Substation inspection path two-stage planning method based on improved fund washing algorithm

Abstract

The invention discloses a transformer substation inspection path two-stage planning method based on an improved gold washing algorithm, and belongs to the technical field of transformer substation operation maintenance, and the method comprises the steps: constructing the improved gold washing algorithm based on the free exploration behavior of a gold washing person and based on a convergence factor in a cosine form; constructing a multi-team travel model and a single-team travel model based on the multiple patrol scenes; respectively optimizing the multi-team travel model and the single-team travel model based on the improved gold washing algorithm, and screening out a reference travel model; and carrying out travel mode optimization on each route of the reference travel model, and screening out an optimal inspection path of the transformer substation. After the free exploration behavior and the convergence factor based on the cosine form are introduced, the earlier-stage search efficiency of the gold washing algorithm is improved, and the global optimization ability is improved to a certain extent.

Description

Technical Field

[0001] The present invention belongs to the technical field of substation operation and maintenance, and in particular relates to a two-stage planning method for a substation inspection path based on an improved gold panning algorithm. Background Art

[0002] In recent years, in order to serve the high-quality development of the social economy, the number of new and expanded substations built by State Grid companies in various provinces and cities is increasing year by year. Some municipal power supply companies are facing a tense situation of insufficient substation operation and inspection personnel and heavy daily inspection tasks. In view of the high investment and maintenance costs of digital inspection equipment such as intelligent inspection robots and drones, and their limited adaptability to complex environments inside and outside the station, they cannot completely replace manual labor and achieve widespread promotion. Therefore, this embodiment proposes a two-stage planning method for substation inspection paths based on an improved gold panning algorithm to reduce the burden and increase the efficiency of inspection and maintenance work for grassroots front-line employees.

[0003] At present, domestic and foreign research on substation inspection path optimization is mainly focused on the intra-station level. By establishing a substation environmental model, an improved optimization algorithm is used to search for the inspection route with the shortest path length. There are few planning for inter-station inspection routes, and most of the research focuses on the optimization of a single inspection type. The impact of multiple inspection types on the optimal inspection path is not considered. At the same time, it does not support inspectors to customize differentiated inspection routes based on actual working conditions. In addition, the quality of the algorithm improvement effect is closely related to the robustness of the original algorithm. As a new heuristic optimization algorithm proposed in 2023, the gold panning algorithm has stronger robustness and higher convergence speed than today's mainstream path optimization algorithms. However, based on path optimization in specific engineering scenarios, how to adjust the search strategy of the gold panning algorithm to ensure global optimization capabilities is a difficult problem that needs to be solved urgently. Summary of the invention

[0004] In order to solve the above technical problems, the present invention proposes a two-stage planning method for substation inspection paths based on an improved gold panning algorithm to solve the problems existing in the above-mentioned prior art.

[0005] To achieve the above object, the present invention provides a two-stage planning method for substation inspection path based on an improved gold panning algorithm, comprising:

[0006] An improved gold mining algorithm is constructed based on the free exploration behavior of gold miners and the convergence factor based on the cosine form;

[0007] Construct a multi-team travel model and a single-team travel model based on multiple patrol scenarios;

[0008] Based on the improved gold panning algorithm, the multi-team travel model and the single-team travel model are optimized respectively to select a benchmark travel model;

[0009] The travel mode of each route of the benchmark travel model is optimized to screen out the optimal inspection path of the substation.

[0010] Preferably, the behavior process of the gold digger's free exploration behavior is expressed as:

[0011]

[0012] Where ω1, ω2, and ω3 are the position weights of gold diggers g1, g2, and i, respectively; are the Euclidean distances of gold diggers g1, g2, and i respectively; g1, g2, and i represent three gold diggers respectively.

[0013] Preferably, the expression of the convergence factor based on the cosine form is:

[0014]

[0015] Wherein, μ is the attenuation factor, 0<μ≤1, here it is taken as 0.95; ζ is the disturbance coefficient, taken as 0.02; r3 is the random quantity between [0,1]; t max is the maximum number of iterations of the algorithm; π is the pi; cos(·) is the cosine function.

[0016] Preferably, the expression of the multi-team peer model is:

[0017]

[0018] Where f1(·) is the time function of the multi-team peer model; n is the number of substations; m is the substation number; t m,m+1 ,t m,m+2 are the average time required for a vehicle to travel from substation m to substation m+1 and m+2 respectively; Δt m is the time compensation amount of substation m.

[0019] Preferably, the expression of the single-team travel model is:

[0020]

[0021] Where f2(·) is the time function of the single-team travel model; t n,0 is the average time taken by a vehicle from substation n to the origin; n is the number of substations; is the inspection duration of substation m; l is the inspection type, where l=1, 2, 3 correspond to comprehensive inspection, routine inspection, and lights-out inspection respectively; d is the importance of the substation, where d=1, 2, 3, 4 represent Class 1 stations, Class 2 stations, Class 3 stations, and Class 4 stations respectively; m is the substation number.

[0022] Preferably, the expression for filtering out the benchmark travel model is:

[0023]

[0024] min F base =[F1,F2];

[0025] Among them, F1 and F2 are the travel time functions under the "multi-team travel" model and the "single team travel" model respectively; C is the number of engineering vehicles; n c is the total number of substations on the route taken by vehicle c; c is the engineering vehicle number; m and k are both substation numbers; is a logical variable, whose value is 0 or 1. For vehicle c, when the vehicle travels from substation m to substation k, Take 1, otherwise take 0; Δt m is the time compensation of substation m; l is the patrol type; L is the total number of types, which is 3 here; Note that when m = 0, t m,k is the average time required for a vehicle to travel from substation m to substation k; is the inspection time of substation m; min(·) is the minimum value function; F base is the benchmark travel model; l is the patrol type, where l = 1, 2, and 3 correspond to comprehensive patrol, routine patrol, and lights-off patrol, respectively.

[0026] Preferably, the expression for optimizing the travel mode for each route of the benchmark travel model is:

[0027]

[0028] Among them, n c is the total number of substations on the route taken by vehicle c; m is the substation number; is the inspection duration of substation m; l is the inspection type; d is the importance of the substation, where d = 1, 2, 3, 4 represent Class 1, Class 2, Class 3, and Class 4 stations respectively; Δt m is the time compensation of substation m; t 1,2 is the average time required for vehicles on the inspection route to travel from substation 1 to substation 2; t m,m+2 is the average time required for vehicles on the path to travel from substation m to substation m+2; The vehicle on this path starts from substation n c -1 to substation n c The average time required.

[0029] Preferably, the expression of the objective function for optimizing the travel mode for each route of the benchmark travel model is:

[0030]

[0031] Among them, F new (·) is the travel time function of the new route; κ is the route number in the benchmark model; R base is the total number of routes in the benchmark model; λ is the number of the new route division scheme; is the total number of substations passed by vehicle c under scheme λ; is the optimization function. When the benchmark model is the "single team travel" model, otherwise, C(·) is the combinatorial number function; The total travel time of the remaining routes after the routes in solution λ are removed from the baseline model; is the new benchmark model; F best is the optimal inspection path model for substations; C is the number of engineering vehicles; c is the engineering vehicle number; m and k are both substation numbers; ι is the number of routes to be taken in the combination function.

[0032] Compared with the prior art, the present invention has the following advantages and technical effects:

[0033] 1) After the present invention introduces free exploration behavior and a convergence factor based on the cosine form, the early search efficiency of the gold panning algorithm is improved, and the global optimization capability is enhanced to a certain extent.

[0034] 2) The planning of the present invention can take into account various types of inspections, and integrate the respective advantages of the "multi-team travel" model and the "single-team travel" model to optimize the optimal inspection path with the least total time consumption within the cycle and a compromise in the total number of route travels.

[0035] 3) The present invention supports patrol personnel to customize the passage time limit, patrol type and number of vehicles according to actual conditions, and reasonably plan differentiated patrol plans.

[0036] 4) The planning method of the present invention has strong scalability and is not limited to the field of substation. For power distribution and transmission, the present invention is still applicable by appropriately adjusting the inspection model and presetting relevant parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0038] Figure 1 A schematic diagram of free exploration behavior according to an embodiment of the present invention;

[0039] Figure 2 A comparison diagram of convergence factors of an embodiment of the present invention;

[0040] Figure 3 A schematic diagram of a "multi-team traveling together" model according to an embodiment of the present invention;

[0041] Figure 4 This is a schematic diagram of a “single-team travel” model according to an embodiment of the present invention;

[0042] Figure 5 A two-stage planning flow chart of an embodiment of the present invention;

[0043] Figure 6 A geographical distribution map of the substations under the jurisdiction of the substation area in an embodiment of the present invention;

[0044] Figure 7 It is a graph of algorithm convergence curve of an embodiment of the present invention;

[0045] Figure 8 It is a graph of algorithm convergence curves under different convergence factors of an embodiment of the present invention;

[0046] Fig. 9 A geographical distribution map of stations under the jurisdiction of a centralized control station according to an embodiment of the present invention;

[0047] Fig.10 It is a graph showing convergence curves of different algorithms in an embodiment of the present invention;

[0048] Fig.11 1 and 2 are the optimal inspection model route maps of the embodiments of the present invention, wherein (a) is the inspection route 1 map, (b) is the inspection route 2 map, (c) is the inspection route 3 map, (d) is the inspection route 4 map, (e) is the inspection route 5 map, and (f) is the inspection route 6 map. DETAILED DESCRIPTION

[0049] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0050] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0051] Embodiment 1

[0052] This embodiment provides a two-stage planning method for substation inspection paths based on an improved gold panning algorithm, including:

[0053] Step 1: For the emerging gold panning algorithm, the free exploration behavior of gold panners and the convergence factor based on cosine form are introduced to improve the solution accuracy of the algorithm on the substation inspection model.

[0054] Step 2: Plan for multiple inspection types, build two substation inspection models: "multi-team travel" and "single-team travel", preset inspection parameters such as the maximum route travel time, inspection type and number of vehicles, and use the improved gold-mining algorithm to optimize and screen out the benchmark travel model.

[0055] Step 3: Based on the benchmark travel model, combined with the travel advantages of the "multi-team travel" and "single-team travel" models, each route is optimized twice to determine the optimal inspection route with the least total time and a compromise in the total number of route trips within the cycle.

[0056] The preferred embodiments are described in detail below in conjunction with the accompanying drawings:

[0057] 1. The solution process of the gold mining algorithm is divided into three stages: migration, gold mining, and collaboration. The migration process of the gold miners can be described by equations (1) to (2):

[0058]

[0059] In the formula, is the migration vector; For the best gold mine location; is the current position of gold digger i; t is the number of iterations; is the new position of the gold digger i; A1 and C1 are variable coefficients, and their values ​​are shown in formula (3) and (4) respectively:

[0060]

[0061] C1=2·r2 (4)

[0062] In the formula, A1 and C1 are variable coefficients; r1 and r2 are random quantities between [0,1]; l1 is the convergence factor l e When e is 1, its calculation method is shown in formula (5):

[0063]

[0064] Where, t max is the maximum number of iterations; l e is the convergence factor; e is the sequence number; t is the number of iterations.

[0065] The gold panning process of the gold panner is shown in equations (6) to (7):

[0066]

[0067] In the formula, is the gold panning vector; is the position of the randomly selected gold digger g1; is the current location of the gold digger i; is the new position of the gold digger i; A2 is the variable coefficient, and its value is shown in formula (8):

[0068] A2=l2(2r1-1) (8)

[0069] In the formula, A2 is the variable coefficient; r1 is the random quantity between [0,1]; l2 is l e The value when e is 2.

[0070] In order to obtain more gold, gold diggers usually cooperate with each other. The mathematical model of the cooperation process is shown in equations (9) to (10):

[0071]

[0072] In the formula, is the collaboration vector; is the position of the randomly selected gold digger g1; is the position of the randomly selected gold digger g2; is the new position of gold digger i; r1 is a random value between [0,1]; The current location of gold digger i.

[0073] If gold digger i can obtain more gold in the new position, update i's current position.

[0074] 2. The free exploration behavior of the gold diggers exists in the collaborative process. During this period, the gold digger i searches for gold in the direction specified by two randomly selected gold diggers. In order to further find better gold mines, the position update formula of the gray wolf optimization algorithm is improved, and the gold digger's gold-seeking direction is dynamically changed near the center of gravity of the triangle formed by the three gold diggers as vertices. The behavior diagram is shown in the figure below. Figure 1 As shown, the exploration process is shown in formulas (11) to (14):

[0075]

[0076] Where ω1, ω2, and ω3 are the position weights of gold diggers g1, g2, and i, respectively; are the Euclidean distances of gold diggers g1, g2, and i respectively; New location for Gold Diggers i.

[0077] 3. The convergence factor expression based on the cosine form is shown in formula (15), and the function graph is shown in Figure 2 As shown, its function is to replace the original convergence factor l1 to broaden the exploration space of the gold digger and ensure the optimization accuracy of the algorithm.

[0078]

[0079] Wherein, l1′ is the convergence factor; μ is the attenuation factor, 0<μ≤1, and in this embodiment, it is 0.95; ζ is the disturbance coefficient, which is 0.02; r3 is the random quantity between [0,1]; t max is the maximum number of iterations; t is the number of iterations; cos(·) is the cosine function; π is the circumference of a circle.

[0080] 4. Plan to construct two substation inspection models: "multiple teams traveling together" and "single team traveling together" for the three types of inspections: comprehensive inspection, routine inspection and lights-out inspection. Among them, "multiple teams traveling together" means that multiple inspection teams share a vehicle for travel. The model diagram is as follows: Figure 3 As shown in the figure; "Single team travel" means that a patrol team travels by car to complete the inspection work of the substation, and its schematic diagram is as follows Figure 4 shown.

[0081] Figure 3 In the process, the engineering vehicle cooperates with two inspection teams to conduct inspections. The process is: the vehicle first sends inspection team 1 to station A, then sends inspection team 2 to station B, then the vehicle returns to station A to pick up inspection team 1 and sends it to station C, then goes to station B to carry inspection team 2 to station D...;

[0082] The mathematical model of “multiple teams traveling together” is shown in formula (16):

[0083]

[0084] Where f1(·) is the time function of the “multi-team” model; m is the substation number; n is the number of substations; t m,m+1 ,t m,m+2 are the average time required for a vehicle to travel from substation m to substation m+1 and m+2 respectively; t n-1,n is the average time required for a vehicle to travel from substation n-1 to substation n; t n,0 is the average time taken by the vehicle from substation n to the origin; Δt m is the time compensation of substation m, and its value is defined by formula (17):

[0085]

[0086] In the formula, Δt m is the time compensation of substation m; t m,m′ The time it takes for a vehicle to depart from substation m and return to substation m; is the inspection duration of substation m; l is the inspection type, where l=1, 2, 3 correspond to comprehensive inspection, routine inspection, and lights-out inspection respectively; d is the importance of the substation, where d=1, 2, 3, 4 represent Class 1 stations, Class 2 stations, Class 3 stations, and Class 4 stations respectively; m is the substation number.

[0087] The mathematical model of “single-team travel” is shown in formula (18):

[0088]

[0089] Where: f2(·) is the time function of the “single team travel” model; t n,0 is the average time taken by a vehicle from substation n to the origin; m is the substation number; n is the number of substations; t m,m+1 is the average time required for a vehicle to travel from substation m to substation m+1; is the inspection duration of substation m; l is the inspection type, where l=1, 2, 3 correspond to comprehensive inspection, routine inspection, and lights-out inspection respectively; d is the importance of the substation, where d=1, 2, 3, 4 represent Class 1 substation, Class 2 substation, Class 3 substation, and Class 4 substation respectively.

[0090] 5. The longest travel time of the route corresponds to the driving time of the route, and its value is set by the inspection personnel according to the actual situation. By optimizing the "single team travel" and "multi-team travel" models respectively, the travel model with the smallest total travel time is selected as the benchmark travel model. The optimization process is shown in equations (19) to (21):

[0091]

[0092] min F base =[F1,F2] (21)

[0093] Where, F1 and F2 are the travel time functions under the “multi-team travel” model and the “single-team travel” model respectively; C is the number of engineering vehicles; n c is the total number of substations on the route taken by vehicle c; c is the engineering vehicle number; m and k are both substation numbers; is a logical variable, whose value is 0 or 1. For vehicle c, when the vehicle travels from substation m to substation k, Take 1, otherwise take 0; Δt m is the time compensation amount of substation m; l is the patrol type; L is the total number of types, which is 3 in this embodiment; note that when m=0, t m,k is the average time required for a vehicle to travel from substation m to substation k; is the inspection time of substation m; min(·) is the minimum value function; F base is the benchmark travel model; l is the patrol type, where l = 1, 2, and 3 correspond to comprehensive patrol, routine patrol, and lights-off patrol, respectively.

[0094] 6. Model travel advantages: The total time taken by the model to inspect a certain route is taken into consideration. If the travel time of "multiple teams" is shorter than that of "single team", then "multiple teams" will be used for travel; otherwise, "single team" will be selected. The selection criteria of the model are as follows:

[0095]

[0096] Where n c is the total number of substations on the route taken by vehicle c; m is the substation number; is the inspection duration of substation m; l is the inspection type; d is the importance of the substation, where d = 1, 2, 3, 4 represent Class 1, Class 2, Class 3, and Class 4 stations respectively; Δt m is the time compensation of substation m; t 1,2 is the average time required for vehicles on the inspection route to travel from substation 1 to substation 2; t m,m+2 is the average time required for vehicles on the path to travel from substation m to substation m+2; The vehicle on this path starts from substation n c -1 to substation n c The average time required.

[0097] 7. Secondary optimization is to optimize the travel mode of each route in the benchmark travel model, and select the optimal inspection path of the substation based on the steps shown in equations (23) to (25). The two-stage planning process of the substation inspection path is as follows: Figure 5 shown.

[0098]

[0099] In the formula, F new (·) is the travel time function of the new route; κ is the route number in the benchmark model; R base is the total number of routes in the benchmark model; λ is the number of the new route division scheme; is the total number of substations passed by vehicle c under scheme λ; is the optimization function. When the benchmark model is the "single team travel" model, otherwise, C(·) is the combinatorial number function; The total travel time of the remaining routes after the routes in solution λ are removed from the baseline model; is the new benchmark model; F best is the optimal inspection path model for substations; C is the number of engineering vehicles; c is the engineering vehicle number; m and k are both substation numbers; ι is the number of routes to be taken in the combination function.

[0100] The technical effects of the present invention will be further illustrated by examples below:

[0101] The present invention uses a substation and a centralized control station in a certain city as examples to perform simulation analysis and verify the effectiveness and universality of the proposed method. Since the examples in this embodiment only involve Class 3 and Class 4 stations, the patrol parameters of the substation are set as shown in Table 1. In addition, the maximum travel time limit T of the route is set. lim = 240min; General parameters of the gold mining algorithm: Total number of gold miners: 30; t max =100.

[0102] Table 1

[0103]

[0104] (1) Example of stations under the jurisdiction of the substation area

[0105] Figure 6 The geographical distribution of the stations under the jurisdiction of the work area is shown. The travel time between stations is calculated using map navigation software based on the geographical coordinates of each station to generate a substation travel time matrix.

[0106] 1) Algorithm improvement effect

[0107] In order to reflect the accuracy advantage of the algorithm after introducing the cosine-based convergence factor and free exploration behavior, the algorithms are compared respectively. Figure 7 The convergence curves of the two algorithms are given. Figure 8 The convergence curves of algorithms based on two convergence factors are compared.

[0108] Depend on Figure 7 It can be seen that the improved gold panning algorithm saves 3.79 minutes in the total route time compared with the gold panning algorithm. This shows that the improvement strategy proposed in this embodiment can improve the optimization accuracy of the algorithm to a certain extent and enhance the global optimization ability of the algorithm.

[0109] Figure 8 In the example, the iterative curve based on the cosine form of the convergence factor has higher convergence accuracy. This is because the improved convergence factor improves the algorithm's search efficiency in the early iteration and the optimization accuracy in the later iteration, making it easier to search for smaller values ​​and maintain them for a longer time, and the algorithm's global search and local search capabilities are balanced. It is emphasized that since the optimization time is not the main concern of the engineering planning problem, it is not considered in this embodiment.

[0110] 2) Comparison of inspection schemes for work area examples

[0111] The planning period of this embodiment is 1 month, and a total of 72 inspections of the stations under its jurisdiction are required during this period. In order to highlight the advantages of the two-stage planning method of this embodiment, the substation optimal inspection path model is compared with the "multi-team travel" model and the "single team travel" model. The comparison results are shown in Table 2, and Tables 3 to 5 give the optimization solutions of each model.

[0112] Table 2

[0113]

[0114] Table 3

[0115]

[0116]

[0117] Table 4

[0118]

[0119]

[0120] Table 5

[0121]

[0122]

[0123] From Tables 2 to 5, we can see that the optimal inspection route model for substations is based on the "multi-team travel" model with a total time of 7565.57 minutes selected in the first stage of planning, combined with the travel advantages of the "single team travel" model, and finally optimized the optimal inspection route with a total time of 7163.63 minutes. In addition, in terms of the total number of route trips, the optimal inspection model has fewer trips than the benchmark model, which reduces the frequency of outbound inspections by the inspection team during the cycle and improves work efficiency to a certain extent.

[0124] (2) Calculation example of stations under the jurisdiction of the centralized control station

[0125] Fig. 9 The centralized control station shown is responsible for the centralized monitoring and comprehensive management of substations in the western area of ​​a city, including 11 220kV substations, 39 110kV substations and 2 35kV substations, involving 16 Class 3 stations and 36 Class 4 stations. The inspection teams gather at the centralized control station and are under its unified deployment.

[0126] 1) Comparison of optimization algorithms

[0127] In the field of path optimization, the sparrow search algorithm (SSA), greedy algorithm (GA), and GWO are the mainstream algorithms today. Compared with some classic algorithms, they have higher search efficiency and are not easy to fall into local optimality. In order to reflect the competitiveness of the GRO algorithm, a horizontal comparison is now conducted. The comparison results are as follows: Fig.10 shown.

[0128] Depend on Fig.10It can be seen that none of the algorithms fell into local optimality during the iteration process, and all of them searched for the best solution in the global scope. In terms of finding competitive solutions, the GRO algorithm is better than other algorithms, and has a stronger global optimization ability. Its convergence curve tends to be stable in the 50th iteration process, showing a faster convergence speed. It can be seen that the GRO algorithm is a better choice for solving path optimization problems.

[0129] 2) Comparison of inspection schemes for centralized control stations

[0130] To further prove the universality of the planning method of this embodiment, a large-scale centralized control station system is simulated and verified. The first phase of the example selects the "single-team travel" model with a smaller total travel time as the benchmark model. Based on this, the second phase of planning optimizes the travel plan with a total of 99 trips. The route map is as follows: Fig.11 As shown in Table 6, the optimization results of the quadratic programming are shown in Table 6.

[0131] Fig.11 Middle: The thin line represents the inspection route of the "single team travel" model, and the thick line represents the inspection route of the "multi-team travel" model. The advantage of using the "multi-team travel" model for some routes is that if the route time from the first arrival of the vehicle to the return to the station is less than the corresponding inspection time of the station, then compared with the baseline model, this model can save twice the travel time from the station to the next station, significantly reducing the vehicle's travel time.

[0132] Table 6

[0133]

[0134] As shown in Table 6, the total cycle time of the optimal inspection model saves 640.7 minutes, about 11 hours, compared with the baseline model, and the total number of route driving is 7 times less than the baseline model, which greatly improves the inspection efficiency of the inspection team and enhances the management efficiency of the centralized control station.

[0135] The above are only preferred specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A two-stage planning method for substation inspection path based on improved gold panning algorithm, characterized in that: The following steps are involved: An improved gold mining algorithm is constructed based on the free exploration behavior of gold miners and the convergence factor based on the cosine form; Construct a multi-team travel model and a single-team travel model based on multiple patrol scenarios; Based on the improved gold panning algorithm, the multi-team travel model and the single-team travel model are optimized respectively to select a benchmark travel model; The travel mode of each route of the benchmark travel model is optimized to screen out the optimal inspection path of the substation.

2. The two-stage planning method for substation inspection path based on improved gold panning algorithm according to claim 1 is characterized in that: The expression of the behavior process of the gold digger's free exploration behavior is: Where ω1, ω2, and ω3 are the position weights of gold diggers g1, g2, and i, respectively; are the Euclidean distances of gold diggers g1, g2, and i respectively; g1, g2, and i represent three gold diggers respectively.

3. The two-stage planning method for substation inspection path based on improved gold panning algorithm according to claim 1 is characterized in that: The expression of the convergence factor based on the cosine form is: Wherein, μ is the attenuation factor, 0<μ≤1, here it is taken as 0.95; ζ is the disturbance coefficient, taken as 0.02; r3 is the random quantity between [0,1]; t max is the maximum number of iterations of the algorithm; π is the pi; cos(·) is the cosine function.

4. The two-stage planning method for substation inspection path based on improved gold panning algorithm according to claim 1 is characterized in that: The expression of the multi-team peer model is: Where f1(·) is the time function of the multi-team peer model; n is the number of substations; m is the substation number; t m,m+1 ,t m,m+2 are the average time required for a vehicle to travel from substation m to substation m+1 and m+2 respectively; Δt m is the time compensation amount of substation m.

5. The two-stage planning method for substation inspection path based on improved gold panning algorithm according to claim 1 is characterized in that: The expression of the single-team travel model is: Where f2(·) is the time function of the single-team travel model; t n,0 is the average time taken by a vehicle from substation n to the origin; n is the number of substations; is the inspection duration of substation m; l is the inspection type, where l=1, 2, 3 correspond to comprehensive inspection, routine inspection, and lights-out inspection respectively; d is the importance of the substation, where d=1, 2, 3, 4 represent Class 1 stations, Class 2 stations, Class 3 stations, and Class 4 stations respectively; m is the substation number.

6. The two-stage planning method for substation inspection path based on improved gold panning algorithm according to claim 1 is characterized in that: The expression for filtering out the benchmark travel model is: <h2 style=";text-align:left;direction:ltr">min F<h2 style=";text-align:left;direction:ltr"> base <h2 style=";text-align:left;direction:ltr"> ([F1,F2]) Among them, F1 and F2 are the travel time functions under the "multi-team travel" model and the "single team travel" model respectively; C is the number of engineering vehicles; n c is the total number of substations on the route taken by vehicle c; c is the engineering vehicle number; m and k are both substation numbers; is a logical variable, whose value is 0 or 1. For vehicle c, when the vehicle travels from substation m to substation k, Take 1, otherwise take 0; Δt m is the time compensation of substation m; l is the patrol type; L is the total number of types, which is 3 here; Note that when m = 0, t m,k is the average time required for a vehicle to travel from substation m to substation k; is the inspection time of substation m; min(·) is the minimum value function; F base is the benchmark travel model; l is the patrol type, where l = 1, 2, and 3 correspond to comprehensive patrol, routine patrol, and lights-off patrol, respectively.

7. The two-stage planning method for substation inspection path based on improved gold panning algorithm according to claim 1 is characterized in that: The expression for optimizing the travel mode for each route of the benchmark travel model is: Among them, n c is the total number of substations on the route taken by vehicle c; m is the substation number; is the inspection duration of substation m; l is the inspection type; d is the importance of the substation, where d = 1, 2, 3, 4 represent Class 1, Class 2, Class 3, and Class 4 stations respectively; Δt m is the time compensation of substation m; t 1,2 is the average time required for vehicles on the inspection route to travel from substation 1 to substation 2; t m,m+2 is the average time required for vehicles on the path to travel from substation m to substation m+2; The vehicle on this path starts from substation n c -1 to substation n c The average time required.

8. The two-stage planning method for substation inspection path based on improved gold panning algorithm according to claim 1 is characterized in that: The expression of the objective function for optimizing the travel mode for each route of the benchmark travel model is: In the formula, F new (·) is the travel time function of the new route; κ is the route number in the benchmark model; R base is the total number of routes in the benchmark model; λ is the number of the new route division scheme; is the total number of substations passed by vehicle c under scheme λ; is the optimization function. When the benchmark model is the "single team travel" model, otherwise, C(·) is the combinatorial number function; The total travel time of the remaining routes after the routes in solution λ are removed from the baseline model; is the new benchmark model; F best is the optimal inspection path model for substations; C is the number of engineering vehicles; c is the engineering vehicle number; m and k are both substation numbers; ι is the number of routes to be taken in the combination function.

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