An intelligent wind-sand disaster evaluation system based on time series data analysis

Abstract

The present application relates to the technical field of disaster circuit data prediction processing, and relates to a wind-sand disaster intelligent evaluation system based on time series data analysis. Multidimensional time series data of wire tension, wind speed and wind direction angle in high-voltage overhead transmission lines are collected in real time. The dynamic geometric response of the specific span wire under complex wind-sand load is calculated in real time. After the pre-defined dangerous wind deviation early warning signal is captured, the affected target section is intelligently screened by the logic discrimination module, and precise regulation instructions are issued to the transformer substation. The present application can accurately identify the key critical point of the wire discharging to the tower body due to aerodynamic instability, thereby obtaining the operation safety index and real-time risk situation of the to-be-defended section. The present application realizes the advanced warning and fine quantification of the line physical posture hidden danger, greatly improving the accuracy and scientificity of the operation safety control of the transmission corridor.

Description

Technical Field

[0001] This invention belongs to the field of disaster circuit data prediction and processing technology, and relates to an intelligent assessment system for wind and sand disasters based on time series data analysis. Background Technology

[0002] High-voltage overhead transmission lines, as the "main artery" of modern cross-regional energy distribution, bear the core function of maintaining the stability of power grid operation and the security of social energy supply. At the physical structure level, transmission conductors, through their own mechanical tension and suspension geometry, construct a stable transmission space over long spans. At the same time, the wind field in the atmosphere, as a constant external excitation, directly affects the maintenance of the conductor's mechanical equilibrium state through the stability of its velocity and direction. Especially for long-span lines, a stable airflow field is the physical basis for ensuring that the conductor's operating posture and electrical insulation gaps do not become unstable.

[0003] It is worth noting that in deserts, Gobi, or arid regions, transmission lines do not operate solely within pure airflow fields. The "wind-dust flow" formed by the coupling of wind fields and dust particles has a profound and sensitive potential impact on the physical state of the conductors. Asymmetric dust accumulation on the conductor surface alters the conductor's cross-sectional geometry, which, through nonlinear amplification of aerodynamic torque, causes the conductor's simple linear deflection under wind force to evolve into complex "wind deflection" or "dancing." This geometric deformation induced by environmental loads not only changes the stress distribution within the conductor but also, due to the dynamic reduction in the distance between the conductor and the tower and between phases, creates an extremely complex and close interactive mapping relationship driven by aerodynamic parameters between the mechanical safety and electrical discharge risk of the line under extreme weather conditions.

[0004] However, existing technologies still exhibit significant lag and limitations in the safety management of transmission lines under severe weather conditions such as sandstorms. Firstly, traditional monitoring and defense systems rely excessively on static line design values ​​and standardized relay protection settings, neglecting the drift of physical characteristics caused by dynamic environmental disturbances. In actual operation, even if the real-time wind speed does not exceed the design limit, conductors may still experience excessive wind deflection due to increased aerodynamic lift caused by sand accumulation. Current management models are mostly "passive response," meaning they only act after a fault transient such as a discharge trip, failing to provide targeted proactive defense solutions before dangerous conductor deformation occurs by quantifying the coupling relationship between conductor temperature rise, current load, and environmental aerodynamic forces. This results in significant predictive blind spots in the health management of the power grid under extreme microclimates, making it highly susceptible to local flashover faults escalating into large-scale power grid impacts and hindering effective prevention of the cascading expansion of permanent faults. Summary of the Invention

[0005] In view of the problems existing in the prior art, the present invention provides an intelligent assessment system for wind and sand disasters based on time series data analysis to solve the above-mentioned technical problems.

[0006] To achieve the above and other objectives, the technical solution adopted by the present invention is as follows:

[0007] This invention provides an intelligent assessment system for wind and sand disasters based on time-series data analysis, the system comprising:

[0008] The wind and sand data acquisition module obtains time-series data on the wind and sand environment in the area where the transmission line is located, as well as data on the line operation status of the transmission line.

[0009] The aerodynamic characteristic modeling module calculates the rate of dust accumulation on the surface of the conductor based on time-series data of aeolian and sandy environments, and constructs a time-varying aerodynamic model of the influence of dust on the conductor; based on the aerodynamic model of the influence of dust on the conductor, it generates the corresponding time-series curve of aerodynamic lift coefficient.

[0010] The resonance risk assessment module receives the aerodynamic lift coefficient time series curve and line operation status data, calculates the natural vibration frequency of the transmission line at the current moment; at the same time, it predicts the excitation frequency of the wind and sand flow on the conductor based on the wind and sand environment time series data; it compares the excitation frequency with the natural vibration frequency, and if the difference between the two is less than the preset resonance safety threshold, it generates a galloping warning signal.

[0011] The active defense decision module responds to the galloping warning signal, calculates the amount of conductor temperature change caused by adjusting the current value of the transmission line, and simultaneously calculates the amount of conductor tension change and natural vibration frequency drift caused by the conductor temperature change.

[0012] The control command output module filters out target current values ​​that can cause the natural vibration frequency to drift to a level outside the preset resonance safety threshold, and generates corresponding current modulation commands.

[0013] Another aspect of the present invention provides an intelligent assessment device for wind and sand disasters based on time-series data analysis, including a processor, a memory, and a communication bus;

[0014] The memory stores a computer-readable program that can be executed by the processor;

[0015] The communication bus enables communication between the processor and the memory;

[0016] When the processor executes the computer-readable program, it performs modules to implement a wind and sand disaster intelligent assessment system based on time-series data analysis as described in any one of the present invention.

[0017] As described above, the intelligent assessment system for wind and sand disasters based on time-series data analysis provided by the present invention has at least the following beneficial effects:

[0018] This invention provides an intelligent assessment system for wind and sand disasters based on time-series data analysis. By collecting multi-dimensional time-series data on conductor tension, wind speed, and wind direction angle in real time for high-voltage overhead transmission lines, it utilizes fluid dynamics mechanisms to deeply analyze the aerodynamic impact of wind and sand flow on conductors, thereby constructing a probabilistic prediction mechanism for abnormal wind deflection and conductor galloping. Through real-time calculation of the dynamic geometric response of conductors with specific spans under complex wind and sand loads, this invention can accurately identify the critical point at which conductors discharge to the tower due to aerodynamic instability, thus deriving the operational safety indicators and real-time risk status of the sections requiring protection. This process effectively solves the limitations of traditional power grid monitoring in facing extreme microclimates, such as single-dimensional perception and lagging prediction logic. From the perspective of deep coupling of energy and mechanical fields, it achieves advanced early warning and refined quantification of potential physical attitude hazards of transmission lines, greatly improving the accuracy and scientific nature of operational safety management of transmission corridors.

[0019] This invention, upon detecting a predefined dangerous wind deflection warning signal, intelligently filters out the affected target section through a logic discrimination module and issues precise control instructions to the substation. It employs a dynamic capacity reduction strategy, temporarily lowering the current setting of the affected line. This utilizes the temperature drop effect to effectively reduce conductor sag and restore electrical safety clearances, or temporarily blocks the automatic reclosing function to completely eliminate secondary current surges caused by intermittent flashovers due to wind and sand. This proactive defense method, based on real-time physical field feedback, not only effectively eliminates the risk of insulation gap breakdown caused by sudden changes in environmental loads but also directly avoids equipment damage and large-scale power outages caused by permanent tripping and arcing, while ensuring the reliability of continuous power supply to the grid. Through this defensive mode that shifts from passive handling to proactive intervention, this invention ensures the strong robustness of the power grid under extreme wind and sand weather conditions, significantly improving the resilience and asset protection capabilities of the power system in the face of widespread severe weather. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below.

[0021] Figure 1 This is a schematic diagram showing the connections of the various modules in the system of the present invention.

[0022] Figure 2 This is a schematic diagram of the logical structure connection of the present invention.

[0023] Figure 3 This is a schematic diagram of the device structure connection of the present invention. Detailed Implementation

[0024] Overview of this application:

[0025] In existing technologies, high-voltage overhead transmission lines often experience faults such as tower discharge and short circuits due to abnormal wind deflection or galloping of conductors under severe weather conditions like sandstorms. Traditional monitoring methods mostly rely on fixed threshold alarms or offline simulations, making it difficult to perceive the coupling relationship between dynamic aerodynamic forces and the mechanical state of the conductors in real time. Experiments have revealed a strong correlation between the temporal changes in conductor tension and wind speed and direction angle. By analyzing the matching degree between the aerodynamic excitation frequency of sandstorm flow and the natural frequency of the conductor, resonance risk can be predicted in advance. During the research, it was found that conductors are prone to sag changes and swing mode transitions under sandstorm loads. Therefore, a strategy based on real-time data prediction of risks and proactive adjustment of operating parameters is proposed to achieve fault prevention.

[0026] Specifically, the system first synchronously collects time-series data on conductor tension, wind speed, and wind direction angle. By analyzing wind speed and wind direction angle, it calculates the aerodynamic load of windblown sand on the conductor and, combined with conductor tension data, evaluates the conductor's dynamic response characteristics and natural frequency. When the system predicts that the wind deflection amplitude or galloping probability of a specific span of conductor exceeds the safety threshold, it automatically triggers an active defense mechanism. The system issues a command to the substation to temporarily reduce the transmission current setting of the line to reduce the increase in sag caused by conductor temperature rise, thereby improving the conductor's mechanical condition and suppressing excessive swaying. Once the time-series data indicates that the wind force has stabilized and the risk has been eliminated, the system automatically restores the normal operating parameters of the line.

[0027] Compared to existing technologies, traditional methods often rely on static meteorological thresholds or reactive protection actions, lacking real-time analysis of the coupling between dynamic aerodynamic and electrical-thermal states of conductors, which can easily lead to false alarms or failures to operate. This solution integrates multi-source time-series data, achieving proactive early warning through aerodynamic modeling and risk prediction. Unlike fixed strategies, this solution can intelligently switch defense modes based on real-time risk levels and optimize intervention parameters through a feedback mechanism, significantly improving the power grid's adaptive defense capabilities and operational reliability under severe wind and sand weather conditions.

[0028] Through the above technical solution, this application effectively overcomes the problem of difficult accurate prediction and active control of conductor dynamic response in windy and sandy environments, and improves the disaster resistance capability of the power grid while maintaining power supply continuity.

[0029] After introducing the basic concept of the present invention, the embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0030] Example 1:

[0031] Please see Figures 1-2As shown, a wind and sand disaster intelligent assessment system based on time series data analysis is presented. The system includes a wind and sand data acquisition module, an aerodynamic characteristic modeling module, a resonance risk assessment module, an active defense decision-making module, and a control command output module.

[0032] The various modules mentioned above are connected via wired and / or wireless means to enable data transmission between them;

[0033] The wind and sand data acquisition module acquires time-series data on the wind and sand environment in the area where the transmission line is located, as well as the line operation status data of the transmission line. The time-series data on the wind and sand environment in the area where the transmission line is located includes wind speed time-series data, wind direction time-series data, and dust particle concentration time-series data. The line operation status data of the transmission line includes conductor tension data, conductor temperature data, current operating current value, and standard unit mass of conductor.

[0034] It should be added that the wind and sand data acquisition module acquires real-time time-series data of wind speed, wind vane and dust particle concentration by anemometers, wind vanes and dust particle concentration sensors installed in the area where the transmission line is located; at the same time, tension sensors, temperature sensors and current transformers on the conductors are used to monitor conductor tension, conductor temperature and current operation, while the standard unit mass of the conductor is directly determined based on the line design parameters.

[0035] The aerodynamic characteristic modeling module calculates the rate of dust accumulation on the surface of the conductor based on time-series data of the aero-sand environment, and constructs a time-varying aerodynamic model of the influence of dust on the conductor; based on the aerodynamic model of the influence of dust on the conductor, it generates the corresponding time-series curve of the aerodynamic lift coefficient.

[0036] The aerodynamic characteristic modeling module performs the following data processing method when calculating the rate of dust accumulation on the surface of the conductor:

[0037] Time series data on dust particle concentration and wind speed were extracted from the time series data of the wind and sand environment in the area where the transmission line is located; wherein the time series data on the wind and sand environment includes time series data on wind speed, wind direction and dust particle concentration.

[0038] Time-series data of dust particle concentration and wind speed are aligned on the time axis to extract instantaneous concentration and instantaneous wind speed values ​​at the same moment.

[0039] The dust transport flux is generated by multiplying the instantaneous concentration value and the instantaneous wind speed value, where the dust transport flux represents the total mass of dust impacting a unit area per unit time.

[0040] Call the preset aerodynamics database to obtain the capture coefficient corresponding to the instantaneous wind speed value, where the capture coefficient is used to characterize the proportion of sand and dust particles that can adhere to the conductor at the current wind speed;

[0041] The dust transport flux is weighted using the capture coefficient to obtain the current dust accumulation rate on the conductor surface.

[0042] In one specific embodiment, from the time-series data of the wind and sand environment acquired by the pre-installed monitoring device in the area where the transmission line is located, the time-series data of dust particle concentration and wind speed are decoupled and extracted. The dust particle concentration and wind speed time-series data are then preprocessed, including but not limited to data frame alignment and filling data gaps. This allows the extraction of the instantaneous dust concentration C(t), instantaneous wind speed V(t), and instantaneous wind direction angle θ(t) at the same time t. Simultaneously, the accumulated fouling amount on the conductor at the previous time point is retrieved from memory. ;

[0043] It should be noted that the instantaneous dust concentration C(t) is in kilograms per cubic meter, the instantaneous wind speed V(t) is in meters per second, the instantaneous wind direction angle θ(t) is the angle between the wind speed vector and the conductor axis, and the cumulative amount of dirt accumulated on the conductor at the previous moment... The unit is kilograms per meter.

[0044] Next, based on the principle of mass conservation in fluid mechanics, the dust transport flux at that moment is calculated using the following formula: ,in Dust transport flux, measured in kilograms per square meter per second, represents the total mass of dust flowing vertically across a unit area per unit time. The term corrects for flux attenuation under non-vertical wind direction.

[0045] The above calculation formula is based on the principle of mass conservation. The total mass (flux) of dust passing perpendicularly through a unit area per unit time is proportional to the dust concentration in that area and the wind speed component perpendicular to that area. When the wind speed vector makes an angle θ(t) with the conductor axis (i.e., the normal direction of the unit area), the effective transport wind speed is V(t)sin(θ(t)). When the wind direction is parallel to the conductor (θ=0∘), sin(0)=0, the flux is zero, and dust flowing along the conductor direction will not produce vertical deposition; when the wind direction is perpendicular to the conductor (θ=90∘), sin(90∘)=1, and the flux is maximum.

[0046] Subsequently, to quantify the adhesion behavior of dust particles after colliding with the conductor, the system invokes a pre-set aerodynamic database. This database stores the mapping relationship between wind speed and capture coefficient based on wind tunnel experiments. By looking up tables and using spline interpolation algorithms, the system obtains the current instantaneous wind speed value V(t) and the amount of accumulated dirt on the conductor at the previous moment. The common capture coefficients are expressed by the query function as follows: ,in The capture coefficient is a function that reflects the nonlinear characteristics of how the surface roughness and adhesion potential change with the increase of fouling on the surface area, thus affecting the capture efficiency. This is a database query mapping function. The conductor diameter parameter is from the line ledger database.

[0047] It needs to be explained regarding the capture coefficient lookup function. The specific implementation logic is as follows:

[0048] The system is based on the current instantaneous wind speed value V(t) and the conductor diameter. and the preset average particle size of sand and dust and dust particle density The dimensionless Stokes number in computational fluid dynamics is calculated using the following formula: ,in This is the aerodynamic viscosity constant. Subsequently, the system retrieves the baseline collision efficiency corresponding to the St number from an aerodynamic database. It follows the empirical mapping relationship. ;

[0049] The Stokes number (St) is a dimensionless number in fluid mechanics that characterizes the relative importance of particle inertia and fluid viscosity. The numerator reflects the characteristic time of particle inertia, while the denominator reflects the characteristic time of the fluid (air) (related to the diameter of the conductor). A larger St indicates stronger particle inertia, making it more likely to deviate from the streamline and collide with the conductor; conversely, a smaller St indicates that the particle is more likely to follow the airflow and bypass the conductor. This formula is used to evaluate the degree of deviation of particle trajectories in a flow field, providing a theoretical basis for calculating collision efficiency.

[0050] Baseline collision efficiency The calculation formula is an empirical relation obtained by fitting a large amount of wind tunnel experimental data. Its meaning is that when St→0, →0 indicates that particles without inertia completely follow the airflow and do not collide; when St→∞, →1 indicates that particles with extremely high inertia are completely unable to bypass obstacles, and the collision efficiency approaches 100%. Its form can well simulate the nonlinear relationship between collision efficiency and St, which first increases rapidly and then tends to saturate.

[0051] The system then extracts the cumulative amount of dirt deposited on the wires at the previous moment. And calculate the equivalent coverage of the current surface. The gain factor calculation formula is as follows: ,in The surface adhesion characteristic coefficient (with a value ranging from 0.12 to 0.18) This serves as the baseline fouling threshold. This logic reflects: as the amount of fouling on the wire surface increases... As the surface of the conductor increases, it changes from smooth to rough, thus... It exhibits a non-linear upward trend.

[0052] Finally, the system will use the baseline collision efficiency. With surface state gain factor After weighted integration and extreme value constraints are applied using a wind direction correction operator, the final output is: .

[0053] The above formula aims to quantify the enhancing effect of increased surface roughness due to fouling on particle capture capability.

[0054] Using a logarithmic function: This reflects the "gain saturation" effect. Initial buildup ( (Slightly) Significantly alters smooth surfaces, with a noticeable increase in roughness; as the fouling layer thickens, the marginal contribution of the newly added rough structure to the capture efficiency decreases. α is the surface adhesion characteristic coefficient, calibrated experimentally, reflecting the inherent adhesion ability of different conductor surface materials to sand and dust. The baseline fouling threshold serves as the reference quality during calibration. This gain factor, multiplied by the baseline collision efficiency, constitutes two corrections to the collision efficiency of an ideal smooth cylinder. The min(1,·) function is used to ensure that the capture coefficient is no greater than 1, preventing unreasonably high values ​​from being calculated under extreme parameters.

[0055] Finally, the dust transport flux is weighted and corrected using the capture coefficient to obtain the fouling rate on the conductor surface at the current moment. The core calculation formula is as follows: ,in The rate of dust and dirt accumulation is expressed in kilograms per square meter per second. This is the surface roughness correction factor for the conductor, determined based on the geometric characteristics of the conductor strand structure. It is typically set between 1.1 and 1.3 and is used to correct the difference in specific surface area between the ideal cylinder and the actual strand surface.

[0056] Among them, the aerodynamic model of the influence of dust on the conductor that varies over time is constructed, and the specific construction logic is as follows:

[0057] The rate of dust accumulation on the conductor surface is integrated over time to calculate the cumulative mass of dust accumulation from the start of monitoring to the current time.

[0058] Based on the preset dust density parameters, the accumulated dirt mass is converted into an equivalent adhesion thickness, and the equivalent adhesion thickness is superimposed on the pre-stored initial diameter of the conductor to generate an equivalent conductor diameter that dynamically increases over time.

[0059] Extract the wind speed time series data at the current moment, calculate the Reynolds number by combining it with the equivalent conductor diameter, and find the corresponding real-time aerodynamic drag coefficient in the preset Reynolds number-drag coefficient mapping table;

[0060] The aerodynamic load per unit length of the conductor is calculated using real-time aerodynamic drag coefficient, equivalent conductor diameter, and wind speed time series data. The aerodynamic load is then associated with and stored with the corresponding time points to form a time-varying aerodynamic model of the conductor affected by dust.

[0061] In one specific embodiment, a discrete-time series integral algorithm is used to calculate the mass accumulation of sand and dust accumulation rate data. The core integral formula is as follows: ,in The cumulative fouling mass per unit length of the conductor at the nth time step is expressed in kilograms per meter. Let be the rate of sand and dirt accumulation at time i, expressed in kilograms per square meter per second. The equivalent conductor diameter at the previous moment, expressed in meters. The time interval for monitoring sampling is measured in seconds.

[0062] The integral term uses That is, the equivalent diameter at the previous moment. This is because... The deposition area at time is from The wire size is determined at any given time. This process dynamically couples the two processes of fouling-induced size changes and the impact of these size changes on the subsequent fouling area, demonstrating the system's feedback characteristics and providing greater precision than using a fixed initial diameter. It achieves a conversion from deposition rate per unit area to deposition quality rate per unit length of wire.

[0063] Next, the equivalent diameter is reconstructed based on the mass-volume-geometry mapping relationship, using a concentric cylinder covering model. The calculation formula is as follows: ,in The equivalent conductor diameter generated at the current moment and dynamically increasing over time. The initial diameter of the conductor is pre-stored and comes from the transmission line equipment ledger database; The preset dust accumulation density parameter, in kilograms per cubic meter, is determined through on-site sampling or geological survey data. Subsequently, to determine the fluid flow state, the current wind speed time-series data is extracted, and the Reynolds number is calculated using the equivalent conductor diameter, as shown in the formula: ,in Let be the Reynolds number, and be a dimensionless parameter. This is the kinematic viscosity coefficient of air, measured in square meters per second. It is determined by referring to tables based on local altitude and temperature, and is approximately [value missing] at standard atmospheric pressure. Based on the calculated Reynolds number, the corresponding real-time aerodynamic drag coefficient is found in a pre-set "Reynolds number-drag coefficient mapping table" using cubic spline interpolation. The formula uses the current wind speed and dynamic equivalent diameter to ensure that the Reynolds number can reflect the changes in the flow field state under the combined effects of wind speed fluctuations and fouling growth in real time. Finally, the aerodynamic load on the conductor is calculated using the basic equations of fluid mechanics, and the calculation formula is as follows: ,in This represents the aerodynamic load per unit length of the conductor, measured in Newtons per meter. The density of air is expressed in kilograms per cubic meter, which generates a four-dimensional dataset containing time points, mass of accumulated dirt, equivalent conductor diameter, and aerodynamic loads. This dataset is the aerodynamic model of the influence of dust on conductors over time.

[0064] It should be added that the aforementioned "Reynolds number-drag coefficient mapping table" is a pre-constructed structured database based on wind tunnel test data or high-fidelity computational fluid dynamics simulation results. This table uses the Reynolds number as the independent variable and the aerodynamic drag coefficient as the dependent variable. Considering the fluid characteristics of the conductor under different wind speeds and sand accumulation thicknesses, this table covers the Reynolds number range from the subcritical to the supercritical region (typically...). to The table below shows an example of the mapping relationship for a typical sand-accumulated conductor cross section:

[0065] Table 1: Examples of Reynolds Number-Drag Coefficient Mapping for Typical Sand-Accumulated Conductors

[0066]

[0067] Among them, the time-series curves of the corresponding aerodynamic lift coefficients generated based on the aerodynamic model of the influence of dust on the conductor include:

[0068] The aerodynamic model of the influence of sand and dust on the conductor was analyzed, the cumulative distribution data of sand and dust at each time point were extracted, and the geometric asymmetry of the conductor cross section after sand accumulation relative to the original center was calculated.

[0069] Obtain wind direction angle information from wind speed time series data, calculate the angle between the airflow direction and the principal axis direction of the geometric asymmetry, and obtain the effective angle of attack of the airflow.

[0070] Using geometric asymmetry and effective angle of attack as index keys, a pre-set unsteady aerodynamic database is retrieved to match the instantaneous lift coefficient at the current time point. The instantaneous lift coefficient characterizes the vertical mechanical properties of the irregular sand-accumulated conductor under the action of wind flow.

[0071] The instantaneous lift coefficients at each time point are arranged and connected in the order of the time series to generate a time series curve of aerodynamic lift coefficients that reflects the evolution of the conductor's stress state with sand accumulation.

[0072] In one specific embodiment, the aerodynamic model of the influence of sand and dust on the conductor is first structurally analyzed to extract the cumulative distribution data of sand and dust at each time step t. Then, based on the principle of centrifugal moment, the geometric asymmetry of the conductor's cross-section after sand accumulation relative to the original center is calculated. The calculation formula is as follows: ,in For geometric asymmetry, and These are the static moments of the sand-accumulated cross section about the original geometric central axis, with units of kilogram-meter. The total mass of the conductor and the accumulated sand is expressed in kilograms. The reference diameter of the conductor is given. The numerator is the composite vector magnitude of the first-order static moment of the sand mass distribution about the original conductor center. The larger the value, the farther the center of mass deviates from the geometric center, and the stronger the asymmetry. The denominator is used for dimensionless calculation. Dividing by the total mass is to eliminate the influence of the mass size itself, and dividing by the reference diameter is to obtain a scale-independent parameter that purely characterizes the shape distortion.

[0073] Next, the wind direction angle information is extracted from the synchronized wind speed time series data, and the effective angle of attack of the airflow is calculated using the following formula: ,in The effective angle of attack is measured in degrees. The wind direction angle is derived from meteorological monitoring data; The principal axis direction angle of the geometric asymmetry, i.e., the angle of the aforementioned static moment composite vector, determines the relative attitude of the irregular cross-section in the flow field. Then, the calculated geometric asymmetry is... and effective angle of attack Using a dual index key, a pre-set unsteady aerodynamic database (built based on computational fluid dynamics (CFD) simulations and wind tunnel tests, storing lift coefficients under different combinations of asymmetry and angle of attack) is retrieved. The instantaneous lift coefficient at the current time point is then matched, and the query interpolation function is expressed as follows: ,in The instantaneous lift coefficient characterizes the vertical aerodynamic characteristics of an irregularly sand-accumulated conductor under wind action. If no exact match is found in the database, a bilinear interpolation algorithm is used to obtain an approximate value. Finally, according to the time series... The instantaneous lift coefficients calculated at each time point are arranged in the following order. By arranging the wires in an orderly manner and smoothly connecting them with splines, a time-series curve of the aerodynamic lift coefficient, reflecting the stress state of the conductor as sand accumulates, is generated.

[0074] It's worth noting that `interp2d` is a function in the SciPy library used for two-dimensional interpolation. It generates a smooth two-dimensional function surface based on a set of discrete data points, thereby estimating the function value at any location.

[0075] The resonance risk assessment module receives the aerodynamic lift coefficient time series curve and line operation status data, calculates the natural vibration frequency of the transmission line at the current moment; at the same time, it predicts the excitation frequency of the wind and sand flow on the conductor based on the wind and sand environment time series data; it compares the excitation frequency with the natural vibration frequency, and if the difference between the two is less than the preset resonance safety threshold, it generates a galloping warning signal.

[0076] In the resonance risk assessment module, when receiving the aerodynamic lift coefficient time-series curve and line operating status data, and calculating the natural vibration frequency of the transmission line at the current moment, the following data processing method is performed:

[0077] The line operation status data includes conductor tension data, conductor temperature data, current operating current value, and standard unit mass of conductor;

[0078] Analyze the line operation status data to extract the standard unit mass and tension data of the conductors;

[0079] The current moment's accumulated dust mass in the aerodynamic model of the influence of dust on the conductor is retrieved, and the standard unit mass and the accumulated dust mass are superimposed and summed to generate the equivalent linear density under the dust accumulation state.

[0080] Calculate the ratio of the conductor tension data to the equivalent linear density, and take the square root of the ratio to obtain the wave velocity parameter;

[0081] The span length information of the transmission line is obtained, and the wave velocity parameter is divided by twice the span length to obtain the natural vibration frequency of the transmission line at the current moment.

[0082] In one specific embodiment, the received line operation status data is parsed, and the standard unit mass of the conductor is extracted. The unit is kilograms per meter. This value is taken from the transmission line equipment ledger database and represents the physical properties of the conductor itself when it is not affected by the environment. At the same time, the conductor tension data at the current moment is also extracted. The unit is Newton, and this value is obtained in real time by tension sensors installed at the hanging points of the iron tower.

[0083] Next, the accumulated dust mass at the current moment, calculated and generated in the aerodynamic characteristic modeling module, is retrieved. Its unit is kilograms per meter. The equivalent linear density under sand accumulation is generated by algebraically superimposing the standard unit mass with the accumulated dust mass. The calculation formula is: ,in The equivalent linear density, expressed in kilograms per meter, quantifies the corrective effect of sand accumulation load on the conductor's vibration inertia.

[0084] Subsequently, the propagation speed of the transverse wave on the tensioned conductor, i.e., the wave velocity parameter, is calculated based on the wave equation. The calculation formula is as follows: ,in Let be the wave velocity parameter, measured in meters per second. This formula reflects the dynamic balance between the restoring force provided by tension and the inertia of mass; the greater the tension, the faster the wave velocity, and the heavier the mass, the slower the wave velocity. Finally, by introducing the geometric boundary conditions of the transmission line and obtaining the span length L, the wave velocity parameter is divided by twice the span length to obtain the natural vibration frequency of the transmission line at the current moment (primarily calculating the fundamental frequency). The core calculation formula is: ,in The natural vibration frequency is expressed in Hertz.

[0085] The above formula is based on the transverse wave theory of tensioned wires, and is the theoretical foundation for the analysis of wind-induced vibration and galloping of transmission lines. Wave velocity formula. The numerator is the tension (restoring force), and the denominator is the linear density (inertia). The greater the tension, the faster the wave speed; the greater the mass, the slower the wave speed. For a string fixed at both ends, its fundamental frequency (first harmonic) is f = v / (2L). 2L corresponds to the wavelength of the fundamental frequency.

[0086] The formula uses real-time tension and equivalent linear density to achieve a dynamic response to both conductor tension fluctuations and increased weight due to sand accumulation. This allows the calculated natural frequency to track the actual state of the line in real time, which is a prerequisite for accurate resonance risk assessment.

[0087] Among them, the prediction of the excitation frequency of windblown sand flow on the conductor based on windblown sand environment time series data includes:

[0088] The instantaneous wind speed value corresponding to the current moment is extracted from the wind speed time series data of the wind and sand environment, and the equivalent conductor diameter at the current moment is analyzed from the time-varying conductor dust influence aerodynamic model.

[0089] Call the preset list of aerodynamic parameters to obtain the Strouhal number corresponding to the cross-sectional shape of the equivalent conductor diameter, where the Strouhal number is a dimensionless constant for vortex shedding generated when fluid flows through a bluff body.

[0090] The characteristic velocity of the fluid is obtained by multiplying the instantaneous wind speed by the Strouhal number.

[0091] The vortex shedding frequency is calculated by dividing the fluid characteristic velocity by the equivalent conductor diameter, and this vortex shedding frequency is marked as the excitation frequency of the wind and sand flow on the conductor.

[0092] In one specific embodiment, the instantaneous wind speed value V(t) at the current time t is extracted from the wind speed time series data. This data is collected in real time by a micro-weather station installed at the top of the tower. Simultaneously, the equivalent conductor diameter corresponding to this time is analyzed from the "time-varying aerodynamic model of the influence of dust on the conductor" constructed in the previous steps. This parameter includes not only the diameter of the conductor itself, but also the thickness of the dust and dirt layer at the current moment, reflecting the true windward characteristics of the conductor in the wind field.

[0093] Next, the system calls a preset list of aerodynamic parameters, which stores the correspondence between different cross-sectional shapes (such as circular, ice-covered D-shaped, and sand-accumulated elliptical) and the Strouhal number St. The current cross-sectional shape features are used to match and obtain the corresponding Strouhal number St. Then, the product of the instantaneous wind speed and the Strouhal number is calculated to generate the fluid characteristic velocity value. The calculation formula is as follows: ,in The characteristic velocity of the fluid is expressed in meters per second. Finally, based on the eddy shedding frequency formula, the characteristic velocity is divided by the equivalent conductor diameter to calculate the excitation frequency of the wind-blown sand flow on the conductor. The core calculation formula is as follows: ,in The vortex shedding frequency, measured in Hertz, directly reflects the frequency at which the wind-blown sand flow alternately generates vortices and exerts a periodic lateral force on the leeward side of the conductor; the system marks this frequency as the excitation frequency of the wind-blown sand flow on the conductor.

[0094] For a bluff body of a specific shape, within a certain Reynolds number range, the vortex shedding frequency f, the incoming flow velocity V, and the characteristic size D satisfy St = fD / V, which is approximately a constant. From St = fD / V, we can directly derive f = St·V / D, which is the formula presented here. The excitation frequency is directly proportional to the wind speed and inversely proportional to the characteristic size, with the proportionality constant being the Strouhal number. The system matches the St value corresponding to different sand accumulation shapes by looking up a table, and combines this with the dynamic V(t) and... Calculations are performed to ensure that the predicted excitation frequency accurately reflects the actual vortex shedding characteristics of the current sand accumulation shape conductor under the current wind speed.

[0095] The active defense decision module responds to the galloping warning signal, calculates the amount of conductor temperature change caused by adjusting the current value of the transmission line, and simultaneously calculates the amount of conductor tension change and natural vibration frequency drift caused by the conductor temperature change.

[0096] The calculation of the change in conductor temperature caused by adjusting the current value of the transmission line includes:

[0097] Obtain the current operating current value of the transmission line and set the expected current adjustment step size. Perform algebraic addition on the two to obtain the adjusted target current value.

[0098] Calculate the squares of the target current value and the operating current value respectively, and calculate the difference between the two squares to obtain the current square difference data;

[0099] The resistivity parameter of the conductor is called, and the current square difference data is multiplied with the resistivity parameter to generate the Joule heat power increment caused by the current adjustment.

[0100] Obtain the specific heat capacity parameter of the conductor material and retrieve the equivalent linear density under sand accumulation state. Calculate the product of the specific heat capacity parameter and the equivalent linear density to obtain the comprehensive heat capacity data.

[0101] The ratio of the Joule heat power increment to the comprehensive heat capacity data is calculated, and this ratio is used as the temperature change rate per unit time. This ratio is then multiplied by the preset duration to obtain the amount of temperature change in the conductor caused by adjusting the current value.

[0102] In one specific embodiment, the current operating current value is acquired in real time. The expected current adjustment step size is set based on the safety margin allowed by the power grid dispatch. Through algebraic addition The adjusted target current value is obtained, establishing the input source for thermal regulation. Next, based on the physical property that heat generation power is proportional to the square of the current in Joule's law, the squares of the target current and the operating current are calculated and their difference is obtained to obtain the current square difference data. Then, combined with the electrical parameters of the conductor, the Joule heat power increment per unit length is calculated using the following formula: ,in It represents the increase in heat power per unit length in Joules, and its unit is watts per meter. This is the DC resistance per unit length of the conductor, measured in ohms per meter. This parameter needs to be obtained by looking up a table based on the conductor type and correcting for the temperature coefficient of resistance based on the current ambient temperature.

[0103] Subsequently, the system calls the specific heat capacity parameter of the conductor material. Its unit is joules per kilogram per degree Celsius, and the equivalent linear density is obtained through multiplication. The comprehensive heat capacity data, expressed in joules per meter per degree Celsius, effectively incorporates the endothermic effect of attached dust into the thermal model, avoiding the thermal response prediction bias caused by considering only the bare conductor. Finally, assuming the current adjustment process is adiabatic heating or short-duration heating, the ratio of the joule heat power increment to the comprehensive heat capacity data is calculated to obtain the temperature change rate. This rate is then multiplied by a preset duration to arrive at the final conductor temperature change. The core calculation formula is as follows: ,in This refers to the temperature change of the conductor, expressed in degrees Celsius. The duration of action for melting ice or preventing galloping current, measured in seconds; It strictly follows Joule's law. The key innovation here lies in using equivalent linear density, rather than the mass of the conductor itself. This means the model considers the heat capacity of the attached dust. The temperature rise requires heating not only the conductor metal but also the dust layer covering it. This treatment greatly improves the accuracy of temperature rise prediction, avoiding overestimation of the temperature rise effect due to neglecting the heat absorption of dust. The formula assumes that in a short time... Internally, Joule heating is entirely used for system temperature rise, ignoring convective heat dissipation. This is conservative and reasonable for calculating the maximum possible temperature rise caused by current adjustment, and meets the upper limit of assessment capability requirements in active defense decision-making.

[0104] The simultaneous calculation of conductor tension change and natural vibration frequency drift caused by conductor temperature change includes:

[0105] The linear expansion coefficient of the transmission line and the current span length are retrieved. The conductor temperature change is multiplied by the linear expansion coefficient and the current span length to obtain the conductor thermal expansion elongation under heated conditions.

[0106] Substitute the thermal expansion elongation of the conductor into the preset conductor state equation, and under the constraint of keeping the span endpoints fixed, solve for the updated conductor tension value after the conductor sag deformation, and calculate the difference between the updated conductor tension value and the conductor tension data to determine the amount of conductor tension change.

[0107] The equivalent linear density of the transmission line is obtained. Based on the principle of string vibration, the updated natural vibration frequency of the conductor after adjustment is calculated by using the updated conductor tension value and the equivalent linear density. The updated natural vibration frequency is then compared with the natural vibration frequency at the current moment to define the natural vibration frequency drift.

[0108] In one specific embodiment, the basic physical property parameters of the transmission line are retrieved, including the linear expansion coefficient α of the conductor material, which is expressed in degrees Celsius and reflects the material's sensitivity to thermal elongation, as well as the current span length L; the conductor temperature change is then calculated. Multiply the above parameters together to calculate the free thermal expansion elongation ΔL of the conductor under heated conditions. This value represents the increase in the physical length of the conductor under unconstrained conditions.

[0109] Next, based on the state equations of the transmission line, and under the geometric constraint of keeping the span endpoints fixed, considering the sag changes caused by the conductor's self-weight and sand accumulation load, the updated conductor tension value after the conductor sag deformation is solved. This solution process typically uses an iterative method to handle the following cubic state equations: ,in The updated conductor tension value to be solved is expressed in Newtons. Given the current conductor tension data (in Newtons), E (elastic modulus of the conductor, in Pascals), S (cross-sectional area of ​​the conductor, in square meters), and g (acceleration due to gravity), the following values ​​were obtained through numerical calculations: Then, calculate the difference between it and the original tension. Determine the change in conductor tension.

[0110] This is the classic tension-sag-temperature equation of state in transmission line mechanics. Based on the catenary or parabolic theory of conductors, it considers the sag caused by the conductor's own weight (including sand accumulation), the elastic deformation of the material, and the thermal expansion effect, and is derived under the constraint of fixed span endpoints. The first term on the left is the change in tension. The second term on the left is the difference in elastic elongation caused by the change in sag (related to the reciprocal of the square of the tension). The right side is the change in length caused by thermal expansion (temperature rise causes the conductor to "want" to elongate). This equation can be solved numerically to accurately obtain the final equilibrium tension of the conductor under the combined effects of heat, load (self-weight), and geometric constraints.

[0111] Subsequently, in order to evaluate the effect of tension changes on vibration characteristics, based on the principle of string vibration, the updated conductor tension value was used. Equivalent linear density under sand accumulation conditions Recalculate the fundamental frequency of the conductor using the following formula: ,in This is the updated natural vibration frequency after conductor adjustment, measured in Hertz. Finally, this updated frequency is compared with the natural vibration frequency currently monitored by the system. The comparison is performed, and the calculation formula is as follows: This defines the natural vibration frequency drift. .

[0112] The control command output module filters out target current values ​​that can cause the natural vibration frequency to drift to a level outside the preset resonance safety threshold, and generates corresponding current modulation commands.

[0113] The operation logic of the control command output module is as follows:

[0114] Obtain the calculated predicted vibration frequency, and simultaneously call the excitation frequency previously generated based on the time series data of the wind and sand environment;

[0115] Calculate the absolute value of the difference between the predicted vibration frequency and the excitation frequency, and mark this absolute value as the frequency avoidance deviation value;

[0116] The frequency avoidance deviation value is compared with the preset resonance safety threshold. If the frequency avoidance deviation value is greater than the preset resonance safety threshold, it is determined that the target current value corresponding to the generated predicted vibration frequency meets the anti-galling requirements.

[0117] Extract the target current value that meets the anti-galling requirements, convert it into a control message format that the device can recognize, and generate a current modulation command that includes the current adjustment range and execution duration.

[0118] In one specific embodiment, the predicted vibration frequency after applying the target current, calculated by the preceding active defense decision module, and the excitation frequency of the wind and sand flow on the conductor, calculated by the aerodynamic characteristic modeling module based on the current wind speed and the morphology of sand accumulation on the conductor, are retrieved. Next, to quantify the degree to which the currently proposed current adjustment scheme avoids the resonance risk, the absolute value of the frequency deviation between the two is calculated to obtain the frequency avoidance deviation value, which characterizes the separation distance between the system's natural frequency and the external excitation frequency in the frequency domain.

[0119] Subsequently, the calculated frequency avoidance deviation value is logically compared with a preset resonance safety threshold. This threshold is typically set based on the damping characteristics and structural safety factor of the transmission line. If the judgment condition is met: frequency avoidance deviation value > resonance safety threshold, it indicates that the current adjustment scheme can effectively disrupt the "frequency lock-in" mechanism, preventing energy from accumulating and causing galloping. Thus, it is determined that the target current value corresponding to the predicted vibration frequency and its associated duration meet the anti-galloping requirements. Finally, once the judgment is passed, the system extracts the target current value and duration, calls the standard power communication protocol library, encapsulates the physical parameters into a control message executable by the device, and generates a current modulation command containing the current adjustment amplitude and execution duration. This drives the flexible AC transmission device or the grid dispatching terminal to precisely adjust the current flowing through the transmission line, achieving active vibration suppression.

[0120] Example 2:

[0121] like Figure 3 As shown, a smart assessment device for wind and sand disasters based on time-series data analysis includes a processor, a memory, and a communication bus.

[0122] The memory stores a computer-readable program that can be executed by the processor;

[0123] The communication bus enables communication between the processor and the memory;

[0124] When the processor executes the computer-readable program, it performs module logic to implement a wind and sand disaster intelligent assessment system based on time-series data analysis as described in any one of the present invention.

[0125] It should be noted that the interval and threshold sizes are set for ease of comparison. The size of the threshold depends on the amount of sample data and the base number set by those skilled in the art for each set of sample data, as long as it does not affect the proportional relationship between the parameter and the quantized value. Furthermore, the above formulas are all dimensionless calculations, and the formulas are derived from software simulations using a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

Claims

1. A smart assessment system for wind and sand disasters based on time-series data analysis, characterized in that, The system includes: The wind and sand data acquisition module obtains time-series data on the wind and sand environment in the area where the transmission line is located, as well as data on the line operation status of the transmission line. The aerodynamic characteristic modeling module calculates the rate of dust accumulation on the surface of the conductor based on time-series data of aeolian and sandy environments, and constructs a time-varying aerodynamic model of the influence of dust on the conductor; based on the aerodynamic model of the influence of dust on the conductor, it generates the corresponding time-series curve of aerodynamic lift coefficient. The resonance risk assessment module receives the aerodynamic lift coefficient time series curve and line operation status data, calculates the natural vibration frequency of the transmission line at the current moment; at the same time, it predicts the excitation frequency of the wind and sand flow on the conductor based on the wind and sand environment time series data; it compares the excitation frequency with the natural vibration frequency, and if the difference between the two is less than the preset resonance safety threshold, it generates a galloping warning signal. In the resonance risk assessment module, when receiving the aerodynamic lift coefficient time-series curve and line operating status data, and calculating the natural vibration frequency of the transmission line at the current moment, the following data processing method is performed: The line operation status data includes conductor tension data, conductor temperature data, current operating current value, and standard unit mass of conductor; Analyze the line operation status data to extract the standard unit mass and tension data of the conductors; The current moment's accumulated dust mass in the aerodynamic model of the influence of dust on the conductor is retrieved, and the standard unit mass and the accumulated dust mass are superimposed and summed to generate the equivalent linear density under the dust accumulation state. Calculate the ratio of the conductor tension data to the equivalent linear density, and take the square root of the ratio to obtain the wave velocity parameter; The span length information of the transmission line is obtained, and the wave velocity parameter is divided by twice the span length to obtain the natural vibration frequency of the transmission line at the current moment. The active defense decision module responds to the galloping warning signal, calculates the amount of conductor temperature change caused by adjusting the current value of the transmission line, and simultaneously calculates the amount of conductor tension change and natural vibration frequency drift caused by the conductor temperature change. The control command output module filters out target current values ​​that can cause the natural vibration frequency to drift to a level outside the preset resonance safety threshold, and generates corresponding current modulation commands.

2. The intelligent assessment system for wind and sand disasters based on time-series data analysis according to claim 1, characterized in that, When calculating the rate of dust accumulation on the surface of the conductor, the aerodynamic characteristic modeling module performs the following data processing: Time series data of dust particle concentration and wind speed were extracted from the time series data of the wind and sand environment in the area where the power transmission line is located. Time-series data of dust particle concentration and wind speed are aligned on the time axis to extract instantaneous concentration and instantaneous wind speed values ​​at the same moment. The dust transport flux is generated by multiplying the instantaneous concentration value and the instantaneous wind speed value, where the dust transport flux represents the total mass of dust impacting a unit area per unit time. Call the preset aerodynamics database to obtain the capture coefficient corresponding to the instantaneous wind speed value, where the capture coefficient is used to characterize the proportion of sand and dust particles that can adhere to the conductor at the current wind speed; The dust transport flux is weighted using the capture coefficient to obtain the current dust accumulation rate on the conductor surface.

3. The intelligent assessment system for wind and sand disasters based on time-series data analysis according to claim 2, characterized in that, A time-varying aerodynamic model of the influence of dust on the conductor is constructed. The specific construction logic is as follows: The rate of dust accumulation on the conductor surface is integrated over time to calculate the cumulative mass of dust accumulation from the start of monitoring to the current time. Based on the preset dust density parameters, the accumulated dirt mass is converted into an equivalent adhesion thickness, and the equivalent adhesion thickness is superimposed on the pre-stored initial diameter of the conductor to generate an equivalent conductor diameter that dynamically increases over time. Extract the wind speed time series data at the current moment, calculate the Reynolds number by combining it with the equivalent conductor diameter, and find the corresponding real-time aerodynamic drag coefficient in the preset Reynolds number-drag coefficient mapping table; The aerodynamic load per unit length of the conductor is calculated using real-time aerodynamic drag coefficient, equivalent conductor diameter, and wind speed time series data. The aerodynamic load is then associated with and stored with the corresponding time points to form a time-varying aerodynamic model of the conductor affected by dust.

4. The intelligent assessment system for wind and sand disasters based on time-series data analysis according to claim 3, characterized in that, Based on the aerodynamic model of the influence of dust on the conductor, the corresponding time series curves of aerodynamic lift coefficients are generated, including: The aerodynamic model of the influence of sand and dust on the conductor was analyzed, the cumulative distribution data of sand and dust at each time point were extracted, and the geometric asymmetry of the conductor cross section after sand accumulation relative to the original center was calculated. Obtain wind direction angle information from wind speed time series data, calculate the angle between the airflow direction and the principal axis direction of the geometric asymmetry, and obtain the effective angle of attack of the airflow. Using geometric asymmetry and effective angle of attack as index keys, a pre-set unsteady aerodynamic database is retrieved to obtain the instantaneous lift coefficient at the current time point. The instantaneous lift coefficient characterizes the vertical mechanical properties of the irregular sand-accumulated conductor under the action of wind flow. The instantaneous lift coefficients at each time point are arranged and connected in the order of the time series to generate a time series curve of aerodynamic lift coefficients that reflects the evolution of the conductor's stress state with sand accumulation.

5. The intelligent assessment system for wind and sand disasters based on time-series data analysis according to claim 4, characterized in that, Predicting the excitation frequency of windblown sand flow on conductors based on time-series data of windblown sand environments, including: The instantaneous wind speed value corresponding to the current moment is extracted from the wind speed time series data of the wind and sand environment, and the equivalent conductor diameter at the current moment is analyzed from the time-varying conductor dust influence aerodynamic model. Call the preset list of aerodynamic parameters to obtain the Strouhal number corresponding to the cross-sectional shape of the equivalent conductor diameter, where the Strouhal number is a dimensionless constant for vortex shedding generated when fluid flows through a bluff body. The characteristic velocity of the fluid is obtained by multiplying the instantaneous wind speed by the Strouhal number. The vortex shedding frequency is calculated by dividing the fluid characteristic velocity value by the equivalent conductor diameter, and this vortex shedding frequency is marked as the excitation frequency of the wind and sand flow on the conductor.

6. The intelligent assessment system for wind and sand disasters based on time-series data analysis according to claim 4, characterized in that, Calculate the change in conductor temperature caused by adjusting the current value of the transmission line, including: Obtain the current operating current value of the transmission line and set the expected current adjustment step size. Perform algebraic addition on the two to obtain the adjusted target current value. Calculate the squares of the target current value and the operating current value respectively, and calculate the difference between the two squares to obtain the current square difference data; The resistivity parameter of the conductor is called, and the current square difference data is multiplied with the resistivity parameter to generate the Joule heat power increment caused by the current adjustment. Obtain the specific heat capacity parameter of the conductor material and retrieve the equivalent linear density under sand accumulation state. Calculate the product of the specific heat capacity parameter and the equivalent linear density to obtain the comprehensive heat capacity data. The ratio of the Joule heat power increment to the comprehensive heat capacity data is calculated, and this ratio is used as the temperature change rate per unit time. This ratio is then multiplied by the preset duration to obtain the amount of temperature change in the conductor caused by adjusting the current value.

7. The intelligent assessment system for wind and sand disasters based on time-series data analysis according to claim 6, characterized in that, The synchronous calculation of the changes in conductor tension and the drift in natural vibration frequency caused by changes in conductor temperature includes: The linear expansion coefficient of the transmission line and the current span length are retrieved. The conductor temperature change is multiplied by the linear expansion coefficient and the current span length to obtain the conductor thermal expansion elongation under heated conditions. Substitute the thermal expansion elongation of the conductor into the preset conductor state equation, and under the constraint of keeping the span endpoints fixed, solve for the updated conductor tension value after the conductor sag deformation, and calculate the difference between the updated conductor tension value and the conductor tension data to determine the amount of conductor tension change. The equivalent linear density of the transmission line is obtained. Based on the principle of string vibration, the updated natural vibration frequency of the conductor after adjustment is calculated by using the updated conductor tension value and the equivalent linear density. The updated natural vibration frequency is then compared with the natural vibration frequency at the current moment to define the natural vibration frequency drift.

8. The intelligent assessment system for wind and sand disasters based on time-series data analysis according to claim 1, characterized in that, The operation logic of the control command output module is as follows: Obtain the calculated predicted vibration frequency, and simultaneously call the excitation frequency previously generated based on the time series data of the wind and sand environment; Calculate the absolute value of the difference between the predicted vibration frequency and the excitation frequency, and mark this absolute value as the frequency avoidance deviation value; The frequency avoidance deviation value is compared with the preset resonance safety threshold. If the frequency avoidance deviation value is greater than the preset resonance safety threshold, it is determined that the target current value corresponding to the generated predicted vibration frequency meets the anti-galling requirements. Extract the target current value that meets the anti-galling requirements, convert it into a control message format that the device can recognize, and generate a current modulation command that includes the current adjustment range and execution duration.

9. A smart assessment device for wind and sand disasters based on time-series data analysis, characterized in that, Includes processor, memory, and communication bus; The memory stores a computer-readable program that can be executed by the processor; The communication bus enables communication between the processor and the memory; When the processor executes the computer-readable program, it performs module logic to implement the module logic in a wind and sand disaster intelligent assessment system based on time-series data analysis as described in any one of claims 1 to 8.

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