Converter valve insulating material defect detection method based on phase-locked infrared thermal imaging

Abstract

The invention provides a converter valve insulating material defect detection method based on phase-locked infrared thermal imaging, and the method comprises the steps: obtaining an output power curve of a converter valve in a time period T, and carrying out the Fourier transform of the output power curve, and obtaining a spectrogram; selecting a peak point of the spectrogram, and calculating a peak width; acquiring a temperature image of the insulating material of the converter valve in the time period T0, converting the temperature image into a grey-scale map, and performing non-local mean filtering processing to obtain a time-varying grey-scale sequence image; setting a phase locking frequency based on the peak point and the peak width; fourier transform is carried out on the time-varying gray sequence image at a set phase locking frequency to obtain a phase diagram; processing the phase diagram by using a Sobel operator to obtain a gradient directional diagram and an edge detection diagram of the converter valve insulating material; and repeating the above steps, obtaining a plurality of edge detection images for comparison, and marking repeated points in the plurality of images to obtain a converter valve insulating material defect positioning image. With the adoption of the scheme, the defects of the insulating material of the converter valve can be quickly identified and accurately positioned.

Description

Technical Field

[0001] The present application relates to the technical field of non-destructive testing of converter valves, and in particular to a method and device for detecting defects in insulating materials of converter valves based on phase-locked infrared thermal imaging. Background Art

[0002] DC power transmission can adapt to long-distance power transmission, and has low loss and large capacity during power transmission. With these advantages, DC power transmission is widely used in power systems. The converter valve is the core equipment of the DC power transmission project. It connects the three-phase AC voltage to the DC end in sequence to obtain the desired DC voltage and realize power control. Its value accounts for about 22-25% of the total price of the converter station equipment. The defect of the converter valve may cause the converter valve failure and cause economic losses to the power system. Therefore, the defect detection of the converter valve is of great significance to the stable operation of the converter station.

[0003] Existing defect detection technologies for converter valve insulation materials include acoustic detection, machine learning, etc. Acoustic detection has poor anti-interference ability due to electromagnetic interference or other environmental factors. Machine learning requires example support and has a certain training cost. Summary of the invention

[0004] The present application aims to solve one of the technical problems in the related art at least to some extent.

[0005] To this end, the first purpose of the present application is to propose a method for detecting defects in the insulation material of a converter valve based on phase-locked infrared thermal imaging, thereby achieving rapid identification and precise positioning of defects in the insulation material of the converter valve.

[0006] The second object of the present application is to provide a computer device.

[0007] To achieve the above-mentioned purpose, the first embodiment of the present application proposes a method for detecting defects in insulating materials of a converter valve based on phase-locked infrared thermal imaging, comprising:

[0008] Step S1: obtaining an output power curve of a converter valve in a time period T, and performing Fourier transform on the output power curve to obtain a frequency spectrum of the converter valve insulation material;

[0009] Step S2: selecting the peak point of the frequency spectrum of the converter valve insulation material and calculating the peak width;

[0010] Step S3: acquiring a temperature image of the converter valve insulation material within the time period T0, converting the acquired image into a grayscale image, and performing non-local mean filtering on the converted grayscale image to obtain a time-varying grayscale sequence image of the converter valve insulation material;

[0011] Step S4: setting the phase-locked frequency based on the peak point and the peak width;

[0012] Step S5: performing Fourier transform on the time-varying grayscale sequence image of the converter valve insulation material at the set phase-locked frequency to obtain a phase diagram of the converter valve insulation material;

[0013] Step S6: using the Sobel operator to process the phase map of the converter valve insulation material to obtain a gradient direction map of the converter valve insulation material and an edge detection map of the converter valve insulation material;

[0014] Step S7: repeat steps S4-S6, obtain multiple converter valve insulation material edge detection images, compare the multiple converter valve insulation material edge detection images, mark the repeated points in the multiple images, and obtain the converter valve insulation material defect location map.

[0015] Optionally, in one embodiment of the present application, the time period T includes at least one complete cycle.

[0016] Optionally, in one embodiment of the present application, the peak width σ is numerically equal to the peak point f of the frequency spectrum of the converter valve insulation material. max Twice the distance to zero.

[0017] Optionally, in one embodiment of the present application, an infrared thermal imaging camera is used to obtain a temperature image of the converter valve insulation material within a time period T0, where T0 = 2 to 3T.

[0018] Optionally, in one embodiment of the present application, when performing Fourier transform for the first time, the phase-locked frequency f locked Equal to the peak point f max , repeat the Fourier transform to make the phase-locked frequency σ is the peak width;

[0019] When performing steps S4-S6 for the nth time, perform the nth Fourier transform to determine the phase diagram P of the converter valve insulation material n , and use the Sobel operator to process the phase diagram P of the converter valve insulation material n , and obtain the gradient direction diagram G of the converter valve insulation material n And converter valve insulation material edge detection Figure E n .

[0020] Optionally, in one embodiment of the present application, the phase diagram P of the converter valve insulation material n The generation process includes: n Perform Fourier transform and calculate each point f locked The phase diagram P of the converter valve insulation material is obtained n .

[0021] Optionally, in one embodiment of the present application, the phase diagram of the converter valve insulation material is a heat map that spreads outward from the heat source as the center, and the phase gradient drop line is analyzed to mark the phase mutation boundaries of π / 2 and -π / 2.

[0022] Optionally, in one embodiment of the present application, the Sobel operator is a custom convolution kernel, the custom convolution kernel is a given parameter sizen, and a convolution kernel of sizen*sizen is generated. The characteristic of the Sobel operator is that the weight of the pixel point farther from the center point is smaller, and the Sobel operator is used to process the phase map P of the converter valve insulation material. n , and obtain the gradient direction diagram G of the converter valve insulation material n And converter valve insulation material edge detection Figure E n ,include:

[0023] Calculate P by convolution n The gradient of each point along the x direction and along the y direction is calculated, and the gradient direction is obtained to obtain the gradient direction diagram G of the converter valve insulation material n ;

[0024] Set the gradient threshold g0 and use the Sobel operator to process G n When the pixel gradient value exceeds g0, it is marked to generate the converter valve insulation material edge detection map E n .

[0025] Optionally, in one embodiment of the present application, the plurality of converter valve insulation material edge detection images include E1, E2 and E3, and the converter valve insulation material defect location image P out The generation process includes:

[0026] Compare E1, E2, and E3, mark the repeated pixel points of E1, E2, and E3, eliminate the boundary influence, and obtain the defect location map P of the converter valve insulation material. out .

[0027] To achieve the above-mentioned purpose, the second aspect of the present invention proposes a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the above-mentioned method for detecting defects in insulating materials of converter valves based on phase-locked infrared thermal imaging is implemented.

[0028] The defect detection method of the converter valve insulation material based on phase-locked infrared thermal imaging in the embodiment of the present application takes into account that the converter valve will dissipate power during operation, causing the temperature of the converter valve insulation material to fluctuate periodically. The infrared thermal imaging camera is used to obtain the temperature image of the converter valve insulation material within a certain period of time, and the temperature image is denoised by a non-local mean filtering algorithm and then Fourier transformed to obtain the phase information at the phase-locked frequency, and then the defect is located by Sobel operator processing. This defect location method is fast and accurate, can effectively avoid noise interference, has strong anti-interference ability, is easy to implement, and has little impact on the converter valve, and will not interfere with the normal operation of the converter valve.

[0029] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0031] Figure 1 A schematic flow chart of a method for detecting defects in insulating materials of a converter valve based on phase-locked infrared thermal imaging provided in the first embodiment of the present application;

[0032] Figure 2 This is a flow chart of repeatedly performing steps S4-S6 in an embodiment of the present application;

[0033] Figure 3 A schematic diagram of a converter valve output power curve according to an embodiment of the present application;

[0034] Figure 4 A phase diagram of the converter valve insulation material according to an embodiment of the present application;

[0035] Figure 5 Schematic diagram of the Sobel operator convolution kernel of an embodiment of the present application;

[0036] Figure 6 This is a gradient directional diagram of the insulating material of the converter valve according to an embodiment of the present application;

[0037] Figure 7 This is a diagram of the edge detection of the converter valve insulation material according to an embodiment of the present application. DETAILED DESCRIPTION

[0038] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0039] The following describes a method and device for detecting defects in insulating materials of a converter valve based on phase-locked infrared thermal imaging according to an embodiment of the present application with reference to the accompanying drawings.

[0040] Figure 1 A schematic flow chart of a method for detecting defects in insulating materials of a converter valve based on phase-locked infrared thermal imaging provided in Example 1 of the present application.

[0041] like Figure 1 As shown, the method for detecting defects in the insulation material of a converter valve based on phase-locked infrared thermal imaging comprises the following steps:

[0042] Step S1: obtaining an output power curve of a converter valve in a time period T, and performing Fourier transform on the output power curve to obtain a frequency spectrum of the converter valve insulation material;

[0043] In this embodiment, obtaining the output power curve of the converter valve within a period of time T must ensure that it contains at least one complete cycle.

[0044] Step S2: selecting the peak point of the frequency spectrum of the converter valve insulation material and calculating the peak width;

[0045] In this embodiment, the peak width σ is numerically equal to the peak point f of the frequency spectrum of the converter valve insulation material. max Twice the distance to zero.

[0046] Step S3: acquiring a temperature image of the converter valve insulation material within the time period T0, converting the acquired image into a grayscale image, and performing non-local mean filtering on the converted grayscale image to obtain a time-varying grayscale sequence image of the converter valve insulation material;

[0047] In this embodiment, when the temperature image of the converter valve insulation material is obtained within a period of time T0 using an infrared thermal imaging camera, T0 = 2-3T. When the converter valve insulation material temperature image is converted into a grayscale image and processed by a non-local mean filtering algorithm, the similarity parameter h should be small enough to remove Gaussian noise.

[0048] Step S4: setting the phase-locked frequency based on the peak point and the peak width;

[0049] Step S5: performing Fourier transform on the time-varying grayscale sequence image of the converter valve insulation material at the set phase-locked frequency to obtain a phase diagram of the converter valve insulation material;

[0050] Step S6: using the Sobel operator to process the phase map of the converter valve insulation material to obtain a gradient direction map of the converter valve insulation material and an edge detection map of the converter valve insulation material;

[0051] Step S7: repeat steps S4-S6, obtain multiple converter valve insulation material edge detection images, compare the multiple converter valve insulation material edge detection images, mark the repeated points in the multiple images, and obtain the converter valve insulation material defect location map.

[0052] In this embodiment, the number of repetitions is set to 3 times. Figure 2 This is a schematic diagram of a flow chart of repeatedly performing steps S4-S6, where n=1 at the initial stage.

[0053] In this embodiment, the phase-locked frequency f is set locked When f locked =f max , perform Fourier transform on the time-varying grayscale sequence image of the converter valve insulation material, and calculate each point f licjed The phase diagram of the converter valve insulation material is obtained by the phase at.

[0054] Repeat the Fourier transform

[0055] In this embodiment, the Sobel operator used is a custom convolution kernel. Given the parameter sizen, a convolution kernel of sizen*sizen is generated. The characteristic is that the farther the pixel point is from the center point, the smaller the weight is. The gradient of each point along the x direction and along the y direction is calculated by convolution, so as to calculate the gradient direction and obtain the gradient direction map G of the converter valve insulation material. n . When using the Sobel operator to process G n When the gradient value of a pixel exceeds g0, the gradient threshold g0 is set manually, and the pixel is marked to generate the edge detection map E of the converter valve insulation material. n .

[0056] In this embodiment, the phase diagram of the converter valve insulation material is a heat map spreading outward from the heat source as the center, and the phase gradient drop line is analyzed to mark the phase mutation boundaries of π / 2 and -π / 2.

[0057] In this embodiment, the phase-locked frequency f is changed locked Repeat the Fourier transform to obtain E1, E2 and E3, compare E1, E2 and E3, mark the repeated pixel points of E1, E2 and E3, eliminate the boundary effect, and obtain the defect location map P of the converter valve insulation material. out .

[0058] The defect detection method of the converter valve insulation material based on phase-locked infrared thermal imaging in the embodiment of the present application takes into account that the converter valve will dissipate power during operation, causing the temperature of the converter valve insulation material to fluctuate periodically. The infrared thermal imaging camera is used to obtain the temperature image of the converter valve insulation material within a certain period of time, and the temperature image is denoised by a non-local mean filtering algorithm and then Fourier transformed to obtain the phase information at the phase-locked frequency, and then the defect is located by Sobel operator processing. This defect location method is fast and accurate, can effectively avoid noise interference, has strong anti-interference ability, is easy to implement, and has little impact on the converter valve, and will not interfere with the normal operation of the converter valve.

[0059] Assuming that the converter valve insulation material has a rectangular defect, the converter valve insulation material defect detection method based on phase-locked infrared thermal imaging of this embodiment is used to obtain the output power curve. Figure 3 After Fourier transformation, the frequency spectrum of the converter valve insulation material is obtained. After analyzing the frequency spectrum, the phase-locked frequency The sampling rate is f0 = 2, the sampling time is T0 = 500s, and a total of number = 1000 png images are collected. These 1000 images are converted into grayscale images, and the Gaussian noise is removed using the non-local mean filtering algorithm. The image size is 480x640, and the Fourier transform is performed to obtain the phase of each pixel at the phase-locked frequency. Figure 4 , set the Sobel operator to a convolution kernel of size n = 3, as shown in the schematic diagram Figure 5 , perform convolution operation to calculate the phase gradient of each pixel, and then output the gradient direction map, see Figure 6 , and finally generate the edge detection map, see Figure 7 , change the phase-locked frequency and repeat the Fourier transform, and finally locate the rectangular defect.

[0060] In order to implement the above embodiments, the present invention further proposes a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method described in the above embodiments is implemented.

[0061] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example" or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0062] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0063] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0064] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute the instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0065] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0066] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0067] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0068] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for detecting defects in insulation materials of converter valves based on phase-locked infrared thermal imaging, characterized in that: include: Step S1: obtaining an output power curve of a converter valve in a time period T, and performing Fourier transform on the output power curve to obtain a frequency spectrum of an insulating material of the converter valve; Step S2: selecting the peak point of the frequency spectrum of the converter valve insulation material and calculating the peak width; Step S3: acquiring a temperature image of the converter valve insulation material within the time period T0, converting the acquired image into a grayscale image, and performing non-local mean filtering on the converted grayscale image to obtain a time-varying grayscale sequence image of the converter valve insulation material; Step S4: setting the phase-locked frequency based on the peak point and the peak width; Step S5: performing Fourier transform on the time-varying grayscale sequence image of the converter valve insulation material at the set phase-locked frequency to obtain a phase diagram of the converter valve insulation material; Step S6: using the Sobel operator to process the phase map of the converter valve insulation material to obtain a gradient direction map of the converter valve insulation material and an edge detection map of the converter valve insulation material; Step S7: repeat steps S4-S6, obtain multiple converter valve insulation material edge detection images, compare the multiple converter valve insulation material edge detection images, mark repeated points in the multiple images, and obtain a converter valve insulation material defect location map.

2. The method according to claim 1, characterized in that The time period T includes at least one complete cycle.

3. The method according to claim 1, characterized in that The peak width σ is numerically equal to the peak point f of the converter valve insulation material spectrum. max Twice the distance to zero.

4. The method according to claim 1, characterized in that An infrared thermal imaging camera is used to obtain a temperature image of the converter valve insulation material within a time period T0, where T0 = 2 to 3T.

5. The method according to claim 1, characterized in that When the Fourier transform is first performed, the phase-locked frequency f locked Equal to the peak point f max , repeat the Fourier transform to make the phase-locked frequency σ is the peak width; When performing steps S4-S6 for the nth time, perform the nth Fourier transform to determine the phase diagram P of the converter valve insulation material n , and use the Sobel operator to process the phase diagram P of the converter valve insulation material n , and obtain the gradient direction diagram G of the converter valve insulation material n And converter valve insulation material edge detection Figure E n .

6. The method according to claim 5, characterized in that Phase diagram of converter valve insulation material P n The generation process includes: n Perform Fourier transform and calculate each point f locked The phase diagram P of the converter valve insulation material is obtained n .

7. The method according to claim 6, characterized in that The phase diagram of the converter valve insulation material is a heat map that spreads outward from the heat source as the center. The phase gradient drop line is analyzed and the phase mutation boundaries of π / 2 and -π / 2 are marked.

8. The method according to claim 5, characterized in that The Sobel operator is a custom convolution kernel. The custom convolution kernel generates a convolution kernel of sizen*sizen with a given parameter sizen. The characteristic of the Sobel operator is that the weight of the pixel point farther from the center point is smaller. The Sobel operator is used to process the phase map P of the converter valve insulation material. n , and obtain the gradient direction diagram G of the converter valve insulation material n And converter valve insulation material edge detection Figure E n ,include: Calculate P by convolution n The gradient of each point along the x direction and along the y direction is calculated, and the gradient direction is obtained to obtain the gradient direction diagram G of the converter valve insulation material n ; Set the gradient threshold g0 and use the Sobel operator to process G n When the pixel gradient value exceeds g0, it is marked to generate the converter valve insulation material edge detection map E n .

9. The method according to claim 5, characterized in that The plurality of converter valve insulation material edge detection images include E1, E2 and E3, and the converter valve insulation material defect location image P out The generation process include: Compare E1, E2, and E3, mark the repeated pixel points of E1, E2, and E3, eliminate the boundary influence, and obtain the defect location map P of the converter valve insulation material. out .

10. A computer device, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method according to any one of claims 1 to 9 is implemented.

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