A method for predicting short-time ice accretion growth of power transmission line considering micro-topography

Abstract

The application discloses a power transmission line short-time icing growth prediction method considering the influence of micro-topography, and particularly relates to the technical field of icing growth prediction, and comprises the following steps: acquiring a structure state sequence at a tower of a power transmission line and calculating disturbance features, and executing joint judgment to output a structure disturbance state identifier; acquiring the structure state sequence and executing disturbance feature calculation, and jointly judging the structure disturbance state; under the structure disturbance state, forming a disturbance corrected wind speed field based on disturbance calculation and terrain factors, identifying airflow stagnation and calculating water vapor accumulation; then, constructing a structure disturbance feedback path and executing icing growth prediction, dynamically updating feedback path weights based on prediction offset results; and rolling calculation of feedback score values and identification of a structure disturbance dominant area, so as to realize icing growth prediction path identification and incremental output under the linkage of the structure disturbance state, disturbance airflow features and terrain factors.

Description

Technical Field

[0001] This invention relates to the field of icing growth prediction technology, and more specifically, to a method for predicting short-term icing growth of transmission lines that takes into account the influence of micro-topography. Background Technology

[0002] Existing methods for predicting ice thickness on transmission line towers mainly rely on meteorological observation data with temporal continuity and introduce topographic feature parameters that describe local landform changes to construct a data-driven model to achieve rolling predictions within the near time range.

[0003] In terms of methodology, meteorological variables such as temperature, humidity, wind speed, and precipitation intensity, as well as static topographic factors such as slope, aspect, altitude, and terrain roughness are generally used as inputs. Historical icing data is used for training, and machine learning regression algorithms are used to output the amount of icing growth in future time periods.

[0004] In the model construction, the towers and conductors of the transmission line are regarded as structurally constant bodies, and their geometry is assumed to remain unchanged during the prediction period. The icing growth process is determined only by external meteorological conditions and topographic factors.

[0005] However, in actual operation, especially in complex terrain sections where there are steep slopes, local elevation changes, or wind profile changes in the area where the transmission line towers are located, the non-uniform load caused by icing will cause local deformation of the conductor, thereby changing the airflow disturbance characteristics of the surrounding area, such as forming local calm wind zones or wind speed reduction zones, and changing the water vapor accumulation efficiency on the conductor surface. This local modulation effect of structural deformation reacting on the meteorological environment often causes an abnormal increase in the icing growth rate in the initial stage after the deformation occurs, and has obvious positive feedback characteristics.

[0006] Because existing methods do not establish the coupling relationship between structural state and meteorological input, and lack modeling and feedback mechanisms for structural response processes, the prediction results under the above conditions are systematically inaccurate in the abrupt icing growth stage induced by local deformation of the conductor. They cannot identify and respond to the rapid icing evolution process under the condensation growth path caused by structural disturbances, which constitutes a key problem that has not yet been solved in the current prediction model. Summary of the Invention

[0007] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a method for predicting short-term icing growth of transmission lines considering the influence of micro-topography. This method involves acquiring a structural state sequence and performing disturbance feature calculations to jointly determine the structural disturbance state. Under the structural disturbance state, a disturbance-corrected wind speed field is formed based on the disturbance calculations and topographic factors to identify airflow stagnation and calculate water vapor accumulation. Subsequently, a structural disturbance feedback path is constructed, and icing growth prediction is performed. The feedback path weights are dynamically updated based on the prediction offset results. The feedback score is continuously calculated, and the dominant area of ​​structural disturbance is identified, thus achieving the identification and incremental output of the icing growth prediction path under the linkage of structural disturbance state, disturbance airflow characteristics, and topographic factors.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a method for predicting the short-term icing growth of transmission lines considering the influence of micro-topography, comprising:

[0009] Obtain the structural state sequence at the transmission line tower and calculate the disturbance characteristics, then perform a joint judgment to output the structural disturbance state identifier;

[0010] Input structural disturbance status identifiers and historical meteorological observation data set information, construct disturbance-corrected wind speed field and calculate airflow stagnation index, perform airflow stagnation determination and calculate water vapor accumulation;

[0011] Based on the water vapor accumulation and historical meteorological observation data set, a disturbance feedback path is constructed, icing growth is predicted, the feedback path offset is calculated, and the feedback path weight is updated.

[0012] The input feedback path offset value is used to calculate the disturbance feedback score, and it is determined whether to output the dominant area identifier or maintain the current state.

[0013] In a preferred embodiment, a structural state sequence at the transmission line tower is obtained, which includes a tension sequence, an inclination angle sequence, and a vibration frequency sequence; disturbance characteristic calculation is performed on the structural state sequence to calculate the tension change rate, inclination angle change amplitude, and vibration frequency change, respectively, and a set of disturbance variables is constructed.

[0014] The set of disturbance variables is subjected to joint judgment. The three conditions are whether the rate of change of tension exceeds the preset threshold, whether the amplitude of change of tilt angle exceeds the preset threshold, and whether the amount of change of vibration frequency is higher than the preset threshold. If none of the three conditions are satisfied, the normal state indicator is output; otherwise, the structural disturbance state indicator is output.

[0015] In a preferred embodiment, when the output result is a structural disturbance status identifier, the historical meteorological observation data set at the transmission line tower is obtained. The historical meteorological observation data set includes wind speed, wind direction, temperature, humidity, relative humidity and air temperature.

[0016] Perturbation calculations are performed on the wind speed and wind direction in the structural disturbance status markers and historical meteorological observation data sets to form a disturbance-corrected wind speed field;

[0017] The airflow stagnation index is calculated by perturbating and correcting the wind speed field and using topographic factors, including aspect, slope, and topographic roughness. Based on the results of the airflow stagnation index, airflow stagnation identification is achieved.

[0018] In the airflow stagnation identification, it is determined whether the airflow stagnation index is higher than the preset airflow stagnation index threshold and whether the slope and wind direction angle is higher than the preset slope and wind direction angle threshold. These two conditions are used for condition judgment. If both conditions are not higher than the preset threshold, the output is airflow non-stagnation; otherwise, the output is airflow stagnation.

[0019] When the output is airflow stagnation, a fitting calculation is performed on the relative humidity and temperature in the historical meteorological observation data set to output the amount of water vapor accumulation.

[0020] In a preferred embodiment, the water vapor accumulation and temperature, humidity and wind speed in the historical meteorological observation data set are used as elements to jointly construct the structural disturbance feedback path and perform icing growth prediction, and output the structural disturbance prediction value.

[0021] Each element in the structural disturbance feedback path is treated as a sub-path and assigned a corresponding preset weight factor to characterize the relative contribution of the element in the icing growth prediction. The weight factor is defined as the feedback path weight.

[0022] The difference between the predicted structural disturbance value and the basic predicted value is calculated, and the feedback path offset value is output. If the feedback path offset value exceeds the preset feedback path offset threshold in two consecutive prediction periods that are updated in chronological order, the feedback path weight is updated; otherwise, the current feedback path weight remains unchanged.

[0023] In a preferred embodiment, a score calculation is performed on the feedback path offset values ​​within multiple time-sequentially updated prediction periods to output a structural disturbance feedback score value.

[0024] If the structural disturbance feedback score exceeds the preset structural disturbance feedback score threshold, a structural disturbance dominant area identifier is output to guide the priority construction of structural disturbance feedback paths and the execution of icing growth prediction within the current area. Otherwise, the current area status is maintained to prevent the triggering of a regional early warning response if the condition for outputting a structural disturbance dominant area identifier is not met.

[0025] In a preferred embodiment, a structural disturbance state identifier is defined. Represented as:

[0026]

[0027] Define the rate of change of tension Represented as:

[0028]

[0029] Define the amplitude of tilt angle change Represented as:

[0030]

[0031] Define the change in vibration frequency Represented as:

[0032]

[0033] in Joint disturbance judgment function; Joint disturbance judgment function The joint judgment logic includes: execution based on three judgment conditions of the set of disturbance variables; the three conditions are: tension change rate Does it exceed the preset threshold for the rate of change of tension and the amplitude of change of tilt angle? Does it exceed the preset threshold for tilt angle change amplitude and vibration frequency change? Whether it exceeds the preset threshold of vibration frequency change. For any t, if none of the three conditions meet their respective preset threshold conditions, it is defined as a normal state. At this time, the output value of the joint disturbance judgment function is 0, corresponding to the output of the normal state indicator. If any one of the three conditions meets its respective preset threshold condition, it is defined as a structural disturbance state. At this time, the output value of the joint disturbance judgment function is 1, corresponding to the output of the structural disturbance state indicator.

[0034] in This is the current time step; A function representing the change of tension over time in a sequence of structural states; The variable represents the variable relative to the current time step. First derivative operations; A function representing the change of tilt angle over time in a sequence of structural states; For time sampling points; Represents a sliding time window The time corresponding to the previous discrete time step; The length of the time window for perturbation detection; A function representing the change of vibration frequency with time in a structural state sequence; Represents a sliding time window Index variables within; Vibration frequency value; Represents a sliding time window Inner Input the numerical value at that time; This represents a fixed threshold used to determine whether a change in vibration frequency meets the disturbance criteria.

[0035] In a preferred embodiment, a perturbation-corrected wind speed field is defined. Represented as:

[0036]

[0037] Define the airflow stagnation index Represented as:

[0038]

[0039] Define water vapor accumulation Represented as:

[0040]

[0041] in Represents the coordinates in the original wind speed field Wind speed at the location; This refers to the wind speed reduction term within the affected area. This is the gain coefficient in the direction of the disturbance; This represents the vector deflection term after applying a disturbance to the airflow direction; This represents the perturbed, corrected wind speed field variable; For integration time; Indicates the slope angle of the terrain With airflow direction angle The projection weight function of the angle between them; coordinates The slope angle of the terrain at that location; coordinates The airflow direction angle at that location; coordinates Terrain roughness coefficient at the location; This is the amplification factor for the aggregation amount; Relative humidity; This represents the dew point temperature obtained by fitting relative humidity and air temperature. This refers to the current temperature. These are the coefficients for the exponential fit. is the base of the natural logarithm.

[0042] In a preferred embodiment, a structural disturbance prediction value is defined. :

[0043]

[0044] Define the base path prediction value Represented as:

[0045]

[0046] Define feedback path offset value Represented as:

[0047]

[0048] Define the average path offset over two consecutive periods Represented as:

[0049]

[0050] in A prediction function model representing the disturbance path; This represents the wind speed value at the current time step. This represents a combined variable of temperature and humidity at the current time step. This indicates the currently constructed structural perturbation feedback path; It is a prediction function model for the basic path; Indicates the first The absolute difference between the predicted value of the structural disturbance feedback path and the predicted value of the basic path within each rolling prediction period; Number the current rolling cycle.

[0051] In a preferred embodiment, a multi-cycle path offset weighted score is defined. Represented as:

[0052]

[0053] Define structural disturbance feedback score Represented as:

[0054]

[0055] The dominant region output decision condition is defined as follows:

[0056]

[0057] in Indicates the number of rolling forecast periods used for score calculation; Indicates the first The corresponding score weights within each rolling prediction period; The structural disturbance feedback path is represented in the th... Average offset value within each rolling forecast period; For perturbation scoring functions; It is the natural logarithm function; This is the gain factor for the perturbation score; This is a disturbance scoring index factor; This indicates the spatial segment of the transmission line to which the current structural disturbance feedback path belongs and the local area number where the tower is located; The threshold for determining the disturbance score.

[0058] The technical effects and advantages of this invention are as follows:

[0059] This solution introduces structural disturbance state identification and links it with meteorological input to solve the problem that traditional models cannot respond to airflow disturbances caused by the deformation of transmission line towers, thereby improving the prediction accuracy in complex terrain areas.

[0060] By constructing a perturbation-corrected wind speed field and integrating topographic factors, the local calm wind or weakened area is accurately simulated, enhancing the ability to identify stagnant airflow areas.

[0061] By utilizing water vapor accumulation and meteorological elements to construct a feedback path, dynamic prediction of condensation abrupt processes caused by structural disturbances can be achieved.

[0062] Based on the rolling comparison results of the feedback path offset values, the path weights are updated in real time to enhance the model's recognition and response to the persistence of disturbances;

[0063] By using a perturbation scoring mechanism to output the identifier of the dominant area of ​​structural perturbation, the prediction resources can be prioritized and the accuracy controlled in key areas. Attached Figure Description

[0064] Figure 1 This is a flowchart of the method steps of the present invention. Detailed Implementation

[0065] The technical solutions of 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.

[0066] Refer to the instruction manual appendix Figure 1 An embodiment of the present invention provides a method for predicting the short-term icing growth of transmission lines considering the influence of micro-topography, comprising:

[0067] Obtain the structural state sequence at the transmission line tower and calculate the disturbance characteristics, then perform a joint judgment to output the structural disturbance state identifier;

[0068] Input structural disturbance status identifiers and historical meteorological observation data set information, construct disturbance-corrected wind speed field and calculate airflow stagnation index, perform airflow stagnation determination and calculate water vapor accumulation;

[0069] Based on the water vapor accumulation and historical meteorological observation data set, a disturbance feedback path is constructed, icing growth is predicted, the feedback path offset is calculated, and the feedback path weight is updated.

[0070] The input feedback path offset value is used to calculate the disturbance feedback score, and it is determined whether to output the dominant area identifier or maintain the current state.

[0071] It should be noted that in the formula structure involved in this scheme, dimensionless terms can be used as proportional or structural adjustment factors. When combined with quantities with units, they only play a role in numerical scaling and do not introduce new physical dimensions. Therefore, they will not change or confuse the overall unit system. This combination of "dimensionless terms and terms with units" can be understood as a composite structural expression commonly used in mathematical physics modeling. It conforms to the principle of dimensional consistency and has a clear physical interpretation basis.

[0072] Secondly, in the formula structure of this scheme, if multiple variables with different physical units are involved, including but not limited to time, mass or energy variables, their joint appearance is to express the collaborative modeling relationship of multiple physical mechanisms. Each variable can form a unified structure through function mapping, ratio combination or normalization adjustment, with clear units and clear meaning. The overall expression conforms to the principle of dimensional consistency and the conventional formula of engineering modeling.

[0073] In this scheme, constants, weights, adjustment factors, threshold parameters, proportional coefficients, etc., are all adjustable control parameters for different application environments. Their values ​​depend on the target equipment configuration, data input characteristics, and performance optimization goals. During the implementation phase, they are converged within a reasonable range through model verification, performance constraints, or engineering calibration. Although these parameters do not have a unique preset value, they have clear adjustment logic and calculation paths. They belong to the deterministic setting process in engineering implementation. The purpose of this setting is to ensure that the scheme is both universally adaptable and reproducible and operable, without affecting its technical clarity and feasibility.

[0074] Obtain the structural state sequence at the transmission line tower, which includes tension sequence, tilt angle sequence and vibration frequency sequence; perform disturbance characteristic calculation on the structural state sequence to calculate the tension change rate, tilt angle change amplitude and vibration frequency change, and construct a set of disturbance variables;

[0075] The set of disturbance variables is subjected to joint judgment. The three conditions are whether the rate of change of tension exceeds the preset threshold, whether the amplitude of change of tilt angle exceeds the preset threshold, and whether the amount of change of vibration frequency is higher than the preset threshold. If none of the three conditions are satisfied, the normal state indicator is output; otherwise, the structural disturbance state indicator is output.

[0076] When the output result is a structural disturbance status identifier, the historical meteorological observation data set at the transmission line tower is obtained. The historical meteorological observation data set includes wind speed, wind direction, temperature, humidity, relative humidity and air temperature.

[0077] Perturbation calculations are performed on the wind speed and wind direction in the structural disturbance status markers and historical meteorological observation data sets to form a disturbance-corrected wind speed field;

[0078] The airflow stagnation index is calculated by perturbating and correcting the wind speed field and using topographic factors, including aspect, slope, and topographic roughness. Based on the results of the airflow stagnation index, airflow stagnation identification is achieved.

[0079] In the airflow stagnation identification, it is determined whether the airflow stagnation index is higher than the preset airflow stagnation index threshold and whether the slope and wind direction angle is higher than the preset slope and wind direction angle threshold. These two conditions are used for condition judgment. If both conditions are not higher than the preset threshold, the output is airflow non-stagnation; otherwise, the output is airflow stagnation.

[0080] When the output is airflow stagnation, a fitting calculation is performed on the relative humidity and temperature in the historical meteorological observation data set to output the water vapor accumulation.

[0081] The fitting calculation involves selecting sample points whose relative humidity and temperature values ​​fluctuate within a fixed range relative to the current input values, statistically analyzing the frequency of actual water vapor condensation recorded at these sample points within the corresponding time period, constructing a fitting curve based on the growth trend corresponding to the temperature and humidity changes, and then inferring the total volume of water vapor condensation per unit time based on the corresponding position of the current input position in the curve, which serves as the estimation result of water vapor accumulation.

[0082] The structural disturbance feedback path is constructed by taking water vapor accumulation and temperature, humidity and wind speed from historical meteorological observation data as elements, and icing growth prediction is performed to output the structural disturbance prediction value.

[0083] Each element in the structural disturbance feedback path is treated as a sub-path and assigned a corresponding preset weight factor to characterize the relative contribution of the element in the icing growth prediction. The weight factor is defined as the feedback path weight.

[0084] The difference between the predicted structural disturbance value and the baseline predicted value is calculated to output the feedback path offset value. The baseline predicted value refers to the icing prediction value under the traditional meteorological-dominated prediction path. If the feedback path offset value exceeds the preset feedback path offset threshold in two consecutive time-sequential rolling prediction periods, the feedback path weight is updated. Otherwise, the current feedback path weight remains unchanged. The time-sequential rolling prediction period refers to a continuous short-term prediction window that advances forward at fixed time intervals. Each period independently performs icing growth prediction based on the latest input data.

[0085] A score calculation is performed on the feedback path offset values ​​within multiple prediction periods that are updated in chronological order, and the structural disturbance feedback score value is output.

[0086] If the structural disturbance feedback score exceeds the preset structural disturbance feedback score threshold, a structural disturbance dominant area identifier is output to guide the priority construction of structural disturbance feedback paths and the execution of icing growth prediction within the current area. Otherwise, the current area status is maintained to prevent the triggering of a regional early warning response if the condition for outputting a structural disturbance dominant area identifier is not met.

[0087] Define structural disturbance status indicators Represented as:

[0088]

[0089] Define the rate of change of tension Represented as:

[0090]

[0091] Define the amplitude of tilt angle change Represented as:

[0092]

[0093] Define the change in vibration frequency Represented as:

[0094]

[0095] in This is a joint disturbance judgment function, used to output the structural disturbance state indicator; joint disturbance judgment function The joint judgment logic includes: execution based on three judgment conditions of the set of disturbance variables; the three conditions are: tension change rate Does it exceed the preset threshold for the rate of change of tension and the amplitude of change of tilt angle? Does it exceed the preset threshold for tilt angle change amplitude and vibration frequency change? Whether the vibration frequency change exceeds a preset threshold is determined. For any t, if none of the three conditions meet their respective preset threshold conditions, it is defined as a normal state, and the output value of the joint disturbance judgment function is 0, corresponding to the output of a normal state indicator. If any one of the three conditions meets its respective preset threshold condition, it is defined as a structural disturbance state, and the output value of the joint disturbance judgment function is 1, corresponding to the output of a structural disturbance state indicator. Thus, the joint disturbance judgment function is essentially a binary judgment function. By combining the threshold constraints of the three disturbance variables using a logical "OR" combination, the automatic identification of the structural disturbance state is achieved.

[0096] Where t is the current time step, which is used to uniformly identify the time position where the current calculation or prediction is being performed in the time series data; A function representing the change of tension over time in a sequence of structural states; The variable represents the variable relative to the current time step. The first derivative operation, the variable relative to the current time step The first derivative is used to measure the instantaneous rate of change of the variable; A function representing the change of tilt angle over time in a sequence of structural states; These are time sampling points, representing points within a sliding time window. Any time position of the discrete sampling within the interior; Represents a sliding time window The time corresponding to the previous discrete time step; The length of the time window for perturbation detection; A function representing the change of vibration frequency with time in a structural state sequence; Represents a sliding time window Index variables within, sliding time window The index variable within is used to control the sliding time window. Perform traversal and statistical analysis on each frequency value within a time period; Vibration frequency value, used to calculate the amplitude of frequency disturbance; Represents a sliding time window Inner Input the numerical value at that time; This represents a fixed threshold used to determine whether a change in vibration frequency meets the disturbance criteria.

[0097] Define the perturbation-corrected wind speed field Represented as:

[0098]

[0099] Define the airflow stagnation index Represented as:

[0100]

[0101] Define water vapor accumulation Represented as:

[0102]

[0103] in Represents the coordinates in the original wind speed field Wind speed at the location; This refers to the wind speed reduction term within the affected area. The disturbance direction gain coefficient is calculated based on the combined strength of the tension change rate and tilt angle change amplitude in the structural disturbance status indicator. The coefficient is assigned according to the disturbance level to enhance the weight of the influence of high-intensity disturbance on wind direction deflection. This represents the vector deflection term after applying a disturbance to the airflow direction; This represents the perturbed, corrected wind speed field variable; The integration time variable is used in the sliding time window. Perform integration within the time domain to represent consecutive time points; Indicates the slope angle of the terrain With airflow direction angle The projection weight function of the angle between them, and the terrain slope angle. With airflow direction angle The projection weight function of the included angle is based on the proportion of the projection length of the included angle within the range of the prevailing wind direction of the terrain. The mapping function is constructed by the proportional relationship between the projection length and the total range of wind direction changes, and is used to assign weights to the airflow stagnation effect under different wind direction and terrain combinations. coordinates The slope angle of the terrain at that location; coordinates The airflow direction angle at that location; coordinates Terrain roughness coefficient at location, coordinates The terrain roughness coefficient is determined by finding the corresponding terrain roughness level based on the terrain type classification (such as forest, mountain, hill, open land) and mapping it to a preset function to determine the value of the coefficient. The accumulation amplification factor is calculated based on the product of the distribution density of the airflow retention index and the intensity of the structural disturbance state indicator, thus determining the condensation contribution intensity range. This factor is then assigned to weight the extreme growth impact of water vapor accumulation. In practical applications, the accumulation amplification factor... It is an empirical adjustment factor used to characterize the strength of water vapor accumulation effect under different topographic and meteorological conditions. Its magnitude is generally obtained by comparing and calibrating historical icing observation data with model output. The value range is within the dimensionless range of 0-1. The specific value can be determined based on field measurements or experimental calibration.

[0104] in Relative humidity; This represents the dew point temperature obtained by fitting relative humidity and air temperature. This refers to the current temperature. The exponential fitting coefficient is determined by analyzing the residuals of the nonlinear relationship between dew point temperature and relative humidity in historical meteorological observation data. This coefficient is set after optimizing the fitting accuracy to enhance the ability to express condensation trends. In practical applications, due to the exponential fitting coefficient... Used to characterize the nonlinear relationship between dew point temperature and relative humidity, its value is determined by minimizing the residuals of historical meteorological observation data. The value range is between 10 to the power of -3 and 10 to the power of -1. In addition, this value range is chosen because it can reflect the enhanced effect of condensation trend and avoid excessive amplification of the fitting results due to excessively large coefficients.

[0105] Where e is the base of the natural logarithm, and the base of the natural logarithm is used to construct the exponential decay function.

[0106] Define structural disturbance prediction values :

[0107]

[0108] Define the base path prediction value Represented as:

[0109]

[0110] Define feedback path offset value Represented as:

[0111]

[0112] Define the average path offset over two consecutive periods Represented as:

[0113]

[0114] in A prediction function model representing the disturbance path; This represents the wind speed value at the current time step. This represents a combined variable of temperature and humidity at the current time step. This indicates the currently constructed structural perturbation feedback path; It is a prediction function model for the basic path; Indicates the first Within each rolling prediction period, the absolute difference between the predicted value of the structural disturbance feedback path and the predicted value of the basic path is used to measure the impact of the disturbance path on the prediction result in the current period. Number the current rolling cycle;

[0115] It should be noted that This is used to characterize the comprehensive impact of structural disturbances on the icing growth path, taking into account environmental parameters such as water vapor accumulation, wind speed, temperature, and humidity, and combining the current feedback path construction results. The model is established by fitting historical monitoring data with numerical simulation results. It employs multiple nonlinear regression or machine learning methods to determine the weights and effects of each input variable on ice cover growth. When selecting values... The output should be the path prediction increment under disturbance conditions. The value is dynamically adjusted according to the changes in environmental input and feedback path parameters, thereby reflecting the amplification effect of structural disturbance on the predicted path.

[0116] in This is used to characterize the basic correspondence between icing growth paths and meteorological conditions without considering structural disturbances. The model involves fitting or calibrating historical meteorological data with conventional physical mechanism models; common methods include energy balance equations or statistical regression models. When determining values, The output is the path prediction result under ideal uniform airflow and stable weather conditions, which is used as a benchmark for comparison. It is used to subtract the predicted result of the structural disturbance path to obtain the feedback path offset.

[0117] Define multi-cycle path offset weighted score value Represented as:

[0118]

[0119] Define structural disturbance feedback score Represented as:

[0120]

[0121] The dominant region output decision condition is defined as follows:

[0122]

[0123] in Indicates the number of rolling forecast periods used for score calculation; Indicates the first The corresponding score weight within the rolling prediction period, the first The corresponding score weights within each rolling prediction period are constructed using a decreasing time weight function based on the time sequence position of the current period among all periods. This assigns a higher score influence to periods closer to the current time, highlighting the impact of certain time periods (such as recent disturbances). The structural disturbance feedback path is represented in the th... Average offset value within each rolling forecast period; This is the perturbation scoring function, used to input the offset score value. Convert to disturbance feedback scoring results; The natural logarithm function is used to limit the growth rate of the scoring function and ensure interpretability. The perturbation score gain factor is based on the growth rate of the structural perturbation path offset value relative to the basic prediction path offset value within the current rolling prediction period. After normalizing the offset magnitude through a mapping function, a gain factor value is assigned to control the sensitivity of the scoring function to the total score value. The perturbation scoring index factor is constructed based on the length of time that the structural perturbation state remains valid in multiple consecutive rolling cycles. It is an exponential growth function that maps the state duration to a scoring weighting factor, thereby enhancing the nonlinear response of the scoring function to the degree of offset. This indicates the transmission line spatial segment and the local area number of the tower to which the current structural disturbance feedback path belongs. The transmission line spatial segment and the local area number of the tower to which the current structural disturbance feedback path belongs are used to distinguish the scoring calculation and state identification on different spatial units. The disturbance score threshold is used to determine whether a region is marked as a disturbance-dominant region.

[0124] Disturbance scoring threshold The value can be determined by statistical calibration of historical icing monitoring samples. Specific methods include: calculating the mean of the corresponding score values ​​in a sample set of known disturbance events. with standard deviation And set the threshold for perturbation scoring. Set as Where k takes integer values ​​from 1 to 2, the purpose of which is to ensure that the threshold is both higher than the score fluctuation under most normal operating conditions, and can relatively sensitively capture significant increments in actual disturbance sections. In typical site data, It includes a dimensionless range of 0.3 to 0.5, which can be calibrated according to different line environments.

[0125] It should be noted that, including but not limited to: in areas with complex terrain, the towers of transmission lines may undergo local deformation due to icing. This structural disturbance will directly change the local airflow distribution of the towers, thereby affecting the speed and distribution pattern of water vapor condensation.

[0126] Existing methods generally treat tower structures as static bodies, relying solely on traditional meteorological factors and terrain parameters to construct prediction models, failing to identify the key positive feedback chain of "structural disturbance - airflow feedback - water vapor accumulation";

[0127] Therefore, in complex terrain sections, problems such as large prediction errors, slow response, and difficulty in identifying local rapid icing growth often occur. There is an urgent need to establish an icing growth prediction scheme that can sense structural disturbances and dynamically adjust the prediction path.

[0128] This solution includes a structural disturbance state identification phase:

[0129] The system acquires structural state sequences such as tension, tilt angle, and vibration frequency. It constructs a set of disturbance variables by calculating the rate of change of tension, the amplitude of change of tilt angle, and the amount of change of vibration frequency. Then, it performs a joint judgment based on a preset threshold and outputs either a "structural disturbance state identifier" or a "normal state identifier".

[0130] This stage enables accurate identification of structural deformation events, which forms the basis for triggering the correction of meteorological input disturbances;

[0131] This scheme includes the stages of disturbed airflow construction and water vapor accumulation assessment:

[0132] If the first part output is a structural disturbance status identifier, then historical meteorological observation data is obtained, and combined with disturbance characteristics and topographic factors, a disturbance-corrected wind speed field is constructed to simulate the wind speed changes and direction shifts after structural disturbance. Then, the airflow retention index is calculated and it is determined whether it meets the retention conditions. If it does, the water vapor accumulation is further calculated as a prerequisite for condensation growth analysis. This stage establishes the coupling path of "structural disturbance - airflow feedback".

[0133] This scheme includes the stages of perturbation feedback path construction and icing growth prediction:

[0134] When airflow stagnation and significant water vapor accumulation are identified, the amount of water vapor accumulation and meteorological factors are combined to construct a "structural disturbance feedback path". Then, the future icing thickness increment is calculated through a short-time prediction model, which is the "structural disturbance prediction value". The "feedback path offset value" is obtained by comparing it with the traditional basic path prediction value, and it is determined whether the offset is continuous. If the offset is continuous, the weight of the feedback path is dynamically updated to improve the sensitivity of the prediction model to the abnormal response of icing caused by structural disturbance.

[0135] This plan includes the dominant area identification and path control feedback phase:

[0136] The feedback path offset values ​​of multiple consecutive rolling cycles are scored and summarized to calculate the "structural disturbance feedback score value". If the score value exceeds the threshold, the "structural disturbance dominant area identifier" is output to guide the priority construction of disturbance feedback paths and execution of prediction in the current area. If the threshold is not reached, the area status remains unchanged to avoid false alarms and over-response.

[0137] This stage achieves prediction focusing and feedback path control in the spatial dimension, which is the output logic of the entire model.

[0138] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. 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 predicting the short-term icing growth of transmission lines considering the influence of micro-topography, characterized in that, include: Obtain the structural state sequence at the transmission line tower and calculate the disturbance characteristics, then perform a joint judgment to output the structural disturbance state identifier; Obtain the structural state sequence at the transmission line tower, which includes tension sequence, tilt angle sequence and vibration frequency sequence; perform disturbance characteristic calculation on the structural state sequence to calculate the tension change rate, tilt angle change amplitude and vibration frequency change, and construct a set of disturbance variables; The set of disturbance variables is subjected to joint judgment. The three conditions are whether the rate of change of tension exceeds the preset threshold for the rate of change of tension, whether the amplitude of change of tilt angle exceeds the preset threshold for the amplitude of change of tilt angle, and whether the amount of change of vibration frequency is higher than the preset threshold for the amount of change of vibration frequency. If none of the three conditions meet their respective preset threshold conditions, a normal state indicator is output; otherwise, a structural disturbance state indicator is output. Define the structural disturbance state identifier S1(t) as follows: S1(t)=Ψ(φ T (t),φ θ (t),φ f (t)) Define the rate of change of tension φ T (t) is represented as: Define the amplitude of tilt angle change φ θ (t) is represented as: Define the change in vibration frequency φ f (t) is represented as: Where Ψ(·) is the joint disturbance judgment function; the joint judgment logic of the joint disturbance judgment function Ψ(·) includes: execution based on three judgment conditions of the disturbance variable set; the three conditions are: tension change rate φ T (t) Whether the preset tension change rate threshold and tilt angle change amplitude φ are exceeded. θ (t) Whether the preset threshold for tilt angle change amplitude and vibration frequency change φ are exceeded. f (t) Whether it is higher than the preset threshold of vibration frequency change. For any t, if none of the three conditions meet their respective preset threshold conditions, it is defined as a normal state. At this time, the output value of the joint disturbance judgment function is 0, which corresponds to the output of the normal state indicator. If any one of the three conditions meets its respective preset threshold condition, it is defined as a structural disturbance state. At this time, the output value of the joint disturbance judgment function is 1, which corresponds to the output of the structural disturbance state indicator. Where t is the current time step; T(t) represents the function of tension variation with time in the structural state sequence; d represents the first derivative of the variable with respect to the current time step t; θ(t) represents the function of tilt angle variation with time in the structural state sequence; t i t represents the time sampling point; i -1 represents the time corresponding to the previous discrete time step within the sliding time window [t-Δt,t]; Δt is the length of the time window for perturbation judgment; f(t) represents the function of vibration frequency variation with time in the structural state sequence; i represents the index variable within the sliding time window [t-Δt,t]; f(i) is the vibration frequency value; f(i-1) represents the numerical input at the (i-1)th time within the sliding time window [t-Δt,t]; δ f This represents a fixed threshold for determining whether a change in vibration frequency meets the disturbance criteria. Input structural disturbance status identifiers and historical meteorological observation data set information, construct disturbance-corrected wind speed field and calculate airflow stagnation index, perform airflow stagnation determination and calculate water vapor accumulation; When the output result is a structural disturbance status identifier, the historical meteorological observation data set at the transmission line tower is obtained. The historical meteorological observation data set includes wind speed, wind direction, temperature, humidity, relative humidity and air temperature. Perturbation calculations are performed on the wind speed and wind direction in the structural disturbance status markers and historical meteorological observation data sets to form a disturbance-corrected wind speed field; The airflow stagnation index is calculated by perturbating and correcting the wind speed field and using topographic factors, including aspect, slope, and topographic roughness. Based on the results of the airflow stagnation index, airflow stagnation identification is achieved. In the airflow stagnation identification, it is determined whether the airflow stagnation index is higher than the preset airflow stagnation index threshold and whether the slope and wind direction angle is higher than the preset slope and wind direction angle threshold. These two conditions are used for condition judgment. If both conditions are not higher than the preset threshold, the output is airflow non-stagnation; otherwise, the output is airflow stagnation. When the output is airflow stagnation, a fitting calculation is performed on the relative humidity and temperature in the historical meteorological observation data set to output the water vapor accumulation. Define the perturbation-corrected wind speed field Represented as: Define the airflow retention index γ t (x,y) can be represented as: The water vapor accumulation rate η(t) is defined as follows: Where V(x,y,t) represents the wind speed value at coordinate (x,y) in the original wind speed field; ΔV(x,y,t) is the wind speed reduction term in the disturbance influence area; and κ1 is the disturbance direction gain coefficient. This represents the vector deflection term after applying a disturbance to the airflow direction; τ represents the perturbed, corrected wind speed field variable; R(φ,α) represents the projection weight function of the angle between the terrain aspect angle φ and the airflow direction angle α; φ(x,y) is the terrain aspect angle at coordinate (x,y); α(x,y) is the airflow direction angle at coordinate (x,y); Ω(x,y) is the terrain roughness coefficient at coordinate (x,y); κ2 is the accumulation amplification factor; H(t) is the relative humidity; T d (t) represents the dew point temperature obtained by fitting relative humidity and air temperature; T a (t) represents the current temperature; λ represents the exponential fitting coefficient; and e represents the base of the natural logarithm. Based on the water vapor accumulation and historical meteorological observation data set, a disturbance feedback path is constructed, icing growth is predicted, the feedback path offset is calculated, and the feedback path weight is updated. The input feedback path offset value is used to calculate the disturbance feedback score, and it is determined whether to output the dominant area identifier or maintain the current state.

2. The method for predicting short-term icing growth of transmission lines considering the influence of micro-topography according to claim 1, characterized in that: The structural disturbance feedback path is constructed by taking water vapor accumulation and temperature, humidity and wind speed from historical meteorological observation data as elements, and icing growth prediction is performed to output the structural disturbance prediction value. Each element in the structural disturbance feedback path is treated as a sub-path and assigned a corresponding preset weight factor to characterize the relative contribution of the element in the icing growth prediction. The weight factor is defined as the feedback path weight. The difference between the predicted structural disturbance value and the basic predicted value is calculated, and the feedback path offset value is output. If the feedback path offset value exceeds the preset feedback path offset threshold in two consecutive prediction periods that are updated sequentially over time, the feedback path weight is updated; otherwise, the current feedback path weight remains unchanged.

3. The method for predicting short-term icing growth of transmission lines considering the influence of micro-topography according to claim 2, characterized in that: A score calculation is performed on the feedback path offset values ​​within multiple prediction periods that are updated in chronological order, and the structural disturbance feedback score value is output. If the structural disturbance feedback score exceeds the preset structural disturbance feedback score threshold, a structural disturbance dominant area identifier is output to guide the priority construction of structural disturbance feedback paths and the execution of icing growth prediction within the current area. Otherwise, the current area status is maintained to prevent the triggering of a regional early warning response if the condition for outputting a structural disturbance dominant area identifier is not met.

4. The method for predicting short-term icing growth of transmission lines considering the influence of micro-topography according to claim 3, characterized in that: Define the predicted structural disturbance value ΔI(t+τ): Define the basic path prediction value ΔI0(t+τ) as: Define feedback path offset value Represented as: Define the average path offset over two consecutive periods Represented as: in The prediction function model represents the disturbance path; W(t) is the wind speed value at the current time step; P(t) represents the combined variables of temperature and humidity at the current time step. This indicates the currently constructed structural perturbation feedback path; It is a prediction function model for the basic path; This represents the absolute difference between the predicted value of the structural disturbance feedback path and the predicted value of the basic path within the nth rolling prediction period; n is the current rolling period number.

5. The method for predicting short-term icing growth of transmission lines considering the influence of micro-topography according to claim 4, characterized in that: Define the multi-cycle path offset weighted score value Σ s Represented as: Define the structural perturbation feedback score R. s Represented as: The dominant region output decision condition is defined as follows: Where N represents the number of rolling prediction cycles used for scoring calculation; ξ n This represents the score weight corresponding to the nth rolling prediction period; This represents the average offset of the structural disturbance feedback path during the nth rolling prediction period; τ is the disturbance scoring function; log(·) is the natural logarithm function; μ is the disturbance scoring gain factor; q is the disturbance scoring exponential factor; s represents the transmission line spatial segment to which the current structural disturbance feedback path belongs and the local area number of the tower; τ s The threshold for determining the disturbance score.

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