A method for identifying transmission lines based on magnetic field distribution characteristics

Abstract

The present invention discloses a method for identifying power transmission lines based on magnetic field distribution characteristics. The spatial magnetic field intensity of the transmission lines obtained through theoretical calculation or simulation is normalized as the global matrix array M, and the spatial magnetic field intensity of the vertical profile of the transmission lines obtained through actual measurement is normalized as the local matrix array N. A pyramid template matching method is adopted in the spatial electromagnetic field domain to achieve rapid identification of the spatial orientation of a UAV relative to the power transmission line, thus solving the problem of identifying power transmission lines under variable climate and complex backgrounds, and laying the foundation for autonomous inspections along the power transmission lines by UAVs.

Description

Technical Field

[0001] The present invention belongs to the technical field of autonomous inspection of power transmission lines by unmanned aerial vehicles (UAVs), and specifically relates to a power transmission line identification method based on magnetic field distribution characteristics. Background Art

[0002] With the increasing number of transmission lines, the workload and labor intensity of line inspections are becoming increasingly demanding. The use of drones to replace traditional manual inspections is becoming an inevitable trend. Drone inspections primarily target transmission lines, transmission towers, and their accessories. Autonomous drone inspections of transmission towers and their accessories have been implemented using fixed flight paths. However, transmission lines, whose sag is significantly affected by factors such as climate and ambient temperature, cannot be inspected using fixed drone paths. Real-time adjustments to the inspection trajectory are necessary to enable autonomous drone patrols. Currently, real-time, efficient, and accurate transmission line identification methods have become the primary constraint to the success of drone inspections.

[0003] Existing methods for identifying power transmission lines primarily include image recognition and methods based on electric field distribution characteristics. Image-based methods suffer from high recognition error rates in practical applications due to the complex and changing background environment and the slenderness of transmission lines, which are easily interfered with by similar straight lines such as trees, roads, rivers, and tower structures. This can especially lead to missed transmission lines during inspections when passing towers. Furthermore, methods based on electric field distribution characteristics suffer from low transmission line recognition accuracy due to changes in the electric field distribution caused by drones and their onboard conductive equipment. Summary of the Invention

[0004] To solve the above technical problems, the present invention provides a transmission line identification method based on magnetic field distribution characteristics, which realizes the rapid identification of the spatial orientation of the UAV relative to the transmission line, solves the problem of transmission line identification under changeable and complex climate environments, and lays the foundation for realizing autonomous inspections along the line by UAVs.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is:

[0006] A transmission line identification method based on magnetic field distribution characteristics is proposed. The theoretically calculated or simulated transmission line spatial magnetic field intensity is normalized as the global matrix array M, and the actually measured vertical section spatial magnetic field intensity of the transmission line is normalized as the local matrix array N. The template matching method is used to match the measured local matrix array N to the global matrix array M. The spatial position of the local matrix array N is inferred from the known global matrix array M, thereby realizing effective identification of the transmission line.

[0007] Furthermore, it is applicable to high-voltage direct current transmission and high-voltage alternating current transmission; during alternating current transmission, the spatial magnetic field distribution around the transmission line is equivalent to the effective value of the magnetic field intensity with a period of 20ms.

[0008] Furthermore, the global matrix array M is a matrix obtained by normalizing the spatial magnetic field intensity within 20 m with the transmission line as the center.

[0009] Furthermore, the local matrix array N is a normalized matrix of the spatial magnetic field intensity measured by the electromagnetic sensor matrix carried by the drone within the safe area, and the measurement accuracy of a single sensor is not less than 0.05 μT.

[0010] Furthermore, the normalized formula is:

[0011]

[0012] Among them, X max is the maximum sample value, X min is the minimum sample value, X is the original sample value, X * is the normalized sample value.

[0013] Furthermore, the template matching method adopts a pyramid template matching method, sets the number of pyramid layers n according to the actual measured spatial magnetic field range, adopts a mean filtering algorithm, and selects a normalized correlation coefficient as a similarity measurement criterion.

[0014] The beneficial effects of the present invention are as follows: the present invention identifies transmission lines based on magnetic field distribution characteristics, avoiding the defects of complex image recognition algorithms such as high power consumption, poor real-time performance, and line loss when passing towers. In addition, the magnetic field is an inherent property of current-carrying conductors, and the metal equipment carried by the drone will not change the spatial magnetic field distribution of the transmission lines.

[0015] The present invention introduces the idea of ​​template matching in image recognition. The spatial magnetic field intensity of the transmission line calculated theoretically or simulated is normalized as the global matrix array M, and the spatial magnetic field intensity of the vertical section of the transmission line actually measured is normalized as the local matrix array N. The pyramid template matching method is adopted in the spatial electromagnetic field domain to realize the rapid identification of the spatial orientation of the UAV relative to the transmission line, solve the problem of transmission line identification under changeable climate and complex background, and lay the foundation for realizing autonomous inspection along the line by UAV. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 Schematic diagram of the spatial area corresponding to the ±500kV DC dual-circuit spatial electromagnetic field distribution M1 and the electromagnetic sensing matrix measurement area N1.

[0017] Figure 2 Schematic diagram of the spatial area corresponding to the 500kV AC double-circuit spatial electromagnetic field distribution M2 and the electromagnetic sensing matrix measurement area N2.

[0018] Figure 3Schematic diagram of the layout of the electromagnetic sensing matrix.

[0019] Figure 4 Schematic diagram of each layer of the pyramid template matching method. DETAILED DESCRIPTION

[0020] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only intended to illustrate the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.

[0021] The present invention discloses a method for identifying power transmission lines based on magnetic field distribution characteristics. The global matrix array M is normalized using the theoretically calculated or simulated spatial magnetic field intensity of the power transmission line. The local matrix array N is normalized using the actually measured spatial magnetic field intensity of the vertical section of the power transmission line. A template matching method is used to match the measured local matrix array N to the global matrix array M. The spatial position of the local matrix array N is deduced from the known global matrix array M, thereby effectively identifying the power transmission line. The template matching method employs a pyramid template matching method, sets the number of pyramid layers n based on the actually measured spatial magnetic field range, employs a mean filtering algorithm, and selects the normalized correlation coefficient as the similarity measurement criterion. The present invention is applicable to both high-voltage direct current (HVDC) and high-voltage alternating current (HVAC) power transmission. During AC transmission, the spatial magnetic field distribution around the power transmission line is equivalently calculated using the effective value of the magnetic field intensity with a period of 20 ms.

[0022] The global matrix array M is a matrix obtained by normalizing the spatial magnetic field intensity within 20 m with the transmission line as the center.

[0023] The local matrix array N is a normalized matrix of the spatial magnetic field intensity measured by the electromagnetic sensor matrix carried by the drone in the safe area, and the measurement accuracy of a single sensor is not less than 0.05μT.

[0024] Taking the ±500kV DC double circuit as an example, with the transmission line to be inspected as the center, the spatial electromagnetic field distribution within a range of 20m is analyzed according to the simulation. The global matrix of the spatial magnetic field intensity distribution is normalized to M1, and the corresponding spatial area is as follows: Figure 1 shown.

[0025] Taking a 500kV AC double circuit as an example, with the transmission line to be inspected as the center, the spatial electromagnetic field distribution within a 20m range is analyzed based on simulation. Since the magnetic field strength of AC current varies, the effective value of 20ms is taken based on the AC frequency of 50Hz. The global matrix of the spatial magnetic field strength distribution of the 500kV AC double circuit is normalized to M2. The corresponding spatial area is as follows: Figure 2 shown.

[0026] The layout of the electromagnetic sensing matrix is ​​as follows Figure 3 As shown in Figure 1, an electromagnetic sensor matrix is ​​used to measure the magnetic field distribution intensity in a 10cm*20cm area perpendicular to the transmission line, exceeding the safe distance from the transmission line. The measurement accuracy of a single sensor is no less than 0.05μT. The size of the electromagnetic sensor matrix can be adjusted according to the actual project situation. The measured local matrix of the ±500kV DC dual circuit is normalized to N1, and the corresponding spatial area is shown as follows: Figure 1 The local matrix of the measured 500kV AC double circuit effective value is normalized to N2, and the corresponding spatial area is as follows: Figure 2 shown.

[0027] Since the magnetic field strength in actual engineering applications changes with the current in the transmission line, in order to avoid the current change affecting the effectiveness of transmission line identification and make the method of the present invention more suitable for actual engineering applications, it is necessary to normalize the global and local matrices of the magnetic field. According to the relationship between magnetic field strength and current, deviation normalization is selected to retain the relationship between the original data, and the original data is linearly transformed so that the result is mapped between [0 and 1]. The normalization formula is:

[0028]

[0029] Among them, X max is the maximum sample value, X min is the minimum sample value, X is the original sample value, X * is the normalized sample value.

[0030] To find the position of N1 in M1 and the position of N2 in M2, we introduce the pattern matching algorithm in image recognition. To improve the matching speed between the source matrix M and the template matrix N, we use the pyramid pattern matching algorithm.

[0031] Set the number of pyramid layers to 3, create the corresponding pyramid matrices of M and N by downsampling, and use the mean filter algorithm after downsampling to obtain the first layer m1, second layer m2, and third layer m3 of the pyramid matrix corresponding to M, and the first layer n1, second layer n2, and third layer n3 of the pyramid matrix corresponding to N, as shown in the following example: Figure 4 shown.

[0032] Take a matrix S1 of the same size as n1 on m1 and traverse m1 in a sliding time window manner. Use the normalized correlation coefficient as the similarity metric to calculate the similarity value between template n1 and S1. The larger the absolute value, the more similar it is. Obtain the best matching position in the first layer. The formula for the normalized correlation coefficient is:

[0033]

[0034] Where Cov(X,Y) is the covariance of X and Y, Var[X] is the variance of X, and Var[Y] is the variance of Y.

[0035] Cov(X,Y)=E[(XE[X])(YE[Y])]

[0036] Cov(X,Y)=E[XY]-E[X]E[Y]

[0037] Where E[·] represents expectation;

[0038] It is then transferred to the second layer, where scan matching is performed within an 8*8 window according to the transferred position. It is then transferred to the third layer, where scan matching is performed within a 3*3 window according to the transferred position to obtain the final optimal matching position, which is the position of the local matrix N relative to the global matrix M. Since the position of N is known, the position of the output line can be inferred, ultimately achieving effective identification of the transmission line.

[0039] It will be easily understood by those skilled in the art that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for identifying power transmission lines based on magnetic field distribution characteristics, characterized by: The theoretically calculated or simulated transmission line spatial magnetic field intensity is normalized as the global matrix array M, and the actually measured vertical section spatial magnetic field intensity of the transmission line is normalized as the local matrix array N. The measured local matrix array N is matched to the global matrix array M using a template matching method. The spatial position of the local matrix array N is inferred from the known global matrix array M to achieve effective identification of the transmission line; the template matching method adopts a pyramid template matching method; The global matrix array M is a matrix of the normalized magnetic field intensity within 20m with the transmission line as the center; The local matrix array N is a matrix obtained by normalizing the spatial magnetic field intensity measured by the electromagnetic sensor matrix carried by the drone in the safe area.

2. The method for identifying a power transmission line based on magnetic field distribution characteristics according to claim 1, characterized in that: Applicable to high-voltage direct current transmission and high-voltage alternating current transmission; during AC transmission, the spatial magnetic field distribution around the transmission line is equivalent to the effective value of the magnetic field intensity with a period of 20ms.

3. The method for identifying a power transmission line based on magnetic field distribution characteristics according to claim 1, characterized in that: The measurement accuracy of a single sensor is not less than 0.05μT.

4. The method for identifying a power transmission line based on magnetic field distribution characteristics according to claim 1, characterized in that: The normalized formula is: ； in, X max is the maximum sample value, X min is the minimum sample value, X is the original sample value, X * is the normalized sample value.

5. The method for identifying a power transmission line based on magnetic field distribution characteristics according to claim 1, characterized in that: The number of pyramid layers n is set according to the actual measured spatial magnetic field range, the mean filtering algorithm is adopted, and the normalized correlation coefficient is selected as the similarity measurement criterion.

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