Unmanned aerial vehicle transmission, transformation and distribution integrated inspection method

Abstract

The invention relates to the field of unmanned aerial vehicle inspection, in particular to an unmanned aerial vehicle transmission, transformation and distribution integrated inspection method, which comprises the following steps: making an inspection route according to transmission, transformation and distribution equipment, recording and shooting thermal image data in real time during flight, receiving and preprocessing data and images by a ground control center, and identifying the outline of the equipment by combining an image identification technology with an optical image. Different devices are distinguished and divided into small areas on the surfaces of the devices through corresponding algorithms, average temperature is extracted to form a temperature matrix, the temperature matrix is compared with a normal temperature range to judge fault types, temperature abnormal areas are marked on optical images, and data are integrated through a data fusion technology to obtain three-dimensional temperature distribution conditions; according to the method, transformer substations or towers at the two ends of a power transmission line are selected as starting and ending points, a proper division method is adopted for equipment parts in different shapes, the method comprises the steps of marking relevant information of abnormal areas in optical images, integrating marked images and data, integrating the marked images and data with fault types and the like, and the inspection accuracy, the fault diagnosis capability, the maintenance efficiency and the inspection comprehensiveness can be improved.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle inspection, and in particular to an unmanned aerial vehicle transmission, transformation and distribution integrated inspection method. Background Art

[0002] With the rapid economic development and the continuous progress of society, modern society is increasingly dependent on electricity. As a key infrastructure for energy supply, the stable operation of the power system plays a vital role in ensuring the normal industrial production, commercial activities and daily life of residents. As the core component of the power system, the transmission, transformation and distribution equipment is responsible for efficiently and safely transmitting and distributing electric energy from the power plant to various power terminals. Among them, the transmission equipment transmits high-voltage electric energy over long distances, the substation equipment realizes the conversion and distribution of voltage, and the distribution equipment further distributes the electric energy to specific users. Their normal operation is directly related to the reliability, stability and economy of the power system. Failure in any link may cause a large-scale power outage, causing huge economic losses and inconvenience to society.

[0003] In the existing technology, it is often difficult for drones to quickly check transmission lines during inspections, and it is not easy to find and analyze the type of faults, which is inefficient. In addition, for some large substation equipment, it is difficult for drones to conduct detailed inspections inside the substation.

[0004] Therefore, it is necessary to propose an integrated UAV transmission, transformation and distribution inspection method to solve the above problems. Summary of the invention

[0005] The purpose of the present invention is to provide an integrated transmission, transformation and distribution inspection method using a drone to solve the problems that it is often difficult for drones to quickly troubleshoot transmission lines, find and analyze fault types, and have low efficiency during inspections. In addition, for some large substation equipment, it is difficult for drones to conduct detailed inspections inside substations.

[0006] To achieve the above object, the present invention provides the following technical solutions: A method for integrated transmission, transformation and distribution inspection using a drone, the method comprising the following steps: Step S1: According to the transmission and distribution equipment on the transmission line, the inspection route of the UAV for the transmission and distribution equipment is formulated. During the flight of the UAV, the thermal image data of the transmission and distribution equipment is recorded in real time in the inspection area, and photos are taken at the same time; Step S2: The ground control center receives the thermal image data transmitted by the UAV and the optical image of the device captured in real time, and pre-processes the thermal image and the optical image to improve the image quality; Step S3: using image recognition technology combined with optical images to identify the contours of each transmission and distribution equipment in the thermal image. For different types of equipment, adopt corresponding contour recognition algorithms to accurately distinguish different equipment. Within the identified equipment contour, divide the equipment surface into multiple small areas, extract the average temperature of each area, and form a temperature matrix of the equipment surface. Step S4: Compare the temperature of each area with the normal operating temperature range of different areas on the surface of the equipment pre-entered into the system, and preliminarily determine the possible fault type based on the degree and distribution of temperature anomaly; Step S5: For the areas marked as temperature anomalies, annotate them on the corresponding optical images. Through data fusion technology, the annotated images and data taken at different angles and times of the same equipment are integrated to obtain the overall three-dimensional temperature distribution of the transmission and distribution equipment, intuitively displaying the location of the temperature anomaly area in the equipment and the type of fault that may cause the temperature anomaly.

[0007] Preferably, a substation or a tower at one end of the transmission line is selected as the starting point, and a substation or a tower at the other end of the transmission line is selected as the ending point. Before the UAV takes off, the infrared thermal imager and the camera carried by the UAV are checked and calibrated, and a suitable temperature measurement range and image resolution are set. During the acquisition process, the shooting time of the thermal image data, the position of the UAV, and the equipment name information are recorded and stored in a one-to-one correspondence with the thermal image data. The number of optical images at each shooting point depends on the complexity of the equipment, and the optical images are numbered and classified and stored, and associated with the thermal image data and related information.

[0008] Preferably, the temperature data in the thermal image is corrected based on the recorded ambient temperature, humidity and the device's own temperature parameters, combined with the device's emissivity, using the blackbody radiation law to ensure that the measured temperature is close to the actual temperature; the camera captures the device's appearance image at a high frame rate to obtain the device's appearance, connection status, and insulator status information. When shooting, the focus and aperture are automatically adjusted according to the size and distance of the device to ensure a clear image.

[0009] Preferably, for equipment parts with more regular shapes, a rectangular grid division method is used. First, the number of rows and columns of the rectangular grid is determined, and calculated based on the size of the equipment surface and the required area accuracy. For insulators or some circular equipment parts, a circular division method is used. The center of the equipment is taken as the center of the circle, and the radius of a series of concentric rings is determined based on the equipment radius and the required number of areas.

[0010] Preferably, the fault types include: Overheating failure: the temperature of a certain area of ​​the equipment rises significantly, and the temperature distribution shows a trend of gradually decreasing from the area as the center to the surrounding area; Poor heat dissipation failure, the surface temperature of the entire device generally rises, and the temperature distribution area is relatively uniform, but higher than the normal operating temperature range; Discharge failure, abnormal temperature rise occurs locally on the surface, accompanied by discharge marks and local discharge phenomenon.

[0011] Preferably, in step S5, the method of marking the area marked as temperature abnormality on the corresponding optical image comprises the following steps: Step S51: After the temperature abnormality area is marked in the thermal image, the corresponding optical image is found in the optical image library taken at the same time according to the timestamp and the drone position information recorded when the thermal image is collected; Step S52: determining the position information of the temperature abnormality area in the thermal image in the actual device space, and then mapping the position information to the corresponding optical image through a coordinate conversion algorithm to find the approximate position of the temperature abnormality area in the optical image; Step S53: using an image processing software annotation tool to clearly outline the temperature abnormality area with lines on the optical image, and determining the position of the highest temperature point in the temperature abnormality area; Step S54: Analyze the temperature change trend of the temperature abnormality area within a period of time according to the thermal image sequence, make a preliminary guess on the fault type that may cause the temperature abnormality based on the pre-set association rules between the temperature abnormality and the fault type, and mark the guessed fault type in text form at the appropriate position of the optical image.

[0012] Preferably, in the process of analyzing temperature anomaly areas in thermal image sequences, arrows are used on the optical images to indicate the temperature change trend, the arrow direction points to the direction of temperature change, the rising arrow points upward, the falling arrow points downward, and the temperature change rate is marked next to the arrow.

[0013] Preferably, after integrating the annotated images and data taken at different angles and times for the same device, a three-dimensional temperature distribution model of the entire transmission and distribution equipment is obtained, and the pre-established fault type is integrated with the three-dimensional temperature distribution model. In the visualization platform, the user can click on the abnormal temperature area to obtain a list of possible fault types that match the temperature characteristics of the abnormal area and detailed fault analysis information.

[0014] Technical effects and advantages of the present invention: 1. By adopting appropriate division methods for equipment parts of different shapes, the equipment surface temperature information can be extracted more precisely. By comparing the equipment surface temperature with the normal operating temperature range and according to the degree and distribution of temperature anomalies, various fault types can be preliminarily judged, providing rich basis for fault diagnosis; 2. Detailed annotation of abnormal temperature areas on optical images, including outlining, determining the highest temperature point, marking temperature change trends, and inferring fault types. These annotation information provides intuitive and critical clues for maintenance personnel, enabling them to quickly locate the possible location and cause of the fault, thus improving the accuracy of fault diagnosis; 3. Adopt corresponding contour recognition algorithms for different types of transmission and distribution equipment to accurately distinguish equipment, which can adapt to various types of equipment and make the inspection method more flexible and versatile; 4. Develop inspection routes with substations or towers at both ends of the transmission line as the starting and ending points to ensure full coverage of the transmission, transformation and distribution equipment on the transmission line, avoid omissions and ensure the comprehensiveness of the inspection work. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a structural schematic diagram of an unmanned aerial vehicle transmission, transformation and distribution integrated inspection method of the present invention.

[0016] Figure 2 It is a schematic flow chart of the method for marking on the corresponding optical image according to the present invention. DETAILED DESCRIPTION

[0017] 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.

[0018] The present invention provides Figure 1-Figure 2 The method for integrated transmission, transformation and distribution inspection by unmanned aerial vehicles is shown, and the method comprises the following steps: Step S1: According to the transmission and distribution equipment on the transmission line, the inspection route of the UAV for the transmission and distribution equipment is formulated. During the flight of the UAV, the thermal image data of the transmission and distribution equipment is recorded in real time in the inspection area, and photos are taken at the same time; Step S2: The ground control center receives the thermal image data transmitted by the UAV and the optical image of the device captured in real time, and pre-processes the thermal image and the optical image to improve the image quality; Step S3: using image recognition technology combined with optical images to identify the contours of each transmission and distribution equipment in the thermal image. For different types of equipment, adopt corresponding contour recognition algorithms to accurately distinguish different equipment. Within the identified equipment contour, divide the equipment surface into multiple small areas, extract the average temperature of each area, and form a temperature matrix of the equipment surface. Step S4: Compare the temperature of each area with the normal operating temperature range of different areas on the surface of the equipment pre-entered into the system, and preliminarily determine the possible fault type based on the degree and distribution of temperature anomaly; Step S5: For the area marked as temperature abnormality, annotate the corresponding optical image, and integrate the annotated images and data taken at different angles and times of the same equipment through data fusion technology to obtain the overall three-dimensional temperature distribution of the transmission and distribution equipment, intuitively displaying the location of the temperature abnormality area in the equipment and the type of fault that may cause the temperature abnormality; Select the substation or tower at one end of the transmission line as the starting point, and the substation or tower at the other end of the transmission line as the ending point. Before the drone takes off, check and calibrate the infrared thermal imager and camera carried by the drone, set the appropriate temperature measurement range and image resolution, and during the acquisition process, record the shooting time of the thermal image data, the location of the drone, and the name of the equipment, and store them one by one with the thermal image data. The number of optical images at each shooting point depends on the complexity of the equipment, and the optical images are numbered and classified and stored, and associated with the thermal image data and related information; Furthermore, the temperature measurement range is calibrated. An appropriate temperature measurement range setting can ensure that the acquired thermal image data accurately reflects the temperature conditions on the device surface. If the range is set too large, the accuracy of temperature measurement may be reduced. If the range is set too small, some temperature data may exceed the measurement range and cannot be accurately acquired.

[0019] Furthermore, for equipment with complex structures and many parts, more optical images need to be taken to fully reflect the appearance and structural characteristics of the equipment to ensure that no parts that may have problems are missed; while for relatively simple equipment, the number of optical images taken can be appropriately reduced to improve inspection efficiency while ensuring that the equipment status can be accurately judged.

[0020] Furthermore, the optical images are numbered and stored in categories, and are associated with the thermal image data and related information. Numbering facilitates the indexing and management of images, and category storage is divided according to factors such as device type and shooting location, making the image data more organized. By associating with the thermal image data and related information, mutual reference between thermal images and optical images can be achieved.

[0021] Based on the recorded ambient temperature, humidity and temperature parameters of the device, combined with the emissivity of the device, the temperature data in the thermal image is corrected using the blackbody radiation law to ensure that the measured temperature is close to the actual temperature; the camera captures the appearance image of the device at a high frame rate to obtain the appearance, connection status, and insulator status information of the device. When shooting, the focus and aperture are automatically adjusted according to the size and distance of the device to ensure a clear image; Furthermore, the automatic adjustment of the aperture can control the amount of light entering the camera. Under different lighting conditions, by adjusting the aperture size, the image can have appropriate brightness and contrast, avoiding loss of image details due to excessive brightness or darkness, thereby ensuring that the captured image is clear and high-quality, providing good material for subsequent image analysis and equipment status assessment.

[0022] For equipment parts with regular shapes, the rectangular grid division method is used. First, the number of rows and columns of the rectangular grid is determined, and the calculation is based on the size of the equipment surface and the required area accuracy. For insulators or some circular equipment parts, the ring division method is used. The center of the equipment is taken as the center of the circle. According to the equipment radius and the number of required areas, the radius of a series of concentric rings is determined. Failure types include: Overheating failure: the temperature of a certain area of ​​the equipment rises significantly, and the temperature distribution shows a trend of gradually decreasing from the area as the center to the surrounding area; Poor heat dissipation failure, the surface temperature of the entire device generally rises, and the temperature distribution area is relatively uniform, but higher than the normal operating temperature range; Discharge failure, the surface temperature rises abnormally locally, accompanied by discharge marks, and local discharge occurs; In step S5, for the area marked as temperature abnormality, the method of marking the corresponding optical image includes the following steps: Step S51: After the temperature abnormality area is marked in the thermal image, the corresponding optical image is found in the optical image library taken at the same time according to the timestamp and the drone position information recorded when the thermal image is collected; Step S52: determining the position information of the temperature abnormality area in the thermal image in the actual device space, and then mapping the position information to the corresponding optical image through a coordinate conversion algorithm to find the approximate position of the temperature abnormality area in the optical image; Step S53: using an image processing software annotation tool to clearly outline the temperature abnormality area with lines on the optical image, and determining the position of the highest temperature point in the temperature abnormality area; Step S54: analyzing the temperature change trend of the temperature abnormality area within a period of time according to the thermal image sequence, making a preliminary guess on the fault type that may cause the temperature abnormality based on the pre-set association rule between the temperature abnormality and the fault type, and marking the guessed fault type in text at the appropriate position of the optical image; Furthermore, after the abnormal temperature area is identified and marked in the thermal image, the timestamp and the drone location information become the key clues to associate the thermal image and the optical image. At the same time, the drone location information also plays an auxiliary positioning role. At the same time, the thermal image and optical image taken by the drone at a specific location correspond to the same equipment part. In this way, the optical image corresponding to the thermal image with the abnormal temperature area can be accurately found from the many optical images, providing a basis for subsequent annotation work.

[0023] Furthermore, in the temperature abnormality area, the location of the highest temperature point is determined by further analyzing the thermal image data. This highest temperature point may be the key to the fault, such as the most serious local overheating, which may indicate the source of the fault, such as the heat concentration point caused by excessive contact resistance. Marking the location of the highest temperature point on the optical image can provide maintenance personnel with more detailed information, which helps to conduct more targeted troubleshooting.

[0024] Furthermore, the inferred fault type is annotated in text at the appropriate position of the optical image. In this way, when maintenance personnel view the annotated optical image, they can not only see the location and outline of the temperature abnormality area, but also understand the possible cause of the fault, so as to formulate maintenance plans more quickly, improve maintenance efficiency, reduce equipment downtime, and ensure the normal operation of transmission, transformation and distribution equipment.

[0025] In the process of analyzing the temperature abnormality area in the thermal image sequence, arrows are used on the optical image to indicate the temperature change trend. The arrow direction points to the direction of temperature change, with the rising arrow pointing upward and the falling arrow pointing downward. The temperature change rate is marked next to the arrow. After integrating the annotated images and data taken at different angles and times of the same equipment, a 3D temperature distribution model of the entire transmission, transformation and distribution equipment is obtained. The pre-established fault type is integrated with the 3D temperature distribution model. In the visualization platform, users can click on the abnormal temperature area to obtain a list of possible fault types that match the temperature characteristics of the abnormal area and detailed fault analysis information; Furthermore, the corresponding optical image is marked according to the temperature change trend. For example, if the temperature of the winding area shows an upward trend over a period of time, and the temperature change rate is 5°C per hour, an upward arrow is drawn at the position of the winding area in the optical image to indicate the temperature rise, and "5°C / h" is marked next to the arrow. If the temperature shows a downward trend in a subsequent period of time, such as a decrease rate of 2°C per hour, a downward arrow will be drawn in the corresponding subsequent optical image and marked with "-2°C / h". This marking method allows maintenance personnel to intuitively understand the dynamic temperature changes in the abnormal temperature area when viewing the optical image.

[0026] Furthermore, assuming that in a pre-established fault type library, for the case of abnormally high temperature of the transformer winding, possible fault types include turn-to-turn short circuit, multiple-point grounding of the core, etc., after the fault type is integrated with the three-dimensional temperature distribution model, in the visualization platform, maintenance personnel can click on the abnormal winding temperature area in the three-dimensional model of the transformer. At this time, the platform will pop up a list of possible fault types that match the temperature characteristics of the abnormal area, such as "high possibility of turn-to-turn short circuit, medium possibility of multiple-point grounding of the core", and provide detailed fault analysis information, such as the characteristics of temperature changes that may be caused by turn-to-turn short circuit, related electrical parameter changes, and how to further check and confirm the fault. For example, the information will mention that in addition to the increase in winding temperature, turn-to-turn short circuit may also be accompanied by changes in certain gas components in the transformer oil chromatographic analysis, as well as how to further verify through electrical tests, so that maintenance personnel can more accurately and quickly determine the cause of the fault based on this information and take effective maintenance measures.

[0027] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. A UAV transmission, transformation and distribution integrated inspection method, characterized in that: The method comprises the following steps: Step S1: According to the transmission and distribution equipment on the transmission line, the inspection route of the UAV for the transmission and distribution equipment is formulated. During the flight of the UAV, the thermal image data of the transmission and distribution equipment is recorded in real time in the inspection area, and photos are taken at the same time; Step S2: The ground control center receives the thermal image data transmitted by the UAV and the optical image of the device captured in real time, and pre-processes the thermal image and the optical image to improve the image quality; Step S3: using image recognition technology combined with optical images to identify the contours of each transmission and distribution equipment in the thermal image. For different types of equipment, adopt corresponding contour recognition algorithms to accurately distinguish different equipment. Within the identified equipment contour, divide the equipment surface into multiple small areas, extract the average temperature of each area, and form a temperature matrix of the equipment surface. Step S4: Compare the temperature of each area with the normal operating temperature range of different areas on the surface of the equipment pre-entered into the system, and preliminarily determine the possible fault type based on the degree and distribution of temperature anomaly; Step S5: For the areas marked as temperature anomalies, annotate them on the corresponding optical images. Through data fusion technology, the annotated images and data taken at different angles and times of the same equipment are integrated to obtain the overall three-dimensional temperature distribution of the transmission and distribution equipment, intuitively displaying the location of the temperature anomaly area in the equipment and the type of fault that may cause the temperature anomaly.

2. The method for integrated transmission, transformation and distribution inspection by unmanned aerial vehicle according to claim 1 is characterized in that: Select the substation or tower at one end of the transmission line as the starting point, and the substation or tower at the other end of the transmission line as the ending point. Before the drone takes off, check and calibrate the infrared thermal imager and camera carried by the drone, set the appropriate temperature measurement range and image resolution, and during the acquisition process, record the shooting time of the thermal image data, the location of the drone, and the equipment name information, and store them one by one with the thermal image data. The number of optical images at each shooting point depends on the complexity of the equipment, and the optical images are numbered and classified and stored, and associated with the thermal image data and related information.

3. The method for integrated transmission, transformation and distribution inspection by unmanned aerial vehicle according to claim 1 is characterized in that: Based on the recorded ambient temperature, humidity and its own temperature parameters, combined with the emissivity of the equipment, the temperature data in the thermal image is corrected using the blackbody radiation law to ensure that the measured temperature is close to the actual temperature; the camera captures the appearance image of the equipment at a high frame rate to obtain the appearance, connection status, and insulator status information of the equipment. When shooting, the focus and aperture are automatically adjusted according to the size and distance of the equipment to ensure a clear image.

4. The integrated inspection method for transmission, transformation and distribution by unmanned aerial vehicle according to claim 1 is characterized in that: For equipment parts with more regular shapes, the rectangular grid division method is used. First, the number of rows and columns of the rectangular grid is determined, and then calculated based on the size of the equipment surface and the required area accuracy. For insulators or some circular equipment parts, the circular division method is used. The center of the equipment is taken as the center of the circle, and the radius of a series of concentric rings is determined based on the equipment radius and the required number of areas.

5. The UAV transmission, transformation and distribution integrated inspection method according to claim 1 is characterized in that: The fault types include: Overheating failure: the temperature of a certain area of ​​the equipment rises significantly, and the temperature distribution shows a trend of gradually decreasing from the area as the center to the surrounding area; Poor heat dissipation failure, the surface temperature of the entire device generally rises, and the temperature distribution area is relatively uniform, but higher than the normal operating temperature range; Discharge failure, abnormal temperature rise occurs locally on the surface, accompanied by discharge marks and local discharge phenomenon.

6. The method for integrated transmission, transformation and distribution inspection by unmanned aerial vehicle according to claim 1 is characterized in that: In step S5, the method for marking the area marked as temperature abnormality on the corresponding optical image comprises the following steps: Step S51: After the temperature abnormality area is marked in the thermal image, the corresponding optical image is found in the optical image library taken at the same time according to the timestamp and the drone position information recorded when the thermal image is collected; Step S52: determining the position information of the temperature abnormality area in the thermal image in the actual device space, and then mapping the position information to the corresponding optical image through a coordinate conversion algorithm to find the approximate position of the temperature abnormality area in the optical image; Step S53: using an image processing software annotation tool to clearly outline the temperature abnormality area with lines on the optical image, and determining the position of the highest temperature point in the temperature abnormality area; Step S54: Analyze the temperature change trend of the temperature abnormality area within a period of time according to the thermal image sequence, make a preliminary guess on the fault type that may cause the temperature abnormality based on the pre-set association rules between the temperature abnormality and the fault type, and mark the guessed fault type in text form at the appropriate position of the optical image.

7. The method for integrated transmission, transformation and distribution inspection by unmanned aerial vehicle according to claim 6 is characterized in that: In the process of analyzing temperature anomaly areas in thermal image sequences, arrows are used on the optical images to indicate the temperature change trend. The arrow direction points to the direction of temperature change, with an upward arrow pointing upward and a downward arrow pointing downward, and the temperature change rate is marked next to the arrow.

8. The method for integrated transmission, transformation and distribution inspection by unmanned aerial vehicle according to claim 7 is characterized by: After integrating the annotated images and data taken at different angles and times for the same device, we obtain a 3D temperature distribution model of the entire transmission and distribution equipment. We integrate the pre-established fault types with the 3D temperature distribution model. In the visualization platform, users can click on the abnormal temperature area to obtain a list of possible fault types that match the temperature characteristics of the abnormal area, as well as detailed fault analysis information.

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