Typhoon disaster risk assessment method for high-voltage tower based on typhoon path probability distribution

Abstract

This invention relates to the field of wind disaster risk assessment technology, and particularly to a method for assessing the typhoon disaster risk of high-voltage power towers based on the probability distribution of typhoon paths. The method includes: acquiring meteorological, topographical, and soil parameters of the target area to construct an assessment dataset; extrapolating fluctuating wind speed, rainfall intensity, and local wind and rain impact kinetic energy based on the typhoon path probability distribution and local micro-topographical parameters; calculating the tower foundation erosion rate based on the impact kinetic energy and solving for the dynamic burial depth of the tower foundation through time-domain integration; extracting the dominant frequency of the fluctuating wind and solving for the natural frequency of the foundation based on the dynamic burial depth; calculating the ultimate anti-overturning moment after shear softening of the foundation based on the frequency domain matching degree of the two; and simultaneously constructing a dynamic overturning moment considering the geometric amplification of the wind-receiving area and the centroid eccentricity; establishing a dynamic equilibrium equation with the tower foundation tilt angle as a variable; solving for the tilt angle evolution trajectory; and comparing the critical instability threshold to derive the instability probability. This invention improves the accuracy of power tower instability risk assessment under extreme wind disasters.

Description

Technical Field

[0001] This invention relates to the field of wind disaster risk assessment technology, and in particular to a method for assessing the typhoon disaster risk of high-voltage power towers based on the probability distribution of typhoon paths. Background Technology

[0002] High-voltage transmission towers, as core infrastructure of power grids, are exposed to the outdoor natural environment year-round, making them highly vulnerable to extreme weather disasters such as typhoons and torrential rains. To assess the structural safety and collapse risk of high-voltage towers during typhoons, existing technologies typically collect historical meteorological data and geographic information of the target area, calculate the wind overturning moment on the tower using static wind load formulas, and solve for the static anti-overturning moment of the foundation based on the initial physical and mechanical parameters of the soil. Finally, by comparing the ratio of the static anti-overturning moment to the wind overturning moment, a safety factor is calculated, thus assessing the instability risk of the high-voltage towers.

[0003] Existing technologies typically treat the foundation's anti-tilting moment as a fixed constant during the evaluation process, neglecting the frequency domain resonance phenomenon induced by the dynamic overlap between the dominant frequency of typhoon pulsating winds and the natural frequency of the foundation under rainwater soaking. This results in an inability to effectively characterize the dynamic shear softening process of the soil caused by resonance, leading to inaccurate predictions of foundation instability under extreme wind disasters. Summary of the Invention

[0004] To overcome the above shortcomings, this invention provides a typhoon disaster risk assessment method for high-voltage towers based on the probability distribution of typhoon paths. It aims to improve the problem that the dynamic shear softening process of soil caused by resonance cannot be effectively characterized, and that the prediction of foundation instability under extreme wind disasters is inaccurate.

[0005] This invention provides the following technical solution: a method for assessing the typhoon disaster risk of high-voltage power towers based on the probability distribution of typhoon paths, comprising:

[0006] S1. Obtain the typhoon path probability distribution, local micro-topographic parameters, initial burial depth and soil parameters of the target area, and construct the evaluation dataset;

[0007] S2. Based on the probability distribution of the typhoon path, the fluctuating wind speed and rainfall intensity at the target high-voltage tower are inferred, and the local wind and rain impact kinetic energy is calculated by combining local micro-topographic parameters.

[0008] S3. Calculate the tower base erosion rate based on the local wind and rain impact kinetic energy, integrate the tower base erosion rate over time and combine it with the initial burial depth to obtain the dynamic burial depth of the tower base.

[0009] S4. Extract the dominant frequency of the pulsating wind speed to obtain the dominant frequency of the pulsating wind. Calculate the natural frequency of the foundation based on the dynamic burial depth of the tower base and soil parameters. Solve for the frequency domain matching degree between the dominant frequency of the pulsating wind and the natural frequency of the foundation. Calculate the ultimate anti-overturning moment after shear softening of the foundation based on the frequency domain matching degree. At the same time, based on the pulsating wind speed, construct a dynamic overturning moment that considers the amplification of the wind-receiving area and the center of gravity eccentricity of the tower caused by the tower base tilt angle.

[0010] S5. Based on the ultimate anti-tilting moment and dynamic overturning moment, establish a dynamic equilibrium equation with the tower base tilt angle as the variable, solve the evolution trajectory of the tower base tilt angle, compare it with the preset critical instability threshold to derive the instability probability of the high-voltage tower, and complete the risk assessment.

[0011] Preferably, in S2, the fluctuating wind speed and rainfall intensity at the target high-voltage tower are extrapolated based on the typhoon path probability distribution, including:

[0012] Calculate the macroscopic pressure field and macroscopic wind and rain distribution at the target high-voltage tower based on the probability distribution of the typhoon path;

[0013] By combining local micro-topographic parameters, topographic correction is performed to derive average wind speed and rainfall intensity;

[0014] The power spectral density characteristics of the typhoon wind field are extracted and superimposed on the average wind speed to synthesize the pulsating wind speed at the target high-voltage tower.

[0015] Preferably, in S2, the calculation of local wind and rain impact kinetic energy, combined with local micro-topographic parameters, includes:

[0016] Based on local micro-topographic parameters, the fluctuating wind speed is decomposed into lateral wind speed components along the slope direction.

[0017] The vertical falling velocity of raindrops is calculated by combining the rainfall intensity, and the resultant velocity vector of raindrops is derived by vector synthesis with the lateral wind speed component.

[0018] The mass of raindrops is calculated based on the rainfall intensity, and the local wind and rain impact kinetic energy is solved by combining the resultant velocity vector and the lateral wind speed component.

[0019] Preferably, in S3, the calculation of the tower foundation erosion rate based on the local wind and rain impact kinetic energy includes:

[0020] The critical shear stress for erosion resistance of the soil around the tower foundation is determined based on soil parameters.

[0021] When the component of the local wind and rain impact kinetic energy along the slope surface is greater than the critical shear stress for erosion resistance, the excess shear stress difference is calculated.

[0022] Based on the excess difference of shear stress and the slope of the hillside in the local micro-topography parameters, the erosion rate of the tower base is solved by a nonlinear scour erosion empirical model.

[0023] Preferably, in S3, the dynamic burial depth of the tower foundation is obtained by integrating the tower foundation erosion rate over time and combining it with the initial burial depth, including:

[0024] The time step of the typhoon's passage is obtained in the time domain. Discrete numerical integration is performed on the tower base erosion rate within each time step to obtain the cumulative erosion depth during the typhoon's passage.

[0025] Subtract the cumulative erosion depth caused by the typhoon from the initial burial depth, and update and export the dynamic burial depth of the tower foundation in real time.

[0026] Preferably, in S4, the calculation of the foundation natural frequency based on the dynamic burial depth of the tower foundation and soil parameters includes:

[0027] Calculate the shear modulus of softened soil based on the shear wave velocity and soil saturation under rainfall soaking in the soil parameters.

[0028] The lateral constraint stiffness of the foundation is calculated by combining the dynamic burial depth of the tower foundation, and the natural frequency of the foundation is solved based on the lateral constraint stiffness of the foundation and the rotational inertia of the tower and foundation system.

[0029] Preferably, in S4, the frequency domain matching degree between the dominant frequency of the fluctuating wind and the natural frequency of the foundation includes:

[0030] Solve for the absolute frequency domain difference between the extracted pulsating wind dominant frequency and the calculated foundation natural frequency;

[0031] By using a preset Gaussian or exponential frequency domain response attenuation function, the absolute difference in the frequency domain is mapped to a value between 0 and 1, and the frequency domain matching degree is derived.

[0032] Preferably, in S4, the calculation of the ultimate anti-tilting moment after foundation shear softening based on the frequency domain matching degree includes:

[0033] Calculate the dynamic shear softening coefficient of soil based on frequency domain matching degree and typhoon duration.

[0034] By using the dynamic shear softening coefficient to reduce the cohesion and internal friction angle in the soil parameters, the reduced soil strength parameters are obtained.

[0035] Based on the reduced soil strength parameters and the dynamic burial depth of the tower foundation, the ultimate overturning moment after shear softening of the foundation is calculated.

[0036] Preferably, in S4, the dynamic overturning moment that considers the amplification of the tower's wind-receiving area and center of gravity eccentricity caused by the tower base tilt angle includes:

[0037] The triangular magnification correction value of the windward projected area of ​​the tower is calculated based on the current tower base tilt angle, and the sinusoidal magnification value of the eccentricity of the tower's center of gravity relative to the tower base rotation center is calculated.

[0038] The foundation wind load moment is calculated using pulsating wind speed. The additional wind load moment, which is increased by the triangular magnification correction value of the windward projected area, and the additional gravity eccentric moment generated by the sinusoidal magnification value of the eccentricity are superimposed to construct the dynamic overturning moment.

[0039] Preferably, in S5, a dynamic equilibrium equation with the tower base inclination angle as the variable is established based on the ultimate anti-tilting moment and the dynamic overturning moment, and the evolution trajectory of the tower base inclination angle is solved, including:

[0040] Using the tower base tilt angle and its angular acceleration and angular velocity as state variables, the rotational inertia and structural damping moment of the tower and foundation system are introduced to establish a second-order nonlinear rotational differential equation that balances the inertial moment, structural damping moment, ultimate anti-tilting moment and dynamic overturning moment generated by the rotational inertia.

[0041] The second-order nonlinear rotational differential equation is solved iteratively in the time domain using the fourth-order Runge-Kutta method, which outputs the dynamic evolution value of the tower base tilt angle as the typhoon passes, and generates the tower base tilt angle evolution trajectory.

[0042] The present invention has the following beneficial effects:

[0043] 1. In this invention, by introducing the frequency domain matching degree of the main frequency of pulsating wind and the natural frequency of the foundation into the soil shear attenuation model, the time domain attenuation law of the ultimate anti-tilting moment of the foundation under the combined effect of typhoon pulsating wind and rain soaking is accurately characterized, which solves the problem that the traditional static evaluation method ignores the dynamic shear softening of the soil caused by video domain resonance, resulting in inaccurate prediction of foundation instability under extreme wind disasters.

[0044] 2. In this invention, by introducing the triangular magnification correction value of the windward projected area based on the tower base tilt angle and the sinusoidal magnification value of the gravity eccentricity, the overturning moment that dynamically evolves with the tilt angle is corrected in real time. This solves the problem that the traditional model assumes that the tower remains upright and ignores the double geometric nonlinear amplification effect of P-$\Delta$ in the large tilt angle stage, which leads to a serious underestimation of the overturning moment.

[0045] 3. In this invention, by constructing a second-order nonlinear rotational differential equation that combines the balance of rotational inertia, structural damping torque, and dynamic torque, and by using a fourth-order Runge-Kutta operator for time-domain order reduction and iterative solution, a high-precision discrete derivation of the tower base tilt angle evolution trajectory and instability probability is achieved. This solves the problem that traditional black-box models or purely empirical formulas lack clear physical mechanism support and cannot provide transparent and quantifiable basis for disaster prevention and emergency response of high-voltage towers. Attached Figure Description

[0046] Figure 1 This is a flowchart of the typhoon disaster risk assessment method for high-voltage towers based on typhoon path probability distribution proposed in this invention;

[0047] Figure 2 This is a detailed flowchart of the dynamic burial depth calculation for the high-voltage tower foundation based on the typhoon path probability distribution method for typhoon disaster risk assessment proposed in this invention.

[0048] Figure 3 This is a flowchart illustrating the data for solving the ultimate anti-tilting moment and dynamic overturning moment in the high-voltage tower typhoon disaster risk assessment method based on typhoon path probability distribution proposed in this invention.

[0049] Figure 4 This is a diagram illustrating the dynamic equilibrium equation establishment and instability probability calculation logic of the high-voltage tower typhoon disaster risk assessment method based on typhoon path probability distribution proposed in this invention. Detailed Implementation

[0050] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0051] In embodiments of the present invention, the present invention provides a method for assessing the typhoon disaster risk of high-voltage power towers based on the probability distribution of typhoon paths, such as... Figures 1-4 As shown, it includes the following steps:

[0052] S1. Obtain the typhoon path probability distribution, local micro-topographic parameters, initial burial depth and soil parameters of the target area, and construct the evaluation dataset;

[0053] Specifically, in the technical process of building the assessment dataset, the system first needs to collect and preprocess multi-source basic data to form a high-dimensional structured dataset suitable for typhoon disaster risk assessment.

[0054] The typhoon track probability distribution data for the target area is derived from historical typhoon meteorological databases and typhoon probability forecast products released by forecasting agencies. Specifically, the system reads the probability density distribution functions of the typhoon center's latitude and longitude coordinates, central pressure, maximum wind speed, and movement speed within a preset spatiotemporal range. Using the Monte Carlo sampling method, the typhoon center trajectory is discretized and sampled on a latitude and longitude grid to generate a typhoon track ensemble covering the area surrounding the target high-voltage tower. For each sampled track, the spatial straight-line distance and relative azimuth angle between the typhoon center and the high-voltage tower are extracted, and the background meteorological parameter distribution under the influence of the typhoon field is derived by combining this with a typhoon pressure field model.

[0055] The acquisition of local micro-topographic parameters relies on high-resolution digital elevation model data of the area where the target high-voltage tower is located. The system retrieves the elevation matrix of the center point of the high-voltage tower base and its surrounding preset radius, and calculates the elevation gradient in each direction using a two-dimensional grid finite difference method. The slope at the center point of the high-voltage tower base is defined as... The calculation formula is as follows:

[0056] ;

[0057] In the above formula, Indicates elevation. Represents the spatial plane coordinates of the due east direction. Indicates the spatial plane coordinates of true north. The slope is expressed in radians. In a feasible implementation dataset, the slope... The value is 0.35 radians.

[0058] Simultaneously, the slope aspect is calculated using the ratio of gradients in each direction of the elevation matrix. The calculation formula is as follows:

[0059] ;

[0060] In the above formula, It indicates the slope direction, and its unit is radians. It represents the angle between the direction of maximum slope inclination and the due east direction.

[0061] Based on wind tunnel simulation databases or geospatial analysis results, micro-topographic features such as canyons and saddles around high-voltage towers are extracted, and the canyon narrowing coefficient, which characterizes the topographic wind speed amplification effect, is calculated. And the slope correction factor characterizing the steep increase in wind speed under the influence of upwelling on the hillside. In a feasible implementation dataset, the canyon narrowing coefficient... The value is 1.25, which is the slope correction factor. The value is 1.18. These geospatial parameters, after normalization, constitute a micro-topographic dataset characterizing local wind field and hydrological flow field disturbances.

[0062] The initial burial depth and soil parameters were derived from the tower construction project survey report and on-site sensor monitoring data. Initial burial depth The vertical distance from the bottom of the tower foundation to the undisturbed soil layer is used as a reference constant for subsequent time-domain erosion calculations. In feasible implementation data, the initial burial depth... The value is taken as 3.5 meters. Soil parameters include the dry unit weight, natural moisture content, and initial cohesion of the soil. Initial internal friction angle Shear wave velocity and critical shear stress Among them, initial cohesion The value is set to 25.0 kPa, and the initial internal friction angle is... The value is 0.44 radians, representing the soil shear wave velocity. The value is taken as 180 meters per second, and the critical shear stress for erosion resistance is... The value is 12.5 Pascals.

[0063] After completing the reading and feature extraction of the aforementioned basic data, the system performs spatiotemporal alignment and format standardization mapping on the data. The typhoon probability distribution sequence in the time dimension is tensor-stitched with the micro-topographic parameters and physical and mechanical soil parameters in the spatial dimension, under a unified time step and spatial coordinate system. This eliminates inconsistencies in data dimensions, ultimately constructing a high-dimensional assessment dataset containing the typhoon meteorological probability field, local geographic feature field, and foundation structure physical field. This provides complete data support for subsequent projections of pulsating wind speed, rainfall intensity, and wind and rain impact kinetic energy.

[0064] S2. Based on the probability distribution of the typhoon path, the fluctuating wind speed and rainfall intensity at the target high-voltage tower are inferred, and the local wind and rain impact kinetic energy is calculated by combining local micro-topographic parameters.

[0065] Furthermore, in S2, the fluctuating wind speed and rainfall intensity at the target high-voltage tower are extrapolated based on the typhoon path probability distribution, including:

[0066] Calculate the macroscopic pressure field and macroscopic wind and rain distribution at the target high-voltage tower based on the probability distribution of the typhoon path;

[0067] By combining local micro-topographic parameters, topographic correction is performed to derive average wind speed and rainfall intensity;

[0068] The power spectral density characteristics of the typhoon wind field are extracted and superimposed on the average wind speed to synthesize the pulsating wind speed at the target high-voltage tower.

[0069] Furthermore, in S2, the calculation of local wind and rain impact kinetic energy, combined with local micro-topographic parameters, includes:

[0070] Based on local micro-topographic parameters, the fluctuating wind speed is decomposed into lateral wind speed components along the slope direction.

[0071] The vertical falling velocity of raindrops is calculated by combining the rainfall intensity, and the resultant velocity vector of raindrops is derived by vector synthesis with the lateral wind speed component.

[0072] The mass of raindrops is calculated based on the rainfall intensity, and the local wind and rain impact kinetic energy is solved by combining the resultant velocity vector and the lateral wind speed component.

[0073] Specifically, in the technical process of extrapolating meteorological parameters and performing dynamic calculations based on the evaluation dataset, the micro-dynamic input environment under the combined effect of wind and rain is constructed at the target high-voltage tower node by utilizing the typhoon path probability distribution and local micro-topographic parameters.

[0074] The system first reads the typhoon path sampling ensemble and probability density distribution function from the evaluation dataset. For each typhoon path sampling instance, it obtains the real-time geographic coordinates of the typhoon center, the central pressure depth, and the radius of maximum wind speed. Using an empirical meteorological pressure field model, it calculates the sea-level pressure value at the coordinates of the target high-pressure tower, constructing the macroscopic pressure field at the target high-pressure tower. Based on the spatial gradient relationship of the macroscopic pressure field and combined with the meteorological gradient wind equation, it solves for the macroscopic wind speed vector at sea level, and simultaneously derives the macroscopic rainfall intensity through a typhoon rainfall axisymmetric distribution model.

[0075] To map macroscopic meteorological parameters downscale to the microscopic geographical unit where the high-voltage towers are located, the system retrieves the canyon narrowing coefficient from the local micro-topographic parameters. With slope correction factor By multiplying the macroscopic wind speed vector by the canyon narrowing factor... With slope correction factor The terrain was used to correct for the wind acceleration effect, and the average wind speed at the high-voltage tower was derived. Average wind speed The calculation formula is:

[0076] ;

[0077] In the above formula, This represents the macroscopic wind speed at sea level. This represents the average wind speed at the high-voltage tower after terrain correction. Simultaneously, the macro-level rainfall intensity is multiplied by a correction factor for the effect of micro-topographic updrafts on raindrop aggregation. Derive the local rainfall intensity at the high-voltage tower. In a feasible implementation dataset, the correction factor The value is 1.12.

[0078] Obtaining average wind speed Subsequently, the system extracts the turbulent power spectral density characteristics of the typhoon wind field. A power spectral density function suitable for the near-surface wind field of a typhoon is selected, and combined with the friction velocity at the height of the high-voltage tower structure and the ground roughness parameters, a frequency domain wind speed power spectrum is generated. Using the harmonic synthesis method, a discrete inverse Fourier transform is performed on the wind speed power spectrum to generate a random fluctuating wind speed sequence in the time domain that conforms to the typhoon's pulsating characteristics. The random fluctuating wind speed sequence Superimposed on average wind speed The time-domain fluctuating wind speed at the target high-voltage tower was obtained by synthesis. Temporal pulsation wind speed The calculation formula is:

[0079] ;

[0080] In the above formula, Indicates instantaneous pulsating wind speed. This represents the instantaneous random pulsating wind speed component.

[0081] Exporting time-domain fluctuating wind speed With rainfall intensity Then, the system calculates the local wind and rain impact kinetic energy by combining local micro-topographic parameters. The system retrieves the slope gradient. and the prevailing wind direction and slope aspect The angle between Instantaneous pulsating wind speed Projecting onto the coordinate system of the hillside, solve for the lateral wind speed components along the upward or downward direction of the hillside. Lateral wind speed component The calculation formula is:

[0082] ;

[0083] In the above formula, This represents the instantaneous lateral wind speed component along the slope direction.

[0084] Based on rainfall intensity The system uses an empirical formula based on the median raindrop size to calculate the final fall velocity of raindrops. (Raindrop final fall velocity) The calculation formula is:

[0085] ;

[0086] In the above formula, This indicates the vertical falling speed of raindrops. This indicates the median raindrop size. (Raindrop median size) The calculation formula is:

[0087] ;

[0088] The vertically falling raindrops are affected by the lateral wind speed component. The drag force is used to synthesize the velocity vector, and the resultant velocity vector of the raindrop hitting the slope surface of the tower base is derived. Resultant velocity vector The formula for calculating the modulus is:

[0089] ;

[0090] In the above formula, This represents the magnitude of the resultant velocity vector of the raindrop.

[0091] The system is based on rainfall intensity Calculate the cumulative rainfall mass flow rate per unit slope area per unit time. Rainfall mass flow rate The calculation formula is:

[0092] ;

[0093] In the above formula, This indicates the density of water. This indicates the mass flow rate of rainfall.

[0094] Combined with raindrop resultant velocity vector With lateral wind speed component The system calculates the local wind and rain impact kinetic energy acting on the slope surface of the tower base per unit time. Local wind and rain impact kinetic energy The calculation formula includes the physical kinetic energy of raindrop impact and the kinetic energy of continuous wind propulsion.

[0095] ;

[0096] In the above formula, Indicates air density, This represents the local wind and rain impact kinetic energy power per unit area.

[0097] Through the aforementioned continuous data vector operations and vector decomposition and synthesis, the system derives the local wind and rain impact kinetic energy within the local space of high-voltage towers during the entire typhoon's evolution. This dynamic energy flow is directly used as the core input parameter for subsequent calculation of the erosion rate of the foundation soil of the tower.

[0098] S3. Calculate the tower base erosion rate based on the local wind and rain impact kinetic energy, integrate the tower base erosion rate over time and combine it with the initial burial depth to obtain the dynamic burial depth of the tower base.

[0099] Furthermore, in S3, the calculation of the tower foundation erosion rate based on the local wind and rain impact kinetic energy includes:

[0100] The critical shear stress for erosion resistance of the soil around the tower foundation is determined based on soil parameters.

[0101] When the component of the local wind and rain impact kinetic energy along the slope surface is greater than the critical shear stress for erosion resistance, the excess shear stress difference is calculated.

[0102] Based on the excess difference of shear stress and the slope of the hillside in the local micro-topography parameters, the erosion rate of the tower base is solved by a nonlinear scour erosion empirical model.

[0103] Furthermore, in S3, the dynamic burial depth of the tower foundation is obtained by integrating the tower foundation erosion rate over time and combining it with the initial burial depth, including:

[0104] The time step of the typhoon's passage is obtained in the time domain. Discrete numerical integration is performed on the tower base erosion rate within each time step to obtain the cumulative erosion depth during the typhoon's passage.

[0105] Subtract the cumulative erosion depth caused by the typhoon from the initial burial depth, and update and export the dynamic burial depth of the tower foundation in real time.

[0106] Specifically, after obtaining the local wind and rain impact kinetic energy power derived from the aforementioned steps, the system calculates the dynamic burial depth of the tower foundation through the soil scour mechanics mechanism and discrete time-domain integration.

[0107] The system first reads the critical shear stress against scour from the soil parameters in the evaluation dataset. To determine the actual shear force applied to the soil surface under the combined action of wind and rain, the system retrieved the local wind and rain impact kinetic energy power. Magnitude of the resultant velocity vector of the raindrop The kinetic energy power is converted into equivalent fluid impact shear stress. The calculation formula is as follows:

[0108] ;

[0109] In the above formula, This represents the dimensionless empirical drag coefficient, in a feasible implementation dataset. The value is 0.05. This represents the equivalent shear stress generated by the instantaneous impact of wind and rain. This represents the critical shear stress against erosion, in a feasible implementation dataset. The value is 12.5 Pascals.

[0110] The system will convert instantaneous equivalent shear stress Critical shear stress against erosion Perform dynamic numerical condition comparison. When Less than or equal to When the wind and rain shear force acting on the surface soil particles does not exceed the erosion resistance threshold formed by the soil's own weight and cohesion, the system determines that no surface erosion has occurred, and the erosion mass rate of the tower base soil is recorded. The value is assigned to 0. When... Greater than This indicates that the wind and rain shear force has overcome the soil's resistance to erosion, resulting in erosion and degradation of the system. minus The excess shear stress is obtained by solving the problem. The calculation formula is as follows:

[0111] ;

[0112] In the above formula, This indicates the excess shear stress.

[0113] In obtaining the excess shear stress Then, the system retrieves the hillside slope from the local micro-topography parameters. Input a nonlinear scour erosion empirical model to calculate the mass rate of soil erosion per unit area per unit time at the tower foundation. The calculation formula is as follows:

[0114] ;

[0115] In the above formula, The soil erosion coefficient represents the soil's scourability coefficient in a feasible implementation dataset. The value is 0.0025, and the index is... The index represents the nonlinear scouring exponent, which, in a feasible implementation dataset, is... The value is 1.5. Indicating the slope gradient, in a feasible implementation dataset, The value is 0.35 radians. This indicates the rate of soil erosion at the base of the tower.

[0116] Furthermore, the system reads the soil dry unit weight from the soil parameters in the evaluation dataset. With the gravitational acceleration constant By using the mass density conversion relationship, the soil erosion mass rate is... Converted to the tower base depth erosion rate, which characterizes the depth of erosion. The calculation formula is as follows:

[0117] ;

[0118] In the above formula, Indicating the dry unit weight of the soil, in a feasible implementation dataset, The value is taken as 16.5 kN per cubic meter. Representing gravitational acceleration, in a feasible implementation of the data, The value is 9.8 meters per second squared. This indicates the rate of erosion at the base depth.

[0119] The calculated time-domain continuous erosion rate of the tower base depth was obtained. The system then performs discrete numerical integration to solve for the cumulative erosion depth. The system obtains the time step of the time-domain simulation of the entire typhoon's evolution. In a feasible implementation dataset, the time step Set to 10 seconds. The system establishes a time loop sequence, with each time step... Within, the depth erosion rate within the current step size. With time step Multiplying these values ​​yields the step-size erosion amount, which is then accumulated along the time axis to derive the amount up to the current moment. The cumulative erosion depth of the typhoon The calculation formula is as follows:

[0120] ;

[0121] In the above formula, Indicates the first Each discrete time step Indicates the time step in the time domain. Indicates the time up to now. The cumulative erosion depth after the typhoon passed through.

[0122] Finally, the system retrieves the initial burial depth from the evaluation dataset. Utilizing the initial burial depth Subtract the cumulative erosion depth at the current moment Dynamically refresh and export the dynamic burial depth of the tower foundation at the current moment. The calculation formula is as follows:

[0123] ;

[0124] In the above formula, This indicates the initial burial depth of the tower foundation, based on feasible implementation data. The value is taken as 3.5 meters. This indicates the dynamic burial depth of the tower foundation at the current moment.

[0125] Through the processing and calculation of the aforementioned time-domain discrete data stream, the system outputs a sequence of dynamic burial depths of the tower foundation that evolves continuously during the typhoon's passage. This dynamic data updates the foundation constraint geometry in real time and directly participates in the subsequent steps of solving the foundation natural frequency and dynamically updating the foundation lateral constraint stiffness.

[0126] S4. Extract the dominant frequency of the pulsating wind speed to obtain the dominant frequency of the pulsating wind. Calculate the natural frequency of the foundation based on the dynamic burial depth of the tower base and soil parameters. Solve for the frequency domain matching degree between the dominant frequency of the pulsating wind and the natural frequency of the foundation. Calculate the ultimate anti-overturning moment after shear softening of the foundation based on the frequency domain matching degree. At the same time, based on the pulsating wind speed, construct a dynamic overturning moment that considers the amplification of the wind-receiving area and the center of gravity eccentricity of the tower caused by the tower base tilt angle.

[0127] Furthermore, in S4, the calculation of the foundation's natural frequency based on the dynamic burial depth of the tower foundation and soil parameters includes:

[0128] Calculate the shear modulus of softened soil based on the shear wave velocity and soil saturation under rainfall soaking in the soil parameters.

[0129] The lateral constraint stiffness of the foundation is calculated by combining the dynamic burial depth of the tower foundation, and the natural frequency of the foundation is solved based on the lateral constraint stiffness of the foundation and the rotational inertia of the tower and foundation system.

[0130] Furthermore, in S4, solving for the frequency domain matching degree between the dominant frequency of the pulsating wind and the natural frequency of the foundation includes:

[0131] Solve for the absolute frequency domain difference between the extracted pulsating wind dominant frequency and the calculated foundation natural frequency;

[0132] By using a preset Gaussian or exponential frequency domain response attenuation function, the absolute difference in the frequency domain is mapped to a value between 0 and 1, and the frequency domain matching degree is derived.

[0133] Furthermore, in S4, the calculation of the ultimate anti-tilting moment after foundation shear softening based on the frequency domain matching degree includes:

[0134] Calculate the dynamic shear softening coefficient of soil based on frequency domain matching degree and typhoon duration.

[0135] By using the dynamic shear softening coefficient to reduce the cohesion and internal friction angle in the soil parameters, the reduced soil strength parameters are obtained.

[0136] Based on the reduced soil strength parameters and the dynamic burial depth of the tower foundation, the ultimate overturning moment after shear softening of the foundation is calculated.

[0137] Furthermore, in S4, the dynamic overturning moment considering the amplification of the tower's wind-receiving area and center of gravity eccentricity caused by the tower base tilt angle includes:

[0138] The triangular magnification correction value of the windward projected area of ​​the tower is calculated based on the current tower base tilt angle, and the sinusoidal magnification value of the eccentricity of the tower's center of gravity relative to the tower base rotation center is calculated.

[0139] The foundation wind load moment is calculated using pulsating wind speed. The additional wind load moment, which is increased by the triangular magnification correction value of the windward projected area, and the additional gravity eccentric moment generated by the sinusoidal magnification value of the eccentricity are superimposed to construct the dynamic overturning moment.

[0140] Specifically, after obtaining the dynamic burial depth sequence of the tower foundation and the time-domain fluctuating wind speed derived from the aforementioned steps, during the execution of step S4, the ultimate anti-overturning moment and dynamic overturning moment after the foundation shear softening are solved through the synergy of calculation processes such as frequency domain analysis, soil state update and dynamic moment calculation.

[0141] In the specific process of extracting the dominant frequency of pulsating wind, the discrete sampling sequence of time-domain pulsating wind speed generated in the previous steps is first read from the memory buffer. The time-domain wind speed signal is converted into a frequency-domain power spectral density feature array by performing a Fast Fourier Transform (FFT) algorithm; then, the maximum value index in the power spectral density array is extracted by a numerical extremum retrieval command, and the frequency node corresponding to the index is determined as the dominant frequency of the pulsating wind. And save temporarily.

[0142] In the specific process of calculating the natural frequency of the foundation based on the dynamic burial depth of the tower base and soil parameters, the initial shear wave velocity of the soil is read from the stored evaluation dataset. Natural density of soil and the soil saturation input from the real-time data interface Perform floating-point multiplication operations, introducing saturation-based... Rainwater soaking softening correction coefficient Calculate the shear modulus of soil softened by rainwater. The calculation formula is as follows:

[0143] ;

[0144] In the above formula, Indicates the natural density of the soil. Indicates the initial shear wave velocity. This represents the empirical coefficient for saturation softening, in a feasible implementation dataset. The value is 0.35. Indicating soil saturation, in a feasible implementation dataset, The value is 0.95. This represents the shear modulus of softened soil.

[0145] In the further process of solving for the natural frequency of the foundation, the dynamic burial depth variable of the tower foundation at the current time step is retrieved. Combining the basic geometric features of the storage with the foundation shape coefficient The lateral constraint stiffness of the foundation is calculated logically using matrix multiplication. The calculation formula is as follows:

[0146] ;

[0147] In the above formula, The foundation shape factor, in a feasible implementation dataset, The value is 1.8. This indicates the dynamic burial depth of the tower foundation at the current moment. This indicates the lateral constraint stiffness of the foundation.

[0148] Read the rotational inertia scalar of the high-voltage tower and foundation system from the database. The calculated lateral constraint stiffness of the foundation With moment of inertia Input the quadratic root calculation command, execute the dynamic characteristic calculation logic, and calculate the natural frequency of the foundation. The calculation formula is as follows:

[0149] ;

[0150] In the above formula, This represents the moment of inertia of the tower and foundation system relative to the axis of rotation. This represents the natural frequency of the foundation.

[0151] In the specific process of solving the frequency domain matching degree, the temporarily stored pulsating wind main frequency is retrieved. The calculated natural frequency of the foundation Perform subtraction and absolute value logical operations to solve for the absolute difference in the frequency domain. The calculation formula is as follows:

[0152] ;

[0153] absolute difference in the frequency domain Substituting the preset Gaussian frequency response attenuation algorithm instruction, the frequency domain difference is nonlinearly mapped to a scalar between 0 and 1 through floating-point exponentiation, thus deriving the frequency domain matching degree. The calculation formula is as follows:

[0154] ;

[0155] In the above formula, This represents the frequency domain response bandwidth coefficient, in a feasible implementation dataset. The value is 0.2 Hz. This indicates the frequency domain matching degree.

[0156] In the specific process of calculating the ultimate anti-tilting moment based on the frequency domain matching degree, the frequency domain matching degree is derived. Then, combined with the duration of the typhoon's influence as transmitted by the timer. The exponential decay algorithm is used to calculate the dynamic shear softening coefficient of the soil. The calculation formula is as follows:

[0157] ;

[0158] In the above formula, This represents the empirical constant for cyclic shear attenuation, in a feasible implementation dataset. The value is 0.0015. This represents the dynamic shear softening coefficient.

[0159] Using dynamic shear softening coefficient Initial cohesion in the database With initial internal friction angle Perform a multiplicative reduction update and solve for the reduced soil cohesion scalar. Scalar of friction angle within the soil after reduction The calculation formula is as follows:

[0160] ;

[0161] ;

[0162] In the above formula, Indicates initial cohesion. Indicates the initial internal friction angle. This represents the reduced cohesion. This represents the reduced internal friction angle.

[0163] Reduced cohesion internal friction angle and the current dynamic burial depth of the tower foundation Substituting into the pre-set ultimate tensile and tilting failure model of the foundation, solve for the ultimate tilting moment after shear softening of the foundation at the current moment. The calculation formula is as follows:

[0164] ;

[0165] In the above formula, and The empirical coefficient representing the ultimate bearing capacity of the foundation shape, in a feasible implementation dataset, The value is 2.2. The value is 1.5. This represents the ultimate anti-tilting moment of the foundation after shear softening at the current moment.

[0166] In the specific process of constructing the dynamic overturning moment, the tower base tilt angle variable under the current time step is read simultaneously. The trigonometric function command is used to calculate the trigonometric magnification correction value of the windward projected area of ​​the tower. And the sinusoidal amplification of the eccentricity of the tower's center of gravity relative to the center of rotation of the tower base. The calculation formula is as follows:

[0167] ;

[0168] ;

[0169] In the above formula, This represents the initial windward projected area of ​​the tower. This represents the geometric correction factor for the tilt of the windward surface, in a feasible implementation dataset. The value is 0.25. Indicates the height of the tower's center of gravity from its center of rotation. Indicates the effective windward projected area. This indicates the gravitational eccentricity.

[0170] Adjust instantaneous pulsating wind speed Calculate the base wind load, superimpose the additional moment caused by the wind load due to the enlarged windward projected area and the additional moment caused by the gravitational eccentricity, and synthesize the dynamic overturning moment. And it is written to the output buffer, the calculation formula is:

[0171] ;

[0172] In the above formula, Indicates air density, The tower drag coefficient is represented by a feasible set of data. The value is 1.2. Indicates the height of the equivalent point of application of the wind load. This represents the total weight of the tower structure. This indicates the dynamic overturning moment.

[0173] After step S4 is completed, the ultimate overturning moment of the foundation will be updated in real time. With dynamic overturning moment The equations are passed to subsequent steps to construct a dynamic equilibrium equation, which is then used to solve the evolution trajectory of the tower base tilt angle in the time domain.

[0174] S5. Based on the ultimate anti-tilting moment and dynamic overturning moment, establish a dynamic equilibrium equation with the tower base tilt angle as the variable, solve the evolution trajectory of the tower base tilt angle, compare it with the preset critical instability threshold to derive the instability probability of the high-voltage tower, and complete the risk assessment.

[0175] Furthermore, in S5, a dynamic equilibrium equation with the tower base inclination angle as the variable is established based on the ultimate anti-tilting moment and the dynamic overturning moment. The solution to the evolution trajectory of the tower base inclination angle includes:

[0176] Using the tower base tilt angle and its angular acceleration and angular velocity as state variables, the rotational inertia and structural damping moment of the tower and foundation system are introduced to establish a second-order nonlinear rotational differential equation that balances the inertial moment, structural damping moment, ultimate anti-tilting moment and dynamic overturning moment generated by the rotational inertia.

[0177] The fourth-order Runge-Kutta method is used to solve the second-order nonlinear rotational differential equation in the time domain, outputting the dynamic evolution value of the tower base tilt angle with the passage of the typhoon, and generating the tower base tilt angle evolution trajectory.

[0178] Specifically, after obtaining the ultimate anti-tilting moment and dynamic overturning moment of the foundation obtained from the aforementioned steps, during the execution of step S5, the dynamic evolution trajectory of the tower foundation tilt angle is solved through the coordinated calculation process of dynamic equilibrium equation construction, numerical integration solution, and risk threshold comparison, and finally the instability probability of the high-voltage tower is derived.

[0179] In the specific process of constructing the second-order nonlinear rotational differential equation, the tower base inclination angle is selected as the core state variable, and its derivative with respect to time is defined as angular velocity and angular acceleration, respectively. The system retrieves the scalar of the moment of inertia of the tower and foundation system stored in the aforementioned steps. The inertial moment generated by rotational acceleration is calculated. Simultaneously, the structural damping moment is calculated using the structural Raleigh damping coefficient and dynamic rotational stiffness. The Raleigh damping coefficient is a linear combination of the mass damping coefficient and the stiffness damping coefficient. In a feasible implementation, the mass damping coefficient is set to 0.05, and the stiffness damping coefficient to 0.02. Substituting the inertial moment generated by rotational inertia, the structural damping moment derived from angular velocity and damping coefficient, the ultimate anti-overturning moment obtained in the previous steps, and the dynamic overturning moment into the rotational dynamics equilibrium relationship, a second-order nonlinear rotational differential equation is constructed. The calculation formula is as follows:

[0180] ;

[0181] In the above formula, This represents the moment of inertia of the tower and foundation system relative to the axis of rotation. Indicates the rotational damping coefficient. Indicates the base inclination angle variable. Represents a continuous variable over time. This represents the ultimate anti-tilting moment of the foundation after shear softening at the current moment. This indicates the dynamic overturning moment.

[0182] In the specific process of using the fourth-order Runge-Kutta method for time-domain iterative solution, the second-order nonlinear rotational differential equation is first reduced to a first-order system of state equations. The first state variable is defined as angular displacement, and the second as angular velocity. A time-domain integration step size is set; in a feasible implementation dataset, the integration step size is 0.01 seconds. Within each time step, the slope values ​​for four stages are calculated. The first slope value is calculated based on the state variables and torque values ​​at the initial moment of the current time step; the second slope value is calculated after advancing the current time step by half a step and updating the state variables using the first slope value; the third slope value is also calculated after updating the state variables based on the second slope value at the half-step position; the fourth slope value is calculated after advancing the time to the next full step and updating the state variables using the third slope value.

[0183] By assigning weighted average coefficients to the slope values ​​of the four stages and performing a linear combination, the weighting coefficients for the first and fourth slope values ​​are both set to one-sixth, while the weighting coefficients for the second and third slope values ​​are both set to one-third. The angular displacement and angular velocity state variables for the next time step are updated using the weighted average slope values. As the typhoon passes, the above-mentioned fourth-order operator iterative update logic is executed repeatedly within each time step, and the continuously calculated discrete angular displacement values ​​are arranged in chronological order and written into the memory buffer, generating a discrete time-domain numerical sequence of the tower base tilt angle evolving with the typhoon's passage, i.e., the tower base tilt angle evolution trajectory.

[0184] In the specific process of deriving the instability probability of high-voltage towers, the critical instability tilt angle threshold of the high-voltage towers is read from a preset database. In a feasible implementation dataset, the critical instability tilt angle threshold is set to 5.0 degrees. The generated tower base tilt angle evolution trajectory sequence is numerically compared point by point with the critical instability tilt angle threshold, and the maximum tilt angle peak value in the evolution trajectory sequence is retrieved. Simultaneously, the Gaussian random disturbance variance, considering measurement errors and soil parameter uncertainties, is retrieved to construct an instability probability distribution function. The difference between the maximum tilt angle peak value and the critical instability tilt angle threshold is input into the cumulative distribution function for integration, mapping and deriving a tower instability probability scalar with a value between 0 and 1. This instability probability scalar is written into the risk assessment result data package, completing the dynamic risk assessment of high-voltage towers under typhoon conditions.

[0185] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for assessing the typhoon disaster risk of high-voltage power towers based on the probability distribution of typhoon paths, characterized in that, include: S1. Obtain the typhoon path probability distribution, local micro-topographic parameters, initial burial depth and soil parameters of the target area, and construct the evaluation dataset; S2. Based on the probability distribution of the typhoon path, the fluctuating wind speed and rainfall intensity at the target high-voltage tower are inferred, and the local wind and rain impact kinetic energy is calculated by combining local micro-topographic parameters. S3. Calculate the tower base erosion rate based on the local wind and rain impact kinetic energy, integrate the tower base erosion rate over time and combine it with the initial burial depth to obtain the dynamic burial depth of the tower base. S4. Extract the dominant frequency of the pulsating wind speed to obtain the dominant frequency of the pulsating wind. Calculate the natural frequency of the foundation based on the dynamic burial depth of the tower base and soil parameters. Solve for the frequency domain matching degree between the dominant frequency of the pulsating wind and the natural frequency of the foundation. Calculate the ultimate anti-overturning moment after shear softening of the foundation based on the frequency domain matching degree. At the same time, based on the pulsating wind speed, construct a dynamic overturning moment that considers the amplification of the wind-receiving area and the center of gravity eccentricity of the tower caused by the tower base tilt angle. S5. Based on the ultimate anti-tilting moment and dynamic overturning moment, establish a dynamic equilibrium equation with the tower base tilt angle as the variable, solve the evolution trajectory of the tower base tilt angle, compare it with the preset critical instability threshold to derive the instability probability of the high-voltage tower, and complete the risk assessment.

2. The method for assessing the typhoon disaster risk of high-voltage towers based on the probability distribution of typhoon paths according to claim 1, characterized in that, In S2, the fluctuating wind speed and rainfall intensity at the target high-voltage tower are inferred based on the typhoon path probability distribution, including: Calculate the macroscopic pressure field and macroscopic wind and rain distribution at the target high-voltage tower based on the probability distribution of the typhoon path; By combining local micro-topographic parameters, topographic correction is performed to derive average wind speed and rainfall intensity; The power spectral density characteristics of the typhoon wind field are extracted and superimposed on the average wind speed to synthesize the pulsating wind speed at the target high-voltage tower.

3. The method for assessing the typhoon disaster risk of high-voltage towers based on the probability distribution of typhoon paths according to claim 1, characterized in that, In S2, the calculation of local wind and rain impact kinetic energy, combined with local micro-topographic parameters, includes: Based on local micro-topographic parameters, the fluctuating wind speed is decomposed into lateral wind speed components along the slope direction. The vertical falling velocity of raindrops is calculated by combining the rainfall intensity, and the resultant velocity vector of raindrops is derived by vector synthesis with the lateral wind speed component. The mass of raindrops is calculated based on the rainfall intensity, and the local wind and rain impact kinetic energy is solved by combining the resultant velocity vector and the lateral wind speed component.

4. The method for assessing the typhoon disaster risk of high-voltage towers based on the probability distribution of typhoon paths according to claim 1, characterized in that, In S3, the calculation of the tower foundation erosion rate based on the local wind and rain impact kinetic energy includes: The critical shear stress for erosion resistance of the soil around the tower foundation is determined based on soil parameters. When the component of the local wind and rain impact kinetic energy along the slope surface is greater than the critical shear stress for erosion resistance, the excess shear stress difference is calculated. Based on the excess difference of shear stress and the slope of the hillside in the local micro-topography parameters, the erosion rate of the tower base is solved by a nonlinear scour erosion empirical model.

5. The method for assessing the typhoon disaster risk of high-voltage towers based on the probability distribution of typhoon paths according to claim 1, characterized in that, In S3, the dynamic burial depth of the tower foundation is obtained by integrating the tower foundation erosion rate over time and combining it with the initial burial depth, including: The time step of the typhoon's passage is obtained in the time domain. Discrete numerical integration is performed on the tower base erosion rate within each time step to obtain the cumulative erosion depth during the typhoon's passage. Subtract the cumulative erosion depth caused by the typhoon from the initial burial depth, and update and export the dynamic burial depth of the tower foundation in real time.

6. The method for assessing the typhoon disaster risk of high-voltage towers based on the probability distribution of typhoon paths according to claim 1, characterized in that, In S4, the calculation of the foundation natural frequency based on the dynamic burial depth of the tower foundation and soil parameters includes: Calculate the shear modulus of softened soil based on the shear wave velocity and soil saturation under rainfall soaking in the soil parameters. The lateral constraint stiffness of the foundation is calculated by combining the dynamic burial depth of the tower foundation, and the natural frequency of the foundation is solved based on the lateral constraint stiffness of the foundation and the rotational inertia of the tower and foundation system.

7. The method for assessing the typhoon disaster risk of high-voltage towers based on the probability distribution of typhoon paths according to claim 1, characterized in that, In S4, solving for the frequency domain matching degree between the dominant frequency of the fluctuating wind and the natural frequency of the foundation includes: Solve for the absolute frequency domain difference between the extracted pulsating wind dominant frequency and the calculated foundation natural frequency; By using a preset Gaussian or exponential frequency domain response attenuation function, the absolute difference in the frequency domain is mapped to a value between 0 and 1, and the frequency domain matching degree is derived.

8. The method for assessing the typhoon disaster risk of high-voltage towers based on the probability distribution of typhoon paths according to claim 1, characterized in that, In S4, the calculation of the ultimate anti-tilting moment after foundation shear softening based on frequency domain matching degree includes: Calculate the dynamic shear softening coefficient of soil based on frequency domain matching degree and typhoon duration. By using the dynamic shear softening coefficient to reduce the cohesion and internal friction angle in the soil parameters, the reduced soil strength parameters are obtained. Based on the reduced soil strength parameters and the dynamic burial depth of the tower foundation, the ultimate overturning moment after shear softening of the foundation is calculated.

9. The method for assessing the typhoon disaster risk of high-voltage towers based on the probability distribution of typhoon paths according to claim 1, characterized in that, In S4, the dynamic overturning moment considering the amplification of the tower's wind-receiving area and center of gravity eccentricity caused by the tower base tilt angle includes: The triangular magnification correction value of the windward projected area of ​​the tower is calculated based on the current tower base tilt angle, and the sinusoidal magnification value of the eccentricity of the tower's center of gravity relative to the tower base rotation center is calculated. The foundation wind load moment is calculated using pulsating wind speed. The additional wind load moment, which is increased by the triangular magnification correction value of the windward projected area, and the additional gravity eccentric moment generated by the sinusoidal magnification value of the eccentricity are superimposed to construct the dynamic overturning moment.

10. The method for assessing the typhoon disaster risk of high-voltage towers based on the probability distribution of typhoon paths according to claim 1, characterized in that, In S5, a dynamic equilibrium equation with the tower base inclination angle as the variable is established based on the ultimate anti-tilting moment and the dynamic overturning moment. The evolution trajectory of the tower base inclination angle is solved by: Using the tower base tilt angle and its angular acceleration and angular velocity as state variables, the rotational inertia and structural damping moment of the tower and foundation system are introduced to establish a second-order nonlinear rotational differential equation that balances the inertial moment, structural damping moment, ultimate anti-tilting moment and dynamic overturning moment generated by the rotational inertia. The second-order nonlinear rotational differential equation is solved iteratively in the time domain using the fourth-order Runge-Kutta method, which outputs the dynamic evolution value of the tower base tilt angle as the typhoon passes, and generates the tower base tilt angle evolution trajectory.

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