Power distribution network inspection planning method based on double-layer optimization algorithm

Abstract

The invention relates to the technical field of power grid inspection, and provides a power distribution network inspection planning method based on a double-layer optimization algorithm, and the method comprises the steps: carrying out the optimization iteration of an unmanned plane inspection path model based on type 1 station information and an energy consumption optimal principle, and generating an unmanned plane inspection path; generating a battery replacement space window based on the unmanned aerial vehicle routing inspection path, performing iterative optimization on the routing inspection trolley path model based on the battery replacement space window and the type 2 station information, and generating a routing inspection trolley routing inspection path; the inspection trolley and the unmanned aerial vehicle inspect each inspection site according to the corresponding inspection path, and the unmanned aerial vehicle flies to the corresponding inspection trolley in the battery replacement space window to perform battery replacement operation. According to the method, the battery replacement space window is generated through the unmanned aerial vehicle routing inspection path subjected to iterative optimization, then the routing inspection path of the routing inspection trolley is further subjected to energy consumption priority optimization with the battery replacement constraint, the unmanned aerial vehicle endurance supplement problem is converted into a typical time space window problem, the endurance supplement cost is remarkably reduced, and the routing inspection efficiency is improved.

Description

Technical Field

[0001] The present application relates to the field of power grid inspection technology, and in particular to a distribution network inspection planning method based on a double-layer optimization algorithm. Background Art

[0002] The statements in this section merely provide background technical information related to the present application and do not necessarily constitute prior art.

[0003] In order to ensure the normal operation of the power transmission and distribution network, the power grid management department needs to conduct regular inspections of the power transmission and distribution network. With the expansion of urban construction, the distribution of distribution networks has become extremely widespread. The traditional manual line inspection mode can no longer meet the inspection needs. Automatic line inspection equipment represented by drones and unmanned inspection vehicles has begun to be widely used.

[0004] UAV inspection operations have gradually become the mainstream of automatic line inspection equipment due to their outstanding advantages such as fast inspection speed and free path planning. However, due to the wide distribution of distribution network sites, the endurance problem of UAVs has been further magnified. Traditional methods to solve the endurance of UAVs include fixed-site charging and multiple batches of UAV relay patrols. These methods will significantly increase the ineffective flight distance of UAVs, increase energy consumption, have a great impact on inspection efficiency, and also cause great economic costs. This does not meet the reality of large-scale application of power grids. It is urgent to propose a more economical inspection method that does not affect inspection efficiency, so as to reduce the impact of endurance problems on UAV inspection operations. Summary of the invention

[0005] In order to solve the above problems, the present application proposes a distribution network inspection planning method based on a double-layer optimization algorithm.

[0006] The present application provides a distribution network inspection planning method based on a two-layer optimization algorithm, comprising the following steps: S1: Obtain information about sites that need to be inspected, including location information and site types. The site types include sites that are suitable for drone inspections and sites that are suitable for inspection vehicles, which are named type 1 and type 2, respectively. S2: Based on the type 1 site information and the principle of optimal energy consumption, the UAV inspection path model is optimized and iterated to generate the UAV inspection path; S3: Generate a battery swap space window based on the drone inspection path, iteratively optimize the inspection vehicle path model based on the battery swap space window and type 2 site information, and generate the inspection vehicle inspection path; S4: The inspection car and drone inspect each inspection site according to the corresponding inspection route, and the drone flies to the corresponding inspection car in the battery swap space window to perform the battery swap operation.

[0007] Preferably, in step S2, the method for generating the drone inspection path model is specifically as follows: Objective function: (1); The constraints are as follows: (2) (3) (4) (5) Type 1 site collections are , is the take-off and landing point of the UAV, n is the number of type 1 sites, in formula (1), is the shortest path from site i to site j, The value is 0 or 1, indicating whether drone k passes through the path , It is the energy consumption per unit distance when the drone cruises at a preset standard speed. is the average energy consumption of the drone inspection type 1 site, and K is the number of operating drones; In formula (2) to (5), N_total is the number of idle drones, The value is 0 or 1, indicating whether drone k passes through site i; The meanings of the four constraints are: The number of operating drones is less than or equal to the number of idle drones; Each site has only one drone inspection per time; Each site is reached by only one drone inspection path; There is only one inter-site path with each site as the end point.

[0008] Preferably, in step S2, the shortest path of the drone between the two sites is by default a connecting straight line between the two sites.

[0009] Preferably, in step S3, the method for generating the battery swap space window is specifically as follows: S301: Obtaining the inspection trajectory of the drone, and generating the inspection position curve of the drone corresponding to each trajectory based on the historical inspection information of the drone; S302: Select one of the UAV inspection position curves and calculate the energy consumption per unit distance. And the average energy consumption of drone inspection type 1 sites , calculate the remaining energy consumption of the drone at each point on the corresponding inspection trajectory , p is the position coordinate of the UAV; S303: Assume that p_1, p_2, and p_3 are sequentially adjacent sites on the inspection trajectory. If the remaining energy consumption of the drone after inspecting site p_2 is If it is not possible to support the drone to inspect to p_3, the inspection airspace between p_1 and p_end is used as the first battery replacement interval L1 of the corresponding drone, and p_end is the energy consumption end point of the drone between p_2 and p_3; S304: Obtain the location information of type 2 sites, scan with the midpoint of L_1 as the center and Length(L_1) / 2 as the radius, put the type 2 sites within the scanning range into the candidate set C1, and perform enable identification on the elements in the set C1 according to the preset strategy. If the preset strategy is met, jump to step S306; if not, jump to step S305; S305: Adjust the starting point of the first battery swapping interval L1 to a station before the p_1 station, and jump to step S304; S306: Generate a set C2 of inter-site paths between the elements in the set C1, and select the inter-site path D_1 closest to the midpoint of L_1 as the first transmission interval; S307: Plan the drone to start from the midpoint of L_1 to exchange power, and then directly return to the first station it has not inspected, calculate its return energy consumption based on the maximum distance from the midpoint of L_1 to D_1, and then generate other power exchange intervals and power transmission intervals of the corresponding drone inspection trajectory according to steps S03 to S306; S308: Generate a battery swap space window set C3 for each drone inspection trajectory based on steps S302 to S307. The elements of set C3 are {k,{L_1,D_1},{L_2,D_2},...,{L_m,D_m}}, where k is the drone number and m is the number of battery swaps.

[0010] Preferably, in step S301, based on the average inspection time of type 1 sites in the historical inspection information, and the standard cruising speed of the drone between sites Generate UAV inspection position curve { , , ,..., }.

[0011] Preferably, in step S304, the preset strategy for enabling identification of elements in set C1 is as follows: The elements in the candidate set C1 are greater than or equal to 2; There are at least two elements that are connectable, where the shortest path between the two elements does not contain any other sites. In step S306, the inter-site path in the set C2 refers to the shortest path between two connectable sites.

[0012] Preferably, in step S306, all or part of the inter-site paths in set C2 are located within a scanning range with the midpoint of L_1 as the center and Length(L_1) / 2 as the radius.

[0013] Preferably, in step S3, the method for generating the inspection path model of the inspection vehicle is specifically as follows: Objective function: (6); (7) The constraints are as follows: (8) (9) (10) (11) (12) Type 2 site collections are , is the starting point of the inspection car, n is the number of type 2 stations, in formula (6), is the shortest path from site i to site j, The value is 0 or 1, indicating whether the inspection car k passes through the path , It is the energy consumption per unit distance when the inspection vehicle cruises at the preset standard speed. is the average energy consumption of the inspection vehicle inspection type 2 station, The value is 0 or 1, indicating whether the inspection vehicle k passes through station i; Constraints (11) and (12) are the battery swap constraints. In formula (11), the set C_D represents the set of transmission intervals. The meaning of formula (11) is that if the station belongs to a certain transmission interval, then the same inspection vehicle must pass through this transmission interval and its two ends. In formula (12), D_start is the starting station of the transmission interval, D_end is the end station of the transmission interval, L_start is the corresponding starting station of the battery swap interval, L_middle is the midpoint of the battery swap interval, and t_max is the speed at which the UAV can reach the standard cruising speed. The time taken to fly the longest distance from L_middle to the transmission section, and F(t) and G(t) are the inspection position curves of the corresponding drone and inspection car.

[0014] Preferably, in step S4, the specific method for the drone to fly from the battery swapping space window to the corresponding inspection vehicle to perform the battery swapping operation is as follows: When the UAV flies to the midpoint of a battery swap space window, it obtains the location information of the inspection car, calculates the intersection point with the inspection car on its inspection path based on the standard cruising speed of the inspection car and the UAV, flies to the corresponding intersection point to complete the battery swap operation, and after completion, flies to the first station that the UAV has not inspected.

[0015] Preferably, when the UAV flies to the starting point of a battery swap space window, it communicates wirelessly with the corresponding inspection vehicle to obtain its location information in real time. On the basis of ensuring that the remaining energy consumption when arriving at the same uninspected site is greater than that of swapping batteries at the midpoint of the space window, the optimal position for the UAV to deviate from the path for battery swapping is calculated, and the battery swap operation is performed at the corresponding position.

[0016] Compared with the prior art, the beneficial effects of this application are: This application replaces traditional fixed-station charging by dynamically replacing batteries in patrol drones on patrol carts, which can effectively reduce the ineffective flight distance of drones and divide the inspection sites into drone inspection sites and patrol cart inspection sites. This is conducive to overcoming the safety risks caused by ultra-low-altitude operations of drones, improving the use value of existing patrol carts, and conforming to the development trend of refined power grid operations.

[0017] This application iteratively optimizes the drone inspection path through the principle of optimal energy consumption, which is beneficial to reducing the energy consumption of drone inspections, improving inspection efficiency, saving energy and reducing emissions, and reducing the impact of battery life on the efficiency of drone inspections.

[0018] This application generates a battery swap space window through iteratively optimized UAV inspection paths and the location distribution of vehicle inspection stations, and further optimizes the inspection path of the inspection vehicle with energy consumption priority under battery swap constraints, thereby converting the UAV battery life replenishment problem into a typical space-time window problem, which is conducive to using optimization algorithms to quickly iterate the path of the inspection vehicle, significantly reducing the economic and time costs of UAV battery life replenishment as a whole, and improving the efficiency of UAV inspections. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The drawings in the specification, which constitute a part of the present application, are used to provide further understanding of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute improper limitations on the present application.

[0020] Figure 1 This is a schematic diagram of the system composition of this application. Figure 2 This is a schematic diagram of the method flow of this application, Figure 3 This is a schematic diagram of the implementation of this application. DETAILED DESCRIPTION

[0021] The present application is further described below in conjunction with the accompanying drawings and embodiments.

[0022] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present disclosure. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0023] In the present disclosure, terms such as "upper", "lower", "left", "right", "front", "back", "vertical", "horizontal", "side", "bottom" and the like indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings. They are relational words determined only for the convenience of describing the structural relationships of the various parts or elements of the present disclosure, and do not specifically refer to any part or element in the present disclosure and should not be understood as limitations on the present disclosure.

[0024] Example 1 like Figure 1 and Figure 3 As shown, the present application provides a distribution network inspection planning method based on a two-layer optimization algorithm, comprising the following steps: S1: Obtain information about sites that need to be inspected, including location information and site types. The site types include sites that are suitable for drone inspections and sites that are suitable for inspection vehicles, which are named type 1 and type 2, respectively. S2: Based on the type 1 site information and the principle of optimal energy consumption, the UAV inspection path model is optimized and iterated to generate the UAV inspection path; S3: Generate a battery swap space window based on the drone inspection path, iteratively optimize the inspection vehicle path model based on the battery swap space window and type 2 site information, and generate the inspection vehicle inspection path; S4: The inspection car and drone inspect each inspection site according to the corresponding inspection route, and the drone flies to the corresponding inspection car in the battery swap space window to perform the battery swap operation.

[0025] This application replaces traditional fixed-station charging with dynamic battery replacement operations for inspection drones on inspection carts, which can effectively reduce the ineffective flight distance of drones and divide the inspection sites into drone inspection sites and inspection car inspection sites. This is conducive to overcoming the safety risks caused by ultra-low-altitude operations of drones, improving the use value of existing inspection carts, and conforming to the development trend of refined power grid operations.

[0026] Specifically, in step S2, the method for generating the drone inspection path model is as follows: Objective function: (1); The constraints are as follows: (2) (3) (4) (5) Type 1 site collections are , is the take-off and landing point of the UAV, n is the number of type 1 sites, in formula (1), is the shortest path from site i to site j, The value is 0 or 1, indicating whether drone k passes through the path , It is the energy consumption per unit distance when the drone cruises at a preset standard speed. is the average energy consumption of the drone inspection type 1 site, K is the number of operating drones, and the objective function is to find the solution that minimizes the total energy consumption of the drone E_total; In formula (2) to (5), N_total is the number of idle drones, The value is 0 or 1, indicating whether drone k passes through site i; The meanings of the four constraints are: The number of operating drones is less than or equal to the number of idle drones; Each site has only one drone inspection per time; Each site is reached by only one drone inspection path; There is only one inter-site path with each site as the end point.

[0027] In step S2, an optimization algorithm is used to iteratively optimize the path model. Typically, an ant algorithm can be used to optimize the path model. The ant algorithm, also known as the Ant Colony Optimization (ACO), is an optimization algorithm that simulates the foraging behavior of ants in nature. The algorithm was proposed by Marco Dorigo in 1992 and is inspired by the behavior of ants in finding paths in the process of searching for food. The ant colony algorithm is mainly used to solve combinatorial optimization problems, especially the traveling salesman problem (TSP). The ant algorithm and its optimization edges, such as the maximum-minimum ant colony algorithm and the mutation ant algorithm, are all frequently used algorithms in path optimization. The specific method of using the ant algorithm for iterative path optimization is as follows: S201: Initialize the take-off and landing positions of the drone and the location information of type 1 sites, calculate the shortest path between the sites, and set the path probability selection formula; S202: Initialize the inspection path set; S203: creating a new ant colony including a preset number of ants; S204: According to the path selection probability formula, an unselected site is selected as a candidate node, and it is determined whether the energy consumption of reaching it is greater than the remaining energy consumption of the drone. If so, reselect it; if not, select it as the next inspection object; S205: adding the inspection object determined in S204 to the drone path, and calculating the remaining energy consumption of the drone; S206: Determine whether there are any untraversed sites in the inspection path set, if yes, jump to step S207, if no, jump to step S208; S207: Determine whether the remaining energy consumption of the drone is less than the energy consumption of reaching any untraversed site. If so, add a drone or update the remaining energy consumption of the next drone. If not, jump to step S204. S208: Calculate the target value and obtain an iterative solution; S209: Compare and update the global optimal solution, and update the ant colony pheromone; S210: Determine whether the maximum number of iterations has been reached. If so, output the optimal solution, obtain the set of drone inspection paths, and end the iteration process; If not, jump to step S202.

[0028] In the step S201, the parameters of the path probability selection formula include the pheromone concentration of the path between sites, the attraction from one tower to another, and the correlation coefficient between pheromone and attraction, which are the conventional settings of the ant algorithm and will not be repeated in this application; it is worth noting that in the drone path planning, the drone is set to "infinite energy consumption", and the calculation of the remaining energy consumption of the drone in steps S204 to S207 is only to evenly distribute the sites to each drone. In the step S207, after all drones have been allocated a site once, the remaining energy consumption of the drone is updated in sequence according to the order, and then the site is allocated based on full power.

[0029] Preferably, in step S2, the shortest path of the drone between the two sites is by default a connecting straight line between the two sites.

[0030] Specifically, in step S3, the method for generating the battery swap space window is as follows: S301: Obtaining the drone inspection trajectory { , , ,..., }, based on the historical inspection information of the drone, generate the drone inspection position curve corresponding to each trajectory { , , ,..., }; S302: Select one of the UAV inspection position curves and calculate the energy consumption per unit distance. And the average energy consumption of drone inspection type 1 sites , calculate the remaining energy consumption of the drone at each point on the corresponding inspection trajectory , p is the position coordinate of the UAV; S303: Assume that p_1, p_2, and p_3 are sequentially adjacent sites on the inspection trajectory. If the remaining energy consumption of the drone after inspecting site p_2 is If it is not possible to support the drone to inspect to p_3, the inspection airspace between p_1 and p_end is used as the first battery replacement interval L1 of the corresponding drone, and p_end is the energy consumption end point of the drone between p_2 and p_3; S304: Obtain the location information of type 2 sites, scan with the midpoint of L_1 as the center and Length(L_1) / 2 as the radius, put the type 2 sites within the scanning range into the candidate set C1, and perform enable identification on the elements in the set C1 according to the preset strategy. If the preset strategy is met, jump to step S306; if not, jump to step S305; S305: Adjust the starting point of the first battery swapping interval L1 to a station before the p_1 station, and jump to step S304; S306: Generate a set C2 of inter-site paths between the elements in the set C1, and select the inter-site path D_1 closest to the midpoint of L_1 as the first transmission interval; S307: Plan the drone to start from the midpoint of L_1 to exchange power, and then directly return to the first station it has not inspected, calculate its return energy consumption based on the maximum distance from the midpoint of L_1 to D_1, and then generate other power exchange intervals and power transmission intervals of the corresponding drone inspection trajectory according to steps S03 to S306; S308: Generate a battery swap space window set C3 for each drone inspection trajectory based on steps S302 to S307. The elements of set C3 are {k,{L_1,D_1},{L_2,D_2},...,{L_m,D_m}}, where k is the drone number and m is the number of battery swaps.

[0031] Specifically, in step S301, based on the average inspection time of type 1 sites in the historical inspection information and the standard cruising speed of the drone between sites Generate UAV inspection position curve { , , ,..., }.

[0032] Where Length(L_1) is the length of L_1.

[0033] Specifically, in step S304, the preset strategy for enabling identification of elements in set C1 is as follows: The number of elements in the candidate set C1 is greater than or equal to 2, and at least two elements are connectable, where the connectivity means that there is no other site in the middle of the shortest path between the two elements; In step S306, the inter-site path in the set C2 refers to the shortest path between two connectable sites.

[0034] like Figure 3 As shown in the figure, p_1, p_2, and p_3 are sequentially adjacent sites on a certain drone inspection track. As shown in the installation steps S303 to S306, when the drone has inspected site p_2, the remaining energy consumption is If it is not possible to support drone inspection to p_3, the inspection airspace between p_1 and p_end is used as the first power exchange interval L1 of the corresponding drone, and p_end is the energy consumption end point of the drone between p_2 and p_3. The location information of the type 2 site is obtained, and the scan is carried out with the midpoint of L_1 as the center and Length(L_1) / 2 as the radius. The type 2 sites within the scanning range are placed in the candidate set C1. The arc surrounded by the dotted line in the figure is a partial scanning range, p_middle is the midpoint of L_1, and there are three type 2 sites within the scanning range. If according to step S304, the inter-site paths of the three type 2 sites are all connectable, the inter-site path closest to p_middle is selected as the first transmission interval D_1.

[0035] The method of steps S301 to S308 uses the midpoint of the transmission interval as the preset battery replacement position for the drone to fly away from the inspection path, and uses the type 2 site-to-site path that meets the drone's flight energy consumption as the transmission interval, thereby increasing the spatial redundancy and flexibility of the battery replacement operation, reducing the difficulty of coordination between the drone and the inspection vehicle during battery replacement, reducing the impact of the drone and inspection vehicle on the inspection operations, and preventing the drone from increasing its energy consumption and being unable to reach the battery replacement inspection vehicle in unexpected situations.

[0036] Preferably, in step S306, all or part of the paths between the sites in set C2 are located within a scanning range with the midpoint of L_1 as the center and Length(L_1) / 2 as the radius, further preventing the situation in which the drone's energy consumption increases and it is unable to reach the battery replacement inspection vehicle in an unexpected situation.

[0037] Specifically, in step S3, the method for generating the inspection path model of the inspection vehicle is as follows: Objective function: (6); (7) The constraints are as follows: (8) (9) (10) (11) (12) Type 2 site collections are , is the starting point of the inspection car, n is the number of type 2 stations, in formula (6), is the shortest path from site i to site j, The value is 0 or 1, indicating whether the inspection car k passes through the path , It is the energy consumption per unit distance when the inspection vehicle cruises at the preset standard speed. is the average energy consumption of the inspection vehicle inspection type 2 station, The value is 0 or 1, indicating whether the inspection car k passes through station i. The meaning of objective functions (1) and (2) is to find the solution that minimizes the total energy consumption of the inspection car and the number of inspection cars used; Constraints (8) to (10) are consistent with the constraints of the drone model above and will not be repeated here. Constraints (11) and (12) are battery swap constraints. In formula (11), set C_D represents the set of transmission intervals. The meaning of formula (11) is that if a station belongs to a certain transmission interval, then the same inspection vehicle must pass through this transmission interval and its two ends. In formula (12), D_start is the starting station of the transmission interval, D_end is the end station of the transmission interval, L_start is the corresponding starting station of the battery swap interval, L_middle is the midpoint of the battery swap interval, and t_max is the speed at which the drone can reach the standard cruising speed. The time taken to fly the longest distance from L_middle to the transmission section, and F(t) and G(t) are the inspection position curves of the corresponding drone and inspection car.

[0038] In step S3, an optimization algorithm is used to solve the inspection vehicle path model. Typically, a neighborhood search algorithm can be used to iteratively solve the inspection vehicle path model. Compared with the ant algorithm, the neighborhood search algorithm (NS) and its variants, the adaptive large neighborhood search algorithm ALNS and the chaotic neighborhood search algorithm, are more suitable for optimization iteration under complex conditions, and have the advantages of fast iteration speed, flexible parameters, and avoiding falling into local optimal solutions. The basic steps are as follows: Initialization: Starting from an initial solution, define the neighborhood structure and neighborhood actions; Generate candidate solutions: Generate a set of neighbor solutions of the current solution through neighborhood actions; ‌Evaluate candidate solutions‌: Evaluate the objective function for each candidate solution; ‌Select new solution‌: Select one or more new solutions as the current solution based on the evaluation results; Termination condition: Repeat the above steps until the termination condition is met (such as reaching the preset number of iterations or the quality of the solution no longer improves significantly); ‌Output results‌: Output the final solution and objective function value.

[0039] It is worth noting that when using the neighborhood to process the path optimization problem of the inspection vehicle of this application, the initial inspection paths of multiple inspection vehicles are initialized, and then several inspection sites are removed from the initial inspection paths, and then the removed inspection sites are inserted into the inspection paths to obtain updated inspection paths. In the process of removal and insertion, the sites included in the transmission interval set C_D are retained in the initial inspection paths in the order of the historical inspection paths, so as to reduce the number of iterations and the complexity of the calculations.

[0040] Specifically, in step S4, the specific method for the drone to fly from the battery swapping space window to the corresponding inspection vehicle to perform the battery swapping operation is as follows: When the UAV flies to the midpoint of a battery swap space window, it obtains the location information of the inspection car, calculates the intersection point with the inspection car on its inspection path based on the standard cruising speed of the inspection car and the UAV, flies to the corresponding intersection point to complete the battery swap operation, and after completion, flies to the first station that the UAV has not inspected.

[0041] When the UAV flies to the starting point of a battery swapping space window, it communicates wirelessly with the corresponding inspection vehicle to obtain its location information in real time. On the basis of ensuring that the remaining energy consumption when arriving at the same first uninspected station is greater than the battery swapping at the midpoint of the space window, it calculates the optimal position for the UAV to deviate from the path for battery swapping and performs the battery swapping operation at the corresponding position.

[0042] The present application also provides a distribution network inspection planning system based on a two-layer optimization algorithm, including a drone subsystem, an inspection vehicle subsystem, a path planning subsystem, and a subsystem. The functions of each subsystem are as follows: The drone subsystem includes several drones; Inspection vehicle subsystem, including several inspection vehicles; The path planning subsystem communicates with the drones and inspection cars in the drone subsystem and the inspection car subsystem to obtain the site information that needs to be inspected. The site information includes location information and site type. The site type includes sites that are suitable for drone inspections and sites that are suitable for inspection cars, which are named type 1 and type 2, respectively. Based on the type 1 site information and the principle of optimal energy consumption, the drone inspection path model is optimized and iterated to generate a drone inspection path. A battery swapping space window is generated based on the drone inspection path. The inspection car path model is iteratively optimized based on the battery swapping space window and type 2 site information to generate an inspection path for the inspection car. The drone inspection path and the inspection car inspection path are sent to each drone and inspection car. The battery-swapping electronic system, the drone and the corresponding inspection vehicle communicate wirelessly through the battery-swapping electronic system, and fly to the corresponding inspection vehicle in the battery-swapping space window to perform battery-swapping operations.

[0043] The drone and inspection vehicle are equipped with compatible battery replacement components.

[0044] The above are only preferred embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0045] Although the above describes the specific implementation methods of the present application in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present application. Technical personnel in the relevant field should understand that on the basis of the technical solution of the present application, various modifications or variations that can be made by technical personnel in this field without creative work are still within the scope of protection of the present application.

Claims

1. A distribution network inspection planning method based on a double-layer optimization algorithm, characterized in that: The steps include: S1: Obtain information about sites that need to be inspected, including location information and site types. The site types include sites that are suitable for drone inspections and sites that are suitable for inspection vehicles, which are named type 1 and type 2, respectively. S2: Based on the type 1 site information and the principle of optimal energy consumption, the UAV inspection path model is optimized and iterated to generate the UAV inspection path; S3: Generate a battery swap space window based on the drone inspection path, iteratively optimize the inspection vehicle path model based on the battery swap space window and type 2 site information, and generate the inspection vehicle inspection path; S4: The inspection car and drone inspect each inspection site according to the corresponding inspection route, and the drone flies to the corresponding inspection car in the battery swap space window to perform the battery swap operation.

2. The method for power distribution network inspection planning based on a double-layer optimization algorithm according to claim 1, characterized in that: In step S2, the method for generating the drone inspection path model is specifically as follows: Objective function: （1）； The constraints are as follows: （2） （3） （4） （5） Type 1 site collections are , is the take-off and landing point of the UAV, n is the number of type 1 sites, in formula (1), is the shortest path from site i to site j, The value is 0 or 1, indicating whether drone k passes through the path , It is the energy consumption per unit distance when the drone cruises at a preset standard speed. is the average energy consumption of the drone inspection type 1 site, K is the number of operating drones, and in equations (2) to (5), N_total is the number of idle drones. The value is 0 or 1, indicating whether drone k passes through site i; The meanings of the four constraints are: The number of operating drones is less than or equal to the number of idle drones. Each site has only one drone for inspection. Each site has only one drone inspection path to reach it. There is only one inter-site path with each site as the end point.

3. The power distribution network inspection planning method based on a double-layer optimization algorithm according to claim 2 is characterized in that: In step S2, the shortest path of the drone between the two sites is by default the connecting straight line between the two sites.

4. The method for power distribution network inspection planning based on a double-layer optimization algorithm according to claim 3 is characterized in that: In step S3, the method for generating the battery swap space window is specifically as follows: S301: Obtaining the inspection trajectory of the drone, and generating the inspection position curve of the drone corresponding to each trajectory based on the historical inspection information of the drone; S302: Select one of the UAV inspection position curves and calculate the energy consumption per unit distance. And the average energy consumption of drone inspection type 1 sites , calculate the remaining energy consumption of the drone at each point on the corresponding inspection trajectory , p is the position coordinate of the UAV; S303: Assume that p_1, p_2, and p_3 are sequentially adjacent sites on the inspection trajectory. If the remaining energy consumption of the drone after inspecting site p_2 is If it is not possible to support the drone to inspect to p_3, the inspection airspace between p_1 and p_end is used as the first battery replacement interval L1 of the corresponding drone, and p_end is the energy consumption end point of the drone between p_2 and p_3; S304: Obtain the location information of type 2 sites, scan with the midpoint of L_1 as the center and Length(L_1) / 2 as the radius, put the type 2 sites within the scanning range into the candidate set C1, and perform enable identification on the elements in the set C1 according to the preset strategy. If the preset strategy is met, jump to step S306; if not, jump to step S305; S305: Adjust the starting point of the first battery swapping interval L1 to a station before the p_1 station, and jump to step S304; S306: Generate a set C2 of inter-site paths between the elements in the set C1, and select the inter-site path D_1 closest to the midpoint of L_1 as the first transmission interval; S307: Plan the drone to start from the midpoint of L_1 to exchange power, and then directly return to the first station it has not inspected, calculate its return energy consumption based on the maximum distance from the midpoint of L_1 to D_1, and then generate other power exchange intervals and power transmission intervals of the corresponding drone inspection trajectory according to steps S03 to S306; S308: Generate a battery swap space window set C3 for each drone inspection trajectory based on steps S302 to S307. The elements of set C3 are {k,{L_1,D_1},{L_2,D_2},...,{L_m,D_m}}, where k is the drone number and m is the number of battery swaps.

5. The method for power distribution network inspection planning based on a double-layer optimization algorithm according to claim 4 is characterized in that: In step S301, based on the average inspection time of type 1 sites in the historical inspection information and the standard cruising speed of the drone between sites Generate UAV inspection position curve { , , ,..., }.

6. The method for power distribution network inspection planning based on a double-layer optimization algorithm according to claim 4 is characterized in that: In step S304, the preset strategy for enabling identification of elements in set C1 is as follows: The elements in the candidate set C1 are greater than or equal to 2; There are at least two elements that are connectable, where the shortest path between the two elements does not contain any other sites. In step S306, the inter-site path in the set C2 refers to the shortest path between two connectable sites.

7. A distribution network inspection planning method based on a double-layer optimization algorithm according to claim 6, characterized in that: In step S306, all or part of the inter-site paths in set C2 are located within a scanning range with the midpoint of L_1 as the center and Length(L_1) / 2 as the radius.

8. The method for power distribution network inspection planning based on a double-layer optimization algorithm according to claim 4 is characterized in that: In step S3, the method for generating the inspection path model of the inspection vehicle is specifically as follows: Objective function: （6）； （7） The constraints are as follows: （8） （9） （10） （11） （12） Type 2 site collections are , is the starting point of the inspection car, n is the number of type 2 stations, in formula (6), is the shortest path from site i to site j, The value is 0 or 1, indicating whether the inspection car k passes through the path , It is the energy consumption per unit distance when the inspection vehicle cruises at the preset standard speed. is the average energy consumption of the inspection vehicle inspection type 2 station, The value is 0 or 1, indicating whether the inspection vehicle k passes through station i; Constraints (11) and (12) are the battery swap constraints. In formula (11), the set C_D represents the set of transmission intervals. The meaning of formula (11) is that if the station belongs to a certain transmission interval, then the same inspection vehicle must pass through this transmission interval and its two ends. In formula (12), D_start is the starting station of the transmission interval, D_end is the end station of the transmission interval, L_start is the corresponding starting station of the battery swap interval, L_middle is the midpoint of the battery swap interval, and t_max is the speed at which the UAV can reach the standard cruising speed. The time taken to fly the longest distance from L_middle to the transmission section, and F(t) and G(t) are the inspection position curves of the corresponding drone and inspection car.

9. A distribution network inspection planning method based on a double-layer optimization algorithm according to any one of claims 1, 4, and 8, characterized in that: In step S4, the specific method for the drone to fly from the battery swapping space window to the corresponding inspection vehicle to perform the battery swapping operation is as follows: When the UAV flies to the midpoint of a battery swap space window, it obtains the location information of the inspection car, calculates the intersection point with the inspection car on its inspection path based on the standard cruising speed of the inspection car and the UAV, flies to the corresponding intersection point to complete the battery swap operation, and after completion, flies to the first station that the UAV has not inspected.

10. A distribution network inspection planning method based on a double-layer optimization algorithm according to claim 9, characterized in that: When the UAV flies to the starting point of a battery swapping space window, it communicates wirelessly with the corresponding inspection vehicle to obtain its location information in real time. On the basis of ensuring that the remaining energy consumption when arriving at the same uninspected site is greater than the battery swapping at the midpoint of the space window, it calculates the optimal position for the UAV to deviate from the path for battery swapping and performs the battery swapping operation at the corresponding position.

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