Power transmission line unmanned aerial vehicle deicing operation parameter template library generation method and system based on multi-scale mechanical simulation and verification

Abstract

The invention discloses a power transmission line unmanned aerial vehicle deicing operation parameter template library generation method and system based on multi-scale mechanical simulation and verification, and the method comprises the steps: S1, obtaining the working condition data of a power transmission line, and constructing a power transmission line basic model; s2, constructing a multi-scale mechanical model of ice; S2.1, determining the equivalent elastic modulus, the equivalent tensile strength and the equivalent fracture energy of the ice related to the environment temperature, the porosity and the loading strain rate; the method comprises the following steps: S1, establishing an unmanned aerial vehicle deicing dynamics simulation model, S2, establishing an ice body macroscopic constitutive model comprehensively considering elastic response, viscous damping and damage evolution, and S3, carrying out deicing dynamics simulation and result analysis: S3.1, establishing the unmanned aerial vehicle deicing dynamics simulation model by adopting a finite element and discrete element coupling method; s3.2, the deicing dynamics simulation model is operated, a simulation result used for evaluating the deicing effect and safety is output, and S4, multi-target optimization is carried out according to the simulation result, and an operation parameter template library is generated. According to the invention, high-fidelity reproduction and parameter optimization of an impact and vibration deicing mechanism are realized.

Description

Technical Field

[0001] This invention belongs to the field of power facility maintenance technology, specifically relating to a method and system for generating a template library of parameters for UAV de-icing operations on power transmission lines based on multi-scale mechanical simulation and verification. Background Technology

[0002] Icing on transmission lines is one of the major natural disasters facing power grids in frigid regions. Icing overload can lead to line galloping, hardware damage, and even tower collapse and line breakage, seriously threatening power grid safety. Traditional de-icing methods mainly include mechanical de-icing methods such as manual knocking and pulley scraping, and thermal de-icing methods such as direct current melting. Mechanical de-icing methods are inefficient, dangerous, and dependent on manual labor; while thermal de-icing methods are more effective, they require power outages, are complex to operate, and may affect the stability of power grid operation.

[0003] With the maturity of drone technology, drone de-icing, as an emerging low-contact operation method, has become an important development direction for power grid de-icing technology due to its advantages such as high flexibility, no power outage required, and ability to handle complex terrain. Currently, technological development in this field mainly focuses on two aspects: first, innovation in de-icing devices, such as the development of drone-mounted tools for impact, vibration, flame, and laser applications; and second, the adaptation of drone operating platforms, namely, improving the drone's payload, endurance, and wind resistance stability to meet the needs of de-icing tasks.

[0004] However, drone de-icing technology is still in its early stages, and its core bottleneck lies in the accuracy and reliability of de-icing operations, which heavily relies on the accurate implementation of the following key technical principles: Modeling of the Mechanical Mechanism of Icing: Icing on transmission lines is a complex brittle material whose mechanical properties, such as compressive strength, tensile strength, and bond strength, vary significantly with temperature, density, and crystal structure. Currently, the setting of de-icing operation parameters relies heavily on field experience, lacking research support on the mechanical properties of the icing material itself and the establishment of rigorous mathematical models. This results in low de-icing efficiency and the risk of damaging equipment.

[0005] Impact Dynamics Modeling: UAV-mounted impact rod de-icing is a transient, nonlinear, and complex impact dynamics process. This process involves stress wave propagation between the impact rod and the ice layer, brittle fracture of the ice layer, fragmentation, and energy transfer. Current technologies lack systematic research on the quantitative relationships between key parameters such as impact velocity, angle, and rod head shape, and the de-icing effect, as well as the impact load on the line, making it impossible to accurately predict and optimize operational parameters.

[0006] Model Validation and Evaluation: Although existing studies have attempted to model and simulate the de-icing process using the finite element method (analysis software such as ABAQUS and LS-DYNA) or the discrete element method (DEM), these models often oversimplify material properties and boundary conditions, raising questions about the reliability of their simulation results. Most importantly, there is a lack of a closed-loop validation and feedback mechanism that combines simulation results with physical experimental data to continuously revise the model, thus preventing the formation of a scientific and universally applicable methodology for parameter optimization.

[0007] Furthermore, current research on the de-icing mechanism of transmission lines mainly focuses on the following aspects: 1. Regarding ice statics models: In 2000, Kim et al. published an article in the AIAA Journal entitled "Modeling hail ice impacts and predicting impact damage initiation in composite structures," which used an isotropic elastoplastic strain failure model to describe the mechanical behavior of ice. However, it simplified the crystal structure of ice and did not consider the influence of temperature on the elastic modulus and compressive strength of ice. In 2015, Guo Yingkui et al. published an article in Advances in Water Resources and Hydropower Science and Technology entitled "Construction of Constitutive Model of Ice Mechanics," which obtained the relationship between compressive strength and temperature through river ice tests, but did not form a universal constitutive model.

[0008] 2. Regarding impact de-icing simulation: In 2015, Ji et al. published an article in *Shock and Vibration* entitled "The time-varying characteristics of overhead electric transmission lines considering the induced-ice-shedding effect," which analyzed the impact load on the line using finite element analysis, but did not couple the dynamic characteristics of ice with the impact parameters. In 2009, Chen Kequan et al. published an article in the *Journal of Chongqing University* entitled "Numerical Simulation of Dynamic Response of De-icing of Ultra-High Voltage Transmission Lines," which used ABAQUS to establish a finite element model of the iced conductor to simulate the de-icing process under impact load, but only considered the porous elastic characteristics of the ice and ignored the dynamic response of ice breakage and splashing during impact.

[0009] 3. In terms of experimental verification: existing experiments are mostly aimed at a single type of icing, lacking systematic experiments on mixed frost and icing of different thicknesses, and the experimental parameters are not fully measured, making it difficult to support model optimization.

[0010] Similarly, in the area of ​​impact de-icing simulation, Chinese invention patent CN 118983737 A discloses a UAV-based power transmission line de-icing device and its data-driven control system. This data-driven control system drives an insulating link rod mounted on the UAV to impact the iced power line with an impact force conforming to a collision detection quadtree data model, thus performing de-icing operations. The data-driven control system includes three data paths: a physical path for data modeling, a data signal control path, and a data coordinate initialization path. The data signal control path and the data coordinate initialization path are based on the physical path for data modeling. The physical path for data modeling involves the UAV acquiring instantaneous negative acceleration in front of the iced cable and impacting it based on the inertia of the insulating link rod. The data signal control path of the data-driven control system is constructed as follows: Based on a discrete signal control architecture, proportional paradigm, differential paradigm, and integration paradigm are embedded for signal control. Before takeoff, data is acquired according to the PID algorithm, and parameter detection and adjustment are performed by superimposing the input calculation results. The initialization path of the data coordinates is as follows: the system's energy is modeled using the Lagrange method, and a data program is constructed based on the translation and rotation parameters of the six-degree-of-freedom system, using generalized coordinate vectors in the data representation. The data output direction of the data program is: initiating speed braking in front of the power transmission cable to cause the insulating link rod to impact the line. This scheme simulates the de-icing process under impact load, but it ignores the mechanical behavior of ice, cannot systematically perform impact simulation modeling, and cannot guarantee the accuracy of the de-icing operation.

[0011] Therefore, the modeling of power transmission line de-icing needs further improvement to reduce static analysis errors, narrow the deviation between impact simulation effects and actual de-icing effects, optimize de-icing operation parameters, reduce reliance on human experience in de-icing operations, increase the measurement of key parameters, and effectively verify and correct the model, thereby improving the efficiency of UAV power transmission line de-icing and reducing the risk of damage to lines and equipment. Summary of the Invention

[0012] The purpose of this invention is to overcome the shortcomings of the prior art, optimize the de-icing operation parameters, reduce static analysis errors, narrow the deviation between impact simulation effects and actual de-icing effects, increase the measurement of key parameters and the effective verification and correction of the model, improve the efficiency of UAV de-icing of power transmission lines, reduce the risk of damage to lines and equipment, and provide a method and system for generating a template library of UAV de-icing operation parameters for power transmission lines based on multi-scale mechanical simulation and verification.

[0013] To achieve the above objectives, the present invention adopts the following technical solution: A method for generating a template library of parameters for UAV de-icing operations on power transmission lines based on multi-scale mechanical simulation and verification includes the following steps: S1: Obtain the operating condition data of the transmission line, construct a basic model of the transmission line including conductors and icing structures based on the operating condition data, and set the boundary constraints of the conductors. S2: Constructing a multi-scale mechanical model of ice, including: S2.1: Based on ice crystal structure defects and temperature effects, determine the equivalent elastic modulus, equivalent tensile strength and equivalent fracture energy of ice related to ambient temperature, porosity and loading strain rate, so as to determine the equivalent mechanical parameters at the microscale. S2.2: Establish a macroscopic constitutive model of ice body that comprehensively considers elastic response, viscous damping and damage evolution, wherein the equivalent mechanical parameters determined at the microscale are used as inputs to the macroscopic constitutive model; S3: Perform de-icing dynamics simulation and result analysis, including: S3.1: Based on the aforementioned transmission line foundation model and the multi-scale mechanical model of the ice, a dynamic simulation model for UAV de-icing is established using a coupled finite element and discrete element method; wherein: The de-icing actuator is modeled to simulate its impact on the ice layer with a specific initial velocity, incident angle, and superimposed vibration displacement. The conductor is structurally modeled and an equivalent tension boundary is applied to simulate its dynamic response; The ice layer is modeled using solid elements with damage and failure criteria, and a bond-separation model is set for the ice-conductor interface. S3.2: Run the de-icing dynamics simulation model and output one or more simulation results for evaluating the de-icing effect and safety. The simulation results include at least: ice breaking rate, peak dynamic stress of the conductor, and energy consumption parameters of the UAV de-icing process. S4: With minimizing residual ice rate, line impact and relative energy consumption as optimization objectives, and with the peak dynamic stress of the conductor not exceeding the allowable value of the conductor dynamic stress and the energy consumption parameter not exceeding the energy consumption reference value as constraints, the initial speed, incident angle, end curvature radius, impact interval and / or vibration displacement parameters of the de-icing actuator are used as optimization variables to perform multi-objective optimization and generate an operation parameter template library.

[0014] The micromechanical model of this invention dynamically correlates the equivalent elastic modulus, equivalent tensile strength, and equivalent fracture energy of ice with ambient temperature, porosity (gas content), and loading strain rate. By introducing key variables, it maps the mechanical properties of ice to environmental conditions. Furthermore, by establishing a UAV de-icing dynamics simulation model, it achieves high-fidelity reproduction of the impact and vibration de-icing mechanism. When simulation conditions change, the model can automatically calculate the corresponding accurate mechanical parameters without manual resetting or guesswork. Through multi-scale mechanical models combined with experimental calibration, the accuracy of the simulation model is improved, enabling realistic simulation of the effects of temperature and ice quality (porosity) on de-icing effectiveness and accurately reflecting the destructive behavior of ice accumulation.

[0015] Preferably, the operating data includes conductor characteristic parameters, tower line characteristic parameters, hanging point boundary conditions, and on-site weather conditions; The conductor's characteristic parameters include its type, outer diameter, cross-sectional area, mass per unit length, elastic modulus, Poisson's ratio, and strand structure parameters. The tower line characteristic parameters include span, suspension point height difference, tension, insulator string length, and tower arm installation angle; The boundary conditions of the suspension point include the hinged or fixed constraints used at the tower end suspension point, and the equivalent tension or prestress applied to both ends of the conductor. The on-site meteorological conditions include ambient temperature, relative humidity, wind speed, wind direction, air pressure, porosity, icing thickness, and icing type.

[0016] Preferably, in step S2.1, the equivalent elastic modulus of ice is determined. The method is as follows: Construct the equivalent elastic modulus of ice in relation to ambient temperature and porosity. Its expression is: ; Where T is the current ambient temperature, Φ is the porosity of ice, and E0 represents the reference temperature. The baseline elastic modulus of non-porous ice; The porosity influence index represents the sensitivity of porosity to the decrease in elastic modulus. is the elastic modulus-temperature softening coefficient, which represents the intensity of the effect of increasing temperature on decreasing the elastic modulus.

[0017] Preferably, in step S2.1, the equivalent tensile strength of ice is determined. The method is as follows: Construct the equivalent tensile strength of ice in relation to ambient temperature and loading strain rate. Its expression is: ; Where T is the current ambient temperature. The applied strain rate is the deformation rate per unit time. Reference temperature With reference strain rate The tensile strength below; The strain rate sensitivity coefficient represents the degree to which tensile strength increases with increasing applied strain rate; The tensile strength-temperature softening coefficient represents the effect of increasing temperature on reducing tensile strength.

[0018] Preferably, in step S2.1, the equivalent fracture energy of the ice is determined. The method is as follows: Constructing the fracture energy of ice in relation to ambient temperature Its expression is: ; Where T is the current ambient temperature. Represents reference temperature The baseline fracture energy; It is the temperature effect coefficient, which indicates the extent to which the fracture energy decreases as the temperature increases.

[0019] Preferably, in the macroscopic constitutive model of the ice body established in step S2.2, the stress expression is as follows: ; Where σ is the stress tensor, d For damage variables, (1− d ) 2 This is the effective stiffness reduction factor. C Here is the elastic stiffness matrix; e For strain tensor, b The viscous damping coefficient is... Let be the strain rate tensor.

[0020] Preferably, the bond-separation model in step S3.1 adopts a bilinear bond-separation model, defining key parameters including stiffness, peak strength and fracture energy, and combining the BK criterion to determine the fracture behavior of the interface under mixed-mode loading. The bilinear bond separation model obtains stress-strain curves and fracture energy curves through static loading experiments, three-point bending experiments, and interface peeling experiments, and performs parameter inversion and calibration using the least squares fitting method or the Bayesian update method.

[0021] This invention employs a bilinear bond-separation model and the BK criterion to simulate the ice-conductor interface, enabling the simulation to accurately reproduce whether the ice layer fractures from within or peels off from the interface. This provides guidance for optimizing end-effect parameters (such as shape and vibration mode) to generate the most effective failure mode, thus improving the accuracy of de-icing operations.

[0022] Preferably, in step S4, the vibration displacement parameter includes the vibration frequency. f With vibration amplitude A ; Impact speed V Angle of incidence i End curvature radius Impact interval Δt Vibration frequency f With vibration amplitude A As optimization variables, they constitute the parameter vector. : The objective function to be optimized is: ; in, ; ; ; in, J 1 Residual ice rate, or For icebreaking rate, J 2 For line impact, s max It is the peak value of dynamic stress in the conductor. s allow This represents the allowable value for dynamic stress in the conductor. J 3 It is relative energy consumption. E cons It's an energy consumption parameter. E ref This is an energy consumption reference value; A multi-objective evolutionary algorithm is used to solve the optimization objective function to obtain the Pareto optimal solution set, forming a template library of operational parameters for different icing types and meteorological conditions.

[0023] By selecting impact velocity, incident angle, end curvature radius, impact interval, vibration frequency, and vibration amplitude as optimization variables, and using a multi-objective evolutionary algorithm to solve the problem, a template library of operational parameters under different icing types and meteorological conditions is formed. This fully simulates the dynamic process of de-icing operations, extracts key parameters, and realizes system simulation and effective verification of multiple icing types and multiple parameter combinations. By optimizing parameters and storing the operational parameter template library, the efficiency of de-icing operations is improved, and the problem of relying on manual experience in existing technologies is solved.

[0024] Preferably, the method further includes the step of: S5: Conduct comparative tests in the laboratory and on-site transmission lines, compare the measured results with the simulation results, use parameter assimilation methods to correct the simulation parameters, and update the operation parameter template library; S6: Before the de-icing operation, the drone loads the operation parameter template library and applies it to the de-icing operation. After the de-icing operation is completed, the operation process data is uploaded to the operation parameter template library for the updating and iteration of the operation parameter template library.

[0025] A UAV de-icing system for power transmission lines based on multi-scale mechanical simulation and verification includes: The drone operation unit includes: Unmanned aerial vehicle (UAV) platform; The operating condition data acquisition module is installed on the UAV platform and is used to collect operating condition data of the power transmission line; The de-icing actuator is detachably connected to the UAV platform via a mounting interface; the de-icing actuator includes an end effector, a vibration drive module for driving the end effector to generate vibration, and a buffer and rebound control structure; the de-icing actuator is equipped with a force sensor for real-time monitoring of force signals during the de-icing process; The airborne control unit is configured to control the UAV platform and the de-icing actuator to perform a de-icing operation sequence according to the operation parameter template library generated by the method, and to process the mechanical signal in real time, determine whether the mechanical signal exceeds a preset safety threshold, and issue an instruction when the preset safety threshold is exceeded. The ground monitoring and communication unit includes: The ground station is used for mission planning, issuing operational parameters, and receiving and displaying UAV status and operational data. A communication link is established between the UAV operating unit and the ground monitoring and communication unit to enable bidirectional transmission of commands and data.

[0026] Preferably, it further includes: a quick-release mechanism associated with the mounting interface and responding to a command from the airborne control unit to cause the de-icing actuator to detach from the UAV platform in an emergency.

[0027] This invention, through a multi-scale mechanical model and experimental calibration, can realistically simulate the effects of temperature and ice quality (porosity) on de-icing efficiency, and accurately reflect the destructive behavior of ice accumulation. Through a UAV de-icing dynamics simulation model, the end effector, ice layer, and wire are coupled to achieve high-fidelity reproduction of the impact and vibration de-icing mechanism, providing a reliable basis for parameter optimization. Through closed-loop iteration of simulation, experimentation, and online operation, continuous optimization of de-icing efficiency and dynamic updating of the operation parameter template library are achieved.

[0028] This invention establishes a multi-physics coupled multi-scale mechanical model of ice, which can more accurately describe the mechanical properties, deformation, and damage behavior of ice, providing a more precise theoretical basis for de-icing operations and improving their accuracy and reliability. Through multi-physics coupled impact de-icing modeling and de-icing dynamics simulation, various physical phenomena during the de-icing process are fully considered. The simulation results are closer to actual de-icing conditions, effectively guiding the optimization of de-icing operation parameters, improving de-icing efficiency, and reducing damage to transmission lines. Systematic transmission line de-icing dynamics simulation provides solid data support for the verification and improvement of theoretical models, contributing to the development and widespread application of de-icing technology and ensuring the safe and stable operation of transmission lines. Attached Figure Description

[0029] The present invention will now be described in further detail with reference to the accompanying drawings: Figure 1 This is a method block diagram of the present invention; Figure 2 This is a system schematic diagram of the present invention. Detailed Implementation

[0030] like Figure 1 As shown, the present invention provides a method for generating a template library of parameters for UAV de-icing operations on power transmission lines based on multi-scale mechanical simulation and verification, comprising the following steps: S1: Obtain the operating condition data of the transmission line, construct a basic model of the transmission line including conductors and icing structures based on the operating condition data, and set the boundary constraints of the conductors.

[0031] In this embodiment, a basic model of the transmission line is constructed in the ANSYS Workbench simulation platform, wherein tension is applied to both ends of the conductor through boundary constraints, so that the simulation state is consistent with the stress state of the actual transmission line.

[0032] Operating data includes conductor characteristic parameters, tower characteristic parameters, hanging point boundary conditions, and on-site weather conditions.

[0033] Specifically, conductor characteristic parameters include conductor type, outer diameter, cross-sectional area, mass per unit length, elastic modulus, Poisson's ratio, and strand structure parameters. Tower characteristic parameters include span, suspension point height difference, tension, insulator string length, and tower arm installation angle. Suspension point boundary conditions include the hinged or fixed constraints used at the tower end suspension points, and the equivalent tension or prestress applied to both ends of the conductor. These suspension point boundary conditions are used as boundary constraints, which can be set through theoretical calculations, experimental measurements, or standard conductor specifications. On-site meteorological conditions include ambient temperature, relative humidity, wind speed, wind direction, air pressure, porosity, icing thickness, and icing type. Specifically, porosity represents the volume fraction of air bubbles or microcracks in the ice and can be measured experimentally, such as by weighing and micro-CT scanning. Icing types include, for example, rime, glaze, and mixed glaze.

[0034] S2: Constructing a multi-scale mechanical model of ice, including: S2.1: Based on ice crystal structure defects and temperature effects, determine the equivalent elastic modulus, equivalent tensile strength and equivalent fracture energy of ice related to ambient temperature, porosity and loading strain rate, so as to determine the equivalent mechanical parameters at the microscale, which can accurately describe the mechanical properties of ice.

[0035] Specifically, determine the equivalent elastic modulus of ice. The method is as follows: Construct the equivalent elastic modulus of ice in relation to ambient temperature and porosity. Its expression is: ; Where T is the current ambient temperature, Φ is the porosity of ice, and E0 represents the reference temperature. Reference elastic modulus of non-porous ice, at reference temperature Standard low temperature or experimental reference temperature is usually used. The porosity influence index represents the sensitivity of porosity to the decrease in elastic modulus. The elastic modulus-temperature softening coefficient represents the intensity of the effect of temperature increase on the decrease of elastic modulus. In this embodiment, the porosity influence index and the elastic modulus-temperature softening coefficient can be obtained by fitting actual experimental data.

[0036] Determine the equivalent tensile strength of ice The method is as follows: Construct the equivalent tensile strength of ice in relation to ambient temperature and loading strain rate. Its expression is: ; Where T is the current ambient temperature. The applied strain rate is the deformation rate per unit time. Reference temperature With reference strain rate Tensile strength and reference strain rate at the specified values The experimental calibration value is usually taken. The strain rate sensitivity coefficient represents the degree to which tensile strength increases with increasing applied strain rate; The tensile strength-temperature softening coefficient represents the effect of temperature increase on the decrease in tensile strength. In this embodiment, the strain rate sensitivity coefficient and the tensile strength-temperature softening coefficient can be obtained by fitting actual experimental data or historical research data.

[0037] Determine the equivalent fracture energy of ice The method is as follows: Constructing the fracture energy of ice in relation to ambient temperature Its expression is: ; Where T is the current ambient temperature. Represents reference temperature The baseline fracture energy; It is the temperature effect coefficient, which indicates the extent to which the fracture energy decreases as the temperature increases.

[0038] The micromechanical model dynamically correlates the equivalent elastic modulus, equivalent tensile strength, and equivalent fracture energy of ice with ambient temperature, porosity (gas content), and loading strain rate through functional relationships. By introducing key variables, the mechanical properties of ice are mapped to environmental conditions. When the simulation conditions change, the model can automatically calculate the corresponding accurate mechanical parameters without the need for manual reset or guessing.

[0039] S2.2: Establish a macroscopic constitutive model of ice body that comprehensively considers elastic response, viscous damping and damage evolution, wherein the equivalent mechanical parameters determined at the microscale are used as inputs to the macroscopic constitutive model.

[0040] In the macroscopic constitutive model of the ice body established in step S2.2, the stress expression is: ; Where σ is the stress tensor, representing the stress state of the ice body under external load; e For strain tensor, it describes the degree of deformation of ice after being subjected to force; C This is the elastic stiffness matrix, which reflects the linear relationship between stress and strain in the elastic stage of a material. Its value is related to the elastic modulus and Poisson's ratio. b is the viscous damping coefficient, used to describe the effect of loading rate on material response, reflecting the viscous energy dissipation characteristics of ice at different strain rates; Let be the strain rate tensor, representing the rate of change of strain with time; dThe damage variable, with a value range of [0,1], is used to characterize the evolution process of a material from an undamaged state to complete fracture. d =0 indicates a lossless state. d =1 indicates complete breakage; (1− d ) 2 The effective stiffness reduction factor indicates that as damage progresses, the effective load-bearing stiffness of the material gradually decreases until failure.

[0041] By using a multi-scale mechanical model and combining it with experimental calibration, the accuracy of the simulation model has been improved. It can realistically simulate the effects of temperature and ice quality (porosity) on the de-icing effect and truly reflect the destructive behavior of ice covering.

[0042] S3: Perform de-icing dynamics simulation and result analysis, including: S3.1: Based on the basic model of the power transmission line and the multi-scale mechanical model of ice, a dynamic simulation model for UAV de-icing is established using a coupled finite element and discrete element method. Among them: The de-icing actuator is modeled to simulate its impact on the ice layer with a specific initial velocity, incident angle, and superimposed vibration displacement.

[0043] The conductor is structurally modeled and an equivalent tension boundary is applied to simulate its dynamic response.

[0044] The ice layer is modeled using solid elements with damage and failure criteria, and a bond-separation model is set for the ice-conductor interface.

[0045] The bond-separation model adopts a bilinear bond-separation model, defining key parameters including stiffness, peak strength and fracture energy, and combining the BK criterion to determine the fracture behavior of the interface under mixed-mode loading.

[0046] The bilinear bond separation model obtains stress-strain curves and fracture energy curves through static loading experiments, three-point bending experiments, and interface peeling experiments. Parameter inversion and calibration are performed using the least squares fitting method or the Bayesian update method.

[0047] A bilinear bond-separation model and the BK criterion were used to simulate the ice-conductor interface, enabling the simulation to accurately reproduce whether the ice layer fractured from within or peeled off from the interface. This provides guidance for optimizing end-effect parameters (such as shape and vibration mode) to generate the most effective failure mode, improving the accuracy of de-icing operations.

[0048] In this embodiment, a UAV de-icing dynamics simulation model is established in the ANSYS Explicit Dynamics module. The de-icing actuator is an end effector. In this embodiment, the end effector is modeled as a rigid body, and the conductor is modeled through beam elements or solid elements with equivalent tension boundaries applied.

[0049] By establishing a dynamic simulation model of UAV de-icing, a high-fidelity reproduction of the impact and vibration de-icing mechanism was achieved, providing a reliable basis for parameter optimization.

[0050] S3.2: Run the de-icing dynamics simulation model and output one or more simulation results to evaluate the de-icing effect and safety. The simulation results should include at least: ice breaking rate, peak dynamic stress of the conductor, and energy consumption parameters of the UAV de-icing process. In addition, the simulation results may also include maximum deflection, which can be optimized by setting allowable deflection values.

[0051] S4: The optimization objectives are to minimize the residual ice rate, line impact, and relative energy consumption. The constraints are that the peak dynamic stress of the conductor does not exceed the allowable value of the dynamic stress of the conductor and the energy consumption parameter does not exceed the energy consumption reference value. The optimization variables are the initial speed, incident angle, end curvature radius, impact interval and / or vibration displacement parameters of the de-icing actuator. Multi-objective optimization is performed to generate an operation parameter template library.

[0052] Vibration displacement parameters include vibration frequency f With vibration amplitude A .

[0053] Impact speed V Angle of incidence i End curvature radius Impact interval Δt Vibration frequency f With vibration amplitude A As optimization variables, they constitute the parameter vector. : ; The objective function to be optimized is: ; in, ; ; ; in, J 1 Residual ice rate, or For icebreaking rate, J 2 For line impact, s max It is the peak value of dynamic stress in the conductor. s allow This represents the allowable value for dynamic stress in the conductor. J 3 It is relative energy consumption. E cons It's an energy consumption parameter.E ref This is an energy consumption reference value.

[0054] A multi-objective evolutionary algorithm is used to solve the optimization objective function, obtain the Pareto optimal solution set, and form a template library of operational parameters under different icing types and meteorological conditions.

[0055] By selecting impact velocity, incident angle, end curvature radius, impact interval, vibration frequency, and vibration amplitude as optimization variables, and using a multi-objective evolutionary algorithm to solve the problem, a template library of operational parameters under different icing types and meteorological conditions is formed. This fully simulates the dynamic process of de-icing operations, extracts key parameters, and realizes system simulation and effective verification of multiple icing types and multiple parameter combinations. By optimizing parameters and storing the operational parameter template library, the efficiency of de-icing operations is improved, and the problem of relying on manual experience in existing technologies is solved.

[0056] Multi-objective evolutionary algorithms can employ either the Non-dominated sorting genetic algorithm II (NSGA-II) or the decomposition-based multi-objective evolutionary algorithm (MOEA / D).

[0057] S5: Conduct comparative tests in the laboratory and on-site transmission lines, compare the measured results with the simulation results, and use parameter assimilation methods to correct the simulation parameters and update the operational parameter template library. Correcting model parameters through parameter assimilation methods improves the reliability and generalization ability of the simulation model.

[0058] S6: Before de-icing operations, the drone loads the operation parameter template library and applies it to the de-icing operation. After the de-icing operation is completed, the operation process data is uploaded to the operation parameter template library for the updating and iteration of the operation parameter template library.

[0059] Through closed-loop iteration of simulation, experimentation, and online operation, continuous optimization of de-icing efficiency and dynamic updating of the operation parameter template library were achieved.

[0060] This invention establishes a multi-physics coupled multi-scale mechanical model of ice, which can more accurately describe the mechanical properties, deformation, and damage behavior of ice, providing a more precise theoretical basis for de-icing operations and improving their accuracy and reliability. Through multi-physics coupled impact de-icing modeling and de-icing dynamics simulation, various physical phenomena during the de-icing process are fully considered. The simulation results are closer to actual de-icing conditions, effectively guiding the optimization of de-icing operation parameters, improving de-icing efficiency, and reducing damage to transmission lines. Systematic transmission line de-icing dynamics simulation provides solid data support for the verification and improvement of theoretical models, contributing to the development and widespread application of de-icing technology and ensuring the safe and stable operation of transmission lines.

[0061] The technical solution of this invention is not only applicable to the study of de-icing mechanism of power transmission lines, but can also be applied to de-icing problems in other fields, such as aircraft surface de-icing and wind turbine blade de-icing in the aerospace field, providing a reference for the development of de-icing technology in these fields.

[0062] This invention also provides a UAV de-icing system for power transmission lines based on multi-scale mechanical simulation and verification, such as... Figure 2 As shown, it includes: The drone operation unit includes: In this embodiment, the drone platform is a multi-rotor or compound-wing drone with strong load capacity and stable flight performance.

[0063] The operating condition data acquisition module, installed on the UAV platform, is used to collect operating condition data of power transmission lines. The module includes an inertial measurement unit, a satellite navigation system, meteorological sensors, and environmental sensing devices, used to acquire the UAV's attitude, position, and environmental parameters such as wind speed, temperature, and humidity in real time under complex weather conditions.

[0064] The de-icing actuator is detachably connected to the UAV platform via a mounting interface. The de-icing actuator includes an end effector, a vibration drive module for driving the end effector to vibrate, and a buffer and rebound control structure. The end effector's end shape is one of hemispherical, wedge-shaped, or toothed, with the appropriate end shape selected based on the type of icing and de-icing conditions.

[0065] The de-icing actuator is equipped with force sensors to monitor the mechanical signals during the de-icing process in real time. These force sensors include torque and acceleration sensors, which monitor the contact force spectrum and dynamic response during impact, thus providing data support for the safety and effectiveness of the de-icing process.

[0066] The airborne control unit is configured to control the UAV platform and the de-icing actuator to perform a de-icing operation sequence according to the operation parameter template library generated by the above method, and to process the mechanical signal in real time, determine whether the mechanical signal exceeds a preset safety threshold, and issue an instruction when the preset safety threshold is exceeded. A quick-release mechanism, associated with the mounting interface and responding to commands from the onboard control unit, causes the de-icing actuator to detach from the drone platform in an emergency. The quick-release mechanism can be an electronically controlled release or a clutch mechanism, enabling a detachable connection between the de-icing actuator and the drone platform. It can quickly disengage upon detecting abnormal force, preventing damage to the drone platform and power lines due to jamming or overload.

[0067] The ground monitoring and communication unit includes: The ground station is used for mission planning, issuing operational parameters, and receiving and displaying UAV status and operational data. The ground station communicates bidirectionally with the onboard control unit, enabling the loading of mission parameters and remote manual intervention, thereby improving operational flexibility and safety.

[0068] The communication link, established between the UAV operating unit and the ground monitoring and communication unit, is used to achieve bidirectional transmission of commands and data. The communication link includes a data transmission link, an image transmission link, and a control link to ensure real-time data transmission and communication redundancy during UAV de-icing operations.

[0069] In this embodiment, under different ice types and thicknesses, the airborne control unit adaptively adjusts the impact velocity, incident angle, impact interval, vibration frequency, and vibration amplitude based on the received mechanical signals. The ice response is fed back in real time via an accelerometer. When a decrease in ice-breaking rate or excessive energy consumption is detected, the airborne control unit dynamically adjusts the vibration amplitude to optimize energy distribution and breaking efficiency. When the force on the end effector exceeds a preset threshold, the electronically controlled unloading device is immediately triggered, causing the end effector to detach quickly, and the UAV performs a retreat maneuver. After the operation is completed, the operation process data is uploaded to the operation parameter template library for continuous updates and iterations to improve optimization accuracy.

[0070] The system of this invention collects the operating condition data of the power transmission line through the operating condition data acquisition module on the UAV platform; it detachably connects the de-icing actuator to the UAV platform through the mounting interface; it drives the end effector to generate vibration through the vibration drive module, and performs buffer control through the buffer and rebound control structure; it monitors the mechanical signals during the de-icing process in real time through the mechanical sensor; it controls the UAV platform and the de-icing actuator to execute the de-icing operation sequence through the airborne control unit, and processes the mechanical signals in real time, determines whether the mechanical signals exceed the preset safety threshold, and disconnects the connection with the mounting interface when the safety threshold is exceeded, so that the de-icing actuator is urgently detached from the UAV platform; the de-icing operation sequence is obtained by loading the operation parameter template library to guide the de-icing operation; after the de-icing operation is completed, the operation parameter template library is iteratively updated based on the actual operation feedback data.

[0071] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope defined in the claims.

Claims

1. A method for generating a template library of de-icing operation parameters of a power transmission line unmanned aerial vehicle based on multi-scale mechanical simulation and verification, characterized in that, Includes the following steps: S1: Obtain the operating condition data of the transmission line, construct a basic model of the transmission line including conductors and icing structures based on the operating condition data, and set the boundary constraints of the conductors. S2: Constructing a multi-scale mechanical model of ice, including: S2.1: Based on ice crystal structure defects and temperature effects, determine the equivalent elastic modulus, equivalent tensile strength and equivalent fracture energy of ice related to ambient temperature, porosity and loading strain rate, so as to determine the equivalent mechanical parameters at the microscale. S2.2: Establish a macroscopic constitutive model of ice body that comprehensively considers elastic response, viscous damping and damage evolution, wherein the equivalent mechanical parameters determined at the microscale are used as inputs to the macroscopic constitutive model; S3: Perform de-icing dynamics simulation and result analysis, including: S3.1: Based on the aforementioned transmission line foundation model and the multi-scale mechanical model of the ice, a dynamic simulation model for UAV de-icing is established using a coupled finite element and discrete element method; wherein: The de-icing actuator is modeled to simulate its impact on the ice layer with a specific initial velocity, incident angle, and superimposed vibration displacement. The conductor is structurally modeled and an equivalent tension boundary is applied to simulate its dynamic response; The ice layer is modeled using solid elements with damage and failure criteria, and a bond-separation model is set for the ice-conductor interface. S3.2: Run the de-icing dynamics simulation model and output one or more simulation results for evaluating the de-icing effect and safety. The simulation results include at least: ice breaking rate, peak dynamic stress of the conductor, and energy consumption parameters of the UAV de-icing process. S4: With minimizing residual ice rate, line impact and relative energy consumption as optimization objectives, and with the peak dynamic stress of the conductor not exceeding the allowable value of the conductor dynamic stress and the energy consumption parameter not exceeding the energy consumption reference value as constraints, the initial speed, incident angle, end curvature radius, impact interval and / or vibration displacement parameters of the de-icing actuator are used as optimization variables to perform multi-objective optimization and generate an operation parameter template library.

2. The method for generating a template library of de-icing operation parameters of a power transmission line unmanned aerial vehicle based on multi-scale mechanical simulation and verification according to claim 1, characterized in that, The operating data includes conductor characteristic parameters, tower characteristic parameters, hanging point boundary conditions, and on-site weather conditions; The conductor's characteristic parameters include its type, outer diameter, cross-sectional area, mass per unit length, elastic modulus, Poisson's ratio, and strand structure parameters. The tower line characteristic parameters include span, suspension point height difference, tension, insulator string length, and tower arm installation angle; The boundary conditions of the suspension point include the hinged or fixed constraints used at the tower end suspension point, and the equivalent tension or prestress applied to both ends of the conductor. The on-site meteorological conditions include ambient temperature, relative humidity, wind speed, wind direction, air pressure, porosity, icing thickness, and icing type.

3. The method for generating a template library of parameters for UAV de-icing operations on power transmission lines based on multi-scale mechanical simulation and verification, as described in claim 1, is characterized in that... The method for determining the equivalent elastic modulus of ice in step S2.1 is as follows: The method for determining the equivalent elastic modulus of ice in step S2.1 is as follows: Constructing the ice equivalent elastic modulus related to the ambient temperature, porosity whose expression is: ； where T is the current ambient temperature, Φ is the porosity of ice, and E0 represents the reference temperature E0 is the reference elastic modulus of the ice body; is the porosity influence index, indicating the sensitivity of the elastic modulus decay to the porosity; is the elastic modulus-temperature softening coefficient, indicating the influence intensity of the temperature rise on the elastic modulus reduction.

4. The method for generating a template library of parameters for UAV de-icing operations on power transmission lines based on multi-scale mechanical simulation and verification, as described in claim 1, is characterized in that... In step S2.1, the equivalent tensile strength of ice is determined. The method is as follows: Construct the equivalent tensile strength of ice in relation to ambient temperature and loading strain rate. Its expression is: ； Where T is the current ambient temperature. The applied strain rate is the deformation rate per unit time. Reference temperature With reference strain rate The tensile strength below; The strain rate sensitivity coefficient represents the degree to which tensile strength increases with increasing applied strain rate; The tensile strength-temperature softening coefficient represents the effect of increasing temperature on reducing tensile strength.

5. The method for generating a template library of parameters for UAV de-icing operations on power transmission lines based on multi-scale mechanical simulation and verification, as described in claim 1, is characterized in that... In step S2.1, the equivalent fracture energy of the ice is determined. The method is as follows: Constructing the fracture energy of ice in relation to ambient temperature Its expression is: ； Where T is the current ambient temperature. Represents reference temperature The baseline fracture energy; It is the temperature effect coefficient, which indicates the extent to which the fracture energy decreases as the temperature increases.

6. The method for generating a template library of parameters for UAV de-icing operations on power transmission lines based on multi-scale mechanical simulation and verification, as described in claim 1, is characterized in that... In the macroscopic constitutive model of the ice body established in step S2.2, the stress expression is: ； Where σ is the stress tensor, d For damage variables, (1− d ) 2 This is the effective stiffness reduction factor. C Here is the elastic stiffness matrix; ε For strain tensor, b The viscous damping coefficient is... Let be the strain rate tensor.

7. The method for generating a template library of parameters for UAV de-icing operations on power transmission lines based on multi-scale mechanical simulation and verification, as described in claim 1, is characterized in that... The bond-separation model in step S3.1 adopts a bilinear bond-separation model, defining key parameters including stiffness, peak strength and fracture energy, and combining the BK criterion to determine the fracture behavior of the interface under mixed-mode loading. The bilinear bond separation model obtains stress-strain curves and fracture energy curves through static loading experiments, three-point bending experiments, and interface peeling experiments, and performs parameter inversion and calibration using the least squares fitting method or the Bayesian update method.

8. The method for generating a template library of parameters for UAV de-icing operations on power transmission lines based on multi-scale mechanical simulation and verification, as described in claim 1, is characterized in that... In step S4, the vibration displacement parameters include the vibration frequency. f With vibration amplitude A ; Impact speed V Angle of incidence θ End curvature radius Impact interval Δt Vibration frequency f With vibration amplitude A As optimization variables, they constitute the parameter vector. : ； The objective function to be optimized is: ； in, ； ； ； in, J 1 Residual ice rate, η For icebreaking rate, J 2 For line impact, σ max It is the peak value of dynamic stress in the conductor. σ allow This represents the allowable value for dynamic stress in the conductor. J 3 It is relative energy consumption. E cons It's an energy consumption parameter. E ref This is an energy consumption reference value; A multi-objective evolutionary algorithm is used to solve the optimization objective function to obtain the Pareto optimal solution set, forming a template library of operational parameters for different icing types and meteorological conditions.

9. The method for generating a template library of parameters for UAV de-icing operations on power transmission lines based on multi-scale mechanical simulation and verification, as described in claim 8, is characterized in that... It also includes the following steps: S5: Conduct comparative tests in the laboratory and on-site transmission lines, compare the measured results with the simulation results, use parameter assimilation methods to correct the simulation parameters, and update the operation parameter template library; S6: Before the de-icing operation, the drone loads the operation parameter template library and applies it to the de-icing operation. After the de-icing operation is completed, the operation process data is uploaded to the operation parameter template library for the updating and iteration of the operation parameter template library.

10. A UAV de-icing system for power transmission lines based on multi-scale mechanical simulation and verification, characterized in that, include: The drone operation unit includes: Unmanned aerial vehicle (UAV) platform; The operating condition data acquisition module is installed on the UAV platform and is used to collect operating condition data of the power transmission line; The de-icing actuator is detachably connected to the UAV platform via a mounting interface; the de-icing actuator includes an end effector, a vibration drive module for driving the end effector to generate vibration, and a buffer and rebound control structure; the de-icing actuator is equipped with a force sensor for real-time monitoring of force signals during the de-icing process; The airborne control unit is configured to control the UAV platform and the de-icing actuator to perform a de-icing operation sequence according to the operation parameter template library generated by the method as described in any one of claims 1-9, and to process the mechanical signal in real time, determine whether the mechanical signal exceeds a preset safety threshold, and issue an instruction when the preset safety threshold is exceeded; The ground monitoring and communication unit includes: The ground station is used for mission planning, issuing operational parameters, and receiving and displaying UAV status and operational data. A communication link is established between the UAV operating unit and the ground monitoring and communication unit to enable bidirectional transmission of commands and data.

11. The UAV de-icing system for power transmission lines based on multi-scale mechanical simulation and verification according to claim 10, characterized in that, It also includes a quick-release mechanism associated with the mounting interface and responding to commands from the onboard control unit to cause the de-icing actuator to detach from the UAV platform in an emergency.

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