A method for anti-interference trajectory planning of UAV safety monitoring of high-voltage transmission lines

Abstract

The present invention discloses a method for planning a flight path for safe monitoring of unmanned aerial vehicles (UAVs) for anti-interference high-voltage transmission lines, including obtaining parameters of a pole tower and a UAV; drawing a three-dimensional model of the pole tower and the UAV through the parameters of the pole tower and the UAV; extracting the maximum electric field strength and magnetic induction strength of the UAV surface at different positions of the transmission line through a three-dimensional finite element method; designing a basic fitting function to determine a semi-empirical calculation formula for the maximum distorted electric field strength and magnetic induction strength of the UAV; determining the high-altitude safe monitoring hovering position of the UAV based on the electromagnetic field safety threshold of the UAV and the field of view of the UAV-mounted camera; and solving the optimal flight path for traversing the high-altitude safe monitoring hovering position of the UAV through an ant colony algorithm. The present invention uses a semi-empirical calculation method to accurately calculate the distorted electric field strength and magnetic induction strength of the UAV around the transmission line, ensure the flight safety of the UAV, reduce the amount of calculation and calculation time, and is convenient and fast, which is conducive to engineering applications.

Description

Technical Field

[0001] The present invention belongs to the field of route planning, and in particular relates to a method for planning a track for safety monitoring of an unmanned aerial vehicle (UAV) for anti-interference high-voltage power transmission lines. Background Art

[0002] Drones have the advantages of small size, easy to carry, good operability and flexibility, and little influence from terrain and environment. They are widely used in power transmission line inspection work. Compared with traditional manual monitoring methods, the use of drones for high-altitude safety monitoring can greatly improve the level of monitoring of operators. However, due to the high electric field strength around the transmission line, when the drone is close to the wire, its internal components will be degraded or lost due to dizziness or arcing, and even cause a crash. Therefore, drones need to maintain a certain safe distance from the transmission line when flying. Therefore, it is very important to comprehensively consider the electromagnetic interference of the complex electromagnetic environment near the high-voltage transmission line to the drone in order to determine the optimal trajectory of the drone for high-altitude safety monitoring.

[0003] In the existing methods for determining the trajectory of drone safety monitoring, the finite element simulation method is usually used to place the drone in different directions and at different distances from the power transmission line, extract the maximum distorted electric field strength and magnetic induction strength on the drone surface, and then determine the safe distance between the drone and the power transmission line based on the drone electromagnetic field safety threshold. The drone's high-altitude safety monitoring hovering shooting position is determined based on the drone monitoring safety distance, and finally a path planning algorithm is used to solve the optimal path traversing the hovering position. However, the finite element simulation method has a large amount of calculation and is less suitable for practical engineering applications. Summary of the invention

[0004] The purpose of the present invention is to provide a method for anti-interference high-voltage transmission line UAV safety monitoring trajectory planning, which has small calculation amount, strong applicability, high efficiency and accuracy.

[0005] The anti-interference high-voltage transmission line UAV safety monitoring trajectory planning method provided by the present invention comprises the following steps:

[0006] S1. Obtain the parameters of the tower and the drone;

[0007] S2. Draw the three-dimensional model of the tower and the UAV according to the parameters of the tower and the UAV;

[0008] S3. The maximum electric field intensity and magnetic induction intensity of the UAV surface at different positions of the transmission line are extracted by three-dimensional finite element method;

[0009] S4. Design basic fitting functions to determine the semi-empirical calculation formulas for the maximum distortion electric field strength and magnetic induction strength of the UAV;

[0010] S5. Based on the safety threshold of the drone's electromagnetic field and the field of view of the drone's onboard camera, determine the drone's high-altitude safety monitoring hovering position;

[0011] S6. Use the ant colony algorithm to solve the optimal trajectory of the drone's high-altitude safety monitoring hovering position.

[0012] The parameters of poles and drones include pole size, drone size, insulator type, pole material, drone material, split conductor outer diameter and split spacing.

[0013] The step S3 includes extracting the maximum electric field strength and magnetic induction strength on the surface of the UAV at different positions of the transmission line by the three-dimensional finite element method; using electromagnetic field simulation software to extract the electric field strength at the same position before and after the introduction of the UAV, selecting a voltage excitation source in the electrostatic field solver, and when the voltage of the A-phase conductor reaches the amplitude, calculating and assigning the voltages of each phase of the 220kV three-phase transmission line, and extracting the maximum electric field strength on the surface of the UAV at different positions of the transmission line; selecting a current excitation source in the static magnetic field solver, determining the current of each phase conductor according to the economic transmission capacity of the 220kV AC transmission line, and extracting the maximum magnetic induction strength of the UAV fuselage at different positions of the transmission line.

[0014] The step S4 includes setting a semi-empirical calculation formula for the maximum distortion electric field strength of the drone and setting a semi-empirical calculation formula for the maximum magnetic induction strength of the drone.

[0015] The semi-empirical calculation formula for the maximum distortion electric field strength of a UAV includes:

[0016]

[0017] in, represents the maximum distorted electric field strength on the surface of the drone; x represents the distance between the drone and the split wire; Indicates the horizontal angle between the drone and the split conductor.

[0018] The semi-empirical calculation formula for the maximum magnetic induction intensity of a drone includes:

[0019]

[0020] in, Indicates the maximum magnetic induction intensity of the drone body; x indicates the distance between the drone and the split wire; Indicates the horizontal angle between the drone and the split conductor.

[0021] The step S5 includes determining the high-altitude safety monitoring hovering position of the drone based on the safety threshold of the drone electromagnetic field and the field of view of the drone onboard camera; the field of view of the camera is:

[0022]

[0023] Among them, R is the shooting range of the drone's airborne camera; L is the distance between the drone and the target to be inspected; α is the viewing angle of the drone's airborne camera.

[0024] The step S6 includes, in the ant colony algorithm, assuming that the total number of ants is m, and ant k moves from the current position i(x i ,y i ) moves to the next position j(x j ,y j ) is determined by the pheromone content on the path between the current position i and the next position j. At the current time t, the state transition probability of ant k moving from the current position i to the next position j is for:

[0025]

[0026] Among them, γ is the pheromone factor; β is the heuristic function factor; s represents the location that ant k may move to in the next step; d k is the set of possible locations that ant k may move to in the next step; τ ij (t) is the pheromone concentration on the path between the current position i and the next position j at the current time t; τ is (t) represents the pheromone concentration on the path between the current position i at the current time t and the position s that may be reached in the next step; η is (t) represents the heuristic factor of the ant moving from the current position i to the position s that it may reach in the next step at the current time t; η ij (t) is the heuristic factor for the ant to move from the current position i to the next position j at the current time t, Among them, d ij Represents the Euclidean distance from the current position i to the next position j:

[0027] After each generation of path search is completed, the pheromones in the entire optimization set are updated. The pheromone update formula is:

[0028] τ ij (t+1)=(1-ρ)τ ij (t)+Δτ ij

[0029] Among them, τ ij (t+1) represents the pheromone concentration on the path between the current position i and the next position j at the next time t+1; ρ represents the pheromone volatility coefficient; Δτ ij To increase the pheromone:

[0030]

[0031] in, is the amount of pheromone released by ant k on the path between the current position i and the next position j; k∈[1,m], m represents the total number of ants; the ant cycle model is used to statistically update the amount of pheromone released by ant k on the path between the current position i and the next position j The ant week model is as follows:

[0032]

[0033] Among them, Q is the pheromone enhancement coefficient, which represents the influence of ant pheromone on the algorithm. The larger Q is, the greater the influence of pheromone is. k is the total length of the path taken by ant k, tour k Represents the set of positions represented by the current path searched by ant k, tour(i,j) represents the path between the current position i and the next position j; after the iteration converges, the route map with the shortest total length of the path taken by ant k is output, which is the optimal trajectory for the drone to traverse the high-altitude safety monitoring hovering position.

[0034] The anti-interference high-voltage transmission line UAV safety monitoring trajectory planning method provided by the present invention applies a semi-empirical calculation method to the calculation of the maximum distorted electric field strength and magnetic induction strength on the surface of a high-voltage transmission line inspection UAV, accurately calculates the distorted electric field strength and magnetic induction strength of the UAV around the transmission line, ensures the flight safety of the UAV, reduces the amount of calculation and calculation time, is convenient and fast, and is conducive to engineering applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a schematic diagram of the process of the present invention.

[0036] Figure 2 Schematic diagram of the tower assembly dimensions of an embodiment of the present invention.

[0037] Figure 3 Schematic diagram of a three-dimensional simulation model of a tower according to an embodiment of the present invention.

[0038] Figure 4 Schematic diagram of a three-dimensional simulation model of a UAV according to an embodiment of the present invention.

[0039] Figure 5 a is a schematic diagram comparing the maximum electric field strength on the surface of the drone at various angles obtained by a semi-empirical calculation formula when the distance between the drone and the split conductor in an embodiment of the present invention is 1 m and the finite element simulation results.

[0040] Figure 5b is a schematic diagram comparing the maximum electric field intensity on the surface of the drone at various angles obtained by a semi-empirical calculation formula when the distance between the drone and the split conductor in an embodiment of the present invention is 1.5 m and the finite element simulation results.

[0041] Figure 6 a is a schematic diagram comparing the maximum magnetic induction intensity of the drone body at various angles obtained by a semi-empirical calculation formula when the distance between the drone and the split conductor in an embodiment of the present invention is 1 m and the finite element simulation results.

[0042] Figure 6 b is a schematic diagram comparing the maximum magnetic induction intensity of the drone body at various angles obtained by a semi-empirical calculation formula when the distance between the drone and the split conductor in an embodiment of the present invention is 1.5 m and the finite element simulation results.

[0043] Figure 7 Schematic diagram of drone inspection according to an embodiment of the present invention.

[0044] Figure 8 Schematic diagram of the optimal trajectory of a UAV according to an embodiment of the present invention. DETAILED DESCRIPTION

[0045] Aiming at the optimal trajectory planning of UAV high-altitude safety monitoring, the present invention provides a semi-empirical determination method for the hovering position of UAV high-altitude safety monitoring of high-voltage power transmission lines considering electromagnetic field interference, and solves the optimal trajectory of the UAV traversing the hovering position through the ant colony algorithm. Considering the electric field distortion generated by the UAV around the transmission line, the semi-empirical calculation formula of the maximum distortion electric field intensity and magnetic induction intensity of the UAV is given, which saves calculation time compared with the finite element simulation method and is more suitable for practical engineering applications.

[0046] like Figure 1 The flowchart of the method of the present invention is as follows: the anti-interference high-voltage transmission line UAV safety monitoring trajectory planning method provided by the present invention comprises the following steps:

[0047] S1. Obtain the parameters of the tower and the drone;

[0048] S2. Draw the three-dimensional model of the tower and the UAV according to the parameters of the tower and the UAV;

[0049] S3. The maximum electric field intensity and magnetic induction intensity of the UAV surface at different positions of the transmission line are extracted by three-dimensional finite element method;

[0050] S4. Design basic fitting functions to determine the semi-empirical calculation formulas for the maximum distortion electric field strength and magnetic induction strength of the UAV;

[0051] S5. Based on the safety threshold of the drone's electromagnetic field and the field of view of the drone's onboard camera, determine the drone's high-altitude safety monitoring hovering position;

[0052] S6. Use the ant colony algorithm to solve the optimal trajectory of the drone's high-altitude safety monitoring hovering position.

[0053] In the step S1, the parameters of the tower and the drone include the tower size, the drone size, the insulator type, the tower material, the drone material, the split conductor outer diameter and the split spacing.

[0054] In the step S2, in this embodiment, three-dimensional visualization solid simulation software is used to draw the three-dimensional model of the tower and the UAV. Based on the selection of the tower and the UAV, the three-dimensional visualization solid simulation software is used to draw the three-dimensional simulation model of the transmission line tower and the UAV. In order to simplify the calculation, the hardware such as the wire clamp and the phase spacer is ignored.

[0055] The step S3 includes extracting the maximum electric field strength and magnetic induction strength of the surface of the drone at different positions of the transmission line by the three-dimensional finite element method. The electric field strength at the same position before and after the introduction of the drone is extracted by using ANSYS / Maxwell electromagnetic field simulation software, and the voltage excitation source is selected in the electrostatic field solver. When the voltage of the A-phase conductor reaches the amplitude, the voltage of each phase of the 220kV three-phase transmission line is calculated and assigned, and the maximum electric field strength of the surface of the drone at different positions of the transmission line is extracted. The current excitation source is selected in the static magnetic field solver, and the current of each phase conductor can be determined according to the economic transmission capacity of the 220kV AC transmission line. After assigning the relevant parameters, the maximum magnetic induction strength of the drone fuselage at different positions of the transmission line can be extracted.

[0056] The step S4 includes setting a semi-empirical calculation formula for the maximum distortion electric field strength of the drone and setting a semi-empirical calculation formula for the maximum magnetic induction strength of the drone:

[0057] The semi-empirical calculation formula for the maximum distortion electric field strength of a UAV includes:

[0058]

[0059] in, represents the maximum distorted electric field strength on the surface of the drone, in this embodiment, the unit is V / m; x represents the distance between the drone and the split wire, in meters; Indicates the horizontal angle between the drone and the split conductor, in degrees;

[0060] The semi-empirical calculation formula for the maximum magnetic induction intensity of a drone includes:

[0061]

[0062] in, It represents the maximum magnetic induction intensity of the drone body, in μT; x represents the distance between the drone and the split wire; Indicates the horizontal angle between the drone and the split conductor.

[0063] The step S5 includes determining the drone's high-altitude safety monitoring hovering position based on the drone's electromagnetic field safety threshold and the drone's onboard camera's field of view. In order to avoid electrorheological effects on the drone's surface material, the maximum electric field strength cannot exceed 1×10 6 V / m. The magnetometer used for UAV heading measurement can withstand interference of 3-4 times the magnitude of the geomagnetic field. In order to retain a certain safety margin, the magnetic field interference to the UAV generally does not exceed 180μT. Based on the safety threshold of the UAV electromagnetic field, the safe distance between the UAV and the power transmission line can be determined. Combined with the field of view of the UAV's onboard camera, the high-altitude safety monitoring hovering position of the UAV can be determined. The camera's field of view is:

[0064]

[0065] Among them, R is the shooting range of the drone's airborne camera, in meters; L is the distance between the drone and the target to be inspected, in meters; α is the viewing angle of the drone's airborne camera, in degrees;

[0066] Based on the safety threshold of the drone's electromagnetic field, the safe distance between the drone and the power transmission line can be calculated using a semi-empirical formula, thereby obtaining the shooting field of view of the drone's camera. Taking into account the full coverage of the drone's inspection operation shooting range, the drone's high-altitude safety monitoring hovering position can be determined.

[0067] The flight speed of the drone inspection operation is constant. The shorter the path, the shorter the flight time, the less power consumption, and the higher the inspection efficiency. Therefore, the shortest flight path that the drone traverses the safety monitoring hovering and shooting positions is called the optimal trajectory. There are many paths to traverse these hovering positions. It is difficult to solve the optimal trajectory by relying on exhaustive enumeration to list and compare them one by one. The workload is huge. The ant colony algorithm is a new type of simulated evolutionary algorithm that uses the optimization ability demonstrated by ant colonies in the process of searching for food sources to solve optimization problems in discrete systems. The step S6 includes, in the ant colony algorithm, assuming that the total number of ants is m, and ant k moves from the current position i (x i ,y i ) moves to the next position j(x j ,y j ) is determined by the pheromone content on the path between the current position i and the next position j. At the current time t, the state transition probability of ant k moving from the current position i to the next position j is for:

[0068]

[0069] Among them, γ is the pheromone factor, which indicates the importance of pheromone on the path between the current position i and the next position j. The larger γ is, the greater the influence of pheromone is. β is the heuristic function factor, which represents the attraction of the next position j to the current position i. The larger β is, the greater the attraction is. s indicates the position that ant k may move to next. d k is the set of possible locations that ant k may move to in the next step; τ ij (t) is the pheromone concentration on the path between the current position i and the next position j at the current time t; τ is (t) represents the pheromone concentration on the path between the current position i at the current time t and the position s that may be reached in the next step; η is (t) represents the heuristic factor of the ant moving from the current position i to the position s that it may reach in the next step at the current time t; η ij (t) is the heuristic factor for the ant to move from the current position i to the next position j at the current time t, Among them, d ij Represents the Euclidean distance from the current position i to the next position j:

[0070] After each generation of path search is completed, the pheromones in the entire optimization set must be updated. The pheromone update formula is:

[0071] τ ij (t+1)=(1-ρ)τ ij (t)+Δτ ij

[0072] Among them, τ ij (t+1) represents the pheromone concentration on the path between the current position i and the next position j at the next time t+1; ρ represents the pheromone volatility coefficient, and 0<ρ<1. The larger ρ is, the faster the pheromone left by the ant evaporates; Δτ ij To increase the pheromone:

[0073]

[0074] in, is the amount of pheromone released by ant k on the path between the current position i and the next position j; k∈[1,m], m represents the total number of ants; the ant cycle model is used to statistically update the amount of pheromone released by ant k on the path between the current position i and the next position j The ant week model is as follows:

[0075]

[0076] Among them, Q is the pheromone enhancement coefficient, which represents the influence of ant pheromone on the algorithm. The larger Q is, the greater the influence of pheromone is. k is the total length of the path taken by ant k, tour k Represents the set of positions represented by the path currently searched by ant k, and tour(i,j) represents the path between the current position i and the next position j.

[0077] The specific implementation process includes the following steps:

[0078] like Figure 2 Schematic diagram of the tower assembly dimensions of an embodiment of the present invention. Figure 3 Schematic diagram of a three-dimensional simulation model of a tower according to an embodiment of the present invention. Figure 4 The figure is a schematic diagram of the three-dimensional simulation model of the UAV of the embodiment of the present invention. Step 1: The inspection operation of the UAV on the transmission line mainly includes the tower inspection and the corridor inspection. Since the hovering point of the UAV for the corridor inspection is relatively single, this embodiment mainly determines the hovering point for the tower inspection. The tower selects a 220kV AC Z2K cathead straight tower. The tower material is angle steel. The tower geometric dimensions are shown in the figure below. Figure 2 As shown. Glass insulators are used for the insulator string, and the transmission line uses a two-split conductor made of steel core aluminum stranded wire. The surface material of the drone is carbon fiber, with a relative dielectric constant of 100, a conductivity of 1330S / m, and a relative magnetic permeability of 1.

[0079] Step 2: Use 3D visualization solid simulation software to draw the 3D simulation model of the transmission line and the UAV. The basic parameters of the transmission line and the UAV are shown in Table 1. Table 1 is the basic parameters of the 220kV transmission line cathead tower and the UAV.

[0080] Table 1

[0081] name Numeric Tower Type <![CDATA[Z2K Cathead Tower]]> Exhalation height / m 35.7 Tower Materials Angle steel Number of wire splits 2 Split Wire splitting distance / mm 450 Wire outer diameter / mm 26.8 Wire material Steel core aluminum stranded wire Insulator Type Single vertical type Insulator material Glass Insulator structure height / mm 140 Number of insulators 14 Curvature radius of pressure equalizing ring / mm 200 UAV fuselage length / mm 322 UAV fuselage width / mm 242 UAV fuselage height / mm 84

[0082] according to Figure 2 The geometric dimensions of the Z2K cathead tower shown in the figure, as well as the types and dimensions of hardware such as split conductors and grading rings, are modeled in three dimensions. The three-dimensional simulation model of the transmission line is as follows: Figure 3 As shown in Table 1, since the line hardware and interphase spacers have little effect on the electric and magnetic fields in space, in order to simplify the calculation, the hardware such as wire clamps and interphase spacers are ignored. Based on the UAV fuselage dimensions shown in Table 1, a three-dimensional simulation model of the UAV is established as shown in Table 1. Figure 4 shown.

[0083] Step 3: Extract the maximum electric field strength and magnetic induction strength on the drone surface at different locations:

[0084] The three-phase conductors A, B, and C form a 220kV three-phase voltage system. The formula for the change of each phase voltage over time is as follows:

[0085]

[0086] Among them, u A Indicates the voltage of phase A; u B Indicates the voltage of phase B; u C Indicates the C phase voltage; U m represents amplitude; ω represents angular frequency; t represents the current moment; at a certain moment, the voltage of any phase reaches its peak value, the voltage of the other two phases is 1 / 2 negative peak value, and the spatial field intensity reaches its maximum value. When the voltage of the A phase (i.e. the middle phase) conductor reaches the maximum value, the voltage of each phase is:

[0087]

[0088] In the electrostatic field module of the ANSYS / Maxwell electromagnetic field simulation software, a voltage excitation of 179.63 kV is applied to the middle phase conductor of the AC transmission line, and a voltage excitation of -89.81 kV is applied to the side phase conductor. The drones are placed at different positions near the split conductors, and the relevant parameters are set and calculated. The maximum electric field strength on the surface of the drone at each position is shown in Table 2. Table 2 shows the maximum electric field strength on the surface of the drone at different positions.

[0089] Table 2

[0090]

[0091] The excitation used in static magnetic field simulation calculation is current excitation. The current of the AC transmission line needs to be determined according to the transmission capacity of the line. The economic transmission capacity of my country's 220kV AC transmission line is 300MW. When the mid-phase voltage reaches the peak value, the mid-phase current also reaches the peak value. The current of each phase can be calculated as:

[0092]

[0093] Among them, i A Indicates the current of phase A; i B Indicates the B phase current; i C Represents the C-phase current; the UAV is placed at different positions near the split conductor, and after the relevant parameters are set, the calculation is performed to obtain the maximum magnetic induction intensity of the UAV body at each position as shown in Table 3. Table 3 shows the maximum magnetic induction intensity of the UAV body at different positions.

[0094] Table 3

[0095]

[0096] Step 4: Select a suitable basic fitting function to determine the semi-empirical calculation formula for the maximum distortion electric field strength and magnetic induction strength of the drone:

[0097] Based on the maximum electric field intensity and magnetic induction intensity on the surface of the drone extracted in step 3, a suitable fitting function is selected to determine the semi-empirical calculation formula as follows:

[0098] The semi-empirical calculation formula for the maximum distortion electric field strength of a UAV includes:

[0099]

[0100] in, represents the maximum distorted electric field strength on the surface of the drone, in this embodiment, the unit is V / m; x represents the distance between the drone and the split wire, in meters; Indicates the horizontal angle between the drone and the split conductor, in degrees;

[0101] The semi-empirical calculation formula for the maximum magnetic induction intensity of a drone includes:

[0102]

[0103] in, It represents the maximum magnetic induction intensity of the drone body, in μT; x represents the distance between the drone and the split wire; Indicates the horizontal angle between the drone and the split conductor.

[0104] like Figure 5 a is a schematic diagram showing the comparison between the maximum electric field intensity of the drone surface at each angle obtained by the semi-empirical calculation formula and the finite element simulation result when the distance between the drone and the split wire in the embodiment of the present invention is 1m. Figure 5 b is a schematic diagram comparing the maximum electric field intensity on the surface of the drone at each angle obtained by the semi-empirical calculation formula and the finite element simulation result when the distance between the drone and the split wire in the embodiment of the present invention is 1.5m. Figure 6 a is a schematic diagram showing the comparison between the maximum magnetic induction intensity of the drone fuselage at each angle obtained by a semi-empirical calculation formula and the finite element simulation result when the distance between the drone and the split conductor in the embodiment of the present invention is 1m. Figure 6b is a schematic diagram comparing the maximum magnetic induction intensity of the drone body at various angles obtained by the semi-empirical calculation formula when the distance between the drone and the split conductor is 1.5m in the embodiment of the present invention and the finite element simulation results. Step 5. In order to verify whether the semi-empirical calculation formula determined in step 4 is applicable to the calculation of other tower types at the same voltage level, a 220kV AC transmission line wine glass tower drone inspection simulation model is constructed. Similar to the method described in step 3, the maximum electric field intensity and magnetic induction intensity on the drone surface at different positions are extracted respectively, and compared with the results obtained by the semi-empirical calculation formula. When the drone is 1m and 1.5m away from the transmission conductor, the comparison diagrams of the maximum electric field intensity on the drone surface obtained by the semi-empirical calculation formula and the finite element simulation calculation are shown as follows: Figure 5 a and Figure 5 As shown in Figure 2, the maximum error of the results obtained by the above two methods is 3.4%, which verifies the feasibility of the semi-empirical calculation formula for the maximum distorted electric field strength of the UAV. When the UAV is 1m and 1.5m away from the transmission line, the comparison diagram of the maximum magnetic induction intensity of the UAV body obtained by the semi-empirical calculation formula and the finite element simulation calculation is shown in Figure 2. Figure 6 a and Figure 6 As shown in Figure 2, the maximum error of the results obtained by the above two methods is 2.7%, which verifies the feasibility of the semi-empirical calculation formula for the maximum magnetic induction intensity of the UAV.

[0105] Determination of drone high-altitude safety monitoring hovering position:

[0106] To determine the high-altitude safety monitoring hovering position of the drone, we must first determine the safe distance between the drone and the transmission line. Based on the semi-empirical calculation formula obtained in step 4 and combined with the safety threshold of the drone's electromagnetic field, we can calculate the safe distance of the drone at different angles of the transmission line. Combining the semi-empirical calculation formula of the drone's maximum distorted electric field strength and the semi-empirical calculation formula of the drone's maximum magnetic induction strength, we can calculate that the safe distance between the drone and the transmission line is 1.34m. In order to retain a certain safety margin, when actually determining the drone's hovering position, the safe distance between the drone and the transmission line is set to 1.5m.

[0107] like Figure 7 The following is a schematic diagram of a drone inspection in an embodiment of the present invention. The main inspection targets of drone inspections on transmission towers include insulator strings, voltage-equalizing rings, the connection between insulator strings and towers, and ground wires. When a drone inspects a certain surface of a transmission tower, its flight trajectory can be approximately considered to be within a plane. To simplify the problem, the targets to be inspected are projected onto a plane. Taking into account the flight safety of the drone and the full coverage of the shooting range, the drone is set to fly in a plane 1.5m away from the insulator strings and other inspection targets. The camera's field of view is 1.35m. The drone inspection schematic is shown in the figure. Figure 7As shown, 1 represents an insulator, 2 represents the field of view of the drone, 3 represents the drone, and 4 represents the dangerous area.

[0108] The hovering position of the UAV for high-altitude safety monitoring is based on the principle of being as close to the inspection target as possible on the basis of safety. The coordinates of the hovering shooting point can be determined according to the safe flight area of ​​the UAV and the field of view of the onboard camera as shown in Table 4. Table 4 shows the hovering position of the UAV for high-altitude safety monitoring.

[0109] Table 4

[0110] point coordinate point coordinate 1 (-4.9,42.2) 7 (-6,35.7) 2 (4.9,42.2) 8 (-6,35.1) 3 (0,39.7) 9 (-3.2,35.7) 4 (0,39.1) 10 (3.2,35.7) 5 (-4.1,38.2) 11 (6,35.7) 6 (4.1,38.2) 12 (6,35.1)

[0111] like Figure 8 Schematic diagram of the optimal trajectory of the UAV of the embodiment of the present invention. Step 6, set the number of iterations of path search optimization to 200, the number of ants to 200, the pheromone factor α to 1.5, the heuristic function factor β to 5, the pheromone volatility coefficient ρ to 0.5, and the pheromone enhancement coefficient Q to 10. After the relevant parameters are assigned, the number of ants corresponds to the object of path optimization. For example, if the number of ants on a certain path is the largest, then this path is the optimal path. Optimize the flight trajectory of the UAV's high-altitude safety monitoring hovering position in Table 4, and get the optimal trajectory as shown in Figure 8 As shown in the figure, the optimal trajectory of the UAV inspection operation on the cathead tower of 220kV high-voltage AC transmission line is: 8→7→1→5→4→3→2→6→11→12→10→9, and the shortest distance is 41.5m.

Claims

1. A method for anti-interference high-voltage transmission line UAV safety monitoring trajectory planning, characterized in that The steps include: S1. Obtain the parameters of the tower and the drone; S2. Draw the three-dimensional model of the tower and the UAV according to the parameters of the tower and the UAV; S3. The maximum electric field intensity and magnetic induction intensity of the UAV surface at different positions of the transmission line are extracted by three-dimensional finite element method; S4. Design basic fitting functions to determine the semi-empirical calculation formulas for the maximum distortion electric field strength and magnetic induction strength of the UAV; Including setting a semi-empirical calculation formula for the maximum distortion electric field strength of the drone, and setting a semi-empirical calculation formula for the maximum magnetic induction strength of the drone; Among them, the semi-empirical calculation formula for the maximum distortion electric field strength of the UAV includes: in, represents the maximum distorted electric field strength on the surface of the drone; x represents the distance between the drone and the split wire; Indicates the horizontal angle between the drone and the split conductor; The semi-empirical calculation formula for the maximum magnetic induction intensity of a drone includes: in, Indicates the maximum magnetic induction intensity of the drone body; x indicates the distance between the drone and the split wire; Indicates the horizontal angle between the drone and the split conductor; S5. Based on the safety threshold of the drone's electromagnetic field and the field of view of the drone's onboard camera, determine the drone's high-altitude safety monitoring hovering position; S6. Use the ant colony algorithm to solve the optimal trajectory of the drone's high-altitude safety monitoring hovering position.

2. The anti-interference high-voltage transmission line UAV safety monitoring trajectory planning method according to claim 1 is characterized in that The parameters of poles and drones include pole size, drone size, insulator type, pole material, drone material, split conductor outer diameter and split spacing.

3. The anti-interference high-voltage transmission line UAV safety monitoring trajectory planning method according to claim 2 is characterized in that The step S3 includes extracting the maximum electric field strength and magnetic induction strength on the surface of the UAV at different positions of the transmission line by the three-dimensional finite element method; using electromagnetic field simulation software to extract the electric field strength at the same position before and after the introduction of the UAV, selecting a voltage excitation source in the electrostatic field solver, and when the voltage of the A-phase conductor reaches the amplitude, calculating and assigning the voltages of each phase of the 220kV three-phase transmission line, and extracting the maximum electric field strength on the surface of the UAV at different positions of the transmission line; selecting a current excitation source in the static magnetic field solver, determining the current of each phase conductor according to the economic transmission capacity of the 220kV AC transmission line, and extracting the maximum magnetic induction strength of the UAV fuselage at different positions of the transmission line.

4. The anti-interference high-voltage transmission line UAV safety monitoring trajectory planning method according to claim 1 is characterized in that The step S5 includes determining the high-altitude safety monitoring hovering position of the drone based on the safety threshold of the drone electromagnetic field and the field of view of the drone onboard camera; the field of view of the camera is: Among them, R is the shooting range of the drone's airborne camera; L is the distance between the drone and the target to be inspected; α is the viewing angle of the drone's airborne camera.

5. The anti-interference high-voltage transmission line UAV safety monitoring trajectory planning method according to claim 4 is characterized in that The step S6 includes, in the ant colony algorithm, assuming that the total number of ants is m, and ant k moves from the current position i(x i ,y i ) moves to the next position j(x j ,y j ) is determined by the pheromone content on the path between the current position i and the next position j. At the current time t, the state transition probability of ant k moving from the current position i to the next position j is for: Among them, γ is the pheromone factor; β is the heuristic function factor; s represents the location that ant k may move to in the next step; d k is the set of possible locations that ant k may move to in the next step; τ ij (t) is the pheromone concentration on the path between the current position i and the next position j at the current time t; τ is (t) represents the pheromone concentration on the path between the current position i at the current time t and the position s that may be reached in the next step; η is (t) represents the heuristic factor of the ant moving from the current position i to the position s that it may reach in the next step at the current time t; η ij (t) is the heuristic factor for the ant to move from the current position i to the next position j at the current time t, Among them, d ij Represents the Euclidean distance from the current position i to the next position j: After each generation of path search is completed, the pheromones in the entire optimization set are updated. The pheromone update formula is: t ij (t+1)=(1-ρ)τ ij (t)+Δτ ij Among them, τ ij (t+1) represents the pheromone concentration on the path between the current position i and the next position j at the next time t+1; ρ represents the pheromone volatility coefficient; Δτ ij To increase the pheromone: in, is the amount of pheromone released by ant k on the path between the current position i and the next position j; k∈[1,m], m represents the total number of ants; the ant cycle model is used to statistically update the amount of pheromone released by ant k on the path between the current position i and the next position j The ant week model is as follows: Among them, Q is the pheromone enhancement coefficient, which represents the influence of ant pheromone on the algorithm. The larger Q is, the greater the influence of pheromone is. k is the total length of the path taken by ant k, tour k Represents the set of positions represented by the current path searched by ant k, tour(i,j) represents the path between the current position i and the next position j; after the iteration converges, the route map with the shortest total length of the path taken by ant k is output, which is the optimal trajectory for the drone to traverse the high-altitude safety monitoring hovering position.

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