Uav-mounted ultraviolet imager power transmission line fault detection method and system

Abstract

The application discloses a kind of unmanned aerial vehicle transmission line fault detection method and system of carrying ultraviolet imager, comprising: based on the position information of the transmission line to be detected and the flight information of unmanned aerial vehicle, obtain the rotation angle of holder, ensure that the transmission line to be detected is located in the center of ultraviolet imager record image;The image of transmission line is processed, and the position and frequency of partial discharge in detection range are obtained;Evaluate the consistency of partial discharge in detection range;Judge whether the maximum value of partial discharge consistency appears at the position of transmission line or the consistency value of transmission line exceeds 80% of the maximum value, if so, generate alarm.The application utilizes image processing technology, processes the picture obtained by ultraviolet imager, and outputs corresponding alarm according to the processing result, reduces the workload of manual data processing, improves the accuracy of inspection. And using automatic tracking algorithm, keep the transmission line in the center of ultraviolet imager test field, improve the detection performance and inspection efficiency.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of power transmission line inspection, and relates to a method and system for detecting faults of a power transmission line by using a UAV carrying an ultraviolet imager. BACKGROUND

[0002] With the continuous expansion of the power grid scale of the power system and the continuous improvement of the power load requirements, various types of damage and faults in the power system are increasing. If effective inspection and maintenance are not performed, the normal operation of equipment will be affected or even harm will be caused, resulting in huge economic losses. Therefore, the inspection of the power transmission line is very important.

[0003] After the power system is damaged, the phenomenon of corona discharge occurs, accompanied by sound, light, heat and other effects. According to the characteristics of corona discharge, the severity of defects in the insulation device can be determined. Common detection methods mainly include observation method, light measurement method, leakage current method, infrared detection method and the like. These detection methods mostly have problems such as being greatly affected by the environment, having large errors, being difficult to operate, and being low in efficiency.

[0004] Thanks to the development of ultraviolet imaging technology, the ultraviolet imager can remotely, all-weather, and without affecting the operation of the equipment, observe the corona discharge phenomenon, detect the position of the discharge point in real time, and determine the discharge strength according to the number of ultraviolet photons. It is less affected by the environment and brings great convenience in practical application. However, this detection method still has some defects: lack of digital processing of ultraviolet imaging, lack of intelligent and automatic detection of equipment defects; with the gradual development of the power grid, the power transmission lines in long-distance, cross-regional, unmanned and mountainous areas and other complex terrains are increasing, and the traditional inspection method is difficult to meet the new requirements of circuit inspection. SUMMARY

[0005] The purpose of the present application is to solve the problems in the prior art that lack digital processing of ultraviolet imaging, lack intelligent and automatic detection of equipment defects, and the traditional inspection method is difficult to meet the new requirements of circuit inspection, and to provide a method and system for detecting faults of a power transmission line by using a UAV carrying an ultraviolet imager.

[0006] To achieve the above purpose, the following technical solutions are adopted:

[0007] A method for detecting faults of a power transmission line by using a UAV carrying an ultraviolet imager, comprising:

[0008] acquiring image information of the power transmission line;

[0009] based on the image information of the power transmission line, obtaining position information of the power transmission line to be detected;

[0010] Based on the location information of the transmission line to be inspected and the flight information of the UAV, the rotation angle of the gimbal is obtained to ensure that the transmission line to be inspected is located in the center of the image recorded by the ultraviolet imager; thus realizing the automatic path tracking of the transmission line to be inspected by the UAV.

[0011] Based on the realization of automatic path tracking of the transmission line to be inspected by UAV, the image of the transmission line is processed based on the 3σ outlier detection algorithm to obtain the location and frequency of partial discharge within the detection range;

[0012] Based on the location and frequency of partial discharges within the detection range, the consistency of partial discharges within the detection range is evaluated; and the location of objects where partial discharges continue to occur is determined.

[0013] Determine whether the maximum value of partial discharge consistency has occurred at the location of the transmission line, or whether the consistency value of the transmission line exceeds 80% of the maximum value. If yes, generate an alarm; if no, do not generate an alarm.

[0014] A further improvement of the present invention is that:

[0015] Based on the image information of the transmission line, the location information of the transmission line to be detected is obtained; specifically:

[0016] Based on the Canny edge detection algorithm, image information of power transmission lines is processed to obtain two-dimensional edge images;

[0017] Convolution processing of 2D edge images is performed based on vertical convolution kernels to remove vertical lines from 2D edge images;

[0018] The edge image of the transmission line to be detected is enhanced by performing convolution processing on the 2D edge image with vertical lines removed based on the thickness convolution kernel;

[0019] Based on Hough transform, the edge image of the transmission line to be detected is processed to obtain the location information of the transmission line to be detected.

[0020] Based on the Canny edge detection algorithm, image information of transmission lines is processed to obtain two-dimensional edge images; specifically:

[0021] Edge sharpening is performed on the original input image based on Gaussian filtering and non-maximum suppression;

[0022] Based on dual thresholds, the original image after edge sharpening is distinguished to obtain weak edges and strong edges in the original image after edge sharpening.

[0023] Weak edges that are not connected to strong edges are suppressed to obtain a two-dimensional edge image.

[0024] Based on the Hough transform, the edge image of the transmission line to be detected is processed to obtain the position information of the transmission line to be detected, specifically:

[0025] The edge image of the transmission line to be detected is subjected to Hough transform using a first-order linear polynomial, and the transmission line is approximated as a straight line for processing, and the angle [-θ~θ] of the line slope is extracted, wherein the value of θ is shown in formula (1):

[0026]

[0027] Wherein Z1 and Z2 are the heights of the two transmission towers, and S is the span between the two towers.

[0028] Based on the position information of the transmission line to be detected and the flight information of the unmanned aerial vehicle, the rotation angle of the holder is obtained, specifically:

[0029]

[0030] Wherein z is the expected angle of the holder, m i represents the distance between the center of the ultraviolet imager image and the i-th row of transmission line, L represents the number of detected rows, C I represents the distance between the bottom and the center of the ultraviolet imager image, β and V represent the vertical angle and vertical size of the ultraviolet imager respectively.

[0031] Based on the 3σ outlier detection algorithm, the image of the transmission line is processed to obtain the position and frequency of partial discharge in the detection range; Specifically:

[0032] Remove the background of the image of the transmission line to highlight the partial discharge information recorded by the ultraviolet imager; apply 3σ outlier elimination method to reduce dimension on the two-dimensional image I(M×N) of the transmission line after background processing to obtain one-dimensional matrix K(1×N);

[0033] Assuming that the partial discharge intensity follows a normal distribution, when its discharge intensity is greater than μ+3σ, it is considered that the partial discharge intensity is an outlier, and the corresponding position is the damaged surface; while the partial discharge part uniformly distributed on the image is excluded; The specific formula is:

[0034]

[0035] Wherein K j represents the partial discharge intensity of each element in the one-dimensional matrix K(1×N); K' is a one-dimensional matrix, wherein each element K j ' is the sum of each column element of the two-dimensional image I:

[0036]

[0037] Mapping each element in the one-dimensional matrix K (1×N) to the global coordinates to obtain the distribution of partial discharge intensity in the detection range.

[0038] Mapping each element in the one-dimensional matrix K (1×N) to the global coordinates to obtain the distribution of partial discharge intensity in the detection range.

[0039] Calculate the field of view range R:

[0040]

[0041] Where R represents the actual horizontal length of the range indicated by the one-dimensional matrix K (1×N), r represents the distance between the UAV and the transmission line, and a represents the horizontal viewing angle of the ultraviolet imager.

[0042] Calculate the distance l between the UAV and the starting point center ; Calculate the corresponding l center of each one-dimensional matrix K (1×N) matrix based on the Vincenty formula.

[0043] Calculate the distance l j between the starting point and the jth element in each one-dimensional matrix K (1×N).

[0044]

[0045] Where dir represents the motion direction of the ultraviolet imager relative to the transmission line during inspection: when the UAV moves from left to right, dir is defined as 1, and when the UAV moves from right to left, dir is defined as -1.

[0046] Calculate the distribution cdi(l) of partial discharge intensity on the transmission line; replace each element in all one-dimensional matrices K with the corresponding l j to obtain the distribution cdi(l) of partial discharge intensity on the transmission line length l.

[0047] Based on the location and frequency of partial discharge in the detection range, evaluate the consistency of partial discharge in the detection range; specifically:

[0048] Calculate the average value m of the partial discharge intensity; identify the area with strong partial discharge based on the average value m of the partial discharge intensity; the average value m of the partial discharge intensity is the average value of all non-zero values in the distribution cdi(l) of the partial discharge intensity on the transmission line length l, and the area with high partial discharge intensity is selected.

[0049] Define the ratio of the number of partial discharge intensity higher than the average value m of the partial discharge intensity at l to the total number of partial discharge intensity in the interval [l-T, l+T] as the consistency value C(l); where T is the tolerance value corresponding to the GPS error.

[0050] Determine the position of the object that continuously appears partial discharge; Specifically: map the consistency C(l) of partial discharge to the vertical distance r of the unmanned aerial vehicle and the power transmission line, and determine the position of the object that continuously appears partial discharge according to the consistency of partial discharge parallel and perpendicular to the power transmission line.

[0051] A power transmission line fault detection system of an unmanned aerial vehicle carrying an ultraviolet imager, comprising:

[0052] An acquisition module, configured to acquire image information of the power transmission line;

[0053] A first acquisition module, configured to acquire position information of the power transmission line to be detected based on the image information of the power transmission line;

[0054] A second acquisition module, configured to acquire a rotation angle of the holder based on the position information of the power transmission line to be detected and flight information of the unmanned aerial vehicle, so as to ensure that the power transmission line to be detected is located at the center of the image recorded by the ultraviolet imager; and realize automatic path tracking of the unmanned aerial vehicle to the power transmission line to be detected;

[0055] A third acquisition module, configured to process the image of the power transmission line based on a 3σ outlier detection algorithm on the basis of realizing the automatic path tracking of the unmanned aerial vehicle to the power transmission line to be detected, and acquire the position and frequency of partial discharge in the detection range;

[0056] An evaluation module, configured to evaluate the consistency of partial discharge in the detection range based on the position and frequency of partial discharge in the detection range; and determine the position of the object that continuously appears partial discharge;

[0057] A judgment module, configured to judge whether the maximum value of the consistency of partial discharge appears at the position of the power transmission line, or whether the consistency value of the power transmission line exceeds 80% of the maximum value, if yes, generate an alarm; if no, do not generate an alarm.

[0058] Compared with the prior art, the present application has the following beneficial effects:

[0059] The application obtains the rotation angle of the holder by the position information of the power transmission line to be detected and the flight information of the unmanned aerial vehicle, ensures that the power transmission line to be detected is located at the center of the image recorded by the ultraviolet imager, and processes the image of the power transmission line by the 3σ outlier detection algorithm to obtain the position and frequency of the partial discharge in the detection range, evaluate the consistency of the partial discharge in the detection range, and determine the position of the object with persistent partial discharge, judge whether the maximum value of the consistency of the partial discharge appears at the position of the power transmission line or the consistency value of the power transmission line exceeds 80% of the maximum value, if yes, generate an alarm, if no, do not generate an alarm. The application processes the picture obtained by the ultraviolet imager by the image processing technology, provides data for the automatic tracking algorithm of the unmanned aerial vehicle, and outputs the corresponding alarm according to the processing result, greatly reduces the workload of manual data processing, improves the inspection efficiency and accuracy, uses the automatic tracking algorithm to keep the power transmission line at the center of the test field of the ultraviolet imager, reduces the workload of the operator controlling the holder, and improves the detection performance and inspection efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0060] In order to more clearly illustrate the technical solutions of the embodiments of the application, the following will briefly introduce the drawings needed to be used in the embodiments. It should be understood that the following drawings only show some of the embodiments of the application, and therefore should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can also be obtained without creative labor on the basis of these drawings.

[0061] Figure 1 A flow chart of the power transmission line fault detection method of the unmanned aerial vehicle carrying the ultraviolet imager of the application;

[0062] Figure 2 A structure diagram of the power transmission line fault detection system of the unmanned aerial vehicle carrying the ultraviolet imager of the application;

[0063] Figure 3 Another flow chart of the power transmission line fault detection method of the unmanned aerial vehicle carrying the ultraviolet imager of the application;

[0064] Figure 4 A flow chart of calculating the partial discharge intensity distribution of the application;

[0065] Figure 5 A flow chart of evaluating the consistency of the partial discharge of the application. DETAILED DESCRIPTION

[0066] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some but not all of the embodiments of the present application. The components of the embodiments of the present application described and shown in the drawings can be arranged and designed in various different configurations.

[0067] Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed application, but only represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0068] It should be noted that: similar reference numbers and letters represent similar items in the following drawings, therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0069] In the description of the embodiments of the present application, it should be noted that, if the orientation or position relationship indicated by the terms "upper", "lower", "horizontal", "inner" and the like is based on the orientation or position relationship shown in the drawings, or is the orientation or position relationship when the product of the present application is usually placed, which is only for the convenience of describing the present application and simplifying the description, and does not indicate or imply that the indicated device or element must have a particular orientation, be constructed and operated in a particular orientation, therefore, it cannot be understood as a limitation on the present application. In addition, the terms "first", "second" and the like are only used for differentiation in description, and cannot be understood as indicating or implying relative importance.

[0070] In addition, if the term "horizontal" appears, it does not mean that the component must be absolutely horizontal, but can be slightly inclined. For example, "horizontal" only means that its direction is relatively more horizontal than "vertical", and does not mean that the structure must be completely horizontal, but can be slightly inclined.

[0071] In the description of the embodiments of the present application, it should also be noted that, unless otherwise explicitly specified and limited, if the terms "arrangement", "installation", "connection", "connection" appear, they should be understood in a broad sense, for example, can be fixedly connected, can be detachably connected, or integrally connected; can be mechanically connected, can be electrically connected; can be directly connected, can be indirectly connected through an intermediate medium, can be the communication inside two elements. For those of ordinary skill in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0072] The present application will be described in further detail below with reference to the drawings:

[0073] Referring toFigure 1 The application discloses a method for detecting power transmission line faults by using a UAV (unmanned aerial vehicle) loaded with an ultraviolet imager.

[0074] S101, image information of the power transmission line is collected;

[0075] S102, position information of the power transmission line to be detected is acquired based on the image information of the power transmission line;

[0076] S102.1, the image information of the power transmission line is processed based on a Canny edge detection algorithm to acquire a two-dimensional edge image;

[0077] The original image is subjected to edge sharpening based on Gaussian filtering and non-maximum suppression;

[0078] The original image subjected to edge sharpening is distinguished based on double thresholds to acquire weak edges and strong edges of the original image subjected to edge sharpening;

[0079] The weak edges not connected to the strong edges are suppressed to obtain the two-dimensional edge image.

[0080] S102.2, the two-dimensional edge image is subjected to convolution processing based on a vertical convolution kernel to remove vertical lines in the two-dimensional edge image;

[0081] S102.3, the two-dimensional edge image from which the vertical lines are removed is subjected to convolution processing based on a thickness convolution kernel to enhance the edge image of the power transmission line to be detected;

[0082] S102.4, the edge image of the power transmission line to be detected is processed based on Hough transformation to acquire the position information of the power transmission line to be detected.

[0083] The edge image of the power transmission line to be detected is subjected to Hough transformation using a first-order linear polynomial, the power transmission line is processed by being approximated as a straight line, and the angle [-theta~theta] of the line slope is extracted, wherein the value of theta is shown in formula (1):

[0084]

[0085] wherein Z1 and Z2 are the heights of two power transmission towers respectively, and S is the span between the two towers.

[0086] S103, a pan-tilt rotation angle is acquired based on the position information of the power transmission line to be detected and flight information of the UAV, so that the power transmission line to be detected is ensured to be located at the center of the image recorded by the ultraviolet imager, and automatic path tracking of the UAV on the power transmission line to be detected is realized;

[0087] S103.1, the pan-tilt rotation angle is acquired based on the position information of the power transmission line to be detected and flight information of the UAV, and specifically:

[0088]

[0089] wherein z is the desired angle of the gimbal, m i represents the distance between the center of the ultraviolet imager image and the i-th row of transmission line, L represents the number of rows detected, C I represents the distance between the bottom and the center of the ultraviolet imager image, and β and V represent the vertical angle of view and the vertical size of the ultraviolet imager, respectively.

[0090] S104, on the basis of realizing automatic path tracking of the unmanned aerial vehicle to the transmission line to be detected, processing the image of the transmission line based on the 3σ outlier detection algorithm to obtain the position and frequency of partial discharge in the detection range;

[0091] Processing the image of the transmission line based on the 3σ outlier detection algorithm to obtain the position and frequency of partial discharge in the detection range; specifically:

[0092] Removing the background of the image of the transmission line to highlight the partial discharge information recorded by the ultraviolet imager; applying the 3σ outlier elimination method to the two-dimensional image I(MxN) of the transmission line after background processing to obtain a one-dimensional matrix K(1xN);

[0093] Assuming that the partial discharge intensity obeys a normal distribution, when the discharge intensity is greater than μ+3σ, it is considered that the partial discharge intensity is an outlier, and the corresponding position is the damaged surface; while the partial discharge part uniformly distributed on the image is excluded; the specific formula is:

[0094]

[0095] wherein K j represents the partial discharge intensity corresponding to each element in the one-dimensional matrix K(1xN); K' is a one-dimensional matrix, wherein each element K j ' is the sum of each column element of the two-dimensional image I:

[0096]

[0097] Mapping each element in the one-dimensional matrix K(1xN) to the global coordinates to obtain the distribution of the partial discharge intensity in the detection range.

[0098] 11. Mapping each element of the one-dimensional matrix K(1xN) to the global coordinates to obtain the distribution of the partial discharge intensity in the detection range; specifically:

[0099] Calculate the field of view range R:

[0100]

[0101] wherein R represents the actual horizontal length of the range indicated by the one-dimensional matrix K (1xN), r represents the distance between the UAV and the transmission line, and a represents the horizontal angle of view of the ultraviolet imager.

[0102] calculating the distance l between the UAV and the departure point center ; calculating the l corresponding to each one-dimensional matrix K (1xN) based on the Vincenty formula center ;

[0103] calculating the distance l between the departure point and the jth element in each one-dimensional matrix K (1xN) j :

[0104]

[0105] wherein dir represents the direction of motion of the ultraviolet imager relative to the transmission line during the inspection: dir is defined as 1 when the UAV moves from left to right, and dir is defined as -1 when the UAV moves from right to left;

[0106] calculating the distribution cdi(l) of the partial discharge intensity on the transmission line; replacing each element in all one-dimensional matrices K with the corresponding l j , to obtain the distribution cdi(l) of the partial discharge intensity on the length l of the transmission line.

[0107] S105, based on the position and frequency of the partial discharge in the detection range, evaluating the consistency of the partial discharge in the detection range; and determining the position of the object where the partial discharge persists;

[0108] Based on the position and frequency of the partial discharge in the detection range, the consistency of the partial discharge in the detection range is evaluated; specifically:

[0109] calculating the average value m of the partial discharge intensity; identifying the area where the partial discharge is strong based on the average value m of the partial discharge intensity; the average value m of the partial discharge intensity is the average value of all non-zero values in the distribution cdi(l) of the partial discharge intensity on the length l of the transmission line, and the area where the partial discharge intensity is higher than cdi(l) is selected;

[0110] defining the consistency value C(l) as the ratio of the number of partial discharge intensities higher than the average value m of the partial discharge intensity at l to the total number of partial discharge intensities in the interval [l-T, l+T]; wherein T is a tolerance value corresponding to the GPS error.

[0111] determining the position of the object where the partial discharge persists; specifically: mapping the consistency C(l) of the partial discharge to the vertical distance r between the UAV and the transmission line, and determining the position of the object where the partial discharge persists according to the consistency of the partial discharge parallel and perpendicular to the transmission line.

[0112] S106, judging whether the maximum value of the consistency of the local discharge appears at the position of the power transmission line or the consistency value of the power transmission line exceeds 80% of the maximum value, if yes, generating an alarm, if no, not generating an alarm.

[0113] Referring to Figure 2 The application discloses a UAV power transmission line fault detection system loaded with an ultraviolet imager, comprising:

[0114] The acquisition module is configured to acquire image information of the power transmission line.

[0115] The first acquisition module is configured to acquire position information of the power transmission line to be detected based on the image information of the power transmission line.

[0116] The second acquisition module is configured to acquire a rotation angle of the holder based on the position information of the power transmission line to be detected and flight information of the UAV, so as to ensure that the power transmission line to be detected is located at the center of the image recorded by the ultraviolet imager and to realize automatic path tracking of the power transmission line to be detected by the UAV.

[0117] The third acquisition module is configured to process the image of the power transmission line based on a 3σ outlier detection algorithm on the basis of realizing the automatic path tracking of the power transmission line to be detected by the UAV, so as to acquire the position and frequency of the local discharge in the detection range.

[0118] The evaluation module is configured to evaluate the consistency of the local discharge in the detection range based on the position and frequency of the local discharge in the detection range, and to determine the position of the object with which the local discharge continuously appears.

[0119] The judgment module is configured to judge whether the maximum value of the consistency of the local discharge appears at the position of the power transmission line or the consistency value of the power transmission line exceeds 80% of the maximum value, if yes, generating an alarm, if no, not generating an alarm.

[0120] Referring to Figure 3 The application comprises an automatic path tracking part and a consistency estimation part. The automatic path tracking part receives two-dimensional image information acquired by the ultraviolet imager and GPS information from a flight control system, identifies the position of the power transmission line, calculates a rotation angle of the holder, and transmits the rotation angle to a holder controller loaded with the ultraviolet imager, so as to control the holder to rotate to an ideal position. In the consistency estimation part, the input image uses a 3σ outlier detection algorithm to extract the position and frequency of the local discharge, and generates corresponding alarm information.

[0121] The application relates to a path tracking algorithm part, which helps to keep the target power transmission line in the center of the field of view of the ultraviolet imager. The specific steps are as follows:

[0122] S21: Generate a two-dimensional edge image using a Canny edge detection algorithm. First, apply a Gaussian filter and non-maximum suppression to the input original image to sharpen the edges. Then, use a double threshold to distinguish between weak and strong edges. Finally, obtain the two-dimensional edge image by suppressing the weak edges that are not connected to the strong edges.

[0123] S22: Use a vertical convolution kernel [1 2 3] T Convolve the edge image to remove vertical lines in the two-dimensional edge image. Since in the two-dimensional edge image, only when the three pixel values are vertically arranged as [0 1 0] T , the convolution result is 2, so the pixels whose convolution result is not 2 are suppressed, i.e., the continuous edges with large slope or vertical are excluded, while the horizontal edges are retained.

[0124] S23: Convolve the image output in S22 using a thickness convolution kernel, and suppress the pixels whose convolution result is not 1 to enhance the edge image of the transmission line to be detected. The values of the convolution kernel depend on the specifications of the ultraviolet imager and the distance between the ultraviolet imager and the transmission line, and need to be determined after specific measurement. For example, when using the thickness convolution kernel as [0.33 0.33 0.33 -0.5 -0.5 -0.5 0.67 0.67 0.67] T , the convolution result of the pixels with interval 3-7 in the input image is 1, which is retained.

[0125] S24: Perform Hough transform on the image extracted in S23 using a first-order linear polynomial y = ax + b. Since the field of view of the ultraviolet imager is narrow, the transmission line can be approximated as a straight line. Extract the angle -θ ~ θ of the line slope, and the value of θ needs to be determined according to the specific detection environment:

[0126]

[0127] where Z1 and Z2 are the heights of the two transmission towers, and S is the span between the two towers. At the same time, for the area near the tower, only the left and right side areas of the two-dimensional edge image include the transmission line, so the Hough transform is performed on the binary edge images of the left and right side areas, which reduces the computational burden while ensuring accuracy.

[0128] S25: According to the straight line detected in S24, calculate the vertical distance from the center of the field of view of the ultraviolet imager to the center of the transmission line, and thus calculate the rotation angle required by the pan-tilt:

[0129]

[0130] where z represents the desired angle of the pan-tilt, m iL represents the distance (pixel number) between the center of the ultraviolet imager image and the i-th row of the transmission line, L represents the number of rows detected, C I L represents the distance (pixel number) between the center of the ultraviolet imager image and the i-th row of the transmission line, L represents the number of rows detected, C

[0131] S26: Anti-interference, slow down camera shake. When the standard deviation of m i is more than 35% of V, or compared with the previous image, the average value of m i changes more than 40% of V, the expected pan angle z remains unchanged as the expected angle of the previous image, which can effectively reduce the image shake phenomenon. The percentage can be determined according to the specific detection situation.

[0132] S27: Kalman filter increases robustness. Because the detection environment is complex, such as unmanned aerial vehicles may be affected by gusts, Kalman filter algorithm is used to enhance the stability of the system.

[0133] The present application relates to a consistency estimation part, according to the picture output in step two, the corresponding alarm information is generated. The specific steps are as follows:

[0134] S31: Remove the background of the two-dimensional image in RGB format, highlight the partial discharge information recorded by the ultraviolet imager.

[0135] S32: Apply 3σ outlier elimination method to the two-dimensional image I(M×N) processed by S31 to reduce dimension to one-dimensional matrix K(1×N). Assuming that the partial discharge intensity obeys normal distribution, when its discharge intensity is greater than μ+3σ, it is considered that the partial discharge intensity is an outlier, and the corresponding position is the damaged surface; while the partial discharge part uniformly distributed on the image is excluded. The specific formula is:

[0136]

[0137] Where K j represents the partial discharge intensity of each element in the one-dimensional matrix K(1×N). K' is a one-dimensional matrix, where each element K j ' is the sum of each column element of the two-dimensional image I:

[0138]

[0139] S33: Refer to Figure 4 , map each element of the K(1×N) matrix to the global coordinates to obtain the distribution of the partial discharge intensity in the detection range.

[0140] S34: Refer to Figure 5 , evaluate the consistency of partial discharge.

[0141] S35: generating a corresponding alarm based on the consistency of partial discharge generated in S34.

[0142] Further, the removing background and retaining partial discharge information comprises the following steps:

[0143] S311: converting the image in RGB format into a chroma metric map. Each pixel in the image represents a chroma value, which is calculated from the corresponding normalized RGB values as follows:

[0144]

[0145] where S ij ∈(0,1) is the chroma of the i-th row, j-th column element of the input image. R ij , G ij and B ij are the normalized red, green and blue values of the i-th row, j-th column element in RGB format (these values are defined in the range 0-1). V ij ∈(0,1) represents the luminance value, and the calculation formula is:

[0146] V ij = max(R ij , G ij , B ij )

[0147] Since the partial discharge has a higher chroma value than most of the background, after the image is converted into a chroma metric map, the elements in the area where the chroma value S ij is lower than 0.9 or the luminance value V ij is lower than 0.6 are set to zero.

[0148] S312: correcting the stripes on the pixels located at the partial discharge. Since the ultraviolet imager outputs an analog signal, it is easily affected by vibration, resulting in stripes that will reduce the accuracy of subsequent recognition, therefore, the stripes are removed by adding the values of adjacent rows to each row:

[0149] I ij = I' ij + I' i-1j (i≠1)

[0150] Here, I represents the pre-processed image in the form of a chroma metric with M rows and N columns, I ij represents the element of the i-th row, j-th column after removing the stripes, and I' represents I before removing the stripes.

[0151] Further, the mapping of the coordinate system obtains the distribution of the partial discharge intensity in the detection range, specifically comprising the following steps:

[0152] S331: calculating the field of view range R:

[0153]

[0154] wherein R represents the actual horizontal length (in meters) of the range indicated by the K matrix, r represents the distance between the UAV and the transmission line, and a represents the horizontal angle of view of the ultraviolet imager.

[0155] S332: Calculate the distance l between the UAV and the starting point center . Calculate l corresponding to each K matrix using the Vincenty formula center . The accuracy can be determined according to the specific detection scene, and is about 6.4 mm.

[0156] S333: Calculate the distance l between the starting point and the jth element in each K matrix j :

[0157]

[0158] wherein dir represents the direction of motion of the ultraviolet imager relative to the transmission line at the time of inspection: when the UAV moves from left to right, dir is defined as 1, and when the UAV moves from right to left, dir is defined as -1.

[0159] S334: Calculate the distribution cdi(l) of the partial discharge intensity on the transmission line. Replace each element in all K matrices with the corresponding l j , to obtain the distribution cdi(l) of the partial discharge intensity on the transmission line length l.

[0160] Further, the consistency of the partial discharge is calculated, including the following steps:

[0161] S341: Calculate the average value m of the partial discharge intensity. According to the average value m of the partial discharge intensity, identify the area where the partial discharge is strong. The average value m is the average value of all non-zero values in the distribution cdi(l) of the partial discharge intensity on the transmission line length l, and the area where the partial discharge intensity is higher than cdi(l) is selected.

[0162] S342: Define the ratio of the number of partial discharge intensities higher than m at l to the total number of partial discharge intensities within the interval [l-T, l+T] as the consistency value C(l). Wherein T is a tolerance value corresponding to the GPS error, which needs to be determined according to the specific GPS module used.

[0163] Further, generate a corresponding alarm based on the consistency of the partial discharge, specifically including the following steps:

[0164] S351: generating a pre-warning. According to S32, assuming that the partial discharge intensity follows a normal distribution, the partial discharge intensity from the damaged surface is considered as an outlier, the outlier in the normal distribution is eliminated using the 3σ outlier elimination method, and the pre-warning is generated.

[0165] S352: generating a prompt alarm and a warning alarm. The consistency C(l) of the partial discharge is mapped to the vertical distance r of the unmanned aerial vehicle from the power transmission line, and the position of the object where the partial discharge persists is determined according to the consistency of the partial discharge parallel and perpendicular to the power transmission line. If the maximum value of the consistency of the partial discharge appears at the location of the power transmission line, or the consistency value of the power transmission line exceeds 80% of the maximum value, a prompt alarm or a warning alarm is generated: specifically, if the consistency value deviates from the average value by 3σ, a prompt alarm is generated; if the consistency value deviates from the average value by 6σ, a warning alarm is generated.

[0166] The above is only a preferred embodiment of the present application and is not intended to limit the present application. Those skilled in the art can make various modifications and changes to the present application. Any modification, equivalent replacement, improvement, etc. within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for detecting power transmission line faults using an unmanned aerial vehicle (UAV) equipped with an ultraviolet imager, characterized in that, include: Acquire image information of power transmission lines; Based on the image information of the transmission line, the location information of the transmission line to be detected is obtained; Based on the location information of the transmission line to be inspected and the flight information of the UAV, the rotation angle of the gimbal is obtained to ensure that the transmission line to be inspected is located in the center of the image recorded by the ultraviolet imager. To enable drones to automatically track the path of power transmission lines to be inspected; Based on the realization of automatic path tracking of power transmission lines to be inspected by drones, and based on 3D... The outlier detection algorithm processes images of transmission lines to obtain the location and frequency of partial discharges within the detection range, specifically: Background removal from transmission line images highlights partial discharge information recorded by the ultraviolet imager; two-dimensional images of transmission lines after background removal processing... (M×N) Application Outlier elimination is used to reduce dimensionality and obtain a one-dimensional matrix. (1×N); Assuming the partial discharge intensity follows a normal distribution, when its discharge intensity is greater than When the intensity of the partial discharge is considered an anomalous value, the corresponding location is considered a damaged surface; while partial discharges uniformly distributed across the image are excluded; specific formula: in Represents the corresponding one-dimensional matrix The partial discharge intensity of each element in (1×N); It is a one-dimensional matrix, where each element It is a two-dimensional image The sum of each column's elements: One-dimensional matrix Each element in (1×N) is mapped to global coordinates to obtain the distribution of partial discharge intensity within the detection range; The consistency of partial discharges within the detection range is evaluated based on the location and frequency of the partial discharges. And determine the location of the object where partial discharge continues to occur; Determine whether the maximum value of partial discharge consistency has appeared at the location of the transmission line, or whether the consistency value of the transmission line exceeds 80% of the maximum value. If yes, generate an alarm; if no, do not generate an alarm.

2. The method for detecting power line faults using a UAV equipped with an ultraviolet imager according to claim 1, characterized in that, The location information of the transmission line to be detected is obtained based on the image information of the transmission line; specifically: Based on the Canny edge detection algorithm, image information of power transmission lines is processed to obtain two-dimensional edge images; Convolution processing of 2D edge images is performed based on vertical convolution kernels to remove vertical lines from 2D edge images; The edge image of the transmission line to be detected is enhanced by performing convolution processing on the 2D edge image with vertical lines removed based on the thickness convolution kernel; Based on Hough transform, the edge image of the transmission line to be detected is processed to obtain the location information of the transmission line to be detected.

3. The method for detecting power line faults using a UAV equipped with an ultraviolet imager according to claim 2, characterized in that, The Canny edge detection algorithm is used to process the image information of the transmission line to obtain a two-dimensional edge image; specifically: Edge sharpening is performed on the original input image based on Gaussian filtering and non-maximum suppression; Based on dual thresholds, the original image after edge sharpening is distinguished to obtain weak edges and strong edges in the original image after edge sharpening. Weak edges that are not connected to strong edges are suppressed to obtain a two-dimensional edge image.

4. The method for detecting power line faults using a UAV equipped with an ultraviolet imager according to claim 3, characterized in that, include: Based on the Hough transform, the edge image of the transmission line to be detected is processed to obtain the location information of the transmission line to be detected, specifically: The edge image of the transmission line to be detected is subjected to Hough transform using a first-order linear polynomial, approximating the transmission line as a straight line, and the angle of the line slope is extracted. ~ , The values ​​of are shown in formula (1): in and These are the heights of the two transmission towers, This represents the span between the two towers.

5. The method for detecting power line faults using a UAV equipped with an ultraviolet imager according to claim 4, characterized in that, The process of obtaining the gimbal rotation angle based on the location information of the transmission line to be detected and the flight information of the UAV is as follows: in, For the desired angle of the gimbal, Indicates the image center of the ultraviolet imager and the first The distance between transmission lines Indicates the number of rows detected. This indicates the distance between the bottom and center of the ultraviolet imager image. and These represent the vertical viewing angle and vertical dimensions of the ultraviolet imager, respectively.

6. The method for detecting power transmission line faults using a UAV equipped with an ultraviolet imager according to claim 5, characterized in that, The one-dimensional matrix Each element of the (1×N) matrix is ​​mapped to global coordinates to obtain the distribution of partial discharge intensity within the detection range; specifically: Calculate the field of view : in Representing a one-dimensional matrix (1×N) represents the actual horizontal length of the range. This indicates the distance between the drone and the power transmission line. Indicates the horizontal angle of view of the ultraviolet imager; Calculate the distance between the drone and the starting point ; Calculate each one-dimensional matrix based on Vincenti's formula. (1×N) matrix corresponding to ; The calculation starts from the point of view and each one-dimensional matrix. (1×N) of the th Distance between elements : in, Indicates the direction of movement of the ultraviolet imager relative to the power transmission line during the inspection: when the drone moves from left to right, Defined as 1, when the drone moves from right to left. Defined as -1; Calculate the distribution of partial discharge intensity on the transmission line. ; All one-dimensional matrices Each element in the array is replaced with the corresponding... To obtain the partial discharge intensity over the length of the transmission line Distribution on .

7. The method for detecting power line faults using a UAV equipped with an ultraviolet imager according to claim 6, characterized in that, The consistency of partial discharges within the detection range is evaluated based on the location and frequency of the partial discharges; specifically: Calculate the average value of partial discharge intensity ; Based on the average value of partial discharge intensity Identify areas with strong partial discharge; average partial discharge intensity. The partial discharge intensity over the length of the transmission line Upper distribution The average of all non-zero values ​​is selected, with the partial discharge intensity higher than 1. The area; definition The intensity is higher than the average value of partial discharge. Partial discharge intensity number and interval The ratio of the total partial discharge intensity within the area is the consistency value. ;in, This is the tolerance value corresponding to GPS error.

8. The method for detecting power line faults using a UAV equipped with an ultraviolet imager according to claim 7, characterized in that, The determination of the location of the object where partial discharges continuously occur specifically involves: determining the consistency of partial discharges. Mapped to the vertical distance between the drone and the power transmission line Based on the consistency of partial discharges at locations parallel and perpendicular to the transmission line, the location of the object where partial discharges continuously occur is determined.

9. A UAV-based power transmission line fault detection system equipped with an ultraviolet imager, characterized in that, include: The acquisition module is used to acquire image information of the transmission line; The first acquisition module acquires the location information of the transmission line to be detected based on the image information of the transmission line; The second acquisition module acquires the gimbal rotation angle based on the location information of the transmission line to be detected and the flight information of the UAV, ensuring that the transmission line to be detected is located at the center of the image recorded by the ultraviolet imager. To enable drones to automatically track the path of power transmission lines to be inspected; The third acquisition module, based on the automatic path tracking of the transmission line to be inspected by the UAV, is based on 3 The outlier detection algorithm processes images of transmission lines to obtain the location and frequency of partial discharges within the detection range, specifically: Background removal from transmission line images highlights partial discharge information recorded by the ultraviolet imager; two-dimensional images of transmission lines after background removal processing... (M×N) Application Outlier elimination is used to reduce dimensionality and obtain a one-dimensional matrix. (1×N); Assuming the partial discharge intensity follows a normal distribution, when its discharge intensity is greater than When the intensity of the partial discharge is considered an anomalous value, the corresponding location is considered a damaged surface; while partial discharges uniformly distributed across the image are excluded; specific formula: in Represents the corresponding one-dimensional matrix The partial discharge intensity of each element in (1×N); It is a one-dimensional matrix, where each element It is a two-dimensional image The sum of each column's elements: One-dimensional matrix Each element in (1×N) is mapped to global coordinates to obtain the distribution of partial discharge intensity within the detection range; An evaluation module that evaluates the consistency of partial discharges within the detection range based on the location and frequency of the partial discharges within the detection range; And determine the location of the object where partial discharge continues to occur; The judgment module is used to determine whether the maximum value of partial discharge consistency has appeared at the location of the transmission line, or whether the consistency value of the transmission line exceeds 80% of the maximum value. If yes, an alarm is generated; if no, no alarm is generated.

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