A method for optimizing the construction path of a mountain wind farm tower grounding resistance reduction reconstruction

Abstract

The application provides a mountain wind farm tower grounding resistance reduction reconstruction construction path optimization method, and belongs to the technical field of engineering construction optimization, which can at least partially solve the problems that the existing path planning is carried out by manual measurement, lacks accurate constructability analysis, it is difficult to effectively predict and optimize the grounding electrical performance before construction, the conductivity of traditional conductor materials is limited, the ability to reduce grounding impedance is limited, and it is difficult to meet the requirements of modern wind farms on reliability and safety, and the application comprises the following steps: unmanned aerial vehicle multi-source detection, generation of a simple geographic model containing obstacle labels; planning the obstacle avoidance shortest path of the graphene grounding belt on the model; modeling and simulating the grounding system and checking the grounding resistance, the application assigns a grid to the geographic model and gives a partition passing cost, sets the risk area and non-constructible area as non-passable, avoids underground pipelines and optical cable protection areas, and significantly reduces construction risks such as excavation damage, sliding and mechanical rollover.

Description

Technical Field

[0001] This invention belongs to the field of engineering construction optimization technology, specifically relating to a method for optimizing the construction path of the grounding resistance reduction renovation of towers in mountainous wind farms. Background Technology

[0002] In the construction and operation of wind farms, the safety and reliability of the tower grounding system are crucial for equipment lightning protection, operational safety, and power system stability. Traditional grounding system design often relies on experience and localized on-site surveys, resulting in problems such as uneven grounding impedance, unscientific construction path planning, low construction efficiency, and difficulty in quantifying impedance reduction effects. Especially in mountainous or complex terrain conditions, the large topographic relief, dense vegetation, and widespread distribution of underground pipelines and hard surfaces significantly increase the difficulty and safety risks of laying grounding strips. Current technologies often rely on manual surveying or limited two-dimensional topographic maps for path planning, lacking precise constructability analysis of the construction area and making it difficult to effectively predict and optimize grounding electrical performance before construction. Furthermore, the limited conductivity of traditional conductor materials restricts their ability to reduce grounding impedance, especially in areas with high soil resistivity and complex environments, where the performance of the grounding system often fails to meet the reliability and safety requirements of modern wind farms.

[0003] With the development of UAVs, multi-source remote sensing technology, and computer modeling and simulation technology, new technical means have been provided for the construction of wind farm grounding systems. UAVs can achieve high-precision, non-contact environmental data acquisition, obtaining real-time information on terrain, vegetation, surface structures, and underground facilities; path optimization algorithms can achieve intelligent planning of grounding strip construction paths under the premise of considering safety, accessibility, and economic constraints; combined with grounding simulation software such as CDEGS, grounding impedance and current distribution can be predicted and optimized before construction, improving the resistance reduction effect and construction efficiency of the grounding system. However, existing technologies still lack a holistic solution that combines UAV multi-source sensing data, construction feasibility analysis, path optimization algorithms, and graphene high-conductivity materials to achieve a grounding system optimization method that enables scientific planning of construction paths, quantifiable resistance reduction effects, and safe and reliable construction. Therefore, we propose a construction path optimization method for the grounding resistance reduction retrofit of wind farm towers in mountainous areas. Summary of the Invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art, and provides a method for optimizing the construction path of the grounding resistance reduction retrofit of wind farm towers in mountainous areas.

[0005] This invention provides a method for optimizing the construction path of grounding resistance reduction retrofitting of wind farm towers in mountainous areas, comprising the following steps: S1: Use drones equipped with multi-source sensors to survey the tower and its surroundings, and build and label a geographic model of construction obstacles based on the survey data; S2: Based on the geographical model, plan the shortest obstacle avoidance path for the graphene grounding zone from the starting point S at the top of the tower to the farthest physical endpoint T in the geographical model; S3: Establish a grounding system simulation model according to the described path, and verify whether the grounding resistance in the simulation model meets the standard.

[0006] Furthermore, in step S3, if the grounding resistance parameter does not meet the standard, return to step S2 to adjust the obstacle avoidance shortest path until it meets the standard.

[0007] Specifically, step S1 includes: S1.1, Use the multi-source sensors of UAVs to identify the distribution areas of underground pipelines and optical cable facilities, and determine the safety protection zones for underground pipelines and optical cable facilities; S1.2, using the multi-source sensors of the UAV to detect terrain undulations and surface rocks, and to determine areas where the slope exceeds the threshold and areas with surface rocks; S1.3, use the multi-source sensors of the UAV to identify vegetation distribution and calculate the Normalized Difference Vegetation Index (NDVI) to determine areas where dense vegetation coverage exceeds the threshold. S1.4, Based on the detection results, construct the simplified geographic model and mark the underground pipeline and optical cable facility safety protection zone, the slope exceeding the threshold area, the surface rock area and the dense vegetation coverage area exceeding the threshold area as obstacle areas.

[0008] Specifically, in step S1.4: The radius of the safety protection zone for the underground pipelines and optical cable facilities is set at 0.5 meters, and these areas are marked as risk zones. A construction safety threshold for terrain slope is set at 35°, and areas with slopes exceeding the threshold are marked as non-construction areas. The area with surface rocks is marked as a restricted construction zone; The NDVI threshold for densely vegetated areas is set to 0.5, and areas with dense vegetation coverage exceeding the threshold are marked as restricted construction areas.

[0009] Preferably, step S2 includes: S2.1 Extract the outermost contour of the workable area from the geographic model, calculate the Euclidean distance between each point on the contour and the starting point S of the tower vertex, and determine the point with the largest distance as the farthest endpoint T; S2.2, the geographic model is rasterized and passage costs are assigned to different areas. The Dijkstra algorithm is used to plan the obstacle avoidance path with the minimum cumulative passage cost from the starting point S of the pole vertex to the farthest endpoint T.

[0010] Specifically, in step S2.2: Assign a minimum passage cost to the workable area; A higher passage cost will be allocated to the restricted construction area; Assign an infinitely large passage cost to the risk area and the non-constructable area.

[0011] Further, in step S2.2, the cost function of the Dijkstra algorithm is F(n) = G(n) + H(n), where G(n) is the actual cumulative cost from the starting point S of the tower vertex to the current node, and H(n) is the Euclidean distance from the current node to the farthest endpoint T.

[0012] Further, step S3 includes: S3.1 Based on the planned optimal laying path, a three-dimensional simulation model of the graphene grounding strip is established, and the electrical parameters of the graphene composite material and soil resistivity data are set. S3.2 Perform grounding resistance simulation calculation, compare the results of power frequency grounding resistance and impulse grounding resistance with the national standard. If they are not qualified, return to step 2 to add or adjust the grounding strip laying path and re-simulate until the standard is met.

[0013] Furthermore, in step S3.2: The power frequency grounding resistance value obtained from the simulation Compared with national standard requirements Conduct comparative verification; Establish an iterative optimization mechanism, when Then return to step S2 to optimize the path until... , where k is the number of iterations.

[0014] Specifically, the multi-source sensors include, but are not limited to: lidar, cameras, RGB cameras, multispectral cameras, infrared thermal imagers, ground-penetrating radar, and RTK-GNSS / IMU fusion positioning systems.

[0015] The beneficial effects of this invention are as follows: By rasterizing the geographic model and assigning zoned access costs, and setting risk zones and non-construction zones as "inaccessible" (equivalent to infinite costs), the algorithm forces the avoidance of underground pipeline and fiber optic cable protection zones, ultra-slope zones, and boulder zones, significantly reducing construction risks such as excavation damage, slippage, and machinery rollover. In step S2, obstacle avoidance shortest path planning is performed from the tower apex S to the target T with the farthest physical location on the outer contour, extending the grounding strip's deployment length and coverage as much as possible without touching obstacles. In step S3, the path is linked with material and soil parameters through three-dimensional simulation, and the power frequency / impulse grounding resistance is directly evaluated in the simulation domain, so that the resistance reduction effect can be quantified before construction. Attached Figure Description

[0016] Figure 1 A flowchart illustrating the steps of a construction path optimization method for grounding resistance reduction retrofitting of towers in mountainous wind farms, according to a specific embodiment of the present invention. Figure 2 A map showing the feasible area distribution of a construction path optimization method for the grounding resistance reduction retrofit of towers in a mountainous wind farm, according to a specific embodiment of the present invention. Figure 3 The graphene grounding strip simulation model diagram is shown for a construction path optimization method for the grounding resistance reduction retrofit of mountain wind farm towers according to a specific embodiment of the present invention. Figure 4 This is a simulation model diagram of the tower grounding grid for a construction path optimization method for tower grounding resistance reduction retrofitting in a mountainous wind farm, according to a specific embodiment of the present invention. Detailed Implementation

[0017] To enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0018] This invention provides a method for optimizing the construction path of grounding resistance reduction retrofitting of wind farm towers in mountainous areas, as shown in the specific embodiments. Figure 1 As shown, step S1 involves construction environment modeling and obstacle recognition based on UAV multi-source perception: Prior to construction, drones equipped with multi-source sensors were used to conduct comprehensive environmental surveys of the power collection line towers and surrounding areas of the mountain wind farm. These multi-source sensors included high-definition cameras, LiDAR, infrared thermal imagers, visible light sensors, and multispectral imaging equipment to detect underground optical cables, electrical cables, surface rocks, and dense vegetation, and to establish no-go zones for construction obstacles.

[0019] Specifically, step S1 includes step S1.1, which, to ensure the absolute safety of the graphene grounding strip laying construction without power interruption, requires precise identification and marking of the exact paths and burial depths of all underground optical cables and electrical cables within the construction area before groundwork is laid. A drone equipped with multi-source sensing devices, including a high-resolution visible light camera, an infrared thermal imager, and a ground-penetrating radar (GPR), will be used to perform a full-coverage scan of the construction area to identify the potential distribution areas of underground optical cables, electrical cables, and other facilities.

[0020] Infrared thermal imagers identify potential underground cable areas by monitoring differences in surface temperature distribution. Because underground cables accumulate heat during operation, the heat exchange balance in their burial area is disrupted, resulting in a slight increase in surface temperature relative to the surrounding environment. This manifests as linear or band-shaped thermal anomalies in infrared thermal images, allowing for a preliminary inference of the spatial distribution path of underground cables.

[0021] Ground penetrating radar (GPR) systems transmit high-frequency electromagnetic waves into the ground and analyze the characteristics of the reflected signals to identify changes in the underground medium and abnormal structures, thereby inferring the depth information and morphological characteristics of underground optical cables.

[0022] By spatially registering and overlaying visible light images, infrared thermal images, and ground-penetrating radar anomaly reflection maps, and comparing the corresponding ground features of thermal and electromagnetic anomaly areas in the visible light images, the comprehensive identification and precise positioning of underground optical cables and electrical cables can be achieved, providing a reliable safety guarantee for subsequent grounding zone construction.

[0023] Furthermore, step S1 also includes steps S1.2, S1.3, and S1.4. To comprehensively understand the topographic features, surface obstacle distribution, and hard ground distribution of the construction area, a drone equipped with LiDAR, a high-definition camera, multispectral imaging equipment, and infrared imaging equipment is used to conduct multi-source simultaneous detection of the construction area and acquire multimodal surface data. The drone equipped with LiDAR and a high-definition camera is used to detect ground undulations and the location of surface rocks, while the drone equipped with multispectral and infrared imaging equipment is used to identify vegetation distribution.

[0024] LiDAR (Light Detection and Ranging) acquires high-density point cloud data of the terrain by emitting high-frequency laser beams and receiving echo signals, accurately reflecting topographic relief, slope characteristics, and surface roughness. Based on the point cloud data, digital elevation models (DEMs) and digital surface models (DSMs) can be generated, enabling visualization and quantification of ground undulations. Simultaneously, leveraging the penetrating power of LiDAR, it can identify hard objects such as rocks and bare rocks obscured by sparse vegetation, and extract their spatial location, morphological features, and volumetric dimensions.

[0025] Multispectral and infrared imaging equipment is used to identify vegetation and surface material types within the construction area. Unmanned aerial vehicles (UAVs) simultaneously collect surface reflection and radiation information across multiple bands, including visible, near-infrared, and thermal infrared. Due to the strong reflectivity of vegetation in the near-infrared band and its absorption characteristics in the red band, high-density vegetation areas can be accurately distinguished from low-coverage vegetation areas. Simultaneously, because hard surfaces have lower reflectivity and higher thermal inertia in the infrared band, spectral differences can be used to effectively differentiate between vegetated areas and hard surfaces.

[0026] The multispectral and infrared imaging equipment carried by the drone extracts the reflectance of each wavelength band (visible light). Near-infrared Shortwave infrared Thermal infrared As a representation of vegetation cover status, the Normalized Difference Vegetation Index (NDVI) is calculated using reflectance.

[0027] A higher NDVI value indicates denser vegetation in the area; areas with low or near-zero NDVI values ​​often correspond to exposed rock, hardened ground, or low-reflectivity soil. By combining NDVI with thermal infrared reflectance characteristics, it is possible to classify and identify hard surfaces, high-density vegetation areas, and low-coverage vegetation areas, providing accurate surface type references for grounding zone paving route planning.

[0028] Furthermore, such as Figure 2 As shown, in step S1.4, after the UAV completes the cruise scan, it automatically uploads the collected multi-source perception data to the ground workstation. The ground workstation fuses and preprocesses the multi-source data, and draws a simplified geographic model map of the tower and its surrounding area based on the UAV imagery and ranging information, while identifying and marking construction obstacle areas.

[0029] Based on the detection results, the system classifies different types of risks and limiting factors into categories: For areas with concealed facilities such as underground cables and optical cables, a safety protection zone radius of 0.5 meters is set, and these areas are marked as risk areas on the model map; For areas with a large number of rocks, bare rocks, or hardened structures on the surface, these areas are marked as non-construction areas based on the characteristics of lidar point clouds and infrared image reflection characteristics; For areas with dense vegetation cover, the Normalized Difference Vegetation Index (NDVI) is used as the judgment indicator, with a threshold of 0.5. When the NDVI value is higher than 0.5, it is marked as a restricted construction area; For areas with a large terrain slope, a construction safety threshold of 35° is set. When the slope exceeds this threshold, it is also marked as a restricted construction area.

[0030] A clearly marked and well-defined map of workable areas is generated from the simplified geographic model. This map will serve as the basis for subsequent planning of graphene grounding strip laying paths and optimization of construction schemes, providing accurate geographic information support for subsequent automated path calculations and on-site safe construction.

[0031] Based on the above basic implementation method, the method further includes step S2, which is the optimal laying path planning for graphene grounding strips based on Dijkstra's algorithm: Step S2.1: In the grounding system, the total length of the grounding electrode is closely related to the current dissipation resistance. In order to extend the laying length and coverage of the grounding strip as much as possible within the safe range, based on the simplified geographical model map with obstacle markings generated in step S1.4, firstly, any vertex of the outer edge of the tower foundation is designated as the starting point S, and the point with the longest straight-line distance from the starting point S is determined as the ending point T at the boundary of the workable area.

[0032] In the constructible area, a boundary extraction algorithm is used to identify the outermost constructible contour line. This contour forms a closed polygon, and all points on it are boundary points of the constructible area. By discretizing this contour, a dense set of boundary sampling points is generated as a candidate construction endpoint set. For each boundary point Calculate its Euclidean distance from the starting point S:

[0033] Record and sort the data, and take the point with the largest distance as the "farthest physical location endpoint T".

[0034] By connecting the starting point S and the ending point T, the main path of the grounding strip with the maximum achievable straight length within the construction area can be obtained without touching any obstacles. This maximizes the current dissipation range and efficiency of the grounding electrode, ensuring that the current can be quickly and evenly diffused into the ground under extreme conditions such as lightning strikes or short circuits, thereby improving the safety protection level of equipment and personnel.

[0035] Specifically, step S2 also includes step S2.2, which, after determining the starting point S and the ending point T, calculates the optimal passage path between the two points based on the Dijkstra algorithm, so as to obtain a safe construction route with the minimum cumulative passage cost while avoiding all obstacle areas, thereby planning a shortest path that connects the two points and avoids all obstacle areas, so as to ensure the construction economy, feasibility and safety while satisfying the flow dispersion effect.

[0036] The simplified geographic model map generated in step S1.3 is converted into a raster map, with the size of each raster cell set according to the construction accuracy requirements (e.g., 0.5m × 0.5m). A passage cost function value is assigned to each raster cell, defined as follows: 1. Construction-ready area (green marker): The passage cost is... That is, the lowest cost.

[0037] 2. Restricted construction area (yellow marker): The passage fee is... This means setting a higher cost to encourage the algorithm to prioritize green areas.

[0038] 3. Risk areas and areas where construction is prohibited (marked in red): The cost of passage is... This indicates that passage is prohibited.

[0039] Based on this, Dijkstra's algorithm is used to search for the minimum cost path from the starting point S to the ending point T in the raster map. The path cost function is defined as:

[0040] G(n) is the actual cumulative travel cost from the starting point S to the current node n. H(n) is the heuristically estimated distance from the current node n to the destination T, i.e., the current Euclidean distance. This is used to guide the search direction. During the iteration process, the algorithm automatically avoids red obstacle zones and tends to choose green zones with lower passage costs until a safe path with the minimum total cost is generated.

[0041] By continuously expanding the node with the current minimum F(n), Dijkstra's algorithm ultimately obtains a continuous grid path from the starting point S to the ending point T. This path achieves a comprehensive optimization of minimum cumulative passage cost and maximum flow dispersion path length while ensuring obstacle avoidance. After smoothing, this path is overlaid on the original geographic model map, presenting the optimal laying route of the graphene grounding strip in a visual form, providing data support for subsequent precise on-site construction.

[0042] In one specific implementation, such as Figure 3 As shown, the method also includes step S3, grounding system performance simulation verification and iterative optimization: Step S3.1, based on the optimal laying path obtained in step S2.2, establish a graphene grounding strip simulation model in CDEGS software. The grounding strip material is set to graphene composite material, and its resistivity is defined as: Significantly lower than traditional steel, relative permeability The material is non-magnetic, and a three-dimensional geometric model of the grounding strip is established according to the laying path.

[0043] Input the soil resistivity data measured in the field, establish a layered soil model, and calculate the resistivity of the horizontally layered soil model using the following formula:

[0044] in, Let be the resistivity of the i-th soil layer. Let be the influence coefficient of the i-th layer, and h be the measurement depth.

[0045] Perform frequency domain analysis in CDEGS to calculate the power frequency grounding resistance:

[0046] in, Power frequency grounding resistance ( ), The potential of the grounding device increases (V). The power frequency current (A) injected into the grounding device.

[0047] Simultaneously calculate the impulse grounding resistance:

[0048] in, The impact coefficient is related to soil resistivity and lightning current waveform.

[0049] In another specific embodiment, step S3 further includes step S3.2. The simulation results were compared and verified with national standards (such as DL / T 475-2017 and GB 50065-2011): If the power frequency grounding resistance value is lower than the standard requirement, i.e. Confirm that the design scheme is qualified; if the grounding resistance does not meet the requirements, i.e. Return to step 2 to add a graphene grounding strip laying path. Update the path and re-simulate after each iteration until the standard is met. , where k is the number of iterations.

[0050] Specifically, through simulation verification, it is ensured that the graphene grounding strip laying scheme meets physical construction constraints while also fully complying with safe operation standards in terms of electrical performance.

[0051] In one specific implementation, taking a particular tower as an example, the soil conditions of the tower are as follows: upper soil resistivity 200Ω×m, upper soil thickness 1.5m, middle soil resistivity 100Ω×m, middle soil thickness 0.5m, and lower soil resistivity 50Ω×m; the foundation grounding parameters of the tower are as follows: tower height 27m, number of segments 4, grounding grid shape square, grounding grid side length 4m, grounding electrode radius 0.01m, tower support height 3m, and tower center height 1m.

[0052] Furthermore, simulation calculations showed that the resistance of the tower foundation grounding electrode was 6.42Ω. The grounding resistance of this tower is slightly greater than the specified value. In soil-rich areas, the resistance can be reduced by adding horizontal and vertical grounding electrodes.

[0053] Furthermore, such as Figure 4 The diagram shows a simulation model of the designed tower grounding grid, which is a ring-shaped grounding grid. Based on a square grounding grid, two 4m long, 1m deep horizontal grounding electrodes are radially led out from the two lower corners. Two 1m long vertical grounding wires are led out from the ends of these horizontal grounding electrodes, and similar horizontal grounding electrodes continue to be led out. Due to terrain influences and economic considerations, the horizontal grounding electrodes on both sides are ultimately connected to form one horizontal grounding electrode, resulting in a total of 7 vertical grounding electrodes and 8 horizontal grounding electrodes. Simulation calculations show that the resistance value of the grounding grid is 3.39Ω.

[0054] To aid in a better understanding of the present invention, a more comprehensive and specific embodiment is described, in which the present invention provides a method for optimizing the construction path of grounding resistance reduction retrofitting of wind farm towers in mountainous areas, comprising the following steps: S1: Use drones equipped with multi-source sensors to survey the tower and its surroundings, and build and label a geographic model of construction obstacles based on the survey data; S2: Based on the geographical model, plan the shortest obstacle avoidance path for the graphene grounding zone from the starting point S at the top of the tower to the farthest physical endpoint T in the geographical model; S3: Establish a grounding system simulation model according to the path, and verify whether the grounding resistance in the simulation model meets the standard.

[0055] In this embodiment, if the grounding resistance parameter does not meet the standard in step S3, the process returns to step S2 to adjust the shortest obstacle avoidance path until it meets the standard. Step S1 includes: Step S1.1: Use the multi-source sensors of the UAV to identify the distribution areas of underground pipelines and optical cable facilities, and determine the safety protection zones for underground pipelines and optical cable facilities; Step S1.2: Use the multi-source sensors of the UAV to detect terrain undulations and surface rocks, and determine areas where the slope exceeds the threshold and areas with surface rocks. Step S1.3: Use the multi-source sensors of the UAV to identify vegetation distribution and calculate the Normalized Difference Vegetation Index (NDVI) to determine areas where dense vegetation coverage exceeds the threshold. Step S1.4: Based on the detection results, construct a simplified geographic model and mark the underground pipeline and fiber optic cable facility safety protection zone, the slope exceeding the threshold, the surface rock area, and the dense vegetation coverage area exceeding the threshold as obstacle areas; In step S1.4: The safety protection zone for underground pipelines and fiber optic cables is set at a radius of 0.5 meters and marked as a risk area; The construction safety threshold for terrain slope is set at 35°, and areas with slopes exceeding the threshold are marked as non-construction areas. Areas with surface rocks are marked as restricted construction zones; Set the NDVI threshold for densely vegetated areas to 0.5, and mark areas with dense vegetation coverage exceeding the threshold as restricted construction areas; Step S2 includes: Step S2.1: Extract the outermost contour of the workable area from the geographic model, calculate the Euclidean distance between each point on the contour and the starting point S of the tower apex, and determine the point with the largest distance as the farthest endpoint T. Step S2.2: Rasterize the geographic model and assign passage costs to different areas. Use Dijkstra's algorithm to plan the obstacle avoidance path with the minimum cumulative passage cost from the starting point S at the top of the tower to the farthest end point T. In step S2.2: Allocate minimum passage costs to areas where construction is possible; Higher traffic costs are allocated to restrict construction areas; Assign infinitely large passage costs to risk areas and non-constructable areas; in step S2.2, the cost function of Dijkstra's algorithm is F(n) = G(n) + H(n), where G(n) is the actual cumulative cost from the starting point S at the top of the tower to the current node, and H(n) is the Euclidean distance from the current node to the farthest endpoint T.

[0056] Further, step S3 includes: S3.1 Based on the planned optimal laying path, a three-dimensional simulation model of the graphene grounding strip is established, and the electrical parameters of the graphene composite material and soil resistivity data are set. S3.2 Perform grounding resistance simulation calculation, compare the results of power frequency grounding resistance and impulse grounding resistance with the national standard. If they are not qualified, return to step 2 to add or adjust the grounding strip laying path and re-simulate until the standard is met.

[0057] Furthermore, in step S3.2: The power frequency grounding resistance value obtained from the simulation Compared with national standard requirements Conduct comparative verification; Establish an iterative optimization mechanism, when Return to step S2 to optimize the path until... , where k is the iteration number; multi-source sensors include, but are not limited to: lidar, camera, RGB camera, multispectral camera, infrared thermal imager, ground penetrating radar and RTK-GNSS / IMU fusion positioning system.

[0058] In summary, the embodiments disclosed herein have at least the following technical effects: By rasterizing the geographic model and assigning access costs to different zones, and setting risk zones and non-construction zones as "inaccessible" (equivalent to infinite cost), the algorithm forces the avoidance of underground pipeline and fiber optic cable protection zones, ultra-slope zones, and boulder zones, significantly reducing construction risks such as excavation damage, slippage, and machinery rollover. The UAV is equipped with multiple sensors (LiDAR, RGB / multispectral, thermal imaging, GPR, RTK-GNSS / IMU, etc.) to jointly acquire high-density three-dimensional and spectral information, construct and label a geographic model containing obstacle semantics, which is more comprehensive, detailed and objective than a single sensor or manual reconnaissance, providing highly reliable input for subsequent path planning and simulation; We adopt shortest path planning based on travel cost to improve computational efficiency while ensuring the globally optimal reachable path, thus meeting the needs of rapid decision-making and multiple iterations in mountainous areas. In step S2, obstacle avoidance shortest path planning is performed from the tower top S to the target T with the farthest physical location on the outer contour. The grounding strip is extended as much as possible in terms of length and coverage without touching the obstacle. In step S3, the path is linked with material and soil parameters through three-dimensional simulation. The power frequency / impulse grounding resistance is directly evaluated in the simulation domain so that the resistance reduction effect can be quantified before construction. When the simulation results do not meet the national standards, the system automatically returns to step S2 to adjust or add laying paths and re-simulate, forming a closed loop of "planning-simulation-correction"; avoiding repeated trial digging based on experience, shortening the trial and error cycle, and improving the first-time compliance rate; NDVI is used to identify dense vegetation and designate it as a restricted construction zone to reduce clearing and disturbance; safety buffer zones are set up for underground facilities to avoid destructive excavation. Overall, this reduces the amount of excavated soil and secondary remediation costs, meeting ecological and pipeline safety requirements. Digital front-end reconnaissance and automated path planning reduce a significant amount of manual surveying and exploratory excavation; clearly defined minimum cumulative cost paths reduce empty runs and unnecessary detours, saving time, fuel consumption, and equipment depreciation, thus comprehensively reducing project costs.

[0059] It is understood that the above embodiments are merely exemplary implementations used to illustrate the principles of the present invention, and the present invention is not limited thereto. For those skilled in the art, various modifications and improvements can be made without departing from the spirit and essence of the present invention, and these modifications and improvements are also considered to be within the scope of protection of the present invention.

Claims

1. A method for optimizing the construction path of grounding resistance reduction retrofit for wind farm towers in mountainous areas, characterized in that, Includes the following steps: S1: Use drones equipped with multi-source sensors to survey the tower and its surroundings, and build and label a geographic model of construction obstacles based on the survey data; S2: Based on the geographical model, plan the shortest obstacle avoidance path for the graphene grounding zone from the starting point S at the top of the tower to the farthest physical endpoint T in the geographical model; S3: Establish a grounding system simulation model according to the described path, and verify whether the grounding resistance in the simulation model meets the standard.

2. The method for optimizing the construction path of grounding resistance reduction retrofit for mountain wind farm towers according to claim 1, characterized in that, In step S3, if the grounding resistance parameter does not meet the standard, return to step S2 to adjust the shortest obstacle avoidance path until it meets the standard.

3. The method for optimizing the construction path of grounding resistance reduction retrofit for mountain wind farm towers according to claim 1, characterized in that, Step S1 includes: S1.1, Use the multi-source sensors of UAVs to identify the distribution areas of underground pipelines and optical cable facilities, and determine the safety protection zones for underground pipelines and optical cable facilities; S1.2, using the multi-source sensors of the UAV to detect terrain undulations and surface rocks, and to determine areas where the slope exceeds the threshold and areas with surface rocks; S1.3, use the multi-source sensors of the UAV to identify vegetation distribution and calculate the Normalized Difference Vegetation Index (NDVI) to determine areas where dense vegetation coverage exceeds the threshold. S1.4, Based on the detection results, construct the simplified geographic model and mark the underground pipeline and optical cable facility safety protection zone, the slope exceeding the threshold area, the surface rock area and the dense vegetation coverage area exceeding the threshold area as obstacle areas.

4. The method for optimizing the construction path of grounding resistance reduction retrofit for mountain wind farm towers according to claim 3, characterized in that, In step S1.4: The radius of the safety protection zone for the underground pipelines and optical cable facilities is set at 0.2 to 0.5 meters, and these areas are marked as risk zones. The construction safety threshold range for the terrain undulation slope is set to 25° to 45°, and areas with slopes exceeding the threshold are marked as non-construction areas; The NDVI threshold range for the vegetation distribution area is set to 0.45 to 0.55, and the areas where the vegetation coverage exceeds the threshold and the areas with surface rocks are marked as restricted construction areas.

5. The method for optimizing the construction path of grounding resistance reduction retrofit for mountain wind farm towers according to claim 4, characterized in that, Step S2 includes: S2.1 Extract the outermost contour of the workable area from the geographic model, calculate the Euclidean distance between each point on the contour and the starting point S of the tower vertex, and determine the point with the largest distance as the farthest endpoint T; S2.2, the geographic model is rasterized and passage costs are assigned to different areas. The Dijkstra algorithm is used to plan the obstacle avoidance path with the minimum cumulative passage cost from the starting point S of the pole vertex to the farthest endpoint T.

6. The method for optimizing the construction path of grounding resistance reduction retrofit for mountain wind farm towers according to claim 5, characterized in that, In step S2.2: Assign a minimum passage cost to the workable area; A higher passage cost will be allocated to the restricted construction area; Assign an infinitely large passage cost to the risk area and the non-constructable area.

7. The method for optimizing the construction path of the grounding resistance reduction retrofit for mountain wind farm towers according to claim 1, characterized in that, In step S2.2, the cost function of the Dijkstra algorithm is F(n) = G(n) + H(n), where G(n) is the actual cumulative cost from the starting point S of the tower vertex to the current node, and H(n) is the Euclidean distance from the current node to the farthest endpoint T.

8. The method for optimizing the construction path of the grounding resistance reduction retrofit for mountain wind farm towers according to claim 1, characterized in that, Step S3 includes: S3.1 Based on the planned optimal laying path, a three-dimensional simulation model of the graphene grounding strip is established, and the electrical parameters of the graphene composite material and soil resistivity data are set. S3.2 Perform grounding resistance simulation calculation, compare the results of power frequency grounding resistance and impulse grounding resistance with the national standard. If they are not qualified, return to step 2 to add or adjust the grounding strip laying path and re-simulate until the standard is met.

9. The method for optimizing the construction path of grounding resistance reduction retrofit for mountain wind farm towers according to claim 8, characterized in that, In step S3.2: The power frequency grounding resistance value obtained from the simulation Compared with national standard requirements Conduct comparative verification; Establish an iterative optimization mechanism, when Then return to step S2 to optimize the path until... , where k is the number of iterations.

10. The method for optimizing the construction path of the grounding resistance reduction retrofit for mountain wind farm towers according to any one of claims 1 to 9, characterized in that, The multi-source sensors include, but are not limited to: lidar, cameras, RGB cameras, multispectral cameras, infrared thermal imagers, ground-penetrating radar, and RTK-GNSS / IMU fusion positioning systems.

Images