Transmission line identification method based on magnetic field distribution characteristics

Abstract

An overhead electric transmission line is identified and detected from its magnetic field distribution. A theoretically calculated or simulated spatial magnetic field intensity is used as a global mat

Description

TECHNICAL FIELD

[0001] The present disclosure belongs to the technical field of unmanned aerial vehicle (UAV) autonomous routing inspection for transmission lines, and specifically relates to a transmission line identification method based on magnetic field distribution characteristics. BACKGROUND

[0002] With the increase of transmission lines year by year, the task of line routing inspection is heavy and the labor intensity is high. Inevitably, the traditional manual inspection is replaced with unmanned aerial vehicle (UAV) power routing inspection. The UAV power routing inspection mainly focuses on transmission lines, transmission towers and accessories of the transmission towers. For the transmission towers and accessories of the transmission towers, autonomous routing inspection of UAVs has been realized through fixed tracks. For the transmission lines, since the sag degree is greatly affected by climate, ambient temperature and other factors, routing inspection cannot be realized by fixing the tracks of the UAVs, and the routing inspection tracks need to be adjusted in real time to realize the autonomous routing inspection of the UAVs. At present, the real-time, efficient and accurate identification method of transmission lines has become the main factor that restricts the realization of power routing inspection by the UAVs.

[0003] The existing transmission line identification methods mainly refer to image identification methods and identification methods based on electric field distribution characteristics. The transmission line identification method based on image identification has a high identification error rate in practical application, and easily causes routing inspection wire losing especially when crossing the tower, because the background environment is complex and changeable, the transmission lines are thin, and the method is easily interfered by the background of trees, roads, rivers, towers and other similar lines. However, the identification method based on electric field distribution characteristics can lead to low accuracy of transmission line identification because of the change of electric field distribution by unmanned aerial vehicles and airborne conductive equipment. SUMMARY

[0004] In order to solve the above technical problems, the present disclosure provides a transmission line identification method based on magnetic field distribution characteristics, and the rapid identification of the spatial orientation of an unmanned aerial vehicle (UAV) relative to the transmission lines is realized, so that the difficult problem of transmission line identification under the changeable and complex climate environment is solved, and a foundation is laid for realizing autonomous routing inspection by the UAV.

[0005] To achieve the above objective, the present disclosure has the following technical schemes.

[0006] Disclosed is a transmission line identification method based on magnetic field distribution characteristics. The normalized theoretically calculated or simulated spatial magnetic field intensity of transmission lines is used as a global matrix array M, and the normalized actually measured spatial magnetic field intensity of the transmission linesvertical section is used as a local matrix array N. A template matching method is adopted to match the measured local i matrix array N to the global matrix array M, and the spatial position of the local matrix array N is deduced from the known global matrix array M, so that effective identification of the transmission lines is realized.

[0007] Further, the method is suitable for high-voltage direct-current transmission and high-voltage alternating-current transmission; and during alternating-current transmission, the spatial magnetic field distribution around the transmission lines is equivalent by using the effective value of the magnetic field intensity with a period of 20 ms.

[0008] Further, the global matrix array M is a matrix with the transmission lines as the center and the normalized spatial magnetic field intensity within 20 m.

[0009] Further, the local matrix array N is a normalized matrix of the spatial magnetic field intensity measured in a safe area by an unmanned aerial vehicle carried electromagnetic sensing matrix, and the measurement accuracy of a single sensor is not less than 0.05 pT.

[0010] Further, the normalization formula is as follows: y_ y

[0011] X* =-------““2— '^max ‘^mm

[0012] Wherein, Xmax is the maximum sample value, Xnm is the minimum sample value, Xis the original sample value, andX* is the normalized sample value.

[0013] Further, the template matching method is a pyramid template matching method, the pyramid layer number k is set according to the actually measured spatial magnetic field range, and the mean filtering algorithm is adopted to select the normalized correlation coefficient as the similarity measure criterion.

[0014] The method has the beneficial effects that the transmission lines are identified based on the magnetic field distribution characteristics, and the defects of large power consumption, poor real-time performance, over-tower wire loss and the like caused by complex image identification algorithms are avoided. Moreover, the magnetic field is the inherent property of a current-carrying conductor, and unmanned aerial vehicle carried metal equipment cannot change the spatial magnetic field distribution of the transmission lines.

[0015] The idea of template matching in image identification is introduced. The normalized theoretically calculated or simulated spatial magnetic field intensity of transmission lines is used as a global matrix array M, and the normalized actually measured spatial magnetic field intensity of the transmission line vertical section is used as a local matrix array N. In the spatial electromagnetic field, the pyramid template matching method is adopted to realize the rapid identification of the spatial orientation of an unmanned aerial vehicle (UAV) relative to the transmission lines, so that the difficult problem of transmission line identification under the changeable and complex climate environment is solved, and a foundation is laid for realizing autonomous routing inspection by the UAV. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] FIG. 1 is a schematic diagram of the spatial electromagnetic field distribution Ml in ±500kV direct-current double circuits and the spatial area corresponding to the measurement area N1 of an electromagnetic sensing matrix.

[0017] FIG. 2 is a schematic diagram of the spatial electromagnetic field distribution M2 in 500kV alternating-current double circuits and the spatial area corresponding to the measurement area N2 of an electromagnetic sensing matrix.

[0018] FIG. 3 is a layout diagram of an electromagnetic sensing matrix.

[0019] FIG. 4 is a schematic diagram of each layer in a pyramid template matching method. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] In order to make the objectives, technical solutions and advantages of the present disclosure clearer, the present disclosure will be further described hereinbelow with reference to the attached figures and embodiments thereof. It shall be understood that, the embodiments described herein are only intended to illustrate but not to limit the present disclosure. In addition, the technical features involved in various embodiments of the present disclosure described below can be combined with one another as long as the technical features do not conflict with one another.

[0021] Disclosed is a transmission line identification method based on magnetic field distribution characteristics. The normalized theoretically calculated or simulated spatial magnetic field intensity of transmission lines is used as a global matrix array M, and the normalized actually measured spatial magnetic field intensity of the transmission line vertical section is used as a local matrix array N. A template matching method is adopted to match the measured local matrix array N to the global matrix array M, and the spatial position of the local matrix array N is deduced from the known global matrix array M, so that effective identification of the transmission lines is realized. The template matching method is a pyramid template matching method, the pyramid layer number k is set according to the actually measured spatial magnetic field range, and the mean filtering algorithm is adopted to select the normalized correlation coefficient as the similarity measure criterion. The method is suitable for high-voltage direct-current transmission and high-voltage alternating-current transmission. During alternating-current transmission, the spatial magnetic field distribution around the transmission lines is equivalent by using the effective value of the magnetic field intensity with a period of 20 ms.

[0022] The global matrix array M is a matrix with the transmission lines as the center and the normalized spatial magnetic field intensity within 20 m.

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

[0024] Taking ±500kV direct-current double circuits as an example, taking the transmission lines waiting for routing inspection as the center, according to the simulation analysis of the spatial electromagnetic field distribution within 20 m, the global matrix of the spatial magnetic field intensity distribution is normalized to Ml, and the corresponding spatial area is shown in FIG. 1.

[0025] Taking 500kV alternating-current double circuits as an example, taking the transmission lines waiting for routing inspection as the center, according to the simulation analysis of the spatial electromagnetic field distribution within 20 m, the magnetic field intensity of alternating current is variable, the effective value of 20 ms is taken according to the frequency of 50Hz alternating current, the global matrix of the spatial magnetic field intensity distribution in the 500kV alternating-current double circuits is normalized to M2, and the corresponding spatial area is shown in FIG. 2.

[0026] The layout of the electromagnetic sensing matrix is shown in FIG. 3. The electromagnetic sensing matrix is used for measuring the distribution of a 10cm*20cm spatial magnetic field intensity which is beyond the safe distance from the transmission lines and perpendicular to the transmission lines. The measurement accuracy of a single sensor is not less than 0.05 pT. The size of the electromagnetic sensing matrix can be adjusted according to the actual engineering situation. The measured local matrix of the ±500kV direct-current double circuits is normalized to Nl, and the corresponding spatial area is shown in FIG. 1. The local matrix of the measured effective value of the 500kV alternating-current double circuits is normalized to N2, and the corresponding spatial area is shown in FIG. 2.

[0027] Since the magnetic field intensity can change with the change of current in the transmission lines in practical engineering application, in order to avoid the influence of current change on the effectiveness of transmission line identification, the method is more suitable for practical engineering application, it is necessary to normalize the global matrix and local matrix of the magnetic field. According to the relationship between the magnetic field intensity and the current, the deviation standardization is selected, the existing relationship between the original data is retained, and the original data is linearly transformed, so that the results are mapped in the range of [0~l], The normalization formula is as follows:

[0029] Wherein, Xmax is the maximum sample value, Xmn is the minimum sample value, Xis the original sample value, andX* is the normalized sample value.

[0030] In order to find out the position of Nl in Ml and N2 in M2, the template matching algorithm in image identification is introduced. In order to improve the matching speed between source matrix M and template matrix N, the pyramid template matching algorithm is adopted.

[0031] The pyramid layer number K is set to be 3, and the pyramid matrix corresponding to M and N is established through down-sampling. After down-sampling, the mean filtering algorithm is adopted to obtain the first layer ml, the second layer m2 and the third layer m3 of the pyramid matrix corresponding to M, and the first layer nl, the second layer n2 and the third layer n3 of the pyramid matrix corresponding to N, as shown in FIG. 4.

[0032] The matrix SI with the same size as nl is taken on ml, and ml is traversed by sliding the time window. The normalized correlation coefficient is used as the similarity measure criterion, and the similarity values of templates nl and SI are calculated. The larger the absolute value of the similarity values, the more similarity between templates nl and SI is, and the best matching position of the first layer is obtained. The formula of normalized correlation coefficient is as follows:

[0033] r(XJ) = .................................

[0034] Wherein, 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 [(X - E [X]) (F - E[F])]

[0036] Cm'(XJj =

[0037] Wherein, E[■] represents expected value.

[0038] Then, identification is transmitted to the second layer, scanned and matched in the window range of 8*8 according to the transmission position, and sequentially transmitted to the third layer, and scanned and matched in the window range of 3*3 according to the transmission position to obtain the final best matching position, that 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 transmission line is reversely deduced, and finally the effective identification of the transmission lines is realized.

[0039] Those skilled in the art can easily understand that the foregoing descriptions are merely exemplary embodiments of the present disclosure, but are not intended to limit the present disclosure. Any modification, equivalent replacement or improvement made within the spirit and principle of the present disclosure shall fall within the protection scope of the present disclosure.

Claims

1. A transmission line identification method based on magnetic field distribution characteristics, wherein the normalized theoretically calculated or simulated spatial magnetic field intensity of transmission lines is used as a global matrix array M, and the normalized actually measured spatial magnetic field intensity of the transmission line vertical section is used as a local matrix array N; and a template matching method is adopted to match the measured local matrix array N to the global matrix array M, and the spatial position of the local matrix array N is deduced from the known global matrix array M, so that effective identification of the transmission lines is realized.

2. The transmission line identification method based on magnetic field distribution characteristics according to claim 1, wherein the method is suitable for high-voltage direct-current transmission and high-voltage alternating-current transmission; and during alternating-current transmission, the spatial magnetic field distribution around the transmission lines is equivalent by using the effective value of the magnetic field intensity with a period of 20 ms.

3. The transmission line identification method based on magnetic field distribution characteristics according to claim 1, wherein the global matrix array M is a matrix with the transmission lines as the center and the normalized spatial magnetic field intensity within 20 m.

4. The transmission line identification method based on magnetic field distribution characteristics according to claim 1, wherein the local matrix array N is a normalized matrix of the spatial magnetic field intensity measured in a safe area by an unmanned aerial vehicle carried electromagnetic sensing matrix, and the measurement accuracy of a single sensor is not less than 0.05 pT.

5. The transmission line identification method based on magnetic field distribution characteristics according to claim 3 or 4, wherein the normalization formula is as follows:Wherein, Xmax is the maximum sample value, Xnm is the minimum sample value, X is the original sample value, andX is the normalized sample value.

6. The transmission line identification method based on magnetic field distribution characteristics according to claim 1, wherein the template matching method is a pyramid template matching method, the pyramid layer number k is set according to the actually measured spatial magnetic field range, and the mean filtering algorithm is adopted to select the normalized correlation coefficient as the similarity measure criterion.

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