Power transmission line short-time icing growth prediction method considering microtopography influence

Abstract

The invention discloses a power transmission line short-time icing growth prediction method considering microtopography influence, and particularly relates to the technical field of icing growth prediction.The method comprises the steps that a structure state sequence at a power transmission line tower is obtained, disturbance characteristics are calculated, and combined judgment is executed to output a structure disturbance state identifier; by obtaining a structure state sequence and executing disturbance characteristic calculation, the structure disturbance state is jointly judged; in a structure disturbance state, a disturbance correction wind speed field is formed based on disturbance calculation and terrain factors, airflow retention is recognized, and the water vapor gathering amount is calculated; then constructing a structure disturbance feedback path and executing icing growth prediction, and dynamically updating the feedback path weight based on a prediction offset result; and calculating a feedback score value in a rolling manner and identifying a structure disturbance dominant region, thereby realizing ice coating growth prediction path identification and increment output under linkage of a structure disturbance state, disturbance airflow characteristics and terrain factors.

Description

Technical Field

[0001] The present invention relates to the technical field of ice cover growth prediction, and more particularly to a method for predicting short-term ice cover growth of power transmission lines taking into account the influence of micro-topography. Background Art

[0002] Existing methods for predicting ice thickness on transmission line towers rely primarily on temporally continuous meteorological observation data, introduce terrain characteristic parameters that describe local landform changes, and construct data-driven models to achieve rolling predictions within a short timeframe. In terms of method structure, meteorological variables such as temperature, humidity, wind speed, and precipitation intensity, as well as static terrain factors such as slope, aspect, altitude, and terrain roughness are generally used as inputs. Historical ice cover data is used for training, and the ice cover growth rate in the future time period is output through machine learning regression algorithms. In the model construction, the transmission line towers and their conductors are regarded as constant structures. It is assumed that their geometric shape remains unchanged during the prediction period, and the ice growth process is determined only by external meteorological conditions and terrain factors. However, in actual operation, especially in complex terrain sections with steep slopes, local elevation changes, or inflections in wind profiles, the non-uniform load caused by icing can cause local deformation of the conductors, which in turn changes the airflow disturbance characteristics around them. For example, this can create localized areas of calm wind or reduced wind speed, changing the efficiency of water vapor accumulation on the conductor surface. This local modulation effect, where structural deformation reacts to the meteorological environment, often causes an abnormal increase in the ice growth rate in the initial stage after the deformation occurs, exhibiting a clear positive feedback characteristic. Because existing methods do not establish a coupling relationship between structural state and meteorological input, and lack a modeling and feedback mechanism for the structural response process, the prediction results in the above situation are systematically inaccurate during the sudden icing growth stage induced by local deformation of the conductor. They are unable to identify and respond to the rapid icing evolution process under the condensation growth path caused by structural disturbances, which constitutes a key unresolved problem in current prediction models. Summary of the Invention

[0003] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a method for predicting short-term icing growth of transmission lines taking into account the influence of micro-topography. By obtaining a structural state sequence and performing disturbance feature calculation, the structural disturbance state is jointly judged. Under the structural disturbance state, a disturbance-corrected wind speed field is formed based on the disturbance calculation and terrain factors, airflow retention is identified and water vapor accumulation is calculated. Subsequently, a structural disturbance feedback path is constructed and icing growth prediction is performed, and the feedback path weight is dynamically updated based on the prediction offset result. The feedback score value is rolledly calculated and the structural disturbance dominant area is identified, so as to realize the icing growth prediction path identification and incremental output under the linkage of the structural disturbance state, disturbance airflow characteristics and terrain factors.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a method for predicting short-term ice growth on transmission lines taking into account the influence of micro-topography, comprising: Obtain the structural state sequence at the transmission line tower and calculate the disturbance characteristics, perform joint judgment and output the structural disturbance state identification; Input the structural disturbance state identification and historical meteorological observation data collection information, construct the disturbance-corrected wind speed field and calculate the airflow retention index, perform airflow retention judgment and calculate the water vapor accumulation; Based on the water vapor accumulation and historical meteorological observation data, a disturbance feedback path is constructed, ice growth prediction is performed, feedback path offset values ​​are calculated, and feedback path weights are updated. The feedback path offset value is input to calculate the disturbance feedback score and determine whether to output the dominant area identification or maintain the current state.

[0005] In a preferred embodiment, a structural state sequence of a transmission line tower is obtained, the structural state sequence including a tension sequence, an inclination angle sequence, and a vibration frequency sequence; a disturbance characteristic calculation is performed on the structural state sequence to calculate the tension change rate, the inclination angle change amplitude, and the vibration frequency change, respectively, and construct a disturbance variable set; The disturbance variable set is jointly judged to determine whether the tension change rate exceeds the preset tension change rate threshold, whether the inclination angle change amplitude exceeds the preset inclination angle change amplitude threshold, and whether the vibration frequency change is higher than the preset vibration frequency change threshold. If all three conditions do not meet their corresponding preset threshold conditions, a normal state flag is output; otherwise, a structural disturbance state flag is output.

[0006] In a preferred embodiment, when the output result is a structural disturbance state indicator, a historical meteorological observation data set at the transmission line tower is obtained, where the historical meteorological observation data set includes wind speed, wind direction, temperature, humidity, relative humidity, and air temperature; Perform disturbance calculation on the wind speed and direction in the structural disturbance state identification and historical meteorological observation data set to form a disturbance-corrected wind speed field; The airflow retention index is calculated by using the disturbance-corrected wind speed field and terrain factors, including slope, gradient, and terrain roughness. Airflow retention identification is achieved based on the results of the airflow retention index. In airflow stagnation identification, the airflow stagnation index is judged to be higher than the preset airflow stagnation index threshold, and the angle between the slope and the wind direction is higher than the preset angle between the slope and the wind direction threshold. These two conditions are used for conditional 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 retention, a fitting calculation is performed on the relative humidity and air temperature in the historical meteorological observation data set to output the water vapor accumulation.

[0007] In a preferred embodiment, the water vapor accumulation amount and the temperature, humidity, and wind speed in the historical meteorological observation data set are used as elements to jointly construct a structural disturbance feedback path and perform ice growth prediction, and output a structural disturbance prediction value; Each element in the structural disturbance feedback path is treated as a subpath and assigned a corresponding preset weight factor to characterize the relative contribution of the element in the ice cover growth prediction. The weight factor is defined as the feedback path weight. The difference between the structural disturbance prediction value and the basic prediction value is calculated to output the feedback path offset value; if the feedback path offset value exceeds the preset feedback path offset threshold in two consecutive chronologically updated prediction cycles, the feedback path weight is updated; otherwise, the current feedback path weight remains unchanged.

[0008] In a preferred embodiment, a scoring calculation is performed on the feedback path offset values ​​within a plurality of prediction cycles that are updated in a rolling manner in chronological order, and a structural disturbance feedback scoring value is output; If the structural disturbance feedback score exceeds the preset structural disturbance feedback score threshold, the structural disturbance dominant area identification is output to guide the priority construction of the structural disturbance feedback path in the current area and perform ice growth prediction. Otherwise, the current area status is maintained to prevent the triggering of a regional early warning response when the conditions for outputting the structural disturbance dominant area identification are not met.

[0009] In a preferred embodiment, a structural disturbance state flag is defined as Expressed as:

[0010] Defining the tension change rate Expressed as:

[0011] Define the tilt angle change range Expressed as:

[0012] Define the vibration frequency change Expressed as:

[0013] in is the joint disturbance judgment function; joint disturbance judgment function The joint judgment logic includes: execution of three judgment conditions based on the disturbance variable set; the three conditions are: tension change rate Whether the preset tension change rate threshold and inclination angle change range are exceeded Whether the tilt angle change amplitude threshold and vibration frequency change are exceeded Is it higher than the preset vibration frequency change threshold? For any t, if none of the three conditions meet the corresponding 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 flag; if any one of the three conditions meets the corresponding preset threshold conditions, 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 flag; in is the current time step; A function that represents the change of tension in the structural state sequence over time; Indicates that the variable is relative to the current time step First-order derivative operation; represents the time-varying function of the tilt angle in the structural state sequence; is the time sampling point; Represents a sliding time window The time corresponding to the previous discrete time step; The length of the time window for disturbance judgment; A function that represents the change of vibration frequency over time in a sequence of structural states; Represents a sliding time window Index variables within; Vibration frequency value; Represents a sliding time window Neidi Numerical input when Indicates the fixed threshold for determining whether the vibration frequency change meets the disturbance determination condition.

[0014] In a preferred embodiment, the disturbance-corrected wind speed field is defined as Expressed as:

[0015] Defining the airflow stagnation index Expressed as:

[0016] Defining water vapor accumulation Expressed as:

[0017] in Represents the coordinates in the original wind speed field The wind speed value at is the wind speed reduction term within the disturbance impact area; is the disturbance direction gain coefficient; It represents the vector deflection term after the disturbance is applied to the airflow direction; represents the disturbed and corrected wind speed field variable; is the integral time variable; Represents the terrain aspect angle and wind direction angle The projection weight function of the angle between them; For coordinates The terrain aspect angle at For coordinates The airflow direction angle at For coordinates The terrain roughness coefficient at ; is the aggregation magnification factor; is the relative humidity; Represents the dew point temperature obtained by fitting relative humidity and air temperature; is the current temperature; is the exponential fitting coefficient; is the base of natural logarithms.

[0018] In a preferred embodiment, the structural disturbance prediction value is defined as :

[0019] Defining base path prediction values Expressed as:

[0020] Define the feedback path offset value Expressed as:

[0021] Define the average path deviation of two consecutive cycles Expressed as:

[0022] in A prediction function model representing the disturbance path; is the wind speed value of the current time step; Represents the combined variable of temperature and humidity at the current time step; Represents the currently constructed structural perturbation feedback path; The prediction function model for the basic path; Indicates in The absolute difference between the predicted value of the structural perturbation feedback path and the predicted value of the basic path within a rolling prediction period; Numbers the current scroll cycle.

[0023] In a preferred embodiment, a multi-cycle path deviation weighted score value is defined as Expressed as:

[0024] Define the structural perturbation feedback score value Expressed as:

[0025] The output judgment condition of the dominant area is defined as:

[0026] in Indicates the number of rolling forecast periods used for score calculation; Indicates the The corresponding scoring weight within a rolling forecast period; Indicates the structural perturbation feedback path in The average deviation value within the rolling forecast period; is the perturbation scoring function; is the natural logarithm function; is the perturbation score gain factor; is the disturbance scoring index factor; Indicates the transmission line spatial segment to which the current structural disturbance feedback path belongs and the local area number where the tower is located; is the perturbation score determination threshold.

[0027] The technical effects and advantages of the present invention are as follows: This solution introduces structural disturbance state identification and links it with meteorological inputs to address the problem that traditional models are unable to respond to airflow disturbances caused by transmission line tower deformation, thereby improving prediction accuracy in complex terrain areas. By constructing a disturbance-corrected wind speed field and integrating terrain factors, we can accurately simulate local calm or weakened wind areas and enhance the ability to identify stagnant airflow areas. The water vapor accumulation is combined with meteorological factors to construct a feedback path to achieve dynamic prediction of the condensation mutation process caused by structural disturbances. Based on the rolling comparison results of the feedback path offset values, the path weight is updated in real time to enhance the model's recognition and response to the persistence of disturbances; The disturbance scoring mechanism is used to output the identification of the structural disturbance-dominated area, thereby achieving the priority allocation and precision control of prediction resources in key areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 The figure is a flow chart of the method steps of the present invention. DETAILED DESCRIPTION

[0029] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0030] Refer to the instruction manual Figure 1 According to an embodiment of the present invention, a method for predicting short-term ice growth of a transmission line considering the influence of micro-topography includes: Obtain the structural state sequence at the transmission line tower and calculate the disturbance characteristics, perform joint judgment and output the structural disturbance state identification; Input the structural disturbance state identification and historical meteorological observation data collection information, construct the disturbance-corrected wind speed field and calculate the airflow retention index, perform airflow retention judgment and calculate the water vapor accumulation; Based on the water vapor accumulation and historical meteorological observation data, a disturbance feedback path is constructed, ice growth prediction is performed, feedback path offset values ​​are calculated, and feedback path weights are updated. The feedback path offset value is input to calculate the disturbance feedback score and determine whether to output the dominant area identification or maintain the current state.

[0031] It should be noted that in the formula structure involved in this solution, dimensionless terms can serve as proportionality or structural adjustment factors. When combined with quantities with units, they only play a numerical scaling role and do not introduce new physical dimensions. Therefore, they will not change or confuse the overall unit system of expression. This combination of "dimensionless terms and units" can be understood as a composite structural expression commonly used in mathematical and physical modeling, conforming to the principle of dimensional consistency and having a clear physical interpretation basis. 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 be formed into a unified structure through function mapping, ratio combination or normalization adjustment. The units and meanings are clear, and the overall expression conforms to the principle of dimensional consistency and the common formula of engineering modeling. In this solution, any design constants, weights, adjustment factors, threshold parameters, and proportional coefficients are adjustable control parameters for different application environments. Their values ​​depend on the target device configuration, data input characteristics, and performance optimization goals. During the implementation phase, they are set within a reasonable range through model verification, performance constraints, or engineering calibration. Although these parameters do not have unique preset values, they have clear adjustment logic and calculation paths, and are part of the deterministic setting process in engineering implementation. The purpose of such setting is to ensure that the solution is both universally adaptable, reproducible, and operable, without affecting its technical clarity and feasibility. Obtain the structural state sequence of the transmission line tower, which includes a tension sequence, an inclination angle sequence, and a vibration frequency sequence. Perform disturbance feature calculation on the structural state sequence to calculate the tension change rate, inclination angle change amplitude, and vibration frequency change, respectively, and construct a disturbance variable set. The disturbance variable set is jointly judged to determine whether the tension change rate exceeds the preset tension change rate threshold, whether the inclination angle change amplitude exceeds the preset inclination angle change amplitude threshold, and whether the vibration frequency change is higher than the preset vibration frequency change threshold. If all three conditions do not meet their corresponding preset threshold conditions, a normal state flag is output; otherwise, a structural disturbance state flag is output.

[0032] When the output result is a structural disturbance state indicator, a historical meteorological observation data set at the transmission line tower is obtained, where the historical meteorological observation data set includes wind speed, wind direction, temperature, humidity, relative humidity, and air temperature; Perform disturbance calculation on the wind speed and direction in the structural disturbance state identification and historical meteorological observation data set to form a disturbance-corrected wind speed field; The airflow retention index is calculated by using the disturbance-corrected wind speed field and terrain factors, including slope, gradient, and terrain roughness. Airflow retention identification is achieved based on the results of the airflow retention index. In airflow stagnation identification, the airflow stagnation index is judged to be higher than the preset airflow stagnation index threshold, and the angle between the slope and the wind direction is higher than the preset angle between the slope and the wind direction threshold. These two conditions are used for conditional 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 retention, a fitting calculation is performed on the relative humidity and air temperature in the historical meteorological observation data set to output the water vapor accumulation; The fitting calculation filters out sample points that fluctuate within a fixed range of relative humidity and temperature values ​​that are currently input, counts the actual frequency of water vapor condensation recorded at these sample points within the corresponding time period, and constructs a fitting curve based on the growth trend corresponding to the temperature and humidity changes. Based on the corresponding position of the current input position in the curve, the total volume of water vapor condensation per unit time is inferred as the estimated result of water vapor accumulation.

[0033] The water vapor accumulation and the temperature, humidity, and wind speed in the historical meteorological observation data set are used as factors to jointly construct the structural disturbance feedback path and perform ice growth prediction, outputting the structural disturbance prediction value; Each element in the structural disturbance feedback path is treated as a subpath and assigned a corresponding preset weight factor to characterize the relative contribution of the element in the ice cover growth prediction. The weight factor is defined as the feedback path weight. The difference between the structural disturbance prediction value and the basic prediction value is calculated to output the feedback path offset value, where the basic prediction value refers to the ice cover 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 chronologically updated prediction cycles, the feedback path weight is updated; otherwise, the current feedback path weight remains unchanged, where the chronologically updated prediction cycle refers to a continuous short-term prediction window that continuously advances at fixed time intervals, and each cycle independently performs ice cover growth prediction based on the latest input data.

[0034] Performing score calculation on feedback path offset values ​​within a plurality of prediction cycles that are updated in chronological order, and outputting a structural perturbation feedback score value; If the structural disturbance feedback score exceeds the preset structural disturbance feedback score threshold, the structural disturbance dominant area identification is output to guide the priority construction of the structural disturbance feedback path in the current area and perform ice growth prediction. Otherwise, the current area status is maintained to prevent the triggering of a regional early warning response when the conditions for outputting the structural disturbance dominant area identification are not met.

[0035] Define the structural disturbance state flag Expressed as:

[0036] Defining the tension change rate Expressed as:

[0037] Define the tilt angle change range Expressed as:

[0038] Define the vibration frequency change Expressed as:

[0039] in is a joint disturbance judgment function, which is used to output the structural disturbance state identification; the joint disturbance judgment function The joint judgment logic includes: execution of three judgment conditions based on the disturbance variable set; the three conditions are: tension change rate Whether the preset tension change rate threshold and inclination angle change range are exceeded Whether the tilt angle change amplitude threshold and vibration frequency change are exceeded Whether it is higher than the preset vibration frequency change threshold. For any t, if none of the three conditions meet the corresponding 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 identifier; if any one of the three conditions meets the corresponding preset threshold conditions, 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 identifier; thus, the joint disturbance judgment function is essentially a binary judgment function, which realizes the automatic identification of the structural disturbance state by performing a logical "or" combination on the threshold constraints of the three disturbance variables; Where t is the current time step, which is used to uniformly identify the time position where the current calculation or prediction is performed in the time series data; A function that represents the change of tension in the structural state sequence over time; Indicates that the variable is relative to the current time step The first-order derivative operation of the variable relative to the current time step The first derivative operation is used to measure the instantaneous rate of change of the variable; represents the time-varying function of the tilt angle in the structural state sequence; is the time sampling point, which represents the time in the sliding time window Any time position of discrete sampling within; Represents a sliding time window The time corresponding to the previous discrete time step; The length of the time window for disturbance judgment; A function that represents the change of vibration frequency over time in a sequence of structural states; Represents a sliding time window Index variables within, sliding time window The index variable in the sliding time window is used to Perform traversal and statistics on each frequency value within the time period; Vibration frequency value, which is used to calculate the frequency disturbance amplitude; Represents a sliding time window Neidi Numerical input when Indicates the fixed threshold for determining whether the vibration frequency change meets the disturbance determination condition.

[0040] Define disturbance-corrected wind speed field Expressed as:

[0041] Defining the airflow stagnation index Expressed as:

[0042] Defining water vapor accumulation Expressed as:

[0043] in Represents the coordinates in the original wind speed field The wind speed value at is the wind speed reduction term within the disturbance impact area; is the disturbance direction gain coefficient. The disturbance direction gain coefficient calculates the disturbance level according to the combined intensity of the tension change rate and the inclination angle change amplitude in the structural disturbance state identifier. The coefficient is assigned according to the disturbance level to enhance the influence weight of high-intensity disturbance on wind direction deflection. It represents the vector deflection term after the disturbance is applied to the airflow direction; represents the disturbed and corrected wind speed field variable; The integral time variable is used to calculate the integral time variable in the sliding time window. Perform integration operations within to represent consecutive time points in the time domain; Represents the terrain aspect angle and wind direction angle The projection weight function of the angle between the terrain and the slope angle and wind direction angle The projection weight function of the angle between the two is based on the proportion of the projection length of the angle within the dominant wind direction range of the terrain. The mapping function is constructed through the proportional relationship between the projection length and the total wind direction variation range to assign the airflow retention influence weight under different wind direction and terrain combinations. For coordinates The terrain aspect angle at For coordinates The airflow direction angle at For coordinates The terrain roughness coefficient at The terrain roughness coefficient at the location is based on the terrain type classification (such as forest, mountain, hill, open land) to find the corresponding terrain roughness level, and is mapped to the preset function to determine the coefficient value; is the accumulation amplification coefficient. The accumulation amplification coefficient is calculated based on the product of the distribution density of the airflow retention index and the structural disturbance state identification intensity to calculate the condensation contribution intensity interval. The coefficient is assigned accordingly to weight the extreme growth effect of water vapor accumulation. In practical applications, the accumulation amplification coefficient is It is an empirical adjustment factor used to characterize the strength of the water vapor accumulation effect under different terrain and meteorological conditions. Its magnitude is generally obtained by comparing historical ice cover observation data with model output. Its value range is included in the dimensionless range of 0-1. The specific value can be determined based on field measurements or experimental calibration. in is the relative humidity; Represents the dew point temperature obtained by fitting relative humidity and air temperature; is the current temperature; is the exponential fitting coefficient. The exponential fitting coefficient is based on the nonlinear relationship between dew point temperature and relative humidity in historical meteorological observation data. After optimizing the fitting accuracy, the coefficient is set to enhance the expression of condensation trend. In practical applications, the exponential fitting coefficient is It is used to characterize the nonlinear relationship between dew point temperature and relative humidity. Its value is determined by minimizing the residual error of historical meteorological observation data. The value range is between 10-3 and 10-1. This range is chosen because it can reflect the enhanced effect of condensation trend while avoiding excessive deviation of fitting results caused by excessively large coefficients. Where e is the base of the natural logarithm, and the base of the natural logarithm is used to construct the exponential decay function.

[0044] Define the predicted value of structural disturbance :

[0045] Defining base path prediction values Expressed as:

[0046] Define the feedback path offset value Expressed as:

[0047] Define the average path deviation of two consecutive cycles Expressed as:

[0048] in A prediction function model representing the disturbance path; is the wind speed value of the current time step; Represents the combined variable of temperature and humidity at the current time step; Represents the currently constructed structural perturbation feedback path; The prediction function model for the basic path; Indicates in The absolute difference between the predicted value of the structural perturbation feedback path and the predicted value of the basic path within a rolling forecast period. The absolute difference is used to measure the impact of the perturbation path on the forecast result in the current period. Number the current rolling cycle; It should be noted that, It is used to characterize the comprehensive impact of structural disturbance on the ice 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 includes fitting historical monitoring data with numerical simulation results, and adopting multivariate nonlinear regression or machine learning methods to determine the weight and effect of each input variable on ice growth. The output of should be the path prediction increment under disturbance conditions, and the value should be dynamically adjusted with changes in environmental input and feedback path parameters, thereby reflecting the amplification effect of structural disturbance on the predicted path. in It is used to characterize the basic correspondence between the ice growth path and meteorological conditions when structural disturbance factors are not considered. The model includes fitting or calibration with historical meteorological data and conventional physical mechanism models. Common methods include energy balance equations or statistical regression models. When taking values, The output of is the path prediction result under ideal uniform airflow and stable meteorological conditions, which is used as a comparison benchmark and is subtracted from the prediction result of the structural disturbance path to obtain the feedback path offset.

[0049] Defines the weighted score value for multi-cycle path deviation Expressed as:

[0050] Define the structural perturbation feedback score value Expressed as:

[0051] The output judgment condition of the dominant area is defined as:

[0052] in Indicates the number of rolling forecast periods used for score calculation; Indicates the The corresponding scoring weight within the rolling forecast period is The corresponding score weights within each rolling forecast period are constructed into a decreasing time weight function based on the chronological position of the current period in all periods. The periods closer to the current moment are given a higher score influence, which is used to highlight the impact of certain periods (such as recent disturbances). Indicates the structural perturbation feedback path in The average deviation value within the rolling forecast period; is the perturbation scoring function, which is used to offset the score input value Converted into perturbation feedback scoring results; It is the natural logarithm function, which is used to limit the growth rate of the scoring function and ensure interpretability; The perturbation score gain factor is the gain factor of the perturbation score. The gain factor is calculated based on the growth rate of the structural perturbation path offset value compared to the basic forecast path offset value within the current rolling forecast period. The gain factor value is assigned after normalizing the offset amplitude through a mapping function. It is used to control the response sensitivity of the scoring function to the total score value. The perturbation scoring exponential factor is a factor that constructs an exponential growth function based on the length of time the structural perturbation state remains valid in multiple consecutive rolling cycles. The exponential growth function maps the state duration to a score weighted multiple, which is used to enhance the nonlinear response of the scoring function to the degree of deviation. Indicates the number of the transmission line spatial segment and the local area where the tower is located to which the current structural disturbance feedback path belongs. The number of the transmission line spatial segment and the local area where the tower is located to which the current structural disturbance feedback path belongs is used to distinguish the score calculation and status identification on different spatial units; is the disturbance score determination threshold, which is used to decide whether to mark it as a disturbance-dominated area; Perturbation score judgment threshold The value of can be determined by statistical calibration of historical ice monitoring samples. The specific method includes: calculating the mean of the corresponding score value in the sample set of known disturbance events and standard deviation , and the disturbance score determination threshold Set to , where k is an integer between 1 and 2. The purpose of this is to ensure that the threshold is higher than the score fluctuation under most normal conditions, and can also relatively sensitively capture the significant increment of the actual disturbance section. In typical station data, It includes a dimensionless range of 0.3 to 0.5 and can be calibrated according to different line environments.

[0053] It is important to note that, in complex terrain, transmission line towers can experience local deformation due to icing. This structural disturbance directly alters the local airflow distribution on the towers, thereby affecting the speed and distribution of water vapor condensation. Existing methods generally treat tower structures as static bodies and rely solely on traditional meteorological factors and terrain parameters to build prediction models, failing to identify the key positive feedback chain of "structural disturbance-airflow feedback-water vapor accumulation"; Therefore, in complex terrain sections, problems such as large prediction errors, delayed response, and difficulty in identifying local rapid ice growth often occur. There is an urgent need to establish an ice growth prediction scheme that can sense structural disturbances and dynamically adjust the prediction path. This scheme includes the structural disturbance state identification stage: Obtain a structural state sequence of tension, inclination, vibration frequency, etc., and construct a disturbance variable set by calculating the tension change rate, inclination change amplitude, and vibration frequency change. Then, perform a joint judgment based on a preset threshold and output a "structural disturbance state flag" or "normal state flag." This stage achieves accurate identification of structural deformation events and is the triggering basis for correction of meteorological input disturbances; This scheme includes the stages of disturbed airflow construction and water vapor accumulation judgment: If the output of the first part is a structural disturbance state indicator, historical meteorological observation data is obtained. Combined with the disturbance characteristics and terrain factors, a disturbance-corrected wind speed field is constructed to simulate the wind speed changes and directional deviations after the structural disturbance. The airflow retention index is then calculated to determine whether it meets the retention conditions. If so, the water vapor accumulation is further calculated as a prerequisite for condensation growth analysis. This stage establishes the "structural disturbance-airflow feedback" coupling path. This scheme includes the stages of disturbance feedback path construction and ice cover growth prediction: When air stagnation and significant water vapor accumulation are identified, the water vapor accumulation amount is combined with meteorological factors to construct a "structural disturbance feedback path". The future ice thickness increment, namely the "structural disturbance prediction value", is then calculated through a short-term prediction model. The "feedback path offset value" is compared with the traditional basic path prediction value to determine whether the offset is persistent. If so, the feedback path weight is dynamically updated to improve the prediction model's sensitivity to icing anomalies caused by structural disturbances. This solution includes the dominant area identification and path control feedback stages: The feedback path offset values ​​of multiple consecutive rolling cycles are scored and summarized to calculate a "structural disturbance feedback score value." If the score value exceeds a threshold, a "structural disturbance dominant area identifier" is output to guide the priority construction of disturbance feedback paths and execution of predictions in the current area. If the threshold is not reached, the area status is maintained unchanged to avoid false alarms and over-response. This stage realizes predictive focusing and feedback path control in the spatial dimension, and is the output logic of the entire model.

[0054] The above description is only 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 in the scope of protection of the present invention.

Claims

1. A method for predicting short-term ice growth on 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, perform joint judgment and output the structural disturbance state identification; Input the structural disturbance state identification and historical meteorological observation data collection information, construct the disturbance-corrected wind speed field and calculate the airflow retention index, perform airflow retention judgment and calculate the water vapor accumulation; Based on the water vapor accumulation and historical meteorological observation data, a disturbance feedback path is constructed, ice growth prediction is performed, feedback path offset values ​​are calculated, and feedback path weights are updated. The feedback path offset value is input to calculate the disturbance feedback score and determine whether to output the dominant area identification or maintain the current state.

2. The method for predicting short-term ice growth of power transmission lines considering the influence of micro-topography according to claim 1 is characterized by: Obtain the structural state sequence of the transmission line tower, which includes a tension sequence, an inclination angle sequence, and a vibration frequency sequence. Perform disturbance feature calculation on the structural state sequence to calculate the tension change rate, inclination angle change amplitude, and vibration frequency change, respectively, and construct a disturbance variable set. The disturbance variable set is jointly judged to determine whether the tension change rate exceeds the preset tension change rate threshold, whether the inclination angle change amplitude exceeds the preset inclination angle change amplitude threshold, and whether the vibration frequency change is higher than the preset vibration frequency change threshold. If all three conditions do not meet their corresponding preset threshold conditions, a normal state flag is output; otherwise, a structural disturbance state flag is output.

3. The method for predicting short-term ice growth of power transmission lines considering the influence of micro-topography according to claim 2 is characterized by: When the output result is a structural disturbance state indicator, a historical meteorological observation data set at the transmission line tower is obtained, where the historical meteorological observation data set includes wind speed, wind direction, temperature, humidity, relative humidity, and air temperature; Perform disturbance calculation on the wind speed and direction in the structural disturbance state identification and historical meteorological observation data set to form a disturbance-corrected wind speed field; The airflow retention index is calculated by using the disturbance-corrected wind speed field and terrain factors, including slope, gradient, and terrain roughness. Airflow retention identification is achieved based on the results of the airflow retention index. In airflow stagnation identification, the airflow stagnation index is judged to be higher than the preset airflow stagnation index threshold, and the angle between the slope and the wind direction is higher than the preset angle between the slope and the wind direction threshold. These two conditions are used for conditional 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 retention, a fitting calculation is performed on the relative humidity and air temperature in the historical meteorological observation data set to output the water vapor accumulation.

4. The method for predicting short-term ice growth of power transmission lines considering the influence of micro-topography according to claim 3 is characterized by: The water vapor accumulation and the temperature, humidity, and wind speed in the historical meteorological observation data set are used as factors to jointly construct the structural disturbance feedback path and perform ice growth prediction, outputting the structural disturbance prediction value; Each element in the structural disturbance feedback path is treated as a subpath and assigned a corresponding preset weight factor to characterize the relative contribution of the element in the ice cover growth prediction. The weight factor is defined as the feedback path weight. Calculate the difference between the structural disturbance prediction value and the basic prediction value, and output the feedback path offset value; If the feedback path offset value exceeds the preset feedback path offset threshold in two consecutive prediction cycles that are rolled over in chronological order, the feedback path weight is updated; otherwise, the current feedback path weight remains unchanged.

5. The method for predicting short-term ice growth of power transmission lines considering the influence of micro-topography according to claim 4 is characterized by: Performing score calculation on feedback path offset values ​​within a plurality of prediction cycles that are updated in chronological order, and outputting a structural perturbation feedback score value; If the structural disturbance feedback score exceeds the preset structural disturbance feedback score threshold, the structural disturbance dominant area identification is output to guide the priority construction of the structural disturbance feedback path in the current area and perform ice growth prediction. Otherwise, the current area status is maintained to prevent the triggering of a regional early warning response when the conditions for outputting the structural disturbance dominant area identification are not met.

6. The method for predicting short-term ice growth of power transmission lines considering the influence of micro-topography according to claim 5 is characterized by: Define the structural disturbance state flag Expressed as: ； Defining the tension change rate Expressed as: ； Define the tilt angle change range Expressed as: ； Define the vibration frequency change Expressed as: ； in is the joint disturbance judgment function; joint disturbance judgment function The joint judgment logic includes: execution of three judgment conditions based on the disturbance variable set; the three conditions are: tension change rate Whether the preset tension change rate threshold and inclination angle change range are exceeded Whether the tilt angle change amplitude threshold and vibration frequency change are exceeded Is it higher than the preset vibration frequency change threshold? For any t, if none of the three conditions meet the corresponding 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 flag; if any one of the three conditions meets the corresponding preset threshold conditions, 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 flag; Where t is the current time step; Represents the function of the change of tension in the structural state sequence over time; d represents the first-order derivative operation of the variable relative to the current time step t; represents the time-varying function of the tilt angle in the structural state sequence; is the time sampling point; Represents a sliding time window The time corresponding to the previous discrete time step; The length of the time window for disturbance judgment; Represents the function of the change of vibration frequency in the structural state sequence over time; i represents the sliding time window Index variables within; Vibration frequency value; Represents a sliding time window Neidi Numerical input when Indicates the fixed threshold for determining whether the vibration frequency change meets the disturbance determination condition.

7. The method for predicting short-term ice growth of power transmission lines considering the influence of micro-topography according to claim 6 is characterized by: Define disturbance-corrected wind speed field Expressed as: ； Defining air stagnation index Expressed as: ； Defining water vapor accumulation Expressed as: ； in Represents the coordinates in the original wind speed field The wind speed value at is the wind speed reduction term within the disturbance impact area; is the disturbance direction gain coefficient; It represents the vector deflection term after the disturbance is applied to the airflow direction; represents the disturbed and corrected wind speed field variable; is the integral time variable; Represents the terrain aspect angle and wind direction angle The projection weight function of the angle between them; For coordinates The terrain aspect angle at For coordinates The airflow direction angle at For coordinates The terrain roughness coefficient at ; is the aggregation magnification factor; is the relative humidity; Represents the dew point temperature obtained by fitting relative humidity and air temperature; is the current temperature; is the exponential fitting coefficient; is the base of natural logarithms.

8. The method for predicting short-term ice growth of power transmission lines considering the influence of micro-topography according to claim 7 is characterized by: Define the predicted value of structural disturbance : ； Defining base path prediction values Expressed as: ； Define the feedback path offset value Expressed as: ； Define the average path deviation of two consecutive cycles Expressed as: ； in A prediction function model representing the disturbance path; is the wind speed value of the current time step; Represents the combined variable of temperature and humidity at the current time step; Represents the currently constructed structural perturbation feedback path; The prediction function model for the basic path; Indicates in The absolute difference between the predicted value of the structural perturbation feedback path and the predicted value of the basic path within a rolling prediction period; Numbers the current scroll cycle.

9. The method for predicting short-term ice growth of power transmission lines considering the influence of micro-topography according to claim 8 is characterized by: Defines the weighted score value for multi-cycle path deviation Expressed as: ； Define the structural perturbation feedback score value Expressed as: ； The output judgment condition of the dominant area is defined as: ； in Indicates the number of rolling forecast periods used for score calculation; Indicates the The corresponding scoring weight within a rolling forecast period; Indicates the structural perturbation feedback path in The average deviation value within the rolling forecast period; is the perturbation scoring function; is the natural logarithm function; is the perturbation score gain factor; is the disturbance scoring index factor; Indicates the transmission line spatial segment to which the current structural disturbance feedback path belongs and the local area number where the tower is located; is the perturbation score determination threshold.

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