Insulated lapped wire defect identification method and system applied to high-power transformer

Abstract

The invention discloses an insulation lapped wire defect identification method and system applied to a high-power transformer, and the method comprises the steps: obtaining a target insulation lapped wire, carrying out the fixed-point multi-angle X-ray image collection of the target insulation lapped wire, and obtaining a to-be-identified X-ray image set of the target insulation lapped wire; and inputting the X-ray image set to be identified into a multi-model cooperative defect identification model to enable the multi-model cooperative defect identification model to output defect data of a target insulated lapped wire, the defect data comprising a defect type and a defect position, the defect types comprise internal conductor fracture, gap between an insulating layer and the conductor, abnormal thickness of the insulating layer and falling of the insulating layer; by implementing the method, the comprehensiveness and accuracy of defect identification of the target insulated lapped wire can be improved, and safe and stable operation of the transformer when the insulated lapped wire subjected to defect identification is applied to the high-power transformer is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of insulating winding wire defect recognition, and in particular to an insulating winding wire defect recognition method and system applied to a high-power transformer. Background Art

[0002] Transformers are indispensable and important equipment in power systems. Their main function is to achieve voltage conversion, current transmission and distribution through the principle of electromagnetic induction. In the manufacturing process of transformers, insulating wire, as an important component of the transformer, plays a vital role. The use of insulating wire in transformers can effectively prevent current leakage and short circuit, and reduce the risk of transformer failure. At the same time, the wear resistance of insulating wire can also reduce the damage to the winding caused by mechanical stress and friction, which is of great significance to improving the safe and stable operation of transformers. Before applying insulating wire to transformers, especially to high-power transformers as the core power equipment of the power system, it is very important to identify defects in insulating wire. This is because identifying defects in insulating wire can ensure that the insulating wire will not cause power fluctuations, voltage changes and other problems to the transformer after being applied to the transformer, and ensure the safe and stable operation of the transformer.

[0003] The existing defect recognition method for insulating wrapped wires mainly locates the defects in the captured appearance image by taking a picture of the appearance of the insulating wrapped wire and using algorithms such as target detection. However, this defect recognition method can only identify whether the insulating wrapped wire has surface defects such as deformation and shedding, and cannot promptly detect the internal conductor breakage of the insulating wrapped wire. If the insulating wrapped wire with internal defects is used in the transformer, there will still be problems such as power fluctuation and voltage change in the transformer, and the safe and stable operation of the transformer cannot be guaranteed. Summary of the invention

[0004] The embodiments of the present invention provide a method and system for identifying defects in insulating winding wires applied to high-power transformers, which can solve the problem that the prior art cannot identify internal defects in insulating winding wires. On the basis of realizing the identification of the internal structure of the target insulating winding wire, the comprehensiveness and accuracy of the target insulating winding wire defect identification are improved, thereby ensuring the safe and stable operation of the transformer when the target insulating winding wire that does not have defects after defect identification is applied to the high-power transformer.

[0005] An embodiment of the present invention provides a method for identifying defects of insulating winding wires applied to a high-power transformer, comprising: Acquire a target insulating wrapped wire, and perform fixed-point multi-angle X-ray image acquisition on the target insulating wrapped wire to obtain a set of X-ray images to be identified of the target insulating wrapped wire; wherein the set of X-ray images to be identified includes a plurality of X-ray images to be identified taken from a plurality of angles of the target insulating wrapped wire; Inputting the to-be-identified X-ray image set into a multi-model collaborative defect recognition model, so that the multi-model collaborative defect recognition model outputs defect data of the target insulating wrapped wire, the defect data including defect type and defect location, the defect type including internal conductor breakage, gap between the insulating layer and the conductor, abnormal insulation layer thickness, and insulation layer shedding; Wherein, when the X-ray image set to be identified is input into the multi-model collaborative defect recognition model, the first defect recognition model of the multi-model collaborative defect recognition model constructs a three-dimensional model of the target insulating wrapped wire according to the X-ray image set to be identified, and outputs first defect data according to the three-dimensional model of the target insulating wrapped wire; The second defect recognition model of the multi-model collaborative defect recognition model outputs second defect data according to each X-ray image to be recognized; The collaborative decision model of the multi-model collaborative defect recognition model outputs defect data of the target insulating wrapped wire according to the first defect data and the second defect data.

[0006] Furthermore, the construction of the multi-model collaborative defect recognition model includes: Acquire a training sample set of a multi-model collaborative defect recognition model; wherein the training sample set includes a plurality of X-ray image sample subsets of a plurality of sample insulating wrapped wires, each of the X-ray image sample subsets includes a plurality of X-ray image defect samples of the current sample insulating wrapped wire taken from multiple angles, a reference three-dimensional model of the current sample insulating wrapped wire, and actual defect data of the current sample insulating wrapped wire; Constructing an initial multi-model collaborative defect recognition model; wherein the initial multi-model collaborative defect recognition model includes an initial first defect recognition model, an initial second defect recognition model, a calibration model, a weight adjustment model and an initial collaborative decision model; The multi-model collaborative defect recognition model is trained on the initial multi-model collaborative defect recognition model using the training sample set of the multi-model collaborative defect recognition model until the initial multi-model collaborative defect recognition model converges, thereby generating the multi-model collaborative defect recognition model.

[0007] Furthermore, the multi-model collaborative training of the initial multi-model collaborative defect recognition model using the training sample set of the multi-model collaborative defect recognition model includes: Taking the X-ray image sample subset as the input of the initial multi-model collaborative defect recognition model and taking the predicted defect data of the sample insulation winding wire corresponding to the X-ray image sample subset as the output, the initial multi-model collaborative defect recognition model is trained by multi-model collaboration; In each collaborative training process, the initial first defect recognition model constructs an initial three-dimensional model of the current sample insulating wrapped wire according to each X-ray image defect sample, and transmits the initial three-dimensional model to the calibration model so that the calibration model obtains a reference three-dimensional model of the current sample insulating wrapped wire, determines a three-dimensional model construction error according to the reference three-dimensional model of the current sample insulating wrapped wire and the initial three-dimensional model, and feeds back the three-dimensional model construction error to the initial first defect recognition model so that the initial first defect recognition model adjusts the initial three-dimensional model according to the three-dimensional model construction error, and outputs first predicted defect data according to the adjusted initial three-dimensional model; The initial second recognition model extracts each defect feature data of each X-ray image defect sample, and outputs second predicted defect data according to each defect feature data; The weight adjustment model determines a first prediction error of the first recognition model and a second prediction error of the second recognition model according to the first predicted defect data, the second predicted defect data and the actual defect data, and adjusts the first weight and the second weight of the initial collaborative decision-making model according to the first prediction error, the second prediction error and the three-dimensional model construction error; wherein the first weight is associated with the first predicted defect data, and the second weight is associated with the second predicted defect data; The initial collaborative decision-making model outputs predicted defect data of the current sample insulated winding wire according to the adjusted first weight, the adjusted second weight, the first predicted defect data and the second predicted defect data, and determines the collaborative decision error according to the predicted defect data and the actual defect data.

[0008] Furthermore, the determining of the three-dimensional model construction error according to the reference three-dimensional model of the current sample insulating wrapped wire and the initial three-dimensional model includes: Aligning the reference three-dimensional model with the initial three-dimensional model to obtain an aligned reference three-dimensional model and an aligned initial three-dimensional model; Constructing feature matching geometric space according to the registered reference 3D model; Synchronously moving the registered initial three-dimensional model and the registered reference three-dimensional model to the feature matching geometric space; Repeating the regional feature matching operation in the feature matching geometric space until the registered reference 3D model and the registered initial 3D model have no repeated regions in the feature matching geometric space, and determining the 3D model construction error based on the registered reference 3D model and the registered initial 3D model without repeated regions in the feature matching geometric space; The regional feature matching operation includes: Constructing a number of spheres with radii of random values, and making the spheres randomly cover the feature matching geometric space; For each sphere, the first Euclidean distance between the registered reference 3D model and the registered initial 3D model in the coverage area of ​​the sphere is calculated; The overlapping areas of the registered reference three-dimensional model and the registered initial three-dimensional model are eliminated according to the first Euclidean distances.

[0009] Furthermore, after eliminating the overlapping areas of the registered reference three-dimensional model and the registered initial three-dimensional model according to each of the first Euclidean distances, the method further includes: Calculate the second Euclidean distance between the registered reference three-dimensional model and the registered initial three-dimensional model, which are located outside the preset distance area of ​​the sphere coverage area; The second Euclidean distances are fitted to obtain a current sphere radius adjustment parameter, and the current sphere radius is adjusted according to the current sphere radius adjustment parameter.

[0010] Furthermore, the method of using the X-ray image sample subset as the input of the initial multi-model collaborative defect recognition model includes: Inputting the initial multi-model collaborative defect recognition model in sequence according to the collection order of each X-ray image defect sample in the X-ray image sample subset; The initial second recognition model extracts each defect feature data of each X-ray image defect sample, and outputs second predicted defect data according to each defect feature data, including: The initial second recognition model extracts defect feature data of the first input X-ray image defect sample, and when the initial second recognition model receives the remaining X-ray image defect samples except the first one, determines the overlapping area of ​​the current X-ray image defect sample and the previous X-ray image defect sample according to the previous X-ray image defect sample and the current X-ray image defect sample; After removing the repeated area between the current X-ray image defect sample and the previous X-ray image defect sample, defect feature extraction is performed on the current X-ray image defect sample to obtain defect feature data of the current X-ray image defect sample; The second predicted defect data is output according to each defect feature data of each X-ray image defect sample.

[0011] Furthermore, the fixed-point multi-angle X-ray image acquisition of the target insulating wrapped wire to obtain the X-ray image set to be identified of the target insulating wrapped wire includes: The target insulating wrapped wire is placed on the stage of the X-ray image acquisition device to acquire the X-ray image at the initial angle; After the X-ray image acquisition at the initial angle is completed, the stage is repeatedly rotated to the preset angle and the X-ray image at the current angle is acquired until the stage rotates 360°, the X-ray image acquisition is stopped, and a set of X-ray images to be identified of the target insulated wrapped wire is constructed based on the X-ray images acquired at each angle.

[0012] Based on the above method embodiment, the present invention provides a corresponding system embodiment; An embodiment of the present invention provides a defect recognition system for insulating winding wires applied to a high-power transformer, including: an image data acquisition module and a defect recognition module; The image data acquisition module is used to acquire the target insulating wrapped wire and perform fixed-point multi-angle X-ray image acquisition on the target insulating wrapped wire to obtain a set of X-ray images to be identified of the target insulating wrapped wire; wherein the set of X-ray images to be identified includes a plurality of X-ray images to be identified taken from a plurality of angles of the target insulating wrapped wire; The defect recognition module is used to input the X-ray image set to be identified into the multi-model collaborative defect recognition model so that the multi-model collaborative defect recognition model outputs defect data of the target insulating wrapped wire, the defect data including defect type and defect location, the defect type including internal conductor breakage, gap between the insulating layer and the conductor, abnormal insulation layer thickness and insulation layer shedding; wherein, when the X-ray image set to be identified is input into the multi-model collaborative defect recognition model, the first defect recognition model of the multi-model collaborative defect recognition model constructs a three-dimensional model of the target insulating wrapped wire according to the X-ray image set to be identified, and outputs first defect data according to the three-dimensional model of the target insulating wrapped wire; the second defect recognition model of the multi-model collaborative defect recognition model outputs second defect data according to each X-ray image to be identified; the collaborative decision model of the multi-model collaborative defect recognition model outputs the defect data of the target insulating wrapped wire according to the first defect data and the second defect data.

[0013] Another embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the method for identifying insulation winding wire defects applied to a high-power transformer as described in the above-mentioned embodiment of the invention is implemented.

[0014] Another embodiment of the present invention provides a storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute the method for identifying insulation winding wire defects applied to a high-power transformer as described in the above-mentioned embodiment of the invention.

[0015] The following beneficial effects are achieved by implementing the present invention: The present invention provides an insulating wire defect identification method and system for a high-power transformer. The defect identification method obtains a set of X-ray images to be identified of the target insulating wire based on fixed-point multi-angle shooting, and utilizes the property of the X-ray image to identify the internal structure of the target insulating wire. The method can obtain the internal structural characteristics of the target insulating wire, such as internal conductor breakage, gap between the insulating layer and the conductor, abnormal insulating layer thickness, and insulating layer shedding defects, thereby providing a rich data basis for the identification of internal and external defects of the target insulating wire. Furthermore, by inputting the X-ray image set to be identified into the multi-model collaborative defect recognition model, the first defect recognition model of the multi-model collaborative defect recognition model is used to construct a three-dimensional model of the target insulating winding wire, defects are identified based on the three-dimensional model and first defect data is output, and the second defect recognition model of the multi-model collaborative defect recognition model is used to output the second defect data through two-dimensional image feature extraction, and then the first defect data and the second defect data are integrated through the collaborative defect recognition model to output the defect data of the target insulating winding wire; the multi-model collaborative defect recognition model is used to perform defect identification on the target insulating winding wire from three-dimensional and two-dimensional perspectives, respectively, and on the basis of realizing the identification of the internal structure of the target insulating winding wire, the comprehensiveness and accuracy of the defect identification of the target insulating winding wire are improved, thereby ensuring the safe and stable operation of the transformer when the target insulating winding wire that does not have defects after defect identification is applied to a high-power transformer. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a flow chart of a method for identifying insulation winding wire defects applied to a high-power transformer provided by an embodiment of the present invention.

[0017] Figure 2 It is a structural schematic diagram of an insulation winding wire defect identification system applied to a high-power transformer provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] like Figure 1 As shown, a method for identifying insulation winding wire defects applied to a high-power transformer is provided in one embodiment of the present invention, comprising: Step S1: acquiring a target insulating wrapped wire, and performing fixed-point multi-angle X-ray image acquisition on the target insulating wrapped wire to obtain a set of X-ray images to be identified of the target insulating wrapped wire; wherein the set of X-ray images to be identified includes a plurality of X-ray images to be identified taken from a plurality of angles of the target insulating wrapped wire; Step S2: inputting the to-be-identified X-ray image set into a multi-model collaborative defect recognition model, so that the multi-model collaborative defect recognition model outputs defect data of the target insulating wrapped wire, wherein the defect data includes defect type and defect location, and the defect type includes internal conductor breakage, a gap between the insulating layer and the conductor, abnormal insulation layer thickness, and insulation layer shedding; Among them, when the X-ray image set to be identified is input into the multi-model collaborative defect recognition model, the first defect recognition model of the multi-model collaborative defect recognition model constructs a three-dimensional model of the target insulating winding wire according to the X-ray image set to be identified, and outputs first defect data according to the three-dimensional model of the target insulating winding wire; the second defect recognition model of the multi-model collaborative defect recognition model outputs second defect data according to each X-ray image to be identified; the collaborative decision model of the multi-model collaborative defect recognition model outputs the defect data of the target insulating winding wire according to the first defect data and the second defect data.

[0020] Specifically, a target insulating wrapped wire for which defect identification is required is obtained, and fixed-point multi-angle X-ray image acquisition is performed on the target insulating wrapped wire to obtain multi-angle X-ray images of the target insulating wrapped wire to be identified, thereby obtaining a set of X-ray images of the target insulating wrapped wire to be identified.

[0021] In a preferred embodiment, the fixed-point multi-angle X-ray image acquisition of the target insulating wrapped wire to obtain the X-ray image set of the target insulating wrapped wire to be identified includes: placing the target insulating wrapped wire on the stage of the X-ray image acquisition device to acquire the X-ray image at an initial angle; after the X-ray image acquisition at the initial angle is completed, repeatedly rotating the stage by a preset angle and acquiring the X-ray image at the current angle until the stage rotates 360°, stopping the X-ray image acquisition, and constructing the X-ray image set of the target insulating wrapped wire to be identified based on the X-ray images acquired at each angle.

[0022] Specifically, after obtaining the target insulating wrapped wire, the target insulating wrapped wire is placed on the stage of the X-ray image acquisition device. After determining that the target insulating wrapped wire is placed stably, the X-ray image of the target insulating wrapped wire is acquired by a fixed-point X-ray camera image acquisition device to obtain the X-ray image of the target insulating wrapped wire at an initial angle. Further, after the stage is rotated by a preset angle (such as 30 degrees, 60 degrees or 90 degrees), and after each rotation by the preset angle, the X-ray image at the current angle is acquired until the stage rotates 360°. At this time, it is considered that the X-ray images of the target insulating wrapped wire at all angles have been acquired, and these acquired X-ray images are integrated into a set of X-ray images to be identified of the target insulating wrapped wire in the acquisition order.

[0023] For step S2, the above-collected X-ray image set to be identified is input into the multi-model collaborative defect recognition model, and each X-ray image to be identified of the target insulating wrapped wire is processed by the multi-model collaborative defect recognition model to output the defect type and defect position of the target insulating wrapped wire. In the present invention, the defect types mainly include four categories: internal conductor breakage of the insulating wrapped wire, gap between the insulating layer and the conductor of the insulating wrapped wire, abnormal insulation thickness of the insulating wrapped wire, and insulation layer shedding of the insulating wrapped wire. Among them, internal conductor breakage, gap between the insulating layer and the conductor, and abnormal insulation thickness are internal structural abnormalities, which need to be determined by looking through the internal structure, and insulation layer shedding is a surface abnormality, which can be determined by external structural features. When the X-ray image set to be identified is input into the multi-model collaborative defect recognition model, the first defect recognition model included in the multi-model collaborative defect recognition model constructs a three-dimensional model of the target insulating wrapped wire based on the input data, and outputs the first defect data based on the three-dimensional model. The second defect recognition model outputs the second defect data, and the collaborative decision model outputs the defect data of the target insulating wrapped wire based on the first defect data and the second defect data.

[0024] In a preferred embodiment, the construction of the multi-model collaborative defect recognition model includes: obtaining a training sample set of the multi-model collaborative defect recognition model; wherein the training sample set includes several X-ray image sample subsets of several sample insulating windings, each of the X-ray image sample subsets includes several X-ray image defect samples of the current sample insulating winding from multiple angles, a baseline three-dimensional model of the current sample insulating winding, and real defect data of the current sample insulating winding; constructing an initial multi-model collaborative defect recognition model; wherein the initial multi-model collaborative defect recognition model includes an initial first defect recognition model, an initial second defect recognition model, a calibration model, a weight adjustment model, and an initial collaborative decision model; performing multi-model collaborative training on the initial multi-model collaborative defect recognition model using the training sample set of the multi-model collaborative defect recognition model until the initial multi-model collaborative defect recognition model converges, thereby generating the multi-model collaborative defect recognition model.

[0025] Specifically, the construction of the multi-model collaborative defect recognition model mainly includes three steps: training sample acquisition, initial model construction and model training and tuning.

[0026] First, several sample insulating wrapped wires are obtained as sample objects. It should be noted that in order to be able to identify insulating wrapped wires of various specifications, when obtaining the sample insulating wrapped wires, the sample insulating wrapped wires corresponding to each specification can be obtained based on the specifications of the insulating wrapped wires required for defect identification. For each sample insulating wrapped wire, different defect types are constructed and laid out at different positions, and each defect position and each defect type are marked on the sample insulating wrapped wire as the real defect data of the insulating wrapped wire. The sample insulating wrapped wire is fixed on the stage of the X-ray image acquisition device, and several ray images of the sample insulating wrapped wire taken at different angles are acquired in the same acquisition method as the X-ray image acquisition in step S1, and the images are used as several X-ray image defect samples of the sample insulating wrapped wire. In addition, a reference three-dimensional model of the sample insulating wrapped wire during production is obtained, and a subset of the X-ray image samples of the current sample insulating wrapped wire is constructed by these data. Preferably, for the sample insulating wrapped wires of the same specification, multiple subsets of X-ray image samples are constructed by transforming the defect type and defect position, thereby constituting a training sample set of the multi-model collaborative defect recognition model.

[0027] Secondly, an initial multi-model collaborative defect recognition model is constructed, and the initial multi-model collaborative defect recognition model consists of an initial first defect recognition model, an initial second defect recognition model, a calibration model, a weight adjustment model, and an initial collaborative decision model. The initial multi-model collaborative defect recognition model is multi-model collaboratively trained through a training sample set of the multi-model collaborative defect recognition model until the initial multi-model collaborative defect recognition model converges, such as when the loss function of the initial multi-model collaborative defect recognition model is minimized or when the maximum number of training times is reached, and the multi-model collaborative defect recognition model is generated.

[0028] In a preferred embodiment, the multi-model collaborative training of the initial multi-model collaborative defect recognition model using the training sample set of the multi-model collaborative defect recognition model includes: Taking the X-ray image sample subset as the input of the initial multi-model collaborative defect recognition model and taking the predicted defect data of the sample insulation winding wire corresponding to the X-ray image sample subset as the output, the initial multi-model collaborative defect recognition model is trained by multi-model collaboration; In each collaborative training process, the initial first defect recognition model constructs an initial three-dimensional model of the current sample insulating wrapped wire according to each X-ray image defect sample, and transmits the initial three-dimensional model to the calibration model. The calibration model obtains the reference three-dimensional model of the current sample insulating wrapped wire, determines the three-dimensional model construction error according to the reference three-dimensional model of the current sample insulating wrapped wire and the initial three-dimensional model, and feeds back the three-dimensional model construction error to the initial first defect recognition model. The initial first defect recognition model adjusts the initial three-dimensional model according to the three-dimensional model construction error. After the adjustment of the initial three-dimensional model is completed, the defect feature extraction is performed on the adjusted initial three-dimensional model, and the first predicted defect data is determined according to the extracted defect feature.

[0029] In a preferred embodiment, the method of determining the three-dimensional model construction error based on the reference three-dimensional model of the current sample insulating wrapped wire and the initial three-dimensional model includes: aligning the reference three-dimensional model and the initial three-dimensional model to obtain the aligned reference three-dimensional model and the aligned initial three-dimensional model; constructing a feature matching geometric space based on the aligned reference three-dimensional model; synchronously moving the aligned initial three-dimensional model and the aligned reference three-dimensional model to the feature matching geometric space; and repeatedly performing regional feature matching operations in the feature matching geometric space until the aligned reference three-dimensional model and the aligned initial three-dimensional model in the feature matching geometric space are aligned. The initial three-dimensional model after registration has no repeated areas, and the three-dimensional model construction error is determined according to the registered reference three-dimensional model and the registered initial three-dimensional model without repeated areas in the feature matching geometric space; wherein the regional feature matching operation includes: constructing a number of spheres with radii of random values, and making each of the spheres randomly cover the feature matching geometric space; for each sphere, calculating the first Euclidean distance between the registered reference three-dimensional model and the registered initial three-dimensional model in the sphere coverage area; and eliminating the repeated areas of the registered reference three-dimensional model and the registered initial three-dimensional model according to each of the first Euclidean distances.

[0030] In a supplementary embodiment, after eliminating the repeated areas of the aligned reference three-dimensional model and the aligned initial three-dimensional model according to each of the first Euclidean distances, it also includes: calculating the second Euclidean distances between the aligned reference three-dimensional model and the aligned initial three-dimensional model that are outside the preset distance area of ​​the sphere coverage area; fitting each second Euclidean distance to obtain a current sphere radius adjustment parameter, and adjusting the current sphere radius according to the current sphere radius adjustment parameter.

[0031] Specifically, the reference three-dimensional model and the initial three-dimensional model are registered by the ICP (Iterative Closest Point) algorithm to obtain the registered reference three-dimensional model and the registered initial three-dimensional model. Based on the registered reference three-dimensional model, a feature matching geometric space is constructed that can completely wrap the registered reference three-dimensional model and still retain a certain amount of residual space after wrapping. The geometric shape of the feature matching geometric space can be determined according to the geometric shape of the registered reference three-dimensional model. In the present invention, the reference three-dimensional model of the sample insulating wrapped wire is mainly composed of two upper and lower cylinders with larger diameters and a cylinder with a smaller diameter in the middle. Therefore, when constructing the feature matching geometric space, it can be constructed as a cylinder. After completing the construction of the feature matching geometric space, the registered initial three-dimensional model and the registered reference three-dimensional model are synchronously moved to the feature matching geometric space, and the synchronous movement can ensure that the registration effect is not affected. Perform a regional feature matching operation in the feature matching geometric space, and the regional feature matching operation is mainly to remove the repeated areas of the registered reference three-dimensional model and the registered initial three-dimensional model. The regional feature matching operation specifically includes: obtaining a random value that does not exceed the value threshold, and constructing several initial spheres with the random value as the radius; wherein the value threshold is the minimum value of the length, width, height and height of the reference three-dimensional model. The constructed spheres are randomly covered in the feature matching geometric space. For each sphere, the first Euclidean distance between the two registration points of the registered reference three-dimensional model and the registered initial three-dimensional model in the coverage area of ​​the sphere is calculated. If the first Euclidean distance between the two registration points is equal to zero or almost zero, it is considered that the two registration points basically coincide, that is, they are the registration points corresponding to the repeated area, and they are eliminated at this time; if the first Euclidean distance between the two registration points is not zero, it is considered that the two registration points do not coincide and there is a deviation, and they are not eliminated at this time. Furthermore, the second Euclidean distance between the two registration points outside the preset distance area outside the edge of the sphere is calculated, which is the same as the first Euclidean distance. According to the second Euclidean distance, it is determined whether the two registration points overlap, and then it is determined whether there is an overlapping area in the preset distance area expanded outward on the basis of the current sphere coverage area. If not, the sphere radius value is reduced when the duplicate area is eliminated next time; if so, fitting is performed according to the second Euclidean distance that is not zero, and a fitting result is obtained. When the duplicate area is eliminated next time, the sphere radius value is increased according to the fitting result. The second Euclidean distance can dynamically adjust the radius value of the sphere to improve the efficiency of dynamically eliminating duplicate areas.

[0032] It should be noted that the calculation of the Euclidean distance between the two registration points when multiple spheres are eliminated is a multi-threaded parallel calculation process, that is, it can be understood that each calculation unit only processes the Euclidean distance calculation corresponding to one sphere, and multiple calculation units calculate in parallel to achieve the effect of improving the efficiency of dynamically eliminating duplicate areas.

[0033] The initial second recognition model extracts each defect feature data of each X-ray image defect sample, and outputs second predicted defect data according to each defect feature data.

[0034] In a preferred embodiment, the method of using an X-ray image sample subset as input to the initial multi-model collaborative defect recognition model includes: inputting the initial multi-model collaborative defect recognition model in the order in which the X-ray image defect samples in the X-ray image sample subset are collected in sequence; the initial second recognition model extracts defect feature data of each X-ray image defect sample, and outputs second predicted defect data based on each defect feature data, including: the initial second recognition model extracts the defect feature data of the first input X-ray image defect sample, and when the initial second recognition model receives the remaining X-ray image defect samples except the first one, determines the repeated area between the current X-ray image defect sample and the previous X-ray image defect sample based on the previous X-ray image defect sample and the current X-ray image defect sample; after removing the repeated area between the current X-ray image defect sample and the previous X-ray image defect sample, extracts defect features from the current X-ray image defect sample to obtain the defect feature data of the current X-ray image defect sample; and outputs the second predicted defect data based on the defect feature data of each X-ray image defect sample.

[0035] Specifically, when a subset of X-ray image samples is input into the initial multi-model collaborative defect recognition model, it needs to be input in the order of shooting time. The initial second recognition model is built based on the YOLO algorithm, which is used to extract the defect features of the input X-ray image defect samples one by one, and then output the second predicted defect data according to the defect features. However, if the preset rotation angle is small during shooting, such as 15 degrees or 30 degrees each time, after the first image is taken, there will be a certain overlapping area between each subsequent image and the previous image. If feature recognition and extraction are also performed on the overlapping area, the same feature will be extracted multiple times, resulting in repeated confusion of the final features, which affects the accuracy of judgment. When the first X-ray image defect sample is input, the initial second recognition model directly extracts defect features; when the remaining X-ray image defect samples except the first one are input into the initial second recognition model, the overlapping area between the current X-ray image defect sample and the previous X-ray image defect sample is determined according to the previous X-ray image defect sample and the current X-ray image defect sample, and the overlapping area is removed, and the defect feature extraction is performed on the remaining area after removing the overlapping area to obtain the defect feature data of the current X-ray image defect sample; the second predicted defect data is output according to the defect feature data of each X-ray image defect sample.

[0036] The weight adjustment model determines the first prediction error of the first identification model and the second prediction error of the second identification model based on the first predicted defect data, the second predicted defect data and the actual defect data, and adjusts the first weight and the second weight of the initial collaborative decision-making model based on the first prediction error, the second prediction error and the three-dimensional model construction error; wherein the first weight is associated with the first predicted defect data, and the second weight is associated with the second predicted defect data.

[0037] The initial collaborative decision-making model outputs predicted defect data of the current sample insulated winding wire according to the adjusted first weight, the adjusted second weight, the first predicted defect data and the second predicted defect data, and determines the collaborative decision error according to the predicted defect data and the actual defect data.

[0038] Specifically, the weight adjustment model determines the first prediction error of the first identification model based on the first predicted defect data and the actual defect data; and determines the second prediction error of the second identification model based on the second predicted defect data and the actual defect data. The first weight parameter is adjusted according to the first prediction error and the three-dimensional model construction error, and the second weight parameter is adjusted according to the second prediction error. Among them, the first weight is the proportion parameter of the first predicted defect data considered by the initial collaborative decision model, and the second weight is the proportion parameter of the second predicted defect data considered by the initial collaborative decision model. Exemplarily, the decision formula of the initial collaborative decision model can be expressed as:

[0039] in, represents the output of the initial collaborative decision-making model, i.e., predicted defect data; represents the first weight; represents first predicted defect data; represents the second weight; Indicates the second predicted defect data.

[0040] After the adjustment of the first weight and the second weight is completed, the initial collaborative decision model outputs the predicted defect data of the current sample insulating wrapped wire according to the adjusted first weight, the adjusted second weight, the first predicted defect data and the second predicted defect data. The collaborative decision error is determined according to the predicted defect data and the actual defect data, and a multi-model collaborative defect recognition model is generated when the collaborative decision error is minimized.

[0041] Preferably, the loss function of the initial multi-model collaborative defect recognition model can also be determined based on the collaborative decision error, the first prediction error, the second prediction error and the three-dimensional model construction error, and the multi-model collaborative defect recognition model is generated when each error is less than the preset minimum error.

[0042] Based on the above method embodiments, the present invention provides corresponding system embodiments.

[0043] like Figure 2 As shown, an embodiment of the present invention provides an insulation winding wire defect recognition system applied to a high-power transformer, comprising: an image data acquisition module and a defect recognition module; The image data acquisition module is used to acquire the target insulating wrapped wire and perform fixed-point multi-angle X-ray image acquisition on the target insulating wrapped wire to obtain a set of X-ray images to be identified of the target insulating wrapped wire; wherein the set of X-ray images to be identified includes a plurality of X-ray images to be identified taken from a plurality of angles of the target insulating wrapped wire; The defect recognition module is used to input the X-ray image set to be identified into the multi-model collaborative defect recognition model so that the multi-model collaborative defect recognition model outputs defect data of the target insulating wrapped wire, the defect data including defect type and defect location, the defect type including internal conductor breakage, gap between the insulating layer and the conductor, abnormal insulation layer thickness and insulation layer shedding; wherein, when the X-ray image set to be identified is input into the multi-model collaborative defect recognition model, the first defect recognition model of the multi-model collaborative defect recognition model constructs a three-dimensional model of the target insulating wrapped wire according to the X-ray image set to be identified, and outputs first defect data according to the three-dimensional model of the target insulating wrapped wire; the second defect recognition model of the multi-model collaborative defect recognition model outputs second defect data according to each X-ray image to be identified; the collaborative decision model of the multi-model collaborative defect recognition model outputs the defect data of the target insulating wrapped wire according to the first defect data and the second defect data.

[0044] It should be noted that the system embodiment described above is merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the drawings of the system embodiment provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0045] Those skilled in the art can clearly understand that for the sake of convenience and brevity, the specific working process of the system described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0046] Based on the above method item embodiments, the present invention provides corresponding terminal device item embodiments.

[0047] An embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements a method for identifying defects of insulating winding wires applied to high-power transformers as described in any one of the present invention.

[0048] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0049] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, and uses various interfaces and lines to connect various parts of the entire terminal device.

[0050] The memory can be used to store the computer program, and the processor realizes various functions of the terminal device by running or executing the computer program stored in the memory and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; the data storage area can store data created according to the use of the mobile phone, etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0051] Based on the above method item embodiments, the present invention provides a corresponding storage medium item embodiment.

[0052] An embodiment of the present invention provides a storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute a method for identifying defects of insulating winding wires applied to a high-power transformer as described in any one of the present invention.

[0053] The storage medium is a computer-readable storage medium, and the computer program is stored in the computer-readable storage medium. When the computer program is executed by the processor, the steps of each of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or system that can carry the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0054] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for identifying defects in insulation winding wires of high-power transformers, characterized in that: include: Acquire a target insulating wrapped wire, and perform fixed-point multi-angle X-ray image acquisition on the target insulating wrapped wire to obtain a set of X-ray images to be identified of the target insulating wrapped wire; wherein the set of X-ray images to be identified includes a plurality of X-ray images to be identified taken from a plurality of angles of the target insulating wrapped wire; Inputting the to-be-identified X-ray image set into a multi-model collaborative defect recognition model, so that the multi-model collaborative defect recognition model outputs defect data of the target insulating wrapped wire, the defect data including defect type and defect location, the defect type including internal conductor breakage, gap between the insulating layer and the conductor, abnormal insulation layer thickness, and insulation layer shedding; Wherein, when the X-ray image set to be identified is input into the multi-model collaborative defect recognition model, the first defect recognition model of the multi-model collaborative defect recognition model constructs a three-dimensional model of the target insulating wrapped wire according to the X-ray image set to be identified, and outputs first defect data according to the three-dimensional model of the target insulating wrapped wire; The second defect recognition model of the multi-model collaborative defect recognition model outputs second defect data according to each X-ray image to be recognized; The collaborative decision model of the multi-model collaborative defect recognition model outputs defect data of the target insulating wrapped wire according to the first defect data and the second defect data.

2. The method for identifying defects of insulating winding wires applied to high-power transformers according to claim 1, characterized in that: The construction of the multi-model collaborative defect recognition model includes: Acquire a training sample set of a multi-model collaborative defect recognition model; wherein the training sample set includes a plurality of X-ray image sample subsets of a plurality of sample insulating wrapped wires, each of the X-ray image sample subsets includes a plurality of X-ray image defect samples of the current sample insulating wrapped wire taken from multiple angles, a reference three-dimensional model of the current sample insulating wrapped wire, and actual defect data of the current sample insulating wrapped wire; Constructing an initial multi-model collaborative defect recognition model; wherein the initial multi-model collaborative defect recognition model includes an initial first defect recognition model, an initial second defect recognition model, a calibration model, a weight adjustment model and an initial collaborative decision model; The multi-model collaborative defect recognition model is trained on the initial multi-model collaborative defect recognition model using the training sample set of the multi-model collaborative defect recognition model until the initial multi-model collaborative defect recognition model converges, thereby generating the multi-model collaborative defect recognition model.

3. The method for identifying defects of insulating winding wires applied to high-power transformers according to claim 2, characterized in that: The multi-model collaborative training of the initial multi-model collaborative defect recognition model using the training sample set of the multi-model collaborative defect recognition model includes: Taking the X-ray image sample subset as the input of the initial multi-model collaborative defect recognition model and taking the predicted defect data of the sample insulation winding wire corresponding to the X-ray image sample subset as the output, the initial multi-model collaborative defect recognition model is trained by multi-model collaboration; In each collaborative training process, the initial first defect recognition model constructs an initial three-dimensional model of the current sample insulating wrapped wire according to each X-ray image defect sample, and transmits the initial three-dimensional model to the calibration model so that the calibration model obtains a reference three-dimensional model of the current sample insulating wrapped wire, determines a three-dimensional model construction error according to the reference three-dimensional model of the current sample insulating wrapped wire and the initial three-dimensional model, and feeds back the three-dimensional model construction error to the initial first defect recognition model so that the initial first defect recognition model adjusts the initial three-dimensional model according to the three-dimensional model construction error, and outputs first predicted defect data according to the adjusted initial three-dimensional model; The initial second recognition model extracts each defect feature data of each X-ray image defect sample, and outputs second predicted defect data according to each defect feature data; The weight adjustment model determines a first prediction error of the first recognition model and a second prediction error of the second recognition model according to the first predicted defect data, the second predicted defect data and the actual defect data, and adjusts the first weight and the second weight of the initial collaborative decision-making model according to the first prediction error, the second prediction error and the three-dimensional model construction error; wherein the first weight is associated with the first predicted defect data, and the second weight is associated with the second predicted defect data; The initial collaborative decision-making model outputs predicted defect data of the current sample insulated winding wire according to the adjusted first weight, the adjusted second weight, the first predicted defect data and the second predicted defect data, and determines the collaborative decision error according to the predicted defect data and the actual defect data.

4. The method for identifying defects of insulating winding wires applied to high-power transformers as claimed in claim 3, characterized in that: The step of determining the three-dimensional model construction error according to the reference three-dimensional model of the current sample insulating wrapped wire and the initial three-dimensional model comprises: Aligning the reference three-dimensional model with the initial three-dimensional model to obtain an aligned reference three-dimensional model and an aligned initial three-dimensional model; Constructing feature matching geometric space according to the registered reference 3D model; Synchronously moving the registered initial three-dimensional model and the registered reference three-dimensional model to the feature matching geometric space; Repeating the regional feature matching operation in the feature matching geometric space until the registered reference 3D model and the registered initial 3D model have no repeated regions in the feature matching geometric space, and determining the 3D model construction error based on the registered reference 3D model and the registered initial 3D model without repeated regions in the feature matching geometric space; The regional feature matching operation includes: Constructing a number of spheres with radii of random values, and making the spheres randomly cover the feature matching geometric space; For each sphere, the first Euclidean distance between the registered reference 3D model and the registered initial 3D model in the coverage area of ​​the sphere is calculated; The overlapping areas of the registered reference three-dimensional model and the registered initial three-dimensional model are eliminated according to the first Euclidean distances.

5. The method for identifying defects of insulating winding wires applied to high-power transformers according to claim 4, characterized in that: After eliminating the repeated areas of the registered reference three-dimensional model and the registered initial three-dimensional model according to the first Euclidean distances, the method further includes: Calculate the second Euclidean distance between the registered reference three-dimensional model and the registered initial three-dimensional model, which are located outside the preset distance area of ​​the sphere coverage area; The second Euclidean distances are fitted to obtain a current sphere radius adjustment parameter, and the current sphere radius is adjusted according to the current sphere radius adjustment parameter.

6. The method for identifying defects of insulating winding wires applied to high-power transformers according to claim 3, characterized in that: The input of the initial multi-model collaborative defect recognition model using the X-ray image sample subset includes: Inputting the initial multi-model collaborative defect recognition model in sequence according to the collection order of each X-ray image defect sample in the X-ray image sample subset; The initial second recognition model extracts each defect feature data of each X-ray image defect sample, and outputs second predicted defect data according to each defect feature data, including: The initial second recognition model extracts defect feature data of the first input X-ray image defect sample, and when the initial second recognition model receives the remaining X-ray image defect samples except the first one, determines the overlapping area of ​​the current X-ray image defect sample and the previous X-ray image defect sample according to the previous X-ray image defect sample and the current X-ray image defect sample; After removing the repeated area between the current X-ray image defect sample and the previous X-ray image defect sample, defect feature extraction is performed on the current X-ray image defect sample to obtain defect feature data of the current X-ray image defect sample; The second predicted defect data is output according to each defect feature data of each X-ray image defect sample.

7. The method for identifying defects of insulating winding wires applied to high-power transformers according to claim 1, characterized in that: The method of collecting fixed-point multi-angle X-ray images of the target insulating wrapped wire to obtain a set of X-ray images of the target insulating wrapped wire to be identified includes: The target insulating wrapped wire is placed on the stage of the X-ray image acquisition device to acquire the X-ray image at the initial angle; After the X-ray image acquisition at the initial angle is completed, the stage is repeatedly rotated to the preset angle and the X-ray image at the current angle is acquired until the stage rotates 360°, the X-ray image acquisition is stopped, and a set of X-ray images to be identified of the target insulated wrapped wire is constructed based on the X-ray images acquired at each angle.

8. A defect recognition system for insulation winding wires used in high-power transformers, characterized in that: include: Image data acquisition module and defect recognition module; The image data acquisition module is used to acquire the target insulating wrapped wire and perform fixed-point multi-angle X-ray image acquisition on the target insulating wrapped wire to obtain a set of X-ray images to be identified of the target insulating wrapped wire; wherein the set of X-ray images to be identified includes a plurality of X-ray images to be identified taken from a plurality of angles of the target insulating wrapped wire; The defect recognition module is used to input the X-ray image set to be identified into the multi-model collaborative defect recognition model so that the multi-model collaborative defect recognition model outputs defect data of the target insulating wrapped wire, the defect data including defect type and defect location, the defect type including internal conductor breakage, gap between the insulating layer and the conductor, abnormal insulation layer thickness and insulation layer shedding; wherein, when the X-ray image set to be identified is input into the multi-model collaborative defect recognition model, the first defect recognition model of the multi-model collaborative defect recognition model constructs a three-dimensional model of the target insulating wrapped wire according to the X-ray image set to be identified, and outputs first defect data according to the three-dimensional model of the target insulating wrapped wire; the second defect recognition model of the multi-model collaborative defect recognition model outputs second defect data according to each X-ray image to be identified; the collaborative decision model of the multi-model collaborative defect recognition model outputs the defect data of the target insulating wrapped wire according to the first defect data and the second defect data.

9. A terminal device, characterized in that: It comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements a method for identifying defects of insulating winding wires applied to a high-power transformer as described in any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute the insulation winding wire defect identification method applied to a high-power transformer as described in any one of claims 1 to 7.

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