CN114241040A - Method and system for detecting state and insufficient closing of disconnecting link of transformer substation - Google Patents
Method and system for detecting state and insufficient closing of disconnecting link of transformer substation Download PDFInfo
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- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
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- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
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- G01J5/00—Radiation pyrometry, e.g. infrared or optical thermometry
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Abstract
The application provides a method for detecting the state of a disconnecting link and the incomplete closing of a transformer substation, which comprises the following steps: s1, acquiring an infrared image of the disconnecting link of the transformer substation to be detected, and preprocessing the acquired infrared image; s2, guiding the preprocessed infrared image into an infrared disconnecting link state target detection model for further analysis and calculation, and acquiring position coordinate information; s3, acquiring an image of the disconnecting link in the infrared image according to the position coordinate information; s4, judging whether the state of the disconnecting link is correct or not; s5, further processing the image of the knife switch acquired in the S3 and acquiring a temperature value of the knife switch; s6, comparing the temperature value of the disconnecting link with a preset threshold value of the abnormal temperature value, and judging whether the disconnecting link is in a closing state. By judging the switching-on or switching-off of the disconnecting link and judging whether the disconnecting link is switched on in place or not, the double judgment of the state of the disconnecting link is realized, and the high judgment accuracy of the state of the disconnecting link of the transformer substation is ensured.
Description
Technical Field
The application belongs to the technical field of disconnecting link detection, and particularly relates to a method and a system for detecting the state of a disconnecting link and the incomplete closing of a transformer substation.
Background
The transformer substation is a place for converting voltage and current, receiving electric energy and distributing electric energy in an electric power system. The disconnecting link is an important device in the transformer substation, is also called as an isolating switch, plays a role in isolating voltage and has important significance for ensuring normal and safe work of the transformer substation.
In a conventional transformer substation, operating personnel are required to implement on site and manually judge whether the equipment state is in place before and after the operation of the disconnecting link equipment, and the mode is high in labor intensity, long in operation time and gradually eliminated. With the development of intelligent substations, the operation of the disconnecting link equipment is improved towards remote automatic operation, but operators are still required to participate in each operation and manually confirm whether the operation is accurately completed by field personnel, and although the mode reduces the labor intensity of the operators and shortens the operation time, the subjective judgment of the field personnel is greatly dependent on the conditions such as knowledge and experience of the field personnel, so that misjudgment is easy to occur, and particularly, if severe weather occurs, the field conditions are dangerous, and the smooth operation of the equipment is more influenced.
In view of this, it is very significant to provide a method and a system for detecting the state and the non-complete closing of the disconnecting link of the substation.
Content of application
In order to solve the problem that the existing transformer substation disconnecting link is difficult to judge the state of the disconnecting link, the application provides a method and a system for detecting the state and the closing of the transformer substation disconnecting link, and aims to solve the technical defect problem of the existing transformer substation disconnecting link.
In a first aspect, the present application provides a method for detecting a state of a disconnecting link and an incomplete closing of a transformer substation, including the following steps:
s1, acquiring an infrared image of the disconnecting link of the transformer substation to be detected, and preprocessing the acquired infrared image;
s2, guiding the preprocessed infrared image into an infrared disconnecting link state target detection model for further analysis and calculation, and acquiring position coordinate information;
s3, acquiring an image of the disconnecting link in the infrared image according to the position coordinate information;
s4, judging whether the state of the disconnecting link is correct or not, if the state of the disconnecting link is incorrect, sending an alarm that the state of the disconnecting link is wrong, and if not, not sending the alarm;
s5, further processing the image of the knife switch acquired in the S3 and acquiring a temperature value of the knife switch; and
s6, comparing the temperature value of the disconnecting link with a preset threshold value of an abnormal temperature value, judging whether the disconnecting link is in a closing state, if the obtained disconnecting link temperature value is larger than the threshold value of the abnormal temperature value, sending an alarm that the disconnecting link is not closed in place, otherwise, not sending the alarm.
The image that contains the visible light that ordinary shooting is influenced by the illumination greatly, can't carry out the visible light detection night, this application adopts infrared image to receive the influence of visible light little, and can carry out temperature detection, realize end-to-end detection through using the target detection model, this target detection model adopts popular degree of depth learning mode, only judges the state of switch, closes a floodgate or opens a floodgate, whether the temperature characteristic of reuse infrared image judges that the switch closed a floodgate and targets in place, realize the dual judgement of switch state, guarantee to the judgement accuracy height of transformer substation's switch state.
Preferably, the pretreatment in S1 specifically includes the following steps:
s11, shooting a transformer substation disconnecting link to be detected by a preset unmanned aerial vehicle cradle head to acquire the infrared image;
s12, carrying out color coding on the acquired single-channel gray image, and further acquiring a pseudo color image of the disconnecting link;
s13, zooming the long edge of the obtained pseudo color picture into 640 pixels, obtaining a zoom ratio X and recording;
and S14, multiplying the short side of the obtained false color picture by the scaling ratio X to obtain the false color scaling picture of the infrared image with equal scaling.
Further preferably, the pseudo-coding formula for obtaining the pseudo-color picture of the knife switch in S12 is as follows:
wherein R, G, B represents the three color channels of red, green and blue, respectively.
Further preferably, the obtained pseudo-color zoom map is imported into the infrared switch state target detection model in S2, and the infrared switch state target detection model is trained and established based on the YOLOV5 target detection model.
Further preferably, the training of the infrared switch state target detection model specifically includes the following steps:
a. collecting an infrared picture of the transformer substation disconnecting link shot by the unmanned aerial vehicle as training data;
b. screening and filtering the collected infrared pictures to remove fuzzy pictures without detection targets;
c. marking a candidate frame at a position with a knife switch in the picture by using LabelImg marking software, storing the candidate frame into a marking file in an x.xml format, and converting the x.xml file into a x.txt marking file by using an xml2txt.py tool;
d. the training data is recorded in a way that 5: 2: dividing the training set, the verification set and the test set into a training set, a verification set and a test set, wherein the training set, the verification set and the test set respectively correspond to folders train, val and test, the folders train, val and test all comprise an image folder and a label folder, the image folder is used for storing training picture data, and the label folder is used for storing the mark files in the format of the text;
e. modifying the YOLOV5 target detection model configuration file YOLOV5s.yaml to generate a model structure configuration file yolo-switch.yaml of the infrared switch state target detection model, further modifying configuration parameters of the training model, such as the size of a picture, the total training iteration number, the size of batch gradient data and the like, and starting training after the setting of the parameters which are not set according to default parameters;
f. and after the training is finished, verifying whether the accuracy of the model on the test set meets the requirement, if not, analyzing the data which are identified wrongly or can not be identified by the test set, modifying the label files of the training set and the verification set according to the analysis result, and modifying the configuration parameters for retraining until the accuracy of the infrared switch state target detection model on the test set meets the requirement. The traditional algorithm model is easy to cause instability under the interference of external illumination and shadow, and the advanced deep learning yolov5 algorithm adopted by the method has better robustness and stability compared with the traditional image processing method through a large amount of target learning training.
Preferably, the position coordinate information acquired in S2 is position coordinates of an upper left corner and a lower right corner of the knife switch, and the image of the knife switch is further intercepted according to the position coordinate information in S2 in S3.
Further preferably, the coordinate transformation formula is:
x1＝x-0.5*w
y1＝y-0.5*h
x2＝x+0.5*w
y2＝y+0.5*h
where X and Y are the X, Y axes of the center point coordinates of the knife gate, w is the width of the knife gate, h is the height of the knife gate, X1 and Y1 are the top left X, Y axis coordinates of the knife gate, and X2 and Y are the bottom right X, Y axis coordinates of the knife gate.
Preferably, S5 further includes:
s51, sorting the pixel gray values of the images of the disconnecting link intercepted in the S3 from large to small;
s52, extracting the first 10% pixels in the pixel gray value, which account for the intercepted total pixel gray value number, and solving the average value of the pixel gray value;
and S53, converting the extracted average value by linear fitting or inquiring an encoding table between the infrared image temperature and the pixel to obtain the temperature value of the disconnecting link. The pixel gray values of the intercepted disconnecting link closing state image are sorted from large to small, so that the subsequent pixel value average value extraction is facilitated, the higher the gray value of the pixel is, the higher the temperature is, when the disconnecting link is not closed in place, the highest temperature in the area where the disconnecting link cannot be closed is, and the judgment can be carried out only by taking the pixel with the highest gray value.
In a second aspect, the present application discloses a detection system for a state of a transformer substation disconnecting link and a short-circuit state, including:
the infrared image acquisition module is configured for acquiring an infrared image of the disconnecting link of the transformer substation to be detected;
the image preprocessing module is configured to perform color coding on the acquired single-channel gray level image, further acquire a pseudo color image of the disconnecting link, and perform equal-scale scaling on the pseudo color image to obtain a pseudo color scaling image;
the conversion module is configured to convert the pixel gray value average value to obtain a temperature value of the disconnecting link;
and the alarm module is configured to judge whether the state of the disconnecting link is correct or not and whether the disconnecting link is in a closing state or not, and send out a corresponding alarm.
In a third aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the method as described in any implementation manner of the first aspect.
Compared with the prior art, the beneficial results of this application lie in:
(1) this application adopts infrared image to receive the influence of visible light little, can carry out temperature detection moreover, realizes end-to-end detection through using the target detection model, and this target detection model adopts popular degree of depth learning mode, only judges the state of switch, closes a floodgate or opens a floodgate, and whether the temperature characteristic of recycling infrared image judges that the switch closed a floodgate and targets in place, realizes the dual judgement to the switch state, ensures that the judgement accuracy to the switch state of transformer substation is high.
(2) The traditional algorithm model is easy to cause instability under the interference of external illumination and shadow, and the advanced deep learning yolov5 algorithm adopted by the method has better robustness and stability compared with the traditional image processing method through a large amount of target learning training.
Drawings
The accompanying drawings are included to provide a further understanding of the embodiments and are incorporated in and constitute a part of this specification. The drawings illustrate embodiments and together with the description serve to explain the principles of the application. Other embodiments and many of the intended advantages of embodiments will be readily appreciated as they become better understood by reference to the following detailed description. The elements of the drawings are not necessarily to scale relative to each other. Like reference numerals designate corresponding similar parts.
Fig. 1 is a schematic flow chart of a method for detecting a state of a transformer substation disconnecting link and an incomplete closing state according to an embodiment of the present application;
fig. 2 is a schematic view of a specific flow structure of a method for detecting a state of a transformer substation disconnecting link and an incomplete closing state in an embodiment of the present application;
fig. 3 is a diagram illustrating a closed state of a single-channel infrared disconnecting link in the method for detecting a state and an insufficient closing state of a disconnecting link of a substation according to the embodiment of the present application;
fig. 4 is a training interface diagram of an infrared switch state target detection model in a transformer substation switch state and insufficient switching-on detection method according to an embodiment of the present application;
fig. 5 is a schematic flow chart of a detection system for detecting a state of a transformer substation disconnecting link and an incomplete closing state in an embodiment of the present application;
fig. 6 is a schematic structural diagram of a computer device suitable for implementing an electronic apparatus according to an embodiment of the present application.
Detailed Description
In the following detailed description, reference is made to the accompanying drawings, which form a part hereof, and in which is shown by way of illustration specific embodiments in which the application may be practiced. In this regard, directional terminology, such as "top," "bottom," "left," "right," "up," "down," etc., is used with reference to the orientation of the figures being described. Because components of embodiments can be positioned in a number of different orientations, the directional terminology is used for purposes of illustration and is in no way limiting. It is to be understood that other embodiments may be utilized and logical changes may be made without departing from the scope of the present application. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present application is defined by the appended claims.
Fig. 1 illustrates a method for detecting a state of a transformer substation disconnecting link and a short-circuit state, which is disclosed in an embodiment of the present application, and as shown in fig. 1, the method includes the following steps:
s1, acquiring an infrared image of the disconnecting link of the transformer substation to be detected, and preprocessing the acquired infrared image;
specifically, fig. 2 is a detailed flowchart of a method for detecting a state of a transformer substation disconnecting link and a short-circuit state in an embodiment of the present application, and referring to fig. 1 and fig. 2, the preprocessing in S1 specifically includes the following steps:
s11, shooting a transformer substation disconnecting link to be detected by a preset unmanned aerial vehicle cradle head to obtain an infrared image;
s12, carrying out color coding on the acquired single-channel gray image to further acquire a pseudo color image of the disconnecting link;
the pseudo-coding formula for obtaining the pseudo-color picture of the disconnecting link is as follows:
wherein R, G, B represents the three color channels of red, green and blue, respectively.
S13, zooming the long edge of the obtained pseudo color picture into 640 pixels, obtaining a zoom ratio X and recording;
and S14, multiplying the short edge of the obtained pseudo color picture by the scaling X to obtain a pseudo color scaling picture of the infrared image with equal scaling. Fig. 3 is a diagram of a closed state of a single-channel infrared disconnecting link in the method for detecting a state and an insufficient closing state of a substation disconnecting link according to the embodiment of the present application, as shown in fig. 3.
S2, guiding the preprocessed infrared image into an infrared disconnecting link state target detection model for further analysis and calculation, and acquiring position coordinate information;
specifically, in this embodiment, the obtained pseudo-color zoom map is imported into an infrared switch state target detection model, and the infrared switch state target detection model is trained and established based on a YOLOV5 target detection model. The training of the infrared disconnecting link state target detection model specifically comprises the following steps:
a. collecting an infrared picture of the transformer substation disconnecting link shot by the unmanned aerial vehicle as training data;
b. screening and filtering the collected infrared pictures to remove fuzzy pictures without detection targets;
c. labeling candidate frames of the positions with the disconnecting links in the picture by using LabelImg labeling software, storing the candidate frames into a labeling file in an x.xml format, and converting the x.xml file into a x.txt labeling file by using an xml2txt.
d. Training data were recorded at 5: 2: dividing the training set, the verification set and the test set into a training set, a verification set and a test set, wherein the training set, the verification set and the test set respectively correspond to folders train, val and test, the folders train, val and test all comprise an image folder and a label folder, the image folder is used for storing training picture data, and the label folder is used for storing a tagged file in a txt format;
e. modifying a YOLOV5 target detection model configuration file yolov5s.yaml to generate a model structure configuration file yolo-switch.yaml of the infrared switch state target detection model, further modifying configuration parameters of a training model, such as the size of a picture, the total iteration number of training, the size of batch gradient data and the like, and starting training after the setting of the parameters which are not set according to default parameters;
f. and after the training is finished, verifying whether the accuracy of the model meets the requirement on the test set, if not, analyzing the data which is identified wrongly or can not be identified by the test set, modifying the label files of the training set and the verification set according to the analysis result, and modifying the configuration parameters for retraining until the accuracy of the infrared switch state target detection model on the test set meets the requirement.
Fig. 4 is a training interface diagram of an infrared switch state target detection model in a transformer substation switch state and insufficient closing detection method according to an embodiment of the present application, as shown in fig. 4.
The traditional algorithm model is easy to cause instability under the interference of external illumination and shadow, and the advanced deep learning yolov5 algorithm adopted by the method has better robustness and stability compared with the traditional image processing method through a large amount of target learning training.
S3, acquiring an image of the knife switch in the infrared image according to the position coordinate information;
specifically, the position coordinate information acquired in S2 is the position coordinates of the upper left corner and the lower right corner of the knife switch, and the image of the knife switch is further captured according to the position coordinate information in S2 in S3. The conversion formula of the coordinates is as follows:
x1＝x-0.5*w
y1＝y-0.5*h
x2＝x+0.5*w
y2＝y+0.5*h
where X and Y are the X and Y axes of the center point coordinates of the knife gate, w is the width of the knife gate, h is the height of the knife gate, X1 and Y1 are the top left X, Y axis coordinates of the knife gate, and X2 and Y are the bottom right X, Y axis coordinates of the knife gate.
S4, judging whether the state of the disconnecting link is correct or not, if the state of the disconnecting link is incorrect, giving an alarm of wrong disconnecting link state, and otherwise, not giving an alarm;
s5, further processing the knife switch image obtained in S3 and obtaining a temperature value of the knife switch;
specifically, the method further comprises the following steps:
s51, sorting the pixel gray values of the images of the disconnecting link intercepted in the S3 from large to small;
s52, extracting the first 10% pixels of the pixel gray value which account for the intercepted number of the total pixel gray value, and solving the average value of the pixel gray value;
and S53, converting the extracted average value by linear fitting or inquiring an encoding table between the infrared image temperature and the pixel to obtain the temperature value of the disconnecting link.
The pixel gray values of the intercepted disconnecting link closing state image are sorted from large to small, so that the subsequent pixel value average value extraction is facilitated, the higher the gray value of the pixel is, the higher the temperature is, when the disconnecting link is not closed in place, the highest temperature in the area where the disconnecting link cannot be closed is, and the judgment can be carried out only by taking the pixel with the highest gray value.
S6, comparing the temperature value of the disconnecting link with a preset threshold value of an abnormal temperature value, judging whether the disconnecting link is in a closing state, if the obtained disconnecting link temperature value is larger than the threshold value of the abnormal temperature value, giving an alarm that the disconnecting link is not closed in place, otherwise, not giving an alarm.
This application adopts infrared image to receive the influence of visible light little, can carry out temperature detection moreover, realizes end-to-end detection through using the target detection model, and this target detection model adopts popular degree of depth learning mode, only judges the state of switch, closes a floodgate or opens a floodgate, and whether the temperature characteristic of recycling infrared image judges that the switch closed a floodgate and targets in place, realizes the dual judgement to the switch state, ensures that the judgement accuracy to the switch state of transformer substation is high.
In a second aspect, the present application discloses a system for detecting a state and an insufficient closing of a transformer substation disconnecting link, where fig. 5 is a schematic flow diagram of the system for detecting a state and an insufficient closing of a transformer substation disconnecting link according to an embodiment of the present application, and as shown in fig. 5, the system includes:
the infrared image acquisition module is configured for acquiring an infrared image of the disconnecting link of the transformer substation to be detected;
the image preprocessing module is configured to perform color coding on the acquired single-channel gray level picture, further acquire a pseudo color picture of the disconnecting link, and perform equal-scale scaling on the pseudo color picture to obtain a pseudo color scaling picture;
the conversion module is configured to convert the average value of the pixel gray values to obtain a temperature value of the disconnecting link;
and the alarm module is configured to judge whether the state of the disconnecting link is correct or not and whether the disconnecting link is in a closing state or not, and send out a corresponding alarm.
Referring now to fig. 6, a schematic diagram of a computer device 600 suitable for use in implementing an electronic device (e.g., the server or terminal device shown in fig. 1) according to an embodiment of the present application is shown. The electronic device shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present application.
As shown in fig. 6, the computer apparatus 600 includes a Central Processing Unit (CPU)601 and a Graphics Processing Unit (GPU)602, which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)603 or a program loaded from a storage section 609 into a Random Access Memory (RAM) 606. In the RAM 604, various programs and data necessary for the operation of the apparatus 600 are also stored. The CPU 601, GPU602, ROM 603, and RAM 604 are connected to each other via a bus 605. An input/output (I/O) interface 606 is also connected to bus 605.
The following components are connected to the I/O interface 606: an input portion 607 including a keyboard, a mouse, and the like; an output section 608 including a display such as a Liquid Crystal Display (LCD) and a speaker; a storage section 609 including a hard disk and the like; and a communication section 610 including a network interface card such as a LAN card, a modem, or the like. The communication section 610 performs communication processing via a network such as the internet. The driver 611 may also be connected to the I/O interface 606 as needed. A removable medium 612 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 611 as necessary, so that a computer program read out therefrom is mounted into the storage section 609 as necessary.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such embodiments, the computer program may be downloaded and installed from a network via the communication section 610, and/or installed from the removable media 612. The computer programs, when executed by a Central Processing Unit (CPU)601 and a Graphics Processor (GPU)602, perform the above-described functions defined in the methods of the present application.
It should be noted that the computer readable medium of the present application can be a computer readable signal medium or a computer readable medium or any combination of the two. The computer readable medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor device, apparatus, or any combination of the foregoing. More specific examples of the computer readable medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, a computer readable medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution apparatus, device, or apparatus. In this application, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution apparatus, device, or apparatus. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, or the like, as well as conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based devices that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules described in the embodiments of the present application may be implemented by software or hardware. The modules described may also be provided in a processor.
As another aspect, the present application also provides a computer-readable medium, which may be contained in the electronic device described in the above embodiments; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to perform the method described above.
The above description is only a preferred embodiment of the application and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention herein disclosed is not limited to the particular combination of features described above, but also encompasses other arrangements formed by any combination of the above features or their equivalents without departing from the spirit of the invention. For example, the above features may be replaced with (but not limited to) features having similar functions disclosed in the present application.
Claims (10)
1. A method for detecting the state and the non-in-place closing of a disconnecting link of a transformer substation is characterized by comprising the following steps:
s1, acquiring an infrared image of the disconnecting link of the transformer substation to be detected, and preprocessing the acquired infrared image;
s2, guiding the preprocessed infrared image into an infrared disconnecting link state target detection model for further analysis and calculation, and acquiring position coordinate information;
s3, acquiring an image of the disconnecting link in the infrared image according to the position coordinate information;
s4, judging whether the state of the disconnecting link is correct or not, if the state of the disconnecting link is incorrect, sending an alarm that the state of the disconnecting link is wrong, and if not, not sending the alarm;
s5, further processing the image of the knife switch acquired in the S3 and acquiring a temperature value of the knife switch; and
s6, comparing the temperature value of the disconnecting link with a preset threshold value of an abnormal temperature value, judging whether the disconnecting link is in a closing state, if the obtained disconnecting link temperature value is larger than the threshold value of the abnormal temperature value, sending an alarm that the disconnecting link is not closed in place, otherwise, not sending the alarm.
2. The method for detecting the state of the disconnecting link of the substation and the incomplete closing according to claim 1, wherein the preprocessing in the step S1 specifically comprises the following steps:
s11, shooting a transformer substation disconnecting link to be detected by a preset unmanned aerial vehicle cradle head to acquire the infrared image;
s12, carrying out color coding on the acquired single-channel gray image, and further acquiring a pseudo color image of the disconnecting link;
s13, zooming the long edge of the obtained pseudo color picture into 640 pixels, obtaining a zoom ratio X and recording;
and S14, multiplying the short side of the obtained false color picture by the scaling ratio X to obtain the false color scaling picture of the infrared image with equal scaling.
3. The method for detecting the state of the disconnecting link of the substation and the insufficient closing according to claim 2, wherein the pseudo-coding formula for obtaining the pseudo-color picture of the disconnecting link in S12 is as follows:
wherein R, G, B represents the three color channels of red, green and blue, respectively.
4. The method for detecting the state of the disconnecting link and the insufficient closing of the substation according to claim 3, wherein the obtained pseudo-color zoom map is imported into the infrared disconnecting link state target detection model in S2, and the infrared disconnecting link state target detection model is trained and established based on a YOLOV5 target detection model.
5. The method for detecting the state of the disconnecting link of the transformer substation and the insufficient closing according to claim 4, wherein the training of the infrared disconnecting link state target detection model specifically comprises the following steps:
a. collecting an infrared picture of the transformer substation disconnecting link shot by the unmanned aerial vehicle as training data;
b. screening and filtering the collected infrared pictures to remove fuzzy pictures without detection targets;
c. marking a candidate frame at a position with a knife switch in the picture by using LabelImg marking software, storing the candidate frame into a marking file in an x.xml format, and converting the x.xml file into a x.txt marking file by using an xml2txt.py tool;
d. the training data is recorded in a way that 5: 2: dividing the training set, the verification set and the test set into a training set, a verification set and a test set, wherein the training set, the verification set and the test set respectively correspond to folders train, val and test, the folders train, val and test all comprise an image folder and a label folder, the image folder is used for storing training picture data, and the label folder is used for storing the mark files in the format of the text;
e. modifying the YOLOV5 target detection model configuration file YOLOV5s.yaml to generate a model structure configuration file yolo-switch.yaml of the infrared switch state target detection model, further modifying configuration parameters of the training model, such as the size of a picture, the total training iteration number, the size of batch gradient data and the like, and starting training after the setting of the parameters which are not set according to default parameters;
f. and after the training is finished, verifying whether the accuracy of the model on the test set meets the requirement, if not, analyzing the data which are identified wrongly or can not be identified by the test set, modifying the label files of the training set and the verification set according to the analysis result, and modifying the configuration parameters for retraining until the accuracy of the infrared switch state target detection model on the test set meets the requirement.
6. The method for detecting the state of the substation disconnecting link and the insufficient closing according to claim 1, wherein the position coordinate information obtained in S2 is position coordinates of an upper left corner and a lower right corner of the disconnecting link, and an image of the disconnecting link is further intercepted according to the position coordinate information in S2 in S3.
7. The method for detecting the state of the disconnecting link of the transformer substation and the insufficient closing according to claim 6, wherein the conversion formula of the coordinates is as follows:
x1＝x-0.5*w
y1＝y-0.5*h
x2＝x+0.5*w
y2＝y+0.5*h
where X and Y are the X, Y axes of the center point coordinates of the knife gate, w is the width of the knife gate, h is the height of the knife gate, X1 and Y1 are the top left X, Y axis coordinates of the knife gate, and X2 and Y are the bottom right X, Y axis coordinates of the knife gate.
8. The method for detecting the state and the insufficient closing of the substation disconnecting link based on the infrared image according to claim 1, wherein S5 further includes:
s51, sorting the pixel gray values of the images of the disconnecting link intercepted in the S3 from large to small;
s52, extracting the first 10% pixels in the pixel gray value, which account for the intercepted total pixel gray value number, and solving the average value of the pixel gray value;
and S53, converting the extracted average value by linear fitting or inquiring an encoding table between the infrared image temperature and the pixel to obtain the temperature value of the disconnecting link.
9. The utility model provides a transformer substation's switch state and not in place detecting system closes a floodgate which characterized in that includes:
the infrared image acquisition module is configured for acquiring an infrared image of the disconnecting link of the transformer substation to be detected;
the image preprocessing module is configured to perform color coding on the acquired single-channel gray level image, further acquire a pseudo color image of the disconnecting link, and perform equal-scale scaling on the pseudo color image to obtain a pseudo color scaling image;
the conversion module is configured to convert the pixel gray value average value to obtain a temperature value of the disconnecting link;
and the alarm module is configured to judge whether the state of the disconnecting link is correct or not and whether the disconnecting link is in a closing state or not, and send out a corresponding alarm.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1 to 8.
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| CN115205502A (en) * | 2022-09-16 | 2022-10-18 | 杭州申昊科技股份有限公司 | Knife switch operation method and device and electronic equipment |
| CN115205502B (en) * | 2022-09-16 | 2022-12-27 | 杭州申昊科技股份有限公司 | Knife switch operation method and device and electronic equipment |
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