Power transmission line state monitoring system

Abstract

The invention relates to a power transmission line state monitoring system. The system comprises an instruction distribution device, a content acquisition device, a sequential mapping device, a target extraction mechanism and a level identification mechanism. According to the invention, related operation can be triggered only when whether the current 110-220kV power transmission line is in a proper condition is detected, so that the intelligent management level is improved.

Description

Technical Field

[0001] This invention relates to the field of power transmission lines, and more particularly to a power transmission line condition monitoring system. Background Technology

[0002] The basic process of power transmission lines is to create conditions for the transmission of electromagnetic energy along the direction of the transmission line. The transmission capacity of a line is governed by various laws of electromagnetic fields and circuits. Using the earth's potential as a reference point (zero potential), the line conductors must be under a high voltage applied by the power source, called the transmission voltage. The maximum transmission power determined after comprehensively considering technical, economic, and other factors is called the transmission capacity of the line. The transmission capacity is roughly proportional to the square of the transmission voltage. Therefore, increasing the transmission voltage is the main technical means to achieve large-capacity or long-distance power transmission and a major indicator of the level of development of power transmission technology.

[0003] CN119338799A discloses a method for determining galloping monitoring points, a method for quantitative analysis of galloping monitoring, a method for controlling galloping monitoring, and a device for such control. The method for determining galloping monitoring points includes: acquiring and fusing image data and point cloud data of the transmission line; selecting N monitoring points on the transmission line to determine galloping and half-wave number; in the fused data, determining the position of each monitoring point in the image data based on the position of each monitoring point in the point cloud data; calculating the sag and pixel difference of each monitoring point in the image data and point cloud data respectively; and calculating the physical size represented by each pixel of each monitoring point based on the sag and pixel differences. This technology solves problems such as accurately identifying galloping, precisely tracking the point of maximum amplitude, and resolving large errors in the conversion between amplitude pixel count and physical amplitude in video-based transmission line galloping monitoring, achieving quantitative analysis of galloping, and improving transmission stability and intelligent operation and maintenance levels.

[0004] CN119335299A discloses a fault monitoring system for high-voltage power lines in electrical engineering. The system includes: a data acquisition module for acquiring current signals at monitoring nodes on mining high-voltage power lines at various times; an anomaly analysis module for constructing harmonic anomaly characteristic values ​​for each current signal based on changes in current signals and data mutation characteristics within adjacent time periods; obtaining anomaly score correction values ​​for each current signal based on the probability density and harmonic anomaly characteristic values; and a power transmission control module for acquiring all abnormal current signals based on the anomaly score correction values ​​using an anomaly detection algorithm; and obtaining the judgment result of abnormal faults based on all abnormal current signals. This system enables monitoring of mining high-voltage power lines, improves the accuracy of abnormal fault monitoring of mining high-voltage power lines, and effectively ensures the stability of power supply to mining high-voltage power lines. Summary of the Invention

[0005] To address technical issues in related fields, this invention provides a power transmission line condition monitoring system. This system triggers the data acquisition device to perform image acquisition of the current transmission line's operating scene only when it detects whether the 110-220kV transmission line is undergoing replacement of the single insulator closest to the data acquisition device. This allows the system to obtain and output the corresponding operating scene image, thus avoiding waste of camera resources. Furthermore, it employs a sequential mapping device comprising a first-segment mapping component, a second-segment mapping component, and an end-segment mapping component. This device sequentially performs distortion calibration, piecewise linear grayscale transformation, and homomorphic filtering on the received operating scene image to obtain and output a higher-quality sequentially mapped image. This ensures intelligent detection of the subsequent horizontal placement of the bottle holder. To ensure the reliability and validity of the results, and based on the brightness value distribution range corresponding to the bottle holder, the imaging area of ​​the bottle holder with the largest number of pixels in the received sequentially mapped images is extracted as the target imaging area. An AI identification model is used to intelligently identify whether the bottle holder closest to the content acquisition device is in a horizontal position based on the angle between the imaging plane of the content acquisition device's imaging lens and the horizontal plane, the vertical coordinate values ​​and horizontal coordinate values ​​corresponding to each pixel in the target imaging area, the depth values ​​corresponding to each pixel in the target imaging area, the imaging focal length of the content acquisition device, and the number of each pixel in the target imaging area. This provides a guarantee that the bottle holder associated with the insulator remains in a horizontal position when replacing insulators of 110~220kV transmission lines.

[0006] According to the present invention, a transmission line condition monitoring system is provided, the system comprising: The instruction distribution device is used to detect whether the current 110~220kV transmission line is in the process of replacing the single insulator closest to the content acquisition device, and to issue a first distribution instruction when the current transmission line is detected to be in the process of replacing the single insulator closest to the content acquisition device, and to issue a second distribution instruction when the current transmission line is not detected to be in the process of replacing the single insulator closest to the content acquisition device. The content acquisition device is set on the side of the current transmission line and wirelessly connected to the instruction distribution device. When the first distribution instruction is received, it performs an image content acquisition action on the operating scene of the current transmission line to obtain and output the corresponding operating scene image. A sequential mapping device, connected to the content acquisition device and including a first-segment mapping component, a second-segment mapping component, and an end-segment mapping component, is used to sequentially perform distortion calibration, piecewise linear grayscale transformation, and homomorphic filtering on the received running scene image to obtain and output the corresponding sequentially mapped image. The target extraction mechanism, connected to the sequential mapping device, is used to extract the bottle tray imaging region with the most pixels in the received sequentially mapped image based on the brightness value distribution range corresponding to the bottle tray, and output it as the target imaging region. A horizontal identification mechanism, connected to the target extraction mechanism, is used to intelligently identify whether the bottle holder closest to the content acquisition device is in a horizontal position based on the angle between the imaging plane of the imaging lens of the content acquisition device and the horizontal plane, the vertical coordinate values ​​and horizontal coordinate values ​​corresponding to each pixel of the target imaging area, the depth values ​​corresponding to each pixel of the target imaging area, the imaging focal length of the content acquisition device, and the number of each pixel of the target imaging area. The AI ​​identification model is a deep neural network that has undergone multiple learning iterations, and the number of times the deep neural network has learned is proportional to the total number of pixels in the running scene image. The AI ​​identification model, based on the angle between the imaging plane of the content acquisition device's imaging lens and the horizontal plane, the vertical and horizontal coordinate values ​​corresponding to each pixel in the target imaging area, the depth values ​​corresponding to each pixel in the target imaging area, the imaging focal length of the content acquisition device, and the number of each pixel in the target imaging area, intelligently identifies whether the bottle holder closest to the content acquisition device is in a horizontal position. This includes simultaneously inputting the angle between the imaging plane of the content acquisition device's imaging lens and the horizontal plane, the vertical and horizontal coordinate values ​​corresponding to each pixel in the target imaging area, the depth values ​​corresponding to each pixel in the target imaging area, the imaging focal length of the content acquisition device, and the number of each pixel in the target imaging area into the AI ​​identification model.

[0007] Therefore, it can be seen that the present invention has at least the following four important inventive points: First: The image acquisition device is triggered to perform image content acquisition on the current transmission line from the side only when it detects whether the current 110~220kV transmission line is in the process of replacing the single insulator closest to the content acquisition device, so as to obtain and output the corresponding operation scene image, thereby avoiding the waste of camera resources. Second: A sequential mapping device including a first-segment mapping component, a second-end mapping component, and an end-end mapping component is used to sequentially perform distortion calibration, piecewise linear grayscale transformation, and homomorphic filtering on the received running scene image to obtain and output a sequentially mapped image with better image quality, thereby ensuring the reliability and effectiveness of the intelligent identification results of the subsequent horizontal placement of the bottle holder. Third: Based on the brightness value distribution range corresponding to the bottle holder, the imaging area of ​​the bottle holder with the largest number of pixels in the received sequentially mapped images is extracted as the target imaging area. An AI identification model is used to intelligently identify whether the bottle holder closest to the content acquisition device is in a horizontal position based on the angle between the imaging plane of the content acquisition device's imaging lens and the horizontal plane, the vertical coordinate values ​​and horizontal coordinate values ​​corresponding to each pixel in the target imaging area, the depth values ​​corresponding to each pixel in the target imaging area, the imaging focal length of the content acquisition device, and the number of each pixel in the target imaging area. This ensures that the bottle holder associated with the insulator remains in a horizontal position when replacing the insulators of 110~220kV transmission lines. Fourth: The AI ​​identification model for intelligent identification of the bottle holder in a horizontal position is a deep neural network that has undergone multiple learning iterations. The number of times the deep neural network learns is proportional to the total number of pixels in the scene image. This allows for the customization of AI identification models with different structures for monitoring images of different resolutions, ensuring the stability and effectiveness of the identification results. Detailed Implementation

[0008] Currently, during the construction and use of transmission lines, it is necessary to ensure that the insulator is kept horizontal when the 110~220kV transmission line is in the replacement state of the single insulator closest to the data acquisition device. Obviously, the existing technology lacks a targeted detection solution, which makes it difficult to guarantee the safety and reliability of the transmission line construction and use process.

[0009] The implementation scheme of the power transmission line condition monitoring system of the present invention will be described in detail below.

[0010] Implementation Plan A The transmission line condition monitoring system shown in embodiment A of the present invention includes: The instruction distribution device is used to detect whether the current 110~220kV transmission line is in the process of replacing the single insulator closest to the content acquisition device, and to issue a first distribution instruction when the current transmission line is detected to be in the process of replacing the single insulator closest to the content acquisition device, and to issue a second distribution instruction when the current transmission line is not detected to be in the process of replacing the single insulator closest to the content acquisition device. Specifically, a GAL device can be used to implement the instruction distribution device, which is used to detect whether the current 110~220kV transmission line is in the process of replacing the single insulator closest to the content acquisition device, and to issue a first distribution instruction when the current transmission line is detected to be in the process of replacing the single insulator closest to the content acquisition device, and to issue a second distribution instruction when the current transmission line is not detected to be in the process of replacing the single insulator closest to the content acquisition device. The content acquisition device is set on the side of the current transmission line and wirelessly connected to the instruction distribution device. When the first distribution instruction is received, it performs an image content acquisition action on the operating scene of the current transmission line to obtain and output the corresponding operating scene image. A sequential mapping device, connected to the content acquisition device and including a first-segment mapping component, a second-segment mapping component, and an end-segment mapping component, is used to sequentially perform distortion calibration, piecewise linear grayscale transformation, and homomorphic filtering on the received running scene image to obtain and output the corresponding sequentially mapped image. The target extraction mechanism, connected to the sequential mapping device, is used to extract the bottle tray imaging region with the most pixels in the received sequentially mapped image based on the brightness value distribution range corresponding to the bottle tray, and output it as the target imaging region. A horizontal identification mechanism, connected to the target extraction mechanism, is used to intelligently identify whether the bottle holder closest to the content acquisition device is in a horizontal position based on the angle between the imaging plane of the imaging lens of the content acquisition device and the horizontal plane, the vertical coordinate values ​​and horizontal coordinate values ​​corresponding to each pixel of the target imaging area, the depth values ​​corresponding to each pixel of the target imaging area, the imaging focal length of the content acquisition device, and the number of each pixel of the target imaging area. The AI ​​identification model is a deep neural network that has undergone multiple learning iterations, and the number of times the deep neural network has learned is proportional to the total number of pixels in the running scene image. The AI ​​identification model intelligently identifies whether the bottle holder closest to the content acquisition device is in a horizontal position based on the angle between the imaging plane of the content acquisition device's imaging lens and the horizontal plane, the vertical and horizontal coordinate values ​​corresponding to each pixel in the target imaging area, the depth values ​​corresponding to each pixel in the target imaging area, the imaging focal length of the content acquisition device, and the number of each pixel in the target imaging area. This includes simultaneously inputting the angle between the imaging plane of the content acquisition device's imaging lens and the horizontal plane, the vertical and horizontal coordinate values ​​corresponding to each pixel in the target imaging area, the depth values ​​corresponding to each pixel in the target imaging area, the imaging focal length of the content acquisition device, and the number of each pixel in the target imaging area into the AI ​​identification model. The AI ​​identification model, based on the angle between the imaging plane of the imaging lens of the content acquisition device and the horizontal plane, the vertical coordinate values ​​and horizontal coordinate values ​​corresponding to each pixel in the target imaging area, the depth values ​​corresponding to each pixel in the target imaging area, the imaging focal length of the content acquisition device, and the number of each pixel in the target imaging area, intelligently identifies whether the bottle tray closest to the content acquisition device is in a horizontal position. This also includes the AI ​​identification model outputting a status identifier, and using different values ​​of the status identifier to indicate whether the bottle tray closest to the content acquisition device is in a horizontal position. The process of synchronously inputting the angle between the imaging plane of the content acquisition device's imaging lens and the horizontal plane, the vertical and horizontal coordinate values ​​corresponding to each pixel in the target imaging area, the depth values ​​corresponding to each pixel in the target imaging area, the imaging focal length of the content acquisition device, and the number of each pixel in the target imaging area into the AI ​​identification model includes: using a synchronous drive interface to synchronously input the angle between the imaging plane of the content acquisition device's imaging lens and the horizontal plane, the vertical and horizontal coordinate values ​​corresponding to each pixel in the target imaging area, the depth values ​​corresponding to each pixel in the target imaging area, the imaging focal length of the content acquisition device, and the number of each pixel in the target imaging area into the AI ​​identification model. The content acquisition device is further configured to stop performing image content acquisition actions on the current power transmission line operation scene when it receives the second distribution instruction.

[0011] Implementation Plan B The transmission line condition monitoring system shown in embodiment B of the present invention may further include the following components: A connection service device is provided for connection to the target extraction mechanism, the first segment mapping component, the second segment mapping component, and the last segment mapping component, respectively, so as to receive data from the target extraction mechanism, the first segment mapping component, the second segment mapping component, and the last segment mapping component, respectively, and send parallel data from the connection processing component to the target extraction mechanism, the first segment mapping component, the second segment mapping component, and the last segment mapping component, respectively. The connection service device is configured to be connected to the target extraction mechanism, the first segment mapping component, the second segment mapping component, and the last segment mapping component respectively, so as to receive data from the target extraction mechanism, the first segment mapping component, the second segment mapping component, and the last segment mapping component respectively, and to send parallel data from the connection processing component to the target extraction mechanism, the first segment mapping component, the second segment mapping component, and the last segment mapping component respectively. The connection processing component is either an 8-bit connection processing component or a 16-bit connection processing component.

[0012] Implementation Plan C The transmission line condition monitoring system shown in embodiment C of the present invention may further include the following components: The command transceiver is connected to the target extraction mechanism, the first segment mapping component, the second segment mapping component, and the last segment mapping component, respectively, and is used to receive control commands from the user to the target extraction mechanism, the first segment mapping component, the second segment mapping component, and the last segment mapping component, respectively.

[0013] Next, the specific structure of the transmission line condition monitoring system of the present invention will be further described.

[0014] In a transmission line condition monitoring system according to any embodiment of the present invention: The target extraction mechanism, the first segment mapping component, the second segment mapping component, and the last segment mapping component are connected to the same oscillation execution mechanism to obtain timing data provided by the oscillation execution mechanism. The target extraction mechanism, the first segment mapping component, the second segment mapping component, and the last segment mapping component are each connected to the same content storage chip, which is one of FLASH flash memory, SDRAM memory chip, and DDR memory chip.

[0015] In a transmission line condition monitoring system according to any embodiment of the present invention: The target extraction mechanism, the first segment mapping component, the second segment mapping component, and the last segment mapping component are each connected to the same IIC control bus and are used to receive various control commands sent by the IIC control bus. The various control commands are used to configure the various operating data of the target extraction mechanism, the first segment mapping component, the second segment mapping component, and the last segment mapping component respectively.

[0016] In a transmission line condition monitoring system according to any embodiment of the present invention: The target extraction mechanism, the first segment mapping component, the second segment mapping component, and the last segment mapping component are respectively connected to the IIC control bus and are used to receive various control commands sent through the IIC control bus.

[0017] In a transmission line condition monitoring system according to any embodiment of the present invention: The target extraction mechanism, the first segment mapping component, the second segment mapping component, and the last segment mapping component are each connected to the same MCU controller, and are used to switch between sleep mode and working mode under the control of the same MCU controller.

[0018] And in a transmission line condition monitoring system according to any embodiment of the present invention: The target extraction mechanism, the first segment mapping component, the second segment mapping component, and the last segment mapping component are each connected to the same MCU controller, and are used to switch between sleep mode and working mode under the control of the same MCU controller. The same MCU controller is designed based on the ARM13 core.

[0019] In addition, in the power transmission line condition monitoring system, a sequential mapping device, connected to the content acquisition device and including a first-segment mapping component, a second-end mapping component, and an end-end mapping component, is used to sequentially perform distortion calibration, piecewise linear grayscale transformation, and homomorphic filtering on the received operating scene image to obtain and output the corresponding sequentially mapped image. The first-segment mapping component, the second-end mapping component, and the end-end mapping component are sequentially connected and used to perform distortion calibration, piecewise linear grayscale transformation, and homomorphic filtering on the received image content, respectively.

[0020] The transmission line condition monitoring system of this invention addresses the technical problem in the prior art where it is difficult to ensure that the insulator-associated insulator holders remain horizontally positioned when replacing insulators on 110-220kV transmission lines. This is achieved by triggering the image acquisition device to capture images of the current transmission line's operation only when it detects whether the 110-220kV transmission line is in the process of replacing the single insulator closest to the acquisition device. Furthermore, an AI identification model is used to intelligently determine whether the nearest insulator holder is horizontally positioned. This ensures that the insulator-associated insulator holders remain horizontally positioned when replacing insulators on 110-220kV transmission lines, thus solving the aforementioned technical problem.

[0021] In the foregoing description, the invention has been described with reference to specific exemplary embodiments. However, it is obvious that various modifications and changes can be made without departing from the broad spirit and scope of the invention as set forth in the appended claims. Therefore, this description should be considered illustrative rather than restrictive.

Claims

1. A transmission line condition monitoring system, characterized in that, The system includes: The instruction distribution device is used to detect whether the current 110~220kV transmission line is in the process of replacing the single insulator closest to the content acquisition device, and to issue a first distribution instruction when the current transmission line is detected to be in the process of replacing the single insulator closest to the content acquisition device, and to issue a second distribution instruction when the current transmission line is not detected to be in the process of replacing the single insulator closest to the content acquisition device. The content acquisition device is set on the side of the current transmission line and wirelessly connected to the instruction distribution device. When the first distribution instruction is received, it performs an image content acquisition action on the operating scene of the current transmission line to obtain and output the corresponding operating scene image. A sequential mapping device, connected to the content acquisition device and including a first-segment mapping component, a second-segment mapping component, and an end-segment mapping component, is used to sequentially perform distortion calibration, piecewise linear grayscale transformation, and homomorphic filtering on the received running scene image to obtain and output the corresponding sequentially mapped image. The target extraction mechanism, connected to the sequential mapping device, is used to extract the bottle tray imaging region with the most pixels in the received sequentially mapped image based on the brightness value distribution range corresponding to the bottle tray, and output it as the target imaging region. A horizontal identification mechanism, connected to the target extraction mechanism, is used to intelligently identify whether the bottle holder closest to the content acquisition device is in a horizontal position based on the angle between the imaging plane of the imaging lens of the content acquisition device and the horizontal plane, the vertical coordinate values ​​and horizontal coordinate values ​​corresponding to each pixel of the target imaging area, the depth values ​​corresponding to each pixel of the target imaging area, the imaging focal length of the content acquisition device, and the number of each pixel of the target imaging area. The AI ​​identification model is a deep neural network that has undergone multiple learning iterations, and the number of times the deep neural network has learned is proportional to the total number of pixels in the running scene image. The AI ​​identification model, based on the angle between the imaging plane of the content acquisition device's imaging lens and the horizontal plane, the vertical and horizontal coordinate values ​​corresponding to each pixel in the target imaging area, the depth values ​​corresponding to each pixel in the target imaging area, the imaging focal length of the content acquisition device, and the number of each pixel in the target imaging area, intelligently identifies whether the bottle holder closest to the content acquisition device is in a horizontal position. This includes simultaneously inputting the angle between the imaging plane of the content acquisition device's imaging lens and the horizontal plane, the vertical and horizontal coordinate values ​​corresponding to each pixel in the target imaging area, the depth values ​​corresponding to each pixel in the target imaging area, the imaging focal length of the content acquisition device, and the number of each pixel in the target imaging area into the AI ​​identification model.

2. The transmission line condition monitoring system as described in claim 1, characterized in that: The AI ​​identification model intelligently identifies whether the bottle tray closest to the content acquisition device is in a horizontal position based on the angle between the imaging plane of the imaging lens of the content acquisition device and the horizontal plane, the vertical coordinate values ​​and horizontal coordinate values ​​corresponding to each pixel in the target imaging area, the depth values ​​corresponding to each pixel in the target imaging area, the imaging focal length of the content acquisition device, and the number of each pixel in the target imaging area. This also includes the AI ​​identification model outputting a status identifier, with different values ​​used to indicate whether the bottle tray closest to the content acquisition device is in a horizontal position. The process of synchronously inputting the angle between the imaging plane of the content acquisition device's imaging lens and the horizontal plane, the vertical and horizontal coordinate values ​​corresponding to each pixel in the target imaging area, the depth values ​​corresponding to each pixel in the target imaging area, the imaging focal length of the content acquisition device, and the number of each pixel in the target imaging area into the AI ​​identification model includes: using a synchronous drive interface to synchronously input the angle between the imaging plane of the content acquisition device's imaging lens and the horizontal plane, the vertical and horizontal coordinate values ​​corresponding to each pixel in the target imaging area, the depth values ​​corresponding to each pixel in the target imaging area, the imaging focal length of the content acquisition device, and the number of each pixel in the target imaging area into the AI ​​identification model. The content acquisition device is further configured to stop performing image content acquisition actions on the current power transmission line operation scene when it receives the second distribution instruction.

3. The transmission line condition monitoring system as described in claim 2, characterized in that, The system also includes: A connection service device is provided for connection to the target extraction mechanism, the first segment mapping component, the second segment mapping component, and the last segment mapping component, respectively, so as to receive data from the target extraction mechanism, the first segment mapping component, the second segment mapping component, and the last segment mapping component, respectively, and send parallel data from the connection processing component to the target extraction mechanism, the first segment mapping component, the second segment mapping component, and the last segment mapping component, respectively. The connection service device is configured to be connected to the target extraction mechanism, the first segment mapping component, the second segment mapping component, and the last segment mapping component respectively, so as to receive data from the target extraction mechanism, the first segment mapping component, the second segment mapping component, and the last segment mapping component respectively, and to send parallel data from the connection processing component to the target extraction mechanism, the first segment mapping component, the second segment mapping component, and the last segment mapping component respectively. The connection processing component is either an 8-bit connection processing component or a 16-bit connection processing component.

4. The transmission line condition monitoring system as described in claim 3, characterized in that, The system also includes: The command transceiver is connected to the target extraction mechanism, the first segment mapping component, the second segment mapping component, and the last segment mapping component, respectively, and is used to receive control commands from the user to the target extraction mechanism, the first segment mapping component, the second segment mapping component, and the last segment mapping component, respectively.

5. The transmission line condition monitoring system as described in any one of claims 2-4, characterized in that: The target extraction mechanism, the first segment mapping component, the second segment mapping component, and the last segment mapping component are connected to the same oscillation execution mechanism to obtain timing data provided by the oscillation execution mechanism. The target extraction mechanism, the first segment mapping component, the second segment mapping component, and the last segment mapping component are each connected to the same content storage chip, which is one of FLASH flash memory, SDRAM memory chip, and DDR memory chip.

6. The transmission line condition monitoring system as described in any one of claims 2-4, characterized in that: The target extraction mechanism, the first segment mapping component, the second segment mapping component, and the last segment mapping component are each connected to the same IIC control bus and are used to receive various control commands sent by the IIC control bus. The various control commands are used to configure the various operating data of the target extraction mechanism, the first segment mapping component, the second segment mapping component, and the last segment mapping component respectively.

7. The transmission line condition monitoring system as described in claim 6, characterized in that: The target extraction mechanism, the first segment mapping component, the second segment mapping component, and the last segment mapping component are respectively connected to the IIC control bus and are used to receive various control commands sent through the IIC control bus.

8. The transmission line condition monitoring system as described in any one of claims 2-4, characterized in that: The target extraction mechanism, the first segment mapping component, the second segment mapping component, and the last segment mapping component are each connected to the same MCU controller, and are used to switch between sleep mode and working mode under the control of the same MCU controller.

9. The transmission line condition monitoring system as described in claim 8, characterized in that: The target extraction mechanism, the first segment mapping component, the second segment mapping component, and the last segment mapping component are each connected to the same MCU controller, and are used to switch between sleep mode and working mode under the control of the same MCU controller. The same MCU controller is designed based on the ARM13 core.