CN114881997A - Wind turbine generator defect assessment method and related equipment - Google Patents

Wind turbine generator defect assessment method and related equipment Download PDF

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CN114881997A
CN114881997A CN202210587198.2A CN202210587198A CN114881997A CN 114881997 A CN114881997 A CN 114881997A CN 202210587198 A CN202210587198 A CN 202210587198A CN 114881997 A CN114881997 A CN 114881997A
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杨海胜
张尤君
李秋鹏
梁俊坚
王元强
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Guangdong Wind Power Generation Co ltd
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Abstract

The application discloses a wind turbine generator defect assessment method and related equipment. The method comprises the following steps: identifying a detection picture of a target wind turbine generator to obtain defect parameters of a target defect in the target wind turbine generator, wherein the defect parameters comprise defect types, defect absolute positions and defect size characteristic parameters; acquiring weighting coefficients corresponding to the defect types, the defect absolute positions and the defect size characteristic parameters; and calculating the defect risk probability based on the defect type, the absolute position of the defect, the defect size characteristic parameter and the corresponding weighting coefficient. The method provided by the embodiment of the application comprehensively considers the defect types, defect positions and defect sizes to carry out multi-index description on the defects of the multiple parts. The wind turbine generator defect risk assessment method based on the analytic hierarchy process can be used for scientifically making a maintenance plan of the wind turbine generator.

Description

Wind turbine generator defect assessment method and related equipment
Technical Field
The present disclosure relates to the field of wind turbines, and more particularly, to a method and related apparatus for evaluating defects of a wind turbine.
Background
In the management of equipment in wind power plants, the safe operation of wind turbines is a primary consideration. However, wind power plants generally occupy a relatively large area, have a remote terrain, a complex terrain, a large daily workload of operation and maintenance of equipment, and are prone to careless mistakes due to difficulty in monitoring the operation state of the equipment.
In the prior art, equipment such as a telescope, a ground high-power camera and a hanging basket are usually used for wind turbine generator inspection, and the problems of poor inspection precision and potential safety hazards exist. In addition, most of the existing wind turbine defects are only used for individually identifying and judging the defects of a certain part of the wind turbine, the influences of defect types, positions and sizes on the severity of the defects are not considered, a unified defect risk assessment standard is lacked, and the maintenance time nodes and the maintenance period of each part of the wind turbine cannot be scientifically formulated.
Disclosure of Invention
In this summary, concepts in a simplified form are introduced that are further described in the detailed description. This summary of the invention is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.
In order to scientifically judge the defects of the wind turbine generator, the invention provides a wind turbine generator defect evaluation method in a first aspect, wherein the method comprises the following steps:
identifying a detection picture of a target wind turbine generator to obtain defect parameters of a target defect in the target wind turbine generator, wherein the defect parameters comprise defect types, defect absolute positions and defect size characteristic parameters;
acquiring weighting coefficients corresponding to the defect types, the defect absolute positions and the defect size characteristic parameters;
and calculating the defect risk probability based on the defect type, the absolute position of the defect, the defect size characteristic parameter and the corresponding weighting coefficient.
Optionally, the method further includes:
acquiring unmanned aerial vehicle coordinate information, shooting parameter information and wind generating set size information of a target unmanned aerial vehicle, wherein the shooting parameter information comprises angle information and camera focal length information corresponding to a target camera carried by the target unmanned aerial vehicle, and the target camera is a camera for shooting the detection picture;
and performing coordinate conversion operation based on the coordinate information of the unmanned aerial vehicle, the shooting parameter information and the size information of the wind turbine generator to obtain the absolute position of the defect, wherein the absolute position of the defect comprises a defect length direction position and a defect width direction position.
Optionally, the method further includes:
acquiring the area of a defective pixel of the target defect;
acquiring the part length of the part corresponding to the target defect;
and calculating the defect size characteristic parameter according to the part length and the defect area.
Optionally, the method further includes:
and identifying the detection picture based on a target full convolution neural network model to obtain the defect type, wherein the target full convolution neural network model is obtained by training a training picture with the defect type marked by labelme, and the defect type at least comprises at least one of dirt, abrasion, cracking, delamination, crack and lightning stroke.
Optionally, the obtaining of the weighting coefficients corresponding to the defect type, the defect absolute position, and the defect size characteristic parameter includes:
taking the obtained defect type, the absolute position of the defect and the characteristic parameter of the size of the defect as evaluation parameters to carry out expert scoring operation to obtain relative importance information, wherein the relative importance information is relative importance information between every two defect parameters;
constructing an evaluation matrix based on the relative importance information;
acquiring a maximum characteristic root and an average random consistency index corresponding to the evaluation matrix, wherein the average random consistency index is determined according to the order of the evaluation matrix;
calculating a random consistency index according to the maximum characteristic root and the average random consistency index;
and calculating weighting coefficients corresponding to the defect type, the defect absolute position and the defect size characteristic parameter based on the evaluation matrix under the condition that the random consistency index is smaller than a preset consistency index.
Optionally, the calculating a weighting coefficient corresponding to the defect type, the absolute position of the defect, and the feature parameter of the defect size based on the evaluation matrix includes:
calculating weighting coefficients corresponding to the defect type, the absolute position of the defect and the characteristic parameter of the defect size according to the following formula:
Figure BDA0003666425470000031
in the formula (I), the compound is shown in the specification,
Figure BDA0003666425470000032
is the above-mentioned weighting coefficient, W i For evaluating the product of elements of each row in the matrix
Figure BDA0003666425470000033
To the power, n is the order, Σ W, corresponding to the evaluation matrix described above i For all sigma W i The sum of (1).
Optionally, the method further includes:
and determining the defect risk grade and the corresponding preset maintenance measure according to the defect risk probability and the preset maintenance strategy table.
In a second aspect, the present application further provides a wind turbine generator defect assessment apparatus, including:
the system comprises an identification unit, a defect detection unit and a defect detection unit, wherein the identification unit is used for identifying a detection picture of a target wind turbine generator so as to obtain defect parameters of a target defect in the target wind turbine generator, and the defect parameters comprise defect types, defect absolute positions and defect size characteristic parameters;
the acquiring unit is used for acquiring the defect type, the defect absolute position and a weighting coefficient corresponding to the defect size characteristic parameter;
and the calculating unit is used for calculating the defect risk probability based on the defect type, the defect absolute position, the defect size characteristic parameter and the corresponding weighting coefficient.
In a third aspect, an electronic device includes: a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor is configured to implement the steps of the wind turbine defect assessment method according to any one of the first aspect described above when executing the computer program stored in the memory.
In a fourth aspect, the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the wind turbine defect assessment method according to any one of the above aspects.
In summary, the method for evaluating the defects of the wind turbine generator provided by the embodiment of the application comprises the following steps: identifying a detection picture of a target wind turbine generator to obtain defect parameters of a target defect in the target wind turbine generator, wherein the defect parameters comprise defect types, defect absolute positions and defect size characteristic parameters; acquiring weighting coefficients corresponding to the defect type, the defect absolute position and the defect size characteristic parameter; and calculating the defect risk probability based on the defect type, the absolute position of the defect, the defect size characteristic parameter and the corresponding weighting coefficient. The method provided by the embodiment of the application comprehensively considers the defect types, defect positions and defect sizes to carry out multi-index description on the defects of the multiple parts, so that the defects of the wind turbine generator are more comprehensively and objectively described. The wind turbine generator defect risk assessment method based on the analytic hierarchy process is provided, the defect risk level is determined by comprehensively considering the defect type, the defect position and the defect size, and the method can be used for scientifically making a maintenance plan of the wind turbine generator.
The wind turbine defect assessment method of the present invention, and other advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the specification. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
fig. 1 is a schematic flow chart of a wind turbine generator defect assessment method provided in an embodiment of the present application;
fig. 2 is a schematic structural diagram of a wind turbine provided in an embodiment of the present application;
fig. 3 is a schematic diagram illustrating a coordinate transformation principle provided in an embodiment of the present application;
FIG. 4 is a schematic diagram of another coordinate transformation principle provided in an embodiment of the present application;
fig. 5 is a schematic structural diagram of a wind turbine blade according to an embodiment of the present application;
FIG. 6 is a schematic structural diagram of another wind turbine blade provided in the embodiment of the present application;
FIG. 7 is a schematic structural diagram of another wind turbine blade provided in the embodiment of the present application;
FIG. 8 is a schematic structural diagram of another wind turbine blade according to an embodiment of the present disclosure;
fig. 9 is a defect evaluation device for a wind turbine generator according to an embodiment of the present application;
fig. 10 is a schematic structural diagram of an electronic device for evaluating a wind turbine generator defect provided in an embodiment of the present application.
Detailed Description
The terms "first," "second," "third," "fourth," and the like in the description and in the claims of the present application and in the drawings described above, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It will be appreciated that the data so used may be interchanged under appropriate circumstances such that the embodiments described herein may be practiced otherwise than as specifically illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus. The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments.
Referring to fig. 1, a schematic flow chart of a wind turbine generator defect assessment method in an embodiment of the present application is shown, where the method includes:
s110, identifying a detection picture of a target wind turbine generator to obtain defect parameters of a target defect in the target wind turbine generator, wherein the defect parameters comprise defect types, defect absolute positions and defect size characteristic parameters;
illustratively, a detection picture of a target wind turbine generator is identified through an intelligent algorithm, defects in parts of the wind turbine generator are obtained, and defect parameters of the target defects are obtained, the defect parameters include types of the defects, the types of the defects can be one or more of dirt, abrasion, cracking, layering, cracks and lightning strikes, absolute positions of the defects refer to specific parts of the defects in the wind turbine generator and specific positions on the parts, such as the wind turbine generator shown in fig. 2, mainly including parts of a blade tip 104, a blade 105, a hub 108, a nacelle 106, a tower 107 and the like, and size characteristic parameters of a defect list can include areas of the defects and specific positions of the defects, or comprehensive parameters of the areas and the specific positions.
S120, acquiring weighting coefficients corresponding to the defect types, the defect absolute positions and the defect size characteristic parameters;
for example, the type of defect, the location of the defect, and the size of the defect unit do not affect the wind turbine to the same extent, for example: the severity of the oil stain is lower than the severity of the blade tip falling off, and the severity of the crack appearing in the core material area of the blade is lower than the severity of the crack appearing at the blade tip or the joint of the blade and the hub. According to the method, the weights of three influence factors of defect types, defect positions and defect sizes are given through fan components, if the weight of the defect at the joint of a blade tip and a hub is relatively large, the weight of the defect at the surface of a cabin and the weight of the area of a blade core material is relatively small, and then the defect risk level is calculated according to the defect type weight, the position weight and the range weight.
And S130, calculating the defect risk probability based on the defect type, the defect absolute position, the defect size characteristic parameter and the corresponding weighting coefficient.
Illustratively, the defect type, the absolute position of the defect, and the defect size are multiplied by their corresponding weighting coefficients, and the resulting product sum is the defect risk probability, which can be calculated by the following formula:
Figure BDA0003666425470000071
Figure BDA0003666425470000072
a first weighting factor corresponding to the defect type, f (type) is the defect type,
Figure BDA0003666425470000073
a second weighting factor corresponding to the absolute position of the defect, f (% L,% C) being a characteristic parameter of the size of the defect,
Figure BDA0003666425470000074
and f (S, L) is the defect size characteristic parameter.
In summary, the method provided by the embodiment of the application comprehensively considers the defect types, defect positions and defect sizes to perform multi-index description on the defects of a plurality of parts, so that the description of the defects of the wind turbine generator is more comprehensive and objective. The wind turbine generator defect risk assessment method based on the analytic hierarchy process is provided, the defect risk level is determined by comprehensively considering the defect type, the defect position and the defect size, and the method can be used for scientifically making a maintenance plan of the wind turbine generator.
In some examples, the method further comprises:
acquiring unmanned aerial vehicle coordinate information, shooting parameter information and wind generating set size information of a target unmanned aerial vehicle, wherein the shooting parameter information comprises angle information and camera focal length information corresponding to a target camera carried by the target unmanned aerial vehicle, and the target camera is a camera for shooting the detection picture;
and performing coordinate conversion operation based on the coordinate information of the unmanned aerial vehicle, the shooting parameter information and the size information of the wind turbine generator to obtain the absolute position of the defect, wherein the absolute position of the defect comprises a defect length direction position and a defect width direction position.
For example, the detection picture may be obtained by shooting with a camera by an unmanned aerial vehicle, and specifically may be: the method includes the steps that a fan assembly picture is obtained in a mode that an M300 RTK unmanned aerial vehicle carries a Zen Si H20 camera, limited camera holder postures are used in the flying process of the unmanned aerial vehicle, the rotating angle is between the range of-5 and 5, the pitch angle value is set to be { -90, -60, -30}, and no requirement is set for the yaw angle. And constructing a distributed unmanned aerial vehicle nest, and automatically inspecting through an unmanned aerial vehicle intelligent scheduling algorithm and path planning. The wind turbine generator mainly comprises four parts, namely a blade, a cabin, a hub and a tower. When the unmanned aerial vehicle is controlled to acquire images, the unmanned aerial vehicle flies from bottom to top along the tower of the wind driven generator, then flies along the axis of the blade, flies over the lower surface of the blade along the axis of the blade after flying over the top end of the lower surface of the blade, and all the blades are sequentially flown, so that the images can be shot on the surface of the blade, the edge curved surfaces connecting the two surfaces, and the engine room, the hub and the tower. The number of the blade segments to be detected is divided according to the length of the blade and the focal length of a camera on the unmanned aerial vehicle, and a wind turbine generator defect sample library is established, so that the condition of the wind turbine generator to be detected can be completely reflected.
Processing the picture information according to the acquired picture and unmanned aerial vehicle coordinate information, shooting parameter information and wind generating set size information associated with the picture, firstly calculating the image pixel position of the wind generating set defect, calculating the corresponding geographic coordinate by combining parameters such as unmanned aerial vehicle coordinate information and shooting angle, and judging the position of the defect on the component by referring to the wind generating set size information. The specific coordinate conversion calculation formula is as follows:
Figure BDA0003666425470000081
Figure BDA0003666425470000091
the image coordinate system and the camera coordinate system are shown in fig. 3 and 4. The xy plane is an image physical coordinate plane, the Xc axis is parallel to the x axis of an image coordinate system, the Yc axis is parallel to the y axis of the image coordinate system, the Zc axis is a camera optical axis and is vertical to the image plane, R (alpha, beta, gamma) is a rotation matrix which is the product of three axial rotation matrixes of x, y and z, (alpha, beta and gamma) is an attitude angle, T is a translation vector which represents the translation distance in the three axial directions, and L is a translation vector which represents the translation distance in the three axial directions W Is a 4 x 4 matrix made up of rotational translations, dx is the size of each pixel on the horizontal axis x, dy is the size of each pixel on the vertical axis y, and f is the camera focal length.
Judging which part of the wind turbine generator is defective through coordinates and a three-dimensional model, and then establishing a defect position description formula f (% L,% C) according to the length L and the width C of the part, wherein the% L and the% C are respectively the length proportion (namely the position in the defect length direction) and the width proportion (the position in the defect width direction) of the part, so as to describe the defect position.
In order to uniformly describe the defect risks of different types of blades, and more specifically, to establish a relationship between the defect risks of the blades and the defect positions, the blades are generally divided into a blade tip, a main beam, a blade leading edge, a blade core material region, a blade trailing edge and a blade root according to the structure. The blade structure division schematic diagram is shown in fig. 5 to 8, wherein 101 is a blade root, 102 is a front edge and a rear edge of the blade, 103 is a main beam, and 104 is a blade tip.
To sum up, the method that this application embodiment provided uses the unmanned aerial vehicle platform to realize that fan subassembly detects, carries out automatic patrolling and examining, can improve and patrol and examine efficiency and detection precision. The method combines image recognition and coordinate positioning, can not only automatically recognize the defects, but also accurately position the defects, and can be used for guiding workers to quickly eliminate the defects.
In some examples, the method further comprises:
acquiring the area of a defective pixel of the target defect;
acquiring the part length of the part corresponding to the target defect;
and calculating the defect size characteristic parameter according to the part length and the defect area.
For example, after the defect is identified, the area S of the defective pixel is calculated, and the influence of the actual length L of the component is considered, and a defect size description formula, denoted as f (S, L), is established, and the calculation formula may be:
f(S,L)＝S×L
in summary, according to the method provided by the embodiment of the application, the product of the area of the defective pixel and the actual length of the part is used as a characteristic parameter for describing the size of the defect, and not only are the size factors of the defect considered, but also the influence of the length of the part on the wind turbine generator is considered, so that the defect evaluation method is more accurate.
In some examples, the method further comprises:
and identifying the detection picture based on a target full convolution neural network model to obtain the defect type, wherein the target full convolution neural network model is obtained by training a training picture with the defect type marked by labelme, and the defect type at least comprises at least one of dirt, abrasion, cracking, delamination, crack and lightning stroke.
Illustratively, an artificial intelligence algorithm is adopted to carry out defect recognition on an acquired picture, a full convolution neural network model is specifically selected to carry out intelligent recognition on the defects of the wind turbine generator, a target full convolution neural network model needs to be trained before defect recognition, firstly, a data marking tool labelme is used for marking out defect information in the picture of the wind turbine generator, the defect information comprises the picture of the wind turbine generator and a mask of the wind turbine generator, the picture of the wind turbine generator and the mask of the wind turbine generator form an image-mask pair for training the full convolution neural network model, and a model loss function value is calculated and recorded in the training process. And then, semantic segmentation is carried out on the wind turbine generator pictures obtained in the practical application scene by using the trained model, a wind turbine generator prediction mask is obtained, and then the defects of the wind turbine generator are identified. The defect characteristics are used as input, the defect type is used as output to establish a defect type description formula f (type), and the defect type can be referred to as table 1. Specifically, the wind turbine defects include, but are not limited to, one or more of dirt, wear, cracking, delamination, cracking, and lightning strikes. Specific defect types may include the types described in table 1.
Figure BDA0003666425470000111
Table 1 defect type description
In summary, the method provided by the embodiment of the application adopts the full convolution neural network model, and the labelme marks the defect information in the picture of the wind turbine generator for training, so that various defects such as dirt, abrasion, cracking, layering, cracks, lightning stroke and the like can be identified, and the defect assessment method is more intelligent and accurate.
In some examples, the obtaining of the weighting coefficients corresponding to the defect type, the absolute position of the defect, and the defect size characteristic parameter includes:
taking the obtained defect type, the absolute position of the defect and the characteristic parameter of the size of the defect as evaluation parameters to carry out expert scoring operation to obtain relative importance information, wherein the relative importance information is relative importance information between every two defect parameters;
constructing an evaluation matrix based on the relative importance information;
acquiring a maximum characteristic root and an average random consistency index corresponding to the evaluation matrix, wherein the average random consistency index is determined according to the order of the evaluation matrix;
calculating a random consistency index according to the maximum characteristic root and the average random consistency index;
and calculating weighting coefficients corresponding to the defect type, the defect absolute position and the defect size characteristic parameter based on the evaluation matrix under the condition that the random consistency index is smaller than a preset consistency index.
For example, influence factors and levels are analyzed for the wind turbine defect risk, and influence weights a1, a2 and a3 of the defect type, the defect position and the defect size on the risk occurrence possibility are analyzed by using a level analysis method. And taking the defect type, the absolute position of the defect and the characteristic parameter of the size of the defect as rating parameters to carry out expert scoring operation to obtain relative importance information, carrying out pairwise comparison and scoring by experts according to the relative importance of the three factors and the difference of the same factor in each part when carrying out expert scoring, and rating according to the importance degree of the experts. a is ij The scoring criteria for the comparison of the importance of element i to element j are shown in table 2:
factor i to factor j Quantized value
Of equal importance 1
Of slight importance 3
Of greater importance 5
Of strong importance 7
Of extreme importance 9
Intermediate values of two adjacent judgments 2，4，6，8
TABLE 2 scoring Standard Table
Normalizing the scoring of each expert, and forming an evaluation matrix by the two comparison results, wherein the evaluation matrix has the following properties:
Figure BDA0003666425470000131
the evaluation matrix table can be referred to table 3.
Figure BDA0003666425470000132
Table 3 evaluation matrix table
Carrying out evaluation consistency check to obtain the maximum characteristic root lambda of the evaluation matrix max And determining an average random consistency index RI according to the parameter n of the evaluation matrix, wherein the specific numerical value of RI is shown in Table 4, and solving the random consistency CR, wherein the calculation formula is as follows:
Figure BDA0003666425470000133
order of matrix 1 2 3 4 5 6 7 8 9 10
RI 0 0 0.58 0.90 1.12 1.24 1.32 1.41 1.45 1.49
TABLE 4 average random consistency index RI standard value
The number of the parameters of the defect type, the defect position, and the defect size in this embodiment is 3, so that n is 3, that is, RI is 0.58, and if the consistency CR is smaller than 0.1 (the preset consistency index), the consistency check on the evaluation matrix is passed, and the weighting coefficients of the three parameters can be calculated.
In summary, according to the method provided by the embodiment of the application, the defect type, the defect position and the defect size are comprehensively considered to determine the defect risk level through the wind turbine generator defect risk assessment method based on the analytic hierarchy process, the influence between every two parameters is comprehensively considered by adopting an expert scoring method, the weighting coefficient is obtained according to the influence, the influence between every two parameters can be fully reflected through the defect risk probability obtained through the weighting coefficient, and the influence of the target defect on the wind turbine generator can be better judged based on the defect type, the defect position and the defect size.
In some examples, the calculating the weighting coefficients corresponding to the defect type, the absolute position of the defect, and the defect size characteristic parameter based on the evaluation matrix includes:
calculating weighting coefficients corresponding to the defect type, the absolute position of the defect and the characteristic parameter of the defect size according to the following formula:
Figure BDA0003666425470000141
in the formula (I), the compound is shown in the specification,
Figure BDA0003666425470000142
is the above-mentioned weighting coefficient, W i For evaluating the product of elements of each row in the matrix
Figure BDA0003666425470000143
To the power, n is the order, Σ W, corresponding to the evaluation matrix described above i For all sigma W i The sum of (1).
Illustratively, the multiplication of rows of the matrix
Figure BDA0003666425470000144
The power is W i The weighting coefficients of the three parameters
Figure BDA0003666425470000145
Probability of defect risk
Figure BDA0003666425470000146
In some examples, the method further comprises:
and determining the defect risk grade and the corresponding preset maintenance measure according to the defect risk probability and the preset maintenance strategy table.
Exemplarily, the risk level corresponding to the authority and the corresponding maintenance measure can be determined by searching a preset maintenance strategy table according to the calculated risk probability, so that maintenance personnel can conveniently perform maintenance treatment, and the specific maintenance scheme can be shown in table 5:
Figure BDA0003666425470000151
TABLE 5 maintenance measures taken for different defect risk classes
In summary, according to the method provided by the embodiment of the application, the defect risk probability is calculated, and the risk level and maintenance measure correspondence table corresponding to the defect risk probability is queried, so that a scientific maintenance scheme of the motor group can be formulated, and convenience is brought to judgment and maintenance by constructors.
Referring to fig. 2, the present invention further provides a wind turbine generator defect evaluation apparatus, including:
the identification unit 21 is configured to identify a detection picture of a target wind turbine generator to obtain defect parameters of a target defect in the target wind turbine generator, where the defect parameters include a defect type, a defect absolute position, and a defect size characteristic parameter;
an obtaining unit 22, configured to obtain weighting coefficients corresponding to the defect type, the absolute position of the defect, and the feature parameter of the defect size;
a calculating unit 23, configured to calculate a defect risk probability based on the defect type, the absolute position of the defect, the defect size characteristic parameter, and a corresponding weighting coefficient.
As shown in fig. 3, an electronic device 300 is further provided in the embodiment of the present application, which includes a memory 310, a processor 320, and a computer program 511 stored in the memory 320 and executable on the processor, and when the processor 320 executes the computer program 311, the steps of any method for evaluating the wind turbine defects described above are implemented.
Since the electronic device described in this embodiment is a device used for implementing a wind turbine generator defect evaluation apparatus in this embodiment, based on the method described in this embodiment, a person skilled in the art can understand a specific implementation manner of the electronic device of this embodiment and various variations thereof, so that how to implement the method in this embodiment by the electronic device is not described in detail herein, and as long as the person skilled in the art implements the device used for implementing the method in this embodiment, the device is within the scope of protection intended by this application.
In a specific implementation, the computer program 311 may implement any of the embodiments corresponding to fig. 1 when executed by a processor.
It should be noted that, in the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to relevant descriptions of other embodiments for parts that are not described in detail in a certain embodiment.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The embodiment of the present application further provides a computer program product, where the computer program product includes computer software instructions, and when the computer software instructions are run on a processing device, the processing device is enabled to execute the flow of the wind turbine generator defect assessment method in the embodiment corresponding to fig. 1.
The computer program product includes one or more computer instructions. The procedures or functions according to the embodiments of the present application are all or partially generated when the computer program instructions are loaded and executed on a computer. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored on a computer readable storage medium or transmitted from one computer readable storage medium to another computer readable storage medium, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wire (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). A computer-readable storage medium may be any available medium that a computer can store or a data storage device, such as a server, a data center, etc., that is integrated with one or more available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, a division of a unit is merely a logical division, and an actual implementation may have another division, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
Units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method of the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The above embodiments are only used to illustrate the technical solutions of the present application, and not to limit the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions in the embodiments of the present application.

Claims (10)

1. A wind turbine generator system defect assessment method is characterized by comprising the following steps:
identifying a detection picture of a target wind turbine generator to obtain defect parameters of a target defect in the target wind turbine generator, wherein the defect parameters comprise defect types, defect absolute positions and defect size characteristic parameters;
acquiring weighting coefficients corresponding to the defect type, the defect absolute position and the defect size characteristic parameter;
and calculating the defect risk probability based on the defect type, the defect absolute position, the defect size characteristic parameter and the corresponding weighting coefficient.
2. The method of claim 1, wherein the method further comprises:
acquiring unmanned aerial vehicle coordinate information, shooting parameter information and wind generating set size information of a target unmanned aerial vehicle, wherein the shooting parameter information comprises angle information and camera focal length information corresponding to a target camera carried by the target unmanned aerial vehicle, and the target camera is a camera for shooting the detection picture;
and performing coordinate conversion operation based on the coordinate information of the unmanned aerial vehicle, the shooting parameter information and the size information of the wind turbine generator to obtain the absolute defect position, wherein the absolute defect position comprises a position in the length direction of the defect and a position in the width direction of the defect.
3. The method of claim 1, wherein the method further comprises:
acquiring the area of a defective pixel of the target defect;
acquiring the part length of the part corresponding to the target defect;
and calculating the defect size characteristic parameter according to the part length and the defect area.
4. The method of claim 1, wherein the method further comprises:
and identifying the detection picture based on a target full convolution neural network model to acquire the defect type, wherein the target full convolution neural network model is obtained by training a training picture with the defect type marked by labelme, and the defect type at least comprises at least one of dirt, abrasion, cracking, delamination, crack and lightning stroke.
5. The method of claim 1, wherein the obtaining the weighting coefficients corresponding to the defect type, the absolute position of the defect, and the defect size characteristic parameter comprises:
taking the obtained defect type, the absolute position of the defect and the characteristic parameter of the size of the defect as rating parameters to carry out expert scoring operation to obtain relative importance information, wherein the relative importance information is relative importance information between every two defect parameters;
constructing an evaluation matrix based on the relative importance information;
acquiring a maximum characteristic root and an average random consistency index corresponding to the evaluation matrix, wherein the average random consistency index is determined according to the order of the evaluation matrix;
calculating a random consistency index according to the maximum characteristic root and the average random consistency index;
and calculating the weighting coefficients corresponding to the defect type, the defect absolute position and the defect size characteristic parameter based on the evaluation matrix under the condition that the random consistency index is smaller than a preset consistency index.
6. The method of claim 5, wherein said calculating weighting coefficients corresponding to said defect type, said defect absolute location, and said defect size characterization parameter based on said evaluation matrix comprises:
calculating weighting coefficients corresponding to the defect type, the defect absolute position and the defect size characteristic parameter according to the following formula:
Figure FDA0003666425460000021
in the formula (I), the compound is shown in the specification,
Figure FDA0003666425460000022
as the weighting coefficient, W i For the product of elements of each row of said evaluation matrix
Figure FDA0003666425460000023
To the power, n is the order, Σ W, corresponding to the evaluation matrix i For all sigma W i And (3) is (a).
7. The method of claim 1, wherein the method further comprises:
and determining the defect risk grade and the corresponding preset maintenance measure according to the defect risk probability and a preset maintenance strategy table.
8. A wind turbine generator system defect assessment device is characterized by comprising:
the system comprises an identification unit, a defect detection unit and a defect detection unit, wherein the identification unit is used for identifying a detection picture of a target wind turbine generator so as to obtain defect parameters of a target defect in the target wind turbine generator, and the defect parameters comprise defect types, defect absolute positions and defect size characteristic parameters;
the acquiring unit is used for acquiring the defect type, the defect absolute position and a weighting coefficient corresponding to the defect size characteristic parameter;
and the calculating unit is used for calculating the defect risk probability based on the defect type, the defect absolute position, the defect size characteristic parameter and the corresponding weighting coefficient.
9. An electronic device, comprising: memory, processor and computer program stored in the memory and executable on the processor, characterized in that the processor is configured to implement the steps of the wind turbine defect assessment method according to any of claims 1 to 7 when executing the computer program stored in the memory.
10. A computer-readable storage medium having stored thereon a computer program, characterized in that: the computer program, when executed by a processor, implements a wind turbine defect assessment method according to any of claims 1-7.
CN202210587198.2A 2022-05-27 2022-05-27 Wind turbine generator defect assessment method and related equipment Pending CN114881997A (en)

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