Unmanned aerial vehicle inspection path planning method and system based on autonomous power taking from overhead line

Abstract

This invention discloses a method and system for drone inspection path planning based on autonomous power supply from overhead lines. The method includes: constructing a directed inspection graph of a radial power distribution network; obtaining the time-varying available charging power of each directed inspection arc at different times; determining the cumulative task time and flight energy consumption constraints of the directed inspection arc during the entire inspection phase; constructing a drone charging decision to obtain drone battery state constraints; introducing full-cycle battery safety constraints for the drone; and constructing a joint optimization objective model based on minimizing the cumulative task time of the directed inspection arc during the entire inspection phase as the optimization objective to achieve drone inspection path planning. This invention can improve drone inspection efficiency while satisfying battery safety constraints and inspection topology constraints. As a method and system for drone inspection path planning based on autonomous power supply from overhead lines, this invention can be widely applied in the field of drone scheduling technology.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) scheduling technology, and in particular to a method and system for planning inspection paths for UAVs based on autonomous power supply from overhead lines. Background Technology

[0002] With the continuous expansion of power line scale, overhead lines are widely distributed in complex environments such as mountains, canyons, and rivers. Conventional manual inspection methods suffer from high labor intensity, high operational risks, and low efficiency. Drone inspection, due to its advantages of high mobility, wide coverage, and flexible deployment, has become an important means of intelligent power line inspection. However, limited by the fixed onboard battery capacity of drones, their endurance is insufficient for long-distance, large-area line inspection tasks, typically requiring reliance on ground charging stations, battery swapping facilities, or mobile energy replenishment platforms. These methods not only heavily rely on ground infrastructure but also suffer from high deployment costs and insufficient coverage flexibility in scenarios with limited road conditions, complex terrain, or dynamically changing inspection areas. Overhead power lines themselves possess continuous power transmission characteristics, providing potential energy replenishment conditions for drones operating along the lines. Existing research focuses primarily on the design of drone power collection devices, while considering less the distribution network topology, the time-varying characteristics of branch power flow, and the spatiotemporal differences in available charging power across different lines, making it difficult to support efficient autonomous charging decisions for drones during actual inspection processes. Summary of the Invention

[0003] To address the aforementioned technical problems, the present invention aims to provide a method and system for drone inspection path planning based on autonomous power supply from overhead lines, which can improve drone inspection efficiency while meeting battery safety constraints and inspection topology constraints.

[0004] The first technical solution adopted in this invention is: a drone inspection path planning method based on autonomous power supply from overhead lines, comprising the following steps: Based on the physical overhead lines, a set of directed inspection arcs is determined, binary topology reachability parameters and binary decision variables are defined, and UAV inspection path constraints are introduced to construct a directed inspection graph of the radial distribution network. Based on the directed inspection diagram of the radial distribution network, the correspondence between branch current and available charging power of overhead line is constructed to obtain the time-varying available charging power of each directed inspection arc at different times. Based on the directed inspection graph of the radial distribution network, the inherent flight time of a single directed inspection arc is calculated, and the cumulative task time and flight energy consumption constraints of the entire inspection phase of the directed inspection arc are determined. By constructing a drone charging decision and combining the inherent flight time of a single directed inspection arc with the time-varying available charging power of each directed inspection arc at different times, the drone battery state constraints are obtained. By introducing full-cycle battery safety constraints for UAVs, and taking the minimum cumulative task time of the entire inspection phase of the directed inspection arc as the optimization objective, a joint optimization objective model is constructed by combining UAV battery state constraints and flight energy consumption constraints of the entire inspection phase of the directed inspection arc to realize UAV inspection path planning.

[0005] Furthermore, the step of determining the set of directed inspection arcs based on physical overhead lines, defining binary topology reachability parameters and binary decision variables, and introducing UAV inspection path constraints to construct a directed inspection graph of the radial distribution network specifically includes: Determine the number of physical overhead lines and expand each physical overhead line into two directed inspection arcs in opposite directions to form a set of directed inspection arcs; Based on the set of directed inspection arcs, the entire inspection process is divided into a set of stages, and binary topological reachability parameters and binary decision variables are defined. Based on binary topology reachability parameters and binary decision variables, a directed inspection graph of a radial power distribution network is constructed by introducing UAV inspection path constraints.

[0006] Furthermore, the step of constructing the correspondence between branch currents and available charging power of overhead lines based on the directed inspection diagram of the radial distribution network, and obtaining the time-varying available charging power of each directed inspection arc at different times, specifically includes: Based on the directed inspection diagram of the radial distribution network, each directed inspection arc corresponds to an overhead branch of the distribution network. The available charging power of the directed inspection arc and the current amplitude of the overhead branch of the distribution network are defined to construct a linear mapping relationship between charging power and branch current. Based on the directed inspection diagram of the radial distribution network, the complex power equations of the overhead branches of the distribution network are determined and derived to obtain the current phasors of the overhead branches of the distribution network. Based on the current phasor of the overhead branch of the distribution network, the power flow of the distribution network is iteratively calculated at different times according to the entire inspection cycle, and the current amplitude of the overhead branch of the distribution network corresponding to each directed inspection arc is solved at different times. Substituting the current amplitude into the linear mapping relationship between charging power and branch current, the time-varying available charging power of each directed inspection arc at different times is obtained.

[0007] Furthermore, the step of calculating the inherent flight time of a single directed inspection arc based on the directed inspection graph of the radial distribution network, and determining the cumulative task time and flight energy consumption constraints of the entire inspection phase of the directed inspection arc, specifically includes: Based on the set of directed inspection arcs in the directed inspection graph of the radial distribution network, the physical length of the directed inspection arc is obtained. Combined with the fixed inspection flight speed of the UAV, the inherent flight time of a single directed inspection arc is calculated. For any directed inspection arc, obtain the cumulative completion time of the previous inspection stage, and combine it with the inherent flight time and charging time to determine the cumulative task time of the entire inspection stage of the directed inspection arc. Based on the environmental parameters of the UAV, the propulsion power of the UAV is determined. Combined with the fixed inspection flight speed of the UAV, the flight energy consumption constraints for the entire inspection phase of the directed inspection arc are determined.

[0008] Furthermore, the step of constructing the UAV charging decision and combining the inherent flight time of a single directed inspection arc with the time-varying available charging power of each directed inspection arc at different times to obtain the UAV battery state constraints specifically includes: Define binary decision variables for drone charging, and introduce constraints on drone charging location, upper and lower limits of drone charging duration, and number of drone charging times in a single stage to construct drone charging decision; Based on the drone charging decision, the inherent flight time of a single directed inspection arc and the time-varying available charging power of each directed inspection arc at different times are combined to obtain the drone battery state constraints.

[0009] Furthermore, the mathematical model of the UAV battery state includes the battery power remaining from the previous inspection stage, the total power obtained from charging in this stage, and the total energy consumed during the entire flight in this stage. The total power obtained from charging in this stage is calculated by integrating the time-varying available charging power of each directed inspection arc at different times, and the total energy consumed during the entire flight in this stage is determined by the inherent flight time of a single directed inspection arc.

[0010] Furthermore, the introduction of full-cycle battery safety constraints for the UAV, with the optimization objective being to minimize the cumulative task time during the entire inspection phase of the directed inspection arc, combined with UAV battery state constraints and flight energy consumption constraints during the entire inspection phase of the directed inspection arc, constructs a joint optimization objective model to achieve the UAV inspection path planning step, which specifically includes: Obtain the rated maximum capacity of the drone battery, set the minimum safe power threshold of the drone battery, and construct the full-cycle battery safety constraints for the drone. The optimization objective is to minimize the cumulative task time during the entire inspection phase of the directed inspection arc. A joint optimization objective model is constructed by combining the full-cycle battery safety constraints of the UAV, the battery status constraints of the UAV, and the flight energy consumption constraints during the entire inspection phase of the directed inspection arc. Based on the joint optimization objective model, we realize the unmanned aerial vehicle (UAV) inspection path planning.

[0011] The second technical solution adopted in this invention is: a drone inspection path planning system based on autonomous power supply from overhead lines, comprising: The first module is used to determine the set of directed inspection arcs based on the physical overhead lines, define the binary topology reachability parameters and binary decision variables, and introduce UAV inspection path constraints to construct a directed inspection graph of the radial distribution network. The second module is used to construct the correspondence between branch current and available charging power of overhead lines based on the directed inspection diagram of the radial distribution network, and to obtain the time-varying available charging power of each directed inspection arc at different times. The third module is used to calculate the inherent flight time of a single directed inspection arc based on the directed inspection graph of the radial distribution network, and to determine the cumulative task time and flight energy consumption constraints of the entire inspection phase of the directed inspection arc. The fourth module is used to construct the drone charging decision and combine the inherent flight time of a single directed inspection arc with the time-varying available charging power of each directed inspection arc at different times to obtain the drone battery state constraints. The fifth module introduces full-cycle battery safety constraints for the UAV. Based on minimizing the cumulative task time during the entire inspection phase of the directed inspection arc as the optimization objective, and combining the UAV battery state constraints and the flight energy consumption constraints during the entire inspection phase of the directed inspection arc, a joint optimization objective model is constructed to realize UAV inspection path planning.

[0012] The beneficial effects of the method and system of this invention are as follows: This invention determines the set of directed inspection arcs based on physical overhead lines, defines binary topology reachability parameters and binary decision variables, and introduces UAV inspection path constraints to construct a directed inspection graph of a radial distribution network. Based on the directed inspection graph of the radial distribution network, it constructs the correspondence between branch current and available charging power of overhead lines, obtaining the time-varying available charging power of each directed inspection arc at different times. Overhead lines can be directly used as energy replenishment resources along the line, reducing the need for ground infrastructure deployment, and is suitable for inspection scenarios involving complex terrain and long-distance overhead lines. Based on the directed inspection graph of the radial distribution network, it calculates the inherent flight time of a single directed inspection arc, determines the cumulative task time and flight energy consumption constraints of the entire inspection phase of the directed inspection arc, and can autonomously prioritize the selection of high-availability charging power lines based on the time-varying characteristics of branch power flow, its own remaining power, and the task execution order. Charging is implemented to improve energy replenishment efficiency and shorten the total inspection time. A drone charging decision-making mechanism is constructed, combining the inherent flight time of a single directed inspection arc with the time-varying available charging power of each directed inspection arc at different times to obtain drone battery state constraints. Considering flight time, charging time, flight energy consumption, battery state evolution, and safe operation boundaries, a two-layer solution framework combining outer path generation and inner charging scheduling is adopted to effectively reduce the solution complexity caused by strongly coupled variables in the joint optimization model and improve solution efficiency. A full-cycle battery safety constraint for the drone is introduced. Based on minimizing the cumulative task time of the directed inspection arc throughout the entire inspection phase as the optimization objective, a joint optimization objective model is constructed, combining the drone battery state constraints and the flight energy consumption constraints of the directed inspection arc throughout the entire inspection phase, to realize drone inspection path planning. This enables the minimization of the total drone inspection time while satisfying battery safety constraints and inspection topology constraints, thereby improving inspection efficiency. Attached Figure Description

[0013] Figure 1 This is a flowchart of the steps of the UAV inspection path planning method based on autonomous power supply from overhead lines according to the present invention; Figure 2 This is a structural block diagram of the UAV inspection path planning system based on autonomous power supply from overhead lines, according to the present invention. Figure 3 This is a schematic diagram of a directional inspection of a node-radial power distribution system provided in a specific embodiment of the present invention. Detailed Implementation

[0014] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments. The step numbers in the following embodiments are only for ease of explanation and do not limit the order of the steps. The execution order of each step in the embodiments can be adapted according to the understanding of those skilled in the art.

[0015] Reference Figure 1 This invention provides a method for unmanned aerial vehicle (UAV) inspection path planning based on autonomous power supply from overhead lines. The method includes the following steps: S100. Determine the set of directed inspection arcs based on the physical overhead lines, define the binary topology reachability parameters and binary decision variables, and introduce UAV inspection path constraints to construct a directed inspection graph of the radial distribution network. Specifically, the number of physical overhead lines is determined, and each physical overhead line is expanded into two directed inspection arcs in opposite directions to form a set of directed inspection arcs. Based on the set of directed inspection arcs, the entire inspection process is divided into a set of stages, and binary topology reachability parameters and binary decision variables are defined. Based on the binary topology reachability parameters and binary decision variables, UAV inspection path constraints are introduced to construct a directed inspection graph of the radial distribution network.

[0016] In this embodiment, a directed inspection map corresponding to the radial distribution network is constructed, and each physical overhead line is expanded into two directed inspection arcs in opposite directions. An inspection path model is established in which the UAV starts from the root node, completes the inspection of all lines, and returns to the root node.

[0017] More specifically, let the number of physical overhead lines in a radial distribution network be... Each physical overhead line is expanded into two directed inspection arcs in opposite directions, forming a set of directed inspection arcs. The entire inspection process is divided into a set of stages. Define the reachability parameters of the binary topology. When the inspection arc Can be inspected arc When accessed immediately ;otherwise, Simultaneously define binary decision variables. When the first Each inspection phase accesses the inspection arc. hour, ;otherwise, The drone inspection path satisfies the following constraints: ; ; ; ; ; ; The constraints mentioned above mean that the UAV starts from the designated starting inspection arc and ends at the designated return inspection arc, each inspection stage visits only one directed inspection arc, and the network topology continuity requirement is met between adjacent stages.

[0018] It should also be noted that a radial distribution network is represented as a tree-like network starting from the root node, consisting of one root node and... Each physical overhead line is composed of several terminal nodes, and each line is extended into two directed inspection arcs in opposite directions; when a physical line connects to a parent node... With child nodes At that time, its outward inspection arc is marked with the child node number. The corresponding reverse inspection arc forms a one-to-one correspondence with it, marked as This is used to characterize the arc access sequence during the round-trip inspection process of the UAV.

[0019] S200. Based on the directed inspection diagram of the radial distribution network, the correspondence between branch current and available charging power of overhead line is constructed to obtain the time-varying available charging power of each directed inspection arc at different times. Specifically, based on the directed inspection diagram of the radial distribution network, each directed inspection arc corresponds to one overhead branch of the distribution network. The available charging power of the directed inspection arc and the current amplitude of the overhead branch of the distribution network are defined, and a linear mapping relationship between charging power and branch current is constructed. Based on the directed inspection diagram of the radial distribution network, the complex power equation of the overhead branch of the distribution network is determined and derived to obtain the current phasor of the overhead branch of the distribution network. According to the current phasor of the overhead branch of the distribution network, the power flow iterative calculation of the distribution network is carried out at different times according to the entire inspection cycle, and the current amplitude of the overhead branch of the distribution network corresponding to each directed inspection arc is solved at different times. The current amplitude is substituted into the linear mapping relationship between charging power and branch current to obtain the time-varying available charging power of each directed inspection arc at different times.

[0020] In this embodiment, based on the power flow calculation results of the distribution network branches, a correspondence between the branch current and the available charging power of the overhead line is established to obtain the time-varying available charging power of each inspection arc segment at different times.

[0021] More specifically, the available charging power of an overhead line is determined by the current in the corresponding branch at different times. Let time... Lower inspection arc The available charging power is The corresponding branch current amplitude is Then we have: ; in, Indicates time Inspection arc Available charging power, Indicates the corresponding branch at time [time]. The current amplitude, and These are the charging power model coefficients; the branch current is obtained through power flow calculation of the distribution network.

[0022] Furthermore, the branch current is obtained through power flow calculations. For nodes... To node The branch whose complex power satisfies: ; Accordingly, the branch current phasors satisfy: ; in, Represents a node To node Complex power, Indicates active power. Indicates reactive power. Represents a node The voltage phasor, This represents the branch current phasor. The current amplitude of each branch at different times can be obtained from the above power flow relationships. This allows for the determination of the time-varying available charging power for each inspection arc segment.

[0023] S300. Based on the directed inspection graph of the radial distribution network, calculate the inherent flight time of a single directed inspection arc, and determine the cumulative task time and flight energy consumption constraints of the entire inspection phase of the directed inspection arc. Specifically, based on the set of directed inspection arcs in the directed inspection graph of the radial distribution network, the physical length of the directed inspection arcs is obtained. Combined with the fixed inspection flight speed of the UAV, the inherent flight time of a single directed inspection arc is calculated. For any directed inspection arc, the cumulative completion time of the previous inspection stage is obtained. Combined with the inherent flight time and charging time, the cumulative task time of the entire inspection stage of the directed inspection arc is determined. Based on the environmental parameters of the UAV, the propulsion power of the UAV is determined. Combined with the fixed inspection flight speed of the UAV, the flight energy consumption constraint of the entire inspection stage of the directed inspection arc is determined.

[0024] In this embodiment, an inspection time model and a flight energy consumption model are established based on the UAV inspection speed, inspection arc length, and flight energy consumption characteristics.

[0025] More specifically, set up inspection arcs The length is The drone inspection speed is Then the drone will be in the inspection arc Flight inspection time satisfy: ; in, Indicates the inspection arc segment Flight inspection time, Indicates the inspection arc segment Length, This indicates the speed of drone inspections.

[0026] Let the first The cumulative task completion time at the end of each inspection phase is , No. Each inspection phase is within the inspection arc. The charging time is Then the task completion time satisfies: ; The task completion time is comprised of the cumulative time from the previous stage, the flight inspection time in this stage, and the charging time in this stage. Its initial conditions are: .

[0027] No. Flight energy consumption during each inspection phase satisfy: ; in, Indicates that the drone is used for inspection speed Propulsion power during flight.

[0028] Furthermore, the propulsion power satisfy: ; in, This indicates the blade profile power when the drone is hovering. This indicates the induced power of the drone while it is hovering. Indicates the rotor tip speed. This represents the average induced velocity during hovering. Indicates the fuselage drag ratio. Indicates air density, Indicates rotor solidity, This indicates the area of ​​the rotor disk.

[0029] S400: Construct UAV charging decisions and combine the inherent flight time of a single directed inspection arc with the time-varying available charging power of each directed inspection arc at different times to obtain UAV battery state constraints. Specifically, a binary decision variable for drone charging is defined, and constraints on drone charging location, upper and lower limits of drone charging duration, and number of times drones charge in a single stage are introduced to construct drone charging decisions. Based on the drone charging decisions, the inherent flight time of a single directed inspection arc and the time-varying available charging power of each directed inspection arc at different times are combined to obtain drone battery state constraints.

[0030] The UAV battery state mathematical model includes the battery power remaining from the previous inspection stage, the total power obtained from charging in this stage, and the total energy consumed during the entire flight in this stage. The total power obtained from charging in this stage is calculated by integrating the time-varying available charging power of each directed inspection arc at different times. The total energy consumed during the entire flight in this stage is determined by the inherent flight time of a single directed inspection arc.

[0031] In this embodiment, a charging decision model, a charging duration constraint model, and a battery state mathematical model are established based on the charging behavior of the UAV during the inspection process.

[0032] More specifically, define binary variables When the drone was in Each inspection phase is selected within the inspection arc. When charging is performed ;otherwise, To ensure that the charging behavior is consistent with the inspection path, the charging decision satisfies the following constraints: ; ; ; in, The constant is large enough. The above constraint means that the drone can only perform charging on the currently visited inspection arc, and can perform the charging action at most once per inspection phase.

[0033] Let the first The charging start time for each inspection phase is Assume the drone completes the first The remaining battery charge after each inspection phase is: , The battery state then satisfies: ; Let the first The cumulative task completion time at the end of each inspection phase is , No. Each inspection phase is within the inspection arc. The charging time is If the task completion time satisfies the battery status, then the task completion time will be sufficient.

[0034] It should also be noted that the battery energy state of the drone during the inspection process is determined by both the charging energy and the flight energy consumption; the drone is only allowed to perform charging actions on the currently visited inspection arc, and the battery level must always be between the minimum safe level and the maximum battery capacity during the inspection process.

[0035] The S500 introduces full-cycle battery safety constraints for UAVs. Based on minimizing the cumulative task time during the entire inspection phase of the directed inspection arc as the optimization objective, and combining the UAV battery state constraints and the flight energy consumption constraints during the entire inspection phase of the directed inspection arc, a joint optimization objective model is constructed to realize UAV inspection path planning.

[0036] Specifically, the maximum rated capacity of the drone battery is obtained, the minimum safe power threshold of the drone battery is set, and the full-cycle battery safety constraints of the drone are constructed. Based on the optimization objective of minimizing the cumulative task time of the entire inspection phase of the directed inspection arc, a joint optimization objective model is constructed by combining the full-cycle battery safety constraints of the drone, the drone battery state constraints, and the flight energy consumption constraints of the entire inspection phase of the directed inspection arc. Based on the joint optimization objective model, the drone inspection path planning is realized.

[0037] In this embodiment, with the goal of minimizing the total inspection completion time of the UAV, a joint optimization model of inspection path and charging scheduling is established, which includes path continuity constraints, charging consistency constraints, and battery safety constraints. A two-layer solution method combining outer feasible path generation and inner fixed path charging scheduling is adopted to output the optimal inspection path, charging location, charging time, and total inspection completion time of the UAV.

[0038] More specifically, to ensure the safety of drone flight, the battery power throughout the inspection process must meet the following safety constraints: ; in, This indicates the minimum safe battery threshold for drones. This indicates the maximum capacity of the drone's battery.

[0039] Furthermore, with the goal of minimizing the total time for the UAV to complete all inspection tasks, the objective function of the established joint optimization model is: ; in, This indicates the total inspection completion time after the drone completes all 2N inspection stages.

[0040] The joint optimization model aims to minimize the total inspection completion time of the UAV, and the constraints include at least the following: 1) Task start and return constraints; 2) Unique inspection arc selection constraint for each stage; 3) Arc segment access topology continuity constraints; 4) Consistency constraints between charging actions and access arc segments; 5) Upper bound constraint on charging time; 6) Battery safety operation constraints.

[0041] Furthermore, the outer feasible path generation is used to generate a set of all feasible inspection paths based on the radial distribution network topology and path continuity constraints; the inner fixed path charging scheduling is used to optimize the charging location and charging time of each inspection stage under the given inspection path conditions; finally, by comparing the total inspection completion time corresponding to each candidate path, the globally optimal inspection path and charging joint scheme is obtained.

[0042] More specifically, the two-layer solution framework includes: 1) The outer layer generates a set of feasible inspection paths based on the radial distribution network topology and path continuity constraints; 2) The inner layer solves for the optimal charging position and charging time for each fixed inspection path.

[0043] Finally, by comparing the total inspection completion time corresponding to different candidate paths, the globally optimal path and charging joint scheme are determined.

[0044] In summary, this invention addresses the inspection scenario of overhead lines in radial distribution networks by unifying the modeling of UAV inspection path planning and charging scheduling. Considering the distribution network topology, the time-varying characteristics of branch power flow, UAV flight energy consumption, and battery safety constraints, it collaboratively optimizes the inspection sequence and charging line selection to minimize the total UAV inspection time.

[0045] First, a directed inspection graph of the radial distribution network is constructed, and a set of feasible inspection paths is generated based on physical adjacency and path continuity. Then, based on the power flow calculation results of the distribution network, a mathematical model is established between branch current and available charging power of overhead lines to obtain the available charging power of different inspection segments at different times. Next, given the inspection path, the charging location and charging time of each inspection stage are optimized by combining the real-time battery power, flight energy consumption, and battery safety boundary of the UAV. A two-layer solution framework combining outer path generation and inner charging scheduling is adopted to select the optimal path and charging scheme with the minimum total inspection time. Finally, the optimal inspection sequence, charging scheme, and total inspection time of the UAV are output.

[0046] Compared to inspection methods that rely on ground-based charging facilities or fixed power replenishment nodes, this invention directly utilizes the time-varying available electrical energy of overhead lines to power drones, reducing reliance on ground-based charging infrastructure. This makes it suitable for inspection scenarios involving complex terrain, sparse roads, and large-scale, long-distance overhead transmission lines. Furthermore, this invention can autonomously prioritize charging lines with higher charging power based on differences in branch charging capabilities at different times, improving inspection continuity and overall operational efficiency.

[0047] Therefore, the embodiments of the present invention can comprehensively consider the time-varying charging capacity of the power grid and the endurance requirements of the UAV mission side. Based on the time-varying power flow characteristics and topology constraints, it can achieve joint optimization of UAV inspection path and overhead line charging scheduling, thereby improving the continuity and efficiency of line inspection in complex environments.

[0048] The embodiments of the present invention have the following advantages compared with the prior art: 1) Compared with inspection methods that rely on fixed charging stations, mobile charging platforms or battery swapping devices, the embodiments of the present invention can directly utilize overhead lines as energy replenishment resources along the line, reducing the need for ground infrastructure deployment and making them suitable for inspection scenarios of complex terrain and long-distance overhead lines.

[0049] 2) The embodiments of the present invention integrate the inspection path planning and charging scheduling into a unified model, which can autonomously prioritize the selection of high-availability charging power lines for charging based on the time-varying characteristics of branch power flow, its own remaining power and task execution order, thereby improving energy replenishment efficiency and shortening the total inspection time.

[0050] 3) The embodiments of the present invention explicitly consider flight time, charging time, flight energy consumption, battery state evolution and safe operation boundaries, and the resulting optimization scheme has strong engineering feasibility.

[0051] 4) The embodiments of the present invention adopt a two-layer solution framework that combines outer path generation and inner charging scheduling, which effectively reduces the solution complexity caused by strongly coupled variables in the joint optimization model and improves the solution efficiency.

[0052] Finally, the embodiments of the present invention will be explained and described in conjunction with the accompanying drawings: like Figure 3 As shown, a 4-node radial power distribution system is used as an example for verification. This system includes one root node and three end nodes. The root node is connected to end node 1, and end node 1 is connected to end nodes 2 and 3 respectively. There are three physical overhead lines, each 2000 m long. Following the directed inspection graph construction rules described in this invention, the three physical overhead lines are expanded into six directed inspection arcs, as follows... Figure 3 Select an inspection drone, with the following drone parameters: , , , The drone starts from the root node, continuously inspects all arc segments along the network topology, and then returns to the root node.

[0053] In this embodiment, power flow calculations are performed based on the load curves of three types of nodes: industrial, commercial, and residential. This yields the current variations of each branch at different times, and the time-varying available charging power for each inspection segment is constructed based on the branch current. Then, the two-layer optimization method proposed in this invention is used to generate candidate inspection paths. For each fixed path, the optimal charging location and charging time are further calculated, ultimately obtaining the optimal solution with the shortest total inspection time.

[0054] First, based on the 4-node radial power distribution system structure, a directed inspection graph of the radial power distribution network is constructed, and candidate inspection paths that meet the topology continuity requirements are generated. Then, based on the power flow calculation results of the 4-node radial power distribution system, the current amplitude of each branch at different times is obtained, and the available charging power corresponding to each branch is further calculated. Next, data such as candidate inspection paths, time-varying available charging power of each inspection arc segment, UAV inspection speed, arc length, flight energy consumption parameters, and upper and lower limits of battery capacity are input into a joint optimization model to solve for the optimal charging location and charging time under each candidate path. Then, the total charging time and total inspection time corresponding to different candidate paths are compared, and the path with the shortest total inspection time is selected as the optimal inspection path. Finally, with the optimal path fixed, the UAV inspection speed is further varied, and the total charging time and total inspection time under different speeds are compared and analyzed.

[0055] Furthermore, in this embodiment, two candidate inspection paths that meet the constraints are generated. For each candidate path, the corresponding total charging time and total inspection time are obtained through charging scheduling optimization, while satisfying the UAV battery safety constraints. As shown in Table 1, Table 1 presents the optimization results for different candidate inspection paths.

[0056] Table 1. Optimization results of different candidate inspection paths in a 4-node radial power distribution system. ; Table 1 shows that there are significant differences in charging time and total inspection time for different candidate inspection paths. This indicates that the selection of inspection path directly affects the arrival time of the UAV during the inspection process, the battery energy evolution process, and the available overhead line charging opportunities. Therefore, there is a strong coupling relationship between inspection path and charging scheduling. By comparing the total inspection time of each candidate path, it can be seen that path 2 has the shortest total inspection time of 7422.7511 s, and the corresponding charging time is 5022.7511 s, making it the optimal inspection path in this embodiment.

[0057] Furthermore, for the optimal path determined in Table 1, the optimal charging selection result during the inspection mission of the UAV along the path is solved, and the result shown in Table 2 is obtained.

[0058] Table 2. Results of UAV charging selection under optimal path ; Table 2 presents the charging decisions for each UAV along the optimal path, including the charging step, corresponding overhead line number, charging start time, charging duration, charging capacity, and the power available from the branch at that time. As shown in Table 2, during the inspection process corresponding to the optimal path, the UAV does not charge in all inspection segments, but only on some overhead lines with higher charging power. The charging start time, charging duration, and available power differ for different charging steps, indicating that overhead line charging resources have significant time-varying characteristics and spatial variability. This demonstrates that the method proposed in this invention can adaptively select a better charging location and charging duration based on the UAV's arrival time, remaining battery power, and the power available from the branch, thereby completing all inspection tasks while meeting battery safety constraints.

[0059] Furthermore, to analyze the impact of inspection speed on path selection and charging scheduling results, the joint optimization model of UAV inspection path and charging scheduling was solved under different inspection speed conditions, and the optimal path, charging time and total inspection time results corresponding to different inspection speeds were obtained as shown in Table 3.

[0060] Table 3. Optimization results of inspection paths at different inspection speeds ; Table 3 shows that the UAV inspection speed has a significant impact on the optimal path selection. When the inspection speed is 3 m / s, 4 m / s, 5 m / s, and 6 m / s, the corresponding optimal paths are Path 1, Path 2, Path 2, Path 1, and Path 2, respectively, indicating that the optimal inspection path changes with the inspection speed. This is because changes in inspection speed alter the time when the UAV arrives at each inspection segment, further affecting the available charging opportunities, battery state evolution, and overall mission completion time on each branch. Therefore, the optimal path differs under different speed conditions. Furthermore, as the UAV inspection speed increases, both charging time and total inspection time decrease, indicating that increasing the inspection speed can significantly shorten the mission execution time and effectively reduce the UAV's dependence on mid-course charging. Moreover, when the inspection speed reaches a certain level, the flight phase time is significantly shortened, and the remaining battery energy can meet the energy requirements of the entire inspection process, eliminating the impact of the UAV charging stage on the total inspection time.

[0061] In summary, Tables 1, 2, and 3 verify the effectiveness of the embodiments of the present invention from different perspectives. Table 1 verifies that the present invention can identify the optimal inspection path with the shortest total inspection time from the candidate path set; Table 2 verifies that the present invention can further optimize the charging location, charging duration, and charging time of the UAV given an optimal path; Table 3 verifies that the present invention can still comprehensively consider the coupling relationship between the inspection path, the time-varying charging capability of the branch, and battery constraints under different inspection speed conditions, adaptively obtaining the optimal inspection path and charging scheduling scheme under the corresponding operating conditions, demonstrating strong engineering applicability.

[0062] Reference Figure 2 A drone inspection path planning system based on autonomous power supply from overhead lines includes: The first module 201 is used to determine the set of directed inspection arcs based on the physical overhead lines, define the binary topology reachability parameters and binary decision variables, introduce UAV inspection path constraints, and construct a directed inspection graph of the radial distribution network. The second module 202 is used to construct the correspondence between branch current and overhead line available charging power based on the directed inspection diagram of the radial distribution network, and to obtain the time-varying available charging power of each directed inspection arc at different times. The third module 203 is used to calculate the inherent flight time of a single directed inspection arc based on the directed inspection graph of the radial distribution network, and to determine the cumulative task time and flight energy consumption constraints of the entire inspection phase of the directed inspection arc. The fourth module 204 is used to construct the UAV charging decision and combine the inherent flight time of a single directed inspection arc with the time-varying available charging power of each directed inspection arc at different times to obtain the UAV battery state constraints. Module 5, 205, introduces full-cycle battery safety constraints for UAVs. Based on minimizing the cumulative task time during the full inspection phase of the directed inspection arc as the optimization objective, and combining UAV battery state constraints and flight energy consumption constraints during the full inspection phase of the directed inspection arc, a joint optimization objective model is constructed to realize UAV inspection path planning.

[0063] The content of the above method embodiments is applicable to this system embodiment. The specific functions implemented in this system embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0064] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this is not intended to limit the scope of the embodiments of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the embodiments of the present application.

Claims

1. A method for unmanned aerial vehicle (UAV) inspection path planning based on autonomous power supply from overhead lines, characterized in that, Includes the following steps: Based on the physical overhead lines, a set of directed inspection arcs is determined, binary topology reachability parameters and binary decision variables are defined, and UAV inspection path constraints are introduced to construct a directed inspection graph of the radial distribution network. Based on the directed inspection diagram of the radial distribution network, the correspondence between branch current and available charging power of overhead line is constructed to obtain the time-varying available charging power of each directed inspection arc at different times. Based on the directed inspection graph of the radial distribution network, the inherent flight time of a single directed inspection arc is calculated, and the cumulative task time and flight energy consumption constraints of the entire inspection phase of the directed inspection arc are determined. By constructing a drone charging decision and combining the inherent flight time of a single directed inspection arc with the time-varying available charging power of each directed inspection arc at different times, the drone battery state constraints are obtained. By introducing full-cycle battery safety constraints for UAVs, and taking the minimum cumulative task time of the entire inspection phase of the directed inspection arc as the optimization objective, a joint optimization objective model is constructed by combining UAV battery state constraints and flight energy consumption constraints of the entire inspection phase of the directed inspection arc to realize UAV inspection path planning.

2. The UAV inspection path planning method based on autonomous power supply from overhead lines according to claim 1, characterized in that, The step of determining the set of directed inspection arcs based on physical overhead lines, defining binary topology reachability parameters and binary decision variables, and introducing UAV inspection path constraints to construct a directed inspection graph of the radial distribution network specifically includes: Determine the number of physical overhead lines and expand each physical overhead line into two directed inspection arcs in opposite directions to form a set of directed inspection arcs; Based on the set of directed inspection arcs, the entire inspection process is divided into a set of stages, and binary topological reachability parameters and binary decision variables are defined. Based on binary topology reachability parameters and binary decision variables, a directed inspection graph of a radial power distribution network is constructed by introducing UAV inspection path constraints.

3. The UAV inspection path planning method based on autonomous power supply from overhead lines according to claim 2, characterized in that, The step of constructing the correspondence between branch currents and available charging power of overhead lines based on the directed inspection diagram of the radial distribution network, and obtaining the time-varying available charging power of each directed inspection arc at different times, specifically includes: Based on the directed inspection diagram of the radial distribution network, each directed inspection arc corresponds to an overhead branch of the distribution network. The available charging power of the directed inspection arc and the current amplitude of the overhead branch of the distribution network are defined to construct a linear mapping relationship between charging power and branch current. Based on the directed inspection diagram of the radial distribution network, the complex power equations of the overhead branches of the distribution network are determined and derived to obtain the current phasors of the overhead branches of the distribution network. Based on the current phasor of the overhead branch of the distribution network, the power flow of the distribution network is iteratively calculated at different times according to the entire inspection cycle, and the current amplitude of the overhead branch of the distribution network corresponding to each directed inspection arc is solved at different times. Substituting the current amplitude into the linear mapping relationship between charging power and branch current, the time-varying available charging power of each directed inspection arc at different times is obtained.

4. The UAV inspection path planning method based on autonomous power supply from overhead lines according to claim 3, characterized in that, The step of calculating the inherent flight time of a single directed inspection arc based on the directed inspection graph of the radial distribution network, and determining the cumulative task time and flight energy consumption constraints of the entire inspection phase of the directed inspection arc, specifically includes: Based on the set of directed inspection arcs in the directed inspection graph of the radial distribution network, the physical length of the directed inspection arc is obtained. Combined with the fixed inspection flight speed of the UAV, the inherent flight time of a single directed inspection arc is calculated. For any directed inspection arc, obtain the cumulative completion time of the previous inspection stage, and combine it with the inherent flight time and charging time to determine the cumulative task time of the entire inspection stage of the directed inspection arc. Based on the environmental parameters of the UAV, the propulsion power of the UAV is determined. Combined with the fixed inspection flight speed of the UAV, the flight energy consumption constraints for the entire inspection phase of the directed inspection arc are determined.

5. The UAV inspection path planning method based on autonomous power supply from overhead lines according to claim 4, characterized in that, The step of constructing the UAV charging decision and combining the inherent flight time of a single directed inspection arc with the time-varying available charging power of each directed inspection arc at different times to obtain the UAV battery state constraints specifically includes: Define binary decision variables for drone charging, and introduce constraints on drone charging location, upper and lower limits of drone charging duration, and number of drone charging times in a single stage to construct drone charging decision; Based on the drone charging decision, the inherent flight time of a single directed inspection arc and the time-varying available charging power of each directed inspection arc at different times are combined to obtain the drone battery state constraints.

6. The UAV inspection path planning method based on autonomous power supply from overhead lines according to claim 5, characterized in that, The mathematical model of the UAV battery status includes the battery power remaining from the previous inspection stage, the total power obtained from charging in this stage, and the total energy consumed during the entire flight in this stage. The total power obtained from charging in this stage is calculated by integrating the time-varying available charging power of each directed inspection arc at different times. The total energy consumed during the entire flight in this stage is determined by the inherent flight time of a single directed inspection arc.

7. The UAV inspection path planning method based on autonomous power supply from overhead lines according to claim 6, characterized in that, The introduction of full-cycle battery safety constraints for the UAV, with the optimization objective of minimizing the cumulative task time during the entire inspection phase of the directed inspection arc, combined with UAV battery state constraints and flight energy consumption constraints during the entire inspection phase of the directed inspection arc, constructs a joint optimization objective model to achieve the UAV inspection path planning step, which specifically includes: Obtain the rated maximum capacity of the drone battery, set the minimum safe power threshold of the drone battery, and construct the full-cycle battery safety constraints for the drone. The optimization objective is to minimize the cumulative task time during the entire inspection phase of the directed inspection arc. A joint optimization objective model is constructed by combining the full-cycle battery safety constraints of the UAV, the battery status constraints of the UAV, and the flight energy consumption constraints during the entire inspection phase of the directed inspection arc. Based on the joint optimization objective model, we realize the unmanned aerial vehicle (UAV) inspection path planning.

8. A drone inspection path planning system based on autonomous power supply from overhead lines, characterized in that, Includes the following modules: The first module is used to determine the set of directed inspection arcs based on the physical overhead lines, define the binary topology reachability parameters and binary decision variables, and introduce UAV inspection path constraints to construct a directed inspection graph of the radial distribution network. The second module is used to construct the correspondence between branch current and available charging power of overhead lines based on the directed inspection diagram of the radial distribution network, and to obtain the time-varying available charging power of each directed inspection arc at different times. The third module is used to calculate the inherent flight time of a single directed inspection arc based on the directed inspection graph of the radial distribution network, and to determine the cumulative task time and flight energy consumption constraints of the entire inspection phase of the directed inspection arc. The fourth module is used to construct the drone charging decision and combine the inherent flight time of a single directed inspection arc with the time-varying available charging power of each directed inspection arc at different times to obtain the drone battery state constraints. The fifth module introduces full-cycle battery safety constraints for the UAV. Based on minimizing the cumulative task time during the entire inspection phase of the directed inspection arc as the optimization objective, and combining the UAV battery state constraints and the flight energy consumption constraints during the entire inspection phase of the directed inspection arc, a joint optimization objective model is constructed to realize UAV inspection path planning.

Images