporting that the static angle deviation is abnormal, otherwise, the static angle deviation of the machine set is normal.
In the process of calculating the static deviation of the wind angle, the embodiment combines the operation mechanism of the unit, such as the output power of the unit is mainly related to the wind speed and the wind angle under the condition that the length of the blade, the air density and the main control program are unchanged in the power climbing stage of the unit; by using the multi-parameter automatic weighting method, not only is the static deviation of the wind angle calculated effectively, but also a large amount of parameter adjustment in the later stage of the calculation of the static deviation of the wind angle is avoided, a large amount of manpower and time are saved, and the accuracy of the wind angle abnormity judgment result is further improved.
Example 3
The embodiment provides a wind turbine generator system driftage is to abnormal intelligent early warning system of wind angle, its characterized in that includes:
The data acquisition unit is used for preprocessing the obtained historical data of the target unit to obtain a matrix, wherein the first column of the matrix is wind speed, the second column of the matrix is opposite wind angle and the third column of the matrix is active power;
The bin dividing unit is used for dividing the active power into bins by utilizing wind speed and opposite wind angle to obtain a plurality of wind speed-angle bins;
a matrix construction unit for constructing a two-dimensional matrix and a three-dimensional matrix according to the obtained wind speed-angle bins,
The anomaly judging unit is used for judging whether the dynamic deviation of the wind angle is normal or not by utilizing the obtained two-dimensional matrix; judging whether the target unit operates normally or not by using the three-dimensional matrix, wherein if the unit operates abnormally, constructing a four-dimensional matrix by using the three-dimensional matrix, and judging whether the static deviation of the wind angle is normal or not by using the four-dimensional matrix.
The system can judge the problem of yaw wind angle abnormality of the unit more comprehensively and accurately.
Example 4
The present embodiment provides a processing device corresponding to the yaw versus wind angle abnormality intelligent early warning method of the wind turbine generator set provided in the present embodiment 1, where the processing device may be a processing device for a client, for example, a mobile phone, a notebook computer, a tablet computer, a desktop computer, etc., so as to execute the method of embodiment 1.
The processing device comprises a processor, a memory, a communication interface and a bus, wherein the processor, the memory and the communication interface are connected through the bus so as to complete communication among each other. The memory stores a computer program that can run on the processor, and when the processor runs the computer program, the intelligent early warning method for yaw versus wind angle abnormality of the wind turbine generator set provided in the embodiment 1 is executed.
In some embodiments, the memory may be a high-speed random access memory (RAM: random AccessMemory), and may also include non-volatile memory (non-volatile memory), such as at least one disk memory.
In other embodiments, the processor may be a Central Processing Unit (CPU), a Digital Signal Processor (DSP), or other general purpose processor, which is not limited herein.
Example 5
A wind turbine yaw versus wind angle anomaly intelligent warning method of embodiment 1 may be embodied as a computer program product, which may include a computer readable storage medium having computer readable program instructions loaded thereon for performing a wind turbine yaw versus wind angle anomaly intelligent warning method described in embodiment 1.
The computer readable storage medium may be a tangible device that retains and stores instructions for use by an instruction execution device.
The computer readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination of the preceding.
The above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application.
Claims (10)
1. An intelligent early warning method for yaw diagonal angle abnormality of a wind turbine generator is characterized by comprising the following steps:
Step 1, preprocessing the obtained historical data of the target unit to obtain a matrix, wherein a first column of the matrix is wind speed, a second column of the matrix is opposite wind angle and a third column of the matrix is active power;
Step 2, dividing active power into bins by utilizing wind speed and opposite wind angle to obtain a plurality of wind speed-angle bins;
step 3, respectively constructing a two-dimensional matrix and a three-dimensional matrix according to the obtained wind speed-angle bins;
step 4, judging whether the dynamic deviation of the wind angle is normal or not by using the obtained two-dimensional matrix; judging whether the target unit operates normally or not by using the three-dimensional matrix, wherein if the unit operates abnormally, constructing a four-dimensional matrix by using the three-dimensional matrix, and judging whether the static deviation of the wind angle is normal or not by using the four-dimensional matrix.
2. The intelligent early warning method for yaw versus wind angle abnormality of a wind turbine generator set according to claim 1 is characterized in that in step 1, the obtained historical data of a target turbine generator set is preprocessed to obtain a matrix, and the specific method comprises the following steps:
s11, the historical data comprise a unit state, wind speed, active power, a pitch angle and a wind angle; acquiring configuration parameters of a target unit, wherein the configuration parameters comprise cut-in wind speed, rated wind speed and rated power;
S12, screening data of wind speed larger than cut-in wind speed smaller than rated wind speed, active power with power larger than zero, normal power generation of a unit state and pitch angle smaller than 2 from historical data to form an obtained matrix.
3. The intelligent early warning method for yaw diagonal angle abnormality of a wind turbine generator set according to claim 1 is characterized in that in step 2, active power is divided into bins with the width of 1 by utilizing wind speed and diagonal angle to obtain a plurality of wind speed-angle bins.
4. The intelligent early warning method for yaw versus wind angle abnormality of a wind turbine generator set according to claim 1, wherein in step 3, a two-dimensional matrix is constructed according to a plurality of obtained wind speed-angle bins, and the specific method is as follows:
And counting the data quantity of the active power corresponding to each opposite wind angle in each wind speed-angle bin, and combining the opposite wind angle and the active power data quantity to form a two-dimensional matrix.
5. The intelligent early warning method for yaw versus wind angle abnormality of a wind turbine generator set according to claim 4, wherein in step 3, whether dynamic deviation to wind angle is normal is judged by using the obtained two-dimensional matrix, and the specific method is as follows:
acquiring a wind angle corresponding to the maximum data volume from all the data volumes in the two-dimensional matrix;
If the absolute value of the pair of opposite wind angles is larger than or equal to a set threshold value, the dynamic deviation of the opposite wind angles of the target unit is abnormal; otherwise, the dynamic deviation of the opposite wind angle of the target unit is normal.
6. The intelligent early warning method for yaw versus wind angle abnormality of a wind turbine generator set according to claim 1 is characterized in that in step 3, a three-dimensional matrix is constructed according to a plurality of obtained wind speed-angle bins, and the specific method is as follows:
counting the data quantity of active power in each wind speed-angle bin;
Deleting the wind speed-angle bin corresponding to the data volume smaller than the set value to obtain a residual wind speed-angle bin;
calculating the power average value among all the active powers corresponding to each wind speed-angle bin;
Combining the wind speeds, wind angles and power average values in all the rest wind speed-angle bins to form a three-dimensional matrix;
In the step 3, a four-dimensional matrix is constructed by utilizing a three-dimensional matrix, and the specific method is as follows:
obtaining a maximum power average value corresponding to each wind speed from the three-dimensional matrix;
Obtaining a wind angle corresponding to the maximum power average value;
Acquiring all power average values when the wind angle corresponding to each wind speed is zero from the three-dimensional matrix, and calculating the average value corresponding to all power average values;
calculating the power difference between each maximum power average value and each average value;
acquiring the sum of data amounts of power mean values corresponding to each wind speed;
And combining the wind speed and the wind angle in the three-dimensional matrix and the sum and the power difference of the calculated data quantity to form a four-dimensional matrix.
7. The intelligent early warning method for yaw diagonal angle abnormality of a wind turbine generator set according to claim 6 is characterized in that in step 3, whether static deviation of the diagonal angle is normal is judged by using a four-dimensional matrix, and the specific method is as follows:
Calculating the average value of the power differences corresponding to all the power differences;
Comparing the obtained power difference average value with a set threshold value, wherein if the power difference average value is larger than the set threshold value, calculating the static deviation of the wind angle by using a four-dimensional matrix; otherwise, the target unit operates normally;
comparing the obtained absolute value of the static deviation of the opposite wind angle with a threshold value, wherein if the absolute value of the static deviation of the opposite wind angle is larger than the threshold value, the static deviation of the opposite wind angle is abnormal; otherwise, the static deviation of the wind angle is normal.
8. An intelligent early warning system for yaw wind angle anomaly of a wind turbine generator, comprising:
The data acquisition unit is used for preprocessing the obtained historical data of the target unit to obtain a matrix, wherein the first column of the matrix is wind speed, the second column of the matrix is opposite wind angle and the third column of the matrix is active power;
The bin dividing unit is used for dividing the active power into bins by utilizing wind speed and opposite wind angle to obtain a plurality of wind speed-angle bins;
a matrix construction unit for constructing a two-dimensional matrix and a three-dimensional matrix according to the obtained wind speed-angle bins,
The anomaly judging unit is used for judging whether the dynamic deviation of the wind angle is normal or not by utilizing the obtained two-dimensional matrix; judging whether the target unit operates normally or not by using the three-dimensional matrix, wherein if the unit operates abnormally, constructing a four-dimensional matrix by using the three-dimensional matrix, and judging whether the static deviation of the wind angle is normal or not by using the four-dimensional matrix.
9. A processing device comprising at least a processor and a memory, the memory having stored thereon a computer program, characterized in that the processor executes to implement the steps of the method of any of claims 1 to 7 when the computer program is run by the processor.
10. A computer storage medium having stored thereon computer readable instructions executable by a processor to implement the steps of the method according to any of claims 1 to 7.
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