Wire icing fault prediction method considering meteorological factors

Abstract

The invention relates to the technical field of power grid icing fault prediction, and provides a wire icing fault prediction method considering meteorological factors, which constructs a spatial-temporal characteristic matrix by integrating multi-dimensional parameters such as meteorology, wire deformation, geographical environment and the like, and performs dynamic error compensation by combining real-time sensor data and historical error weight. And single data dependence is eliminated, and prediction deviation caused by meteorological sudden change is corrected. A wire temperature suppression function and a terrain self-adaptive water drop capture efficiency algorithm are innovatively introduced, and the suppression effect of equipment heating on icing and the influence of terrain features on icing formation are quantified. A multi-factor coupling grading model of ice thickness, wind speed and freezing rain duration is established, risks are refined into low, medium and high levels, and differential alarm strategies are matched; the icing prediction precision is improved, and safe and stable operation of a power system is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid icing fault prediction, and in particular to a conductor icing fault prediction method taking meteorological factors into consideration. Background Art

[0002] Conductor icing fault prediction is one of the core areas of power system safety protection technology. Especially in high-altitude and snow-prone areas where extreme weather conditions such as cold waves and freezing rain occur frequently, accidents such as conductor breakage and insulator flashover caused by icing seriously threaten the stable operation of the power grid.

[0003] Current ice prediction technology primarily relies on static thresholds and single-factor modeling. This involves obtaining basic data such as temperature and humidity from weather stations, calculating and determining ice thickness, and then assessing and analyzing the probability of conductor failure based on this thickness. In cases of high failure probability, prompting personnel to address and perform maintenance is crucial. However, using weather stations to obtain basic data such as temperature and humidity and then calculating and determining ice thickness has limitations. In practice, the impact of other external factors on ice thickness cannot be assessed and predicted, leading to biased predictions and the potential for failures. This can delay maintenance time and efficiency, and in severe cases, even lead to conductor failure.

[0004] Based on this, the present invention provides a conductor icing fault prediction method taking meteorological factors into consideration to solve the problem that the existing power grid icing fault prediction method relies on single temperature and humidity data from the meteorological station to estimate the ice thickness, resulting in prediction deviation and inaccurate decision making. Summary of the Invention

[0005] The purpose of the present invention is to address the deficiencies of the existing technology and provide a method for predicting conductor icing faults that takes meteorological factors into consideration. This method solves the problem that traditional methods rely on single temperature and humidity data from meteorological stations to estimate ice thickness, but ignore the dynamic coupling of external factors such as terrain and wind speed, resulting in prediction deviations and inaccurate maintenance decisions, or causing low-risk over-response and high-risk response lag, and even threatening the safe operation of the power grid.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] A method for predicting conductor icing faults taking meteorological factors into consideration includes the following steps:

[0008] S1. Multi-source data collection and fusion: Collect and fuse meteorological information, traverse information, and geographic data to construct a standardized fusion feature matrix;

[0009] S2. Dynamic meteorological error compensation: Based on the error between the sensor's measured data and the weather forecast, and relying on the historical error values ​​of the past week, the correlation weight of the current error and the historical error is calculated. Finally, the weather forecast value is dynamically compensated based on the weight using a dynamic correction formula to generate corrected data.

[0010] S3. Dynamic modeling of ice cover: integrating physical mechanisms with correction data, using a parameterized dynamic balance algorithm to calculate the rate of change of ice cover thickness, and using a numerical integration method to calculate ice cover thickness;

[0011] S4, risk level assessment: Based on the ice thickness obtained in S3, the conductor operation status is graded using the preset risk assessment rules;

[0012] S5, Risk Alert: Based on the risk level obtained in S4, a prompt message is generated and notified to the operation and maintenance personnel.

[0013] Preferably, the meteorological information in S1 includes ambient temperature, wind speed and precipitation phase, the conductor information includes the current and resistance of the conductor, and the geographical environment data includes altitude and terrain roughness.

[0014] Preferably, the fusion feature matrix is ​​specifically:

[0015] X(t)=[T amb ,v,φ p ,I,R,H,τ];

[0016] Where: T amb Environment is temperature; v is wind speed; φ p is the precipitation phase; I is the current; R is the resistance; H is the altitude; τ is the terrain roughness.

[0017] Preferably, the dynamic correction formula in S2 is specifically:

[0018] ΔX(t)=X sensor (t)-X NWP (t)

[0019]

[0020] Y(t)=X NWP (t)+α(t)·ΔX(t)

[0021] Where: X sensor is the actual value measured by the sensor, X sensor (t) is the actual value measured by the sensor at time t, X NWP (t) is the weather forecast value at time t; α(t) is the dynamic weight based on historical errors, reflecting the credibility of the current correction; ΔX histis the historical error between the sensor measured value and the weather forecast value at each moment in the past week, Var(ΔX hist ) is the variance of the historical error, and Y(t) is the revised forecast value.

[0022] Preferably, the ice thickness change rate in S3 is modeled by the following formula:

[0023]

[0024] in: is the rate of change of ice thickness over time, ρ w is the density of water, η represents the water droplet capture efficiency, v is the wind speed, φ p is the precipitation phase, A is the windward area of ​​the conductor in contact with the water droplets, k ice is the thermal conductivity of ice, σ(T amb ,T c ) is the temperature suppression function, I is the current in the wire, R is the resistance of the wire, Q loss is the heat loss of the wire, L f is the latent heat of melting of ice.

[0025] Preferably, the temperature suppression function σ(T amb ,T c ) takes the following form:

[0026]

[0027] Where: T amb is the ambient temperature, T c is the critical temperature, T e is the conductor equivalent temperature, and λ is the conductor temperature suppression coefficient, specifically a modulating factor in the temperature suppression function that controls the decay rate of the exponential function. Specifically, when the ambient temperature is below the critical temperature, the larger λ is, the stronger the inhibitory effect of the conductor equivalent temperature on ice growth (the faster the exponential decay). It is an independent model parameter and has no direct correlation with the specific temperature value, but it can affect temperature-related calculation results.

[0028] Preferably, the water droplet capture efficiency η is modified as follows:

[0029] η=η0(1+β1H+β2τ);

[0030] Where: η0 is the initial water droplet capture efficiency, which is the capture efficiency of water droplets and wires under standard conditions and is the basic value for calculating the actual water droplet capture efficiency; β1 and β2 are terrain influence coefficients, which reflect the influence of altitude H and terrain roughness τ on the water droplet capture efficiency respectively. These two coefficients are used to adjust the water droplet capture efficiency under different terrain conditions.

[0031] H is the altitude. The thin air and low temperature at high altitudes will affect the trajectory and collision probability of water droplets, thereby affecting the icing process. τ is the terrain roughness. Rough terrain will increase the turbulence of the air, making it easier for water droplets to collide with the wire, thereby improving the efficiency of water droplet capture.

[0032] Preferably, the ice thickness is calculated by Euler integration method, specifically:

[0033]

[0034] Among them, n represents the sequence number of the time step, h n Indicates t n Ice thickness at the moment.

[0035] Preferably, the preset risk assessment rules are:

[0036] Low risk: ice thickness ≤ 5mm, wind speed <10m / s, no freezing rain;

[0037] Medium risk: ice thickness >5mm and ≤10mm, or wind speed 10-15m / s, or freezing rain;

[0038] High risk: ice thickness > 10 mm, or wind speed ≥ 15 m / s, or freezing rain lasting more than 2 hours.

[0039] Preferably, in S5, the following response strategy is adopted according to the risk level of S4: when a low risk occurs, there is no need to remind the staff; when a medium risk occurs, an automatic text message reminder is sent to the staff, and the risk location and recommended measures are marked; when a high risk occurs, a buzzer alarm is sounded and the staff is notified immediately by phone.

[0040] The present invention discloses a method for predicting conductor icing faults taking meteorological factors into consideration, which has the following beneficial effects.

[0041] 1. This invention improves ice cover forecast accuracy through multi-source data fusion and dynamic error correction. It integrates meteorological, traverse, and geographic multi-dimensional parameters to construct a spatiotemporal feature matrix, eliminating reliance on a single data source. It also combines real-time sensor data with historical error weights to dynamically compensate for sudden meteorological changes, reducing forecast bias and avoiding missed or redundant alerts.

[0042] 2. The present invention introduces a terrain-adaptive algorithm based on the conductor temperature suppression function and water droplet capture efficiency to quantify the inhibitory effect of the device's own heating on icing, making the factors considered more comprehensive, further in line with actual conditions, and reducing false alarms.

[0043] 3. This invention defines a multi-factor coupling grading rule for ice thickness, wind speed, and freezing rain duration, and refines the risk level into three levels: low, medium, and high. It also matches differentiated alarm methods to avoid distracting staff attention due to indiscriminate alarms, and ensures rapid emergency response to extreme scenarios such as power grid paralysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 The figure is a flow chart of the conductor icing fault prediction method considering meteorological factors according to the present invention.

[0045] Figure 2 This is a comparison chart of data before and after error correction of the present invention.

[0046] Figure 3 This is a graph showing the ice thickness prediction results of the present invention. DETAILED DESCRIPTION

[0047] In order to explain in detail the possible application scenarios, technical principles, specific solutions that can be implemented, and the purpose and effects of this application, the following is a detailed description of the specific embodiments listed in conjunction with the accompanying drawings. The embodiments described herein are only used to more clearly illustrate the technical solutions of this application and are therefore only examples and are not intended to limit the scope of protection of this application.

[0048] References to "embodiments" herein mean that the specific features, structures, or characteristics described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the word "embodiment" in various places in the specification does not necessarily refer to the same embodiment, nor does it particularly limit its independence or relevance to other embodiments. In principle, in this application, as long as there are no technical contradictions or conflicts, the various technical features mentioned in the embodiments can be combined in any manner to form a corresponding implementable technical solution.

[0049] Unless otherwise defined, the technical terms used herein have the same meanings as those generally understood by those skilled in the art to which this application belongs; the use of relevant terms herein is only for describing specific embodiments and is not intended to limit this application.

[0050] In the description of this application, the term "and / or" is used to describe a logical relationship between objects, indicating that three relationships can exist. For example, A and / or B means: A exists, B exists, and both A and B exist. In addition, the character " / " in this document generally indicates that the objects before and after are in a logical "or" relationship.

[0051] Without further limitations, in this application, the words "include", "comprise", "have" or other similar expressions used in the sentences are intended to cover non-exclusive inclusion. These expressions do not exclude the presence of additional elements in the process, method or product including the elements, so that the process, method or product including a series of elements may include not only those defined elements, but also other elements not explicitly listed, or elements inherent to such process, method or product.

[0052] Unless otherwise expressly specified or limited, in the description of the embodiments of the present application, the terms "installed", "connected", "connected", "fixed", "set", etc. used should be understood in a broad sense. For example, the "connection" can be a fixed connection, a detachable connection, or an integrated setting; it can be a mechanical connection, an electrical connection, or a communication connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be the internal connection of two elements or the interaction relationship between two elements. For those skilled in the art of the present application, the specific meanings of the above terms in the embodiments of the present application can be understood according to the specific circumstances.

[0053] Example

[0054] Please refer to Figure 1 A method for predicting conductor icing faults taking meteorological factors into consideration includes the following steps:

[0055] S1. Multi-source data collection and fusion: Collect and fuse meteorological information, traverse information, and geographic data to construct a standardized fusion feature matrix;

[0056] Preferably, in this embodiment, the meteorological information in S1 includes ambient temperature, wind speed, and precipitation phase. Wind speed affects the trajectory and impact kinetic energy of water droplets and is one of the important meteorological parameters affecting ice growth. Precipitation phase is used to distinguish different forms of precipitation, such as rain, snow, and freezing rain. Different precipitation phases have different effects on ice formation and growth.

[0057] Conductor information includes current and resistance, and geographic environment data includes altitude and terrain roughness. Current flowing through a conductor generates heat, which affects the conductor's own heating and, consequently, the ice accumulation and ice melting process. According to Joule's law, current flowing through a conductor generates heat, and the resistance value affects the degree of conductor heating, which in turn affects the ice accumulation and ice melting process.

[0058] Geographical environment data include altitude and terrain roughness. Altitude: The air is thin and the temperature is low in high altitude areas, which will affect the trajectory of water droplets and the probability of collision, thereby affecting the icing process; terrain roughness: Rough terrain will increase the turbulence of the air, making it easier for water droplets to collide with wires, thereby increasing the efficiency of water droplet capture.

[0059] In this embodiment, after collecting meteorological information, conductor information and geographical data, the collected meteorological information: temperature, wind speed, precipitation phase; conductor information: current, resistance; geographical data: altitude, terrain roughness are standardized. In this embodiment, taking temperature data as an example, the collected temperature data range is (X max -X min ), using the linear normalization formula:

[0060]

[0061] Where: X max 、X min Represents the historical maximum / minimum value of the parameter; X represents the current parameter; X norm Represents the standard value after normalization; it is made to have the same scale range as other data, eliminating the dimension effect. Other types of data are also standardized in a similar way;

[0062] Preferably, in this embodiment, the standardized data is arranged in sequence according to the fusion calculation formula to form the input feature matrix X(t), and arranged according to the format of X(t) to form a matrix containing multiple data features. In this way, different types of data are fused in the same matrix, providing a unified data foundation for subsequent analysis and calculation. The fusion feature matrix is ​​specifically:

[0063] X(t)=[T amb ,v,φ p ,I,R,H,τ];

[0064] Where: T amb Environment is temperature; v is wind speed; φ p The precipitation phase state is standardized according to domestic standards. The precipitation phase state is stored in codes, "1" for rain, "3" for snow, "5" for hail, and the start and end time are recorded; I is the current; R is the resistance; H is the altitude; τ is the terrain roughness.

[0065] In this embodiment, the fusion feature matrix is ​​shown in Table 1

[0066] Table 1 Fusion feature matrix

[0067]

[0068] S2. Dynamic meteorological error compensation: Based on the error between the actual sensor data and the meteorological forecast value, and relying on the historical error values ​​of the past week, the correlation weight of the current error and the historical error is calculated. Finally, based on the weight, the meteorological forecast value is dynamically compensated using a dynamic correction formula to generate corrected data. In this embodiment, the sensors include a wind speed sensor and a temperature sensor.

[0069] Preferably, in this embodiment, the dynamic correction formula in S2 is specifically:

[0070] ΔX(t)=X sensor (t)-X NWP (t)

[0071]

[0072] Y(t)=X NWP (t)+α(t)·ΔX(t)

[0073] Where: X sensor is the actual value measured by the sensor, X sensor (t) is the actual sensor measurement value at time t, which is obtained by real-time monitoring by sensors equipped on site, such as wind speed sensors and temperature sensors, and is the direct measurement result of the current environmental meteorological data;

[0074] X NWP (t) is the weather forecast value at time t, which is the estimated data of future weather conditions obtained based on the meteorological model and prediction method; α(t) is the dynamic weight based on historical errors, reflecting the credibility of the current correction. α(t) can effectively reflect the degree of correlation between the current error and the historical error, thereby judging whether the current error is representative. When α(t) approaches 1, it means that the current error is highly consistent with the historical error, and the historical error is relatively stable. At this time, the error has strong predictability and the correction of the weather forecast value is more credible; on the contrary, if α(t) is close to 0 or negative, it means that the current error deviates from the historical error trend and the error is unstable. At this time, the correction amplitude should be reduced to avoid over-correction due to error noise; ΔX hist is the historical error between the sensor measured value and the weather forecast value at each moment in the past week. The weather forecast value is obtained from the meteorological database with a time resolution of 1 hour;

[0075] These historical error data are an important basis for calculating the correlation weight. By analyzing the relationship between the current error and the historical error, the credibility of the revised weather forecast value is determined; Var(ΔX hist) is the variance of the historical error, which measures the degree of dispersion of a set of data. Here, it represents the dispersion of the historical error values ​​over the past week. A larger variance indicates greater volatility in the historical error and a weaker correlation between the current and historical errors. Conversely, a smaller variance indicates a relatively more stable historical error and a stronger correlation between the current and historical errors. Y(t) is the revised forecast value.

[0076] The specific process of dynamic correction is as follows:

[0077] First, using the sensors equipped on site, in this embodiment, wind speed sensors and temperature sensors are used to obtain the current environmental meteorological information in real time, and the actual sensor measurement value X at time t is obtained. sensor (t), and obtain the corresponding weather forecast value X NWP (t), and calculate the difference between the two to get the error ΔX(t) at time t;

[0078] Then, based on the historical error values ​​of the past week, calculate the correlation weight α(t) between the current error and the historical error;

[0079] Finally, the weather forecast value X is modified according to the weight using the dynamic correction formula. NWP (t) is added to the result of multiplying the error ΔX(t) by the weight α(t), thereby obtaining the final corrected forecast value Y(t) which is closer to the true value; the difference before and after error correction is shown in Figure 2 .

[0080] S3. Dynamic modeling of ice cover: integrating physical mechanisms with correction data, using a parameterized dynamic balance algorithm to calculate the rate of change of ice cover thickness, and using a numerical integration method to calculate ice cover thickness;

[0081] Preferably, in this embodiment, the ice thickness change rate in S3 is modeled by the following formula:

[0082]

[0083] in: is the rate of change of ice thickness over time, ρ w is the density of water, η represents the water droplet capture efficiency, which reflects the probability of water droplets in the air colliding with the surface of the wire and adhering to form ice, which is affected by factors such as terrain; v is the wind speed, φ p is the precipitation phase, A is the windward area of ​​the conductor in contact with the water droplets, which affects the collision volume between the water droplets and the conductor and is an important geometric parameter for calculating ice growth; k ice is the thermal conductivity of ice, which is used to measure the characteristics of ice in the heat transfer process when calculating the meltwater; σ(T amb ,T c ) is the temperature suppression function, according to the ambient temperature T amband the critical temperature T of the conductor c The relationship between the conductor surface heat and the icing inhibition effect is quantified, and the conductor critical temperature T c Preset values ​​for staff; I is the current in the wire, R is the resistance of the wire, Q loss The heat loss of the conductor. When calculating the melt water, the heat generated by the conductor and the heat loss must be considered to accurately calculate the heat required for melting ice. f The latent heat of melting of ice is the amount of heat required for ice to melt into water, and is used as a basic physical parameter in calculating the meltwater.

[0084] Preferably, in this embodiment, in order to avoid model distortion caused by sudden temperature change, the temperature suppression function σ(T amb ,T c ) takes the following form:

[0085]

[0086] Where: T amb is the ambient temperature, T c is the critical temperature, T e is the equivalent temperature of the wire. When current passes through the wire, heat energy is generated due to resistance, thus obtaining the equivalent temperature of the wire T e , the calculation formula is:

[0087] Wire equivalent temperature = ambient temperature + wire power / wire area;

[0088] Among them, the ambient temperature T amb The data is collected through temperature sensors. The power and area of ​​the wires are recorded and saved during the construction of the current wires. λ is the temperature suppression coefficient, which specifically refers to the adjustment factor in the temperature suppression function and is used to control the decay rate of the exponential function. That is, when the ambient temperature is below the critical temperature, the larger λ is, the stronger the effect of the wire equivalent temperature on ice growth (the faster the exponential decay). It is an independent model parameter and has no direct correspondence with the specific temperature value, but it will affect the temperature-related calculation results.

[0089] Preferably, in this embodiment, the water droplet capture efficiency η is modified as follows:

[0090] η=η0(1+β1H+β2τ);

[0091] Where: η0 is the initial water droplet capture efficiency, which is the capture efficiency of water droplets and wires under standard conditions and is the basic value for calculating the actual water droplet capture efficiency; β1 and β2 are terrain influence coefficients, which reflect the influence of altitude H and terrain roughness τ on the water droplet capture efficiency respectively. These two coefficients are used to adjust the water droplet capture efficiency under different terrain conditions.

[0092] H is the altitude. The thin air and low temperature at high altitudes will affect the trajectory and collision probability of water droplets, thereby affecting the icing process. τ is the terrain roughness. Rough terrain will increase the turbulence of the air, making it easier for water droplets to collide with the wire, thereby improving the efficiency of water droplet capture.

[0093] Preferably, in this embodiment, the ice thickness is calculated by the Euler integration method, specifically:

[0094]

[0095] Among them, n represents the sequence number of the time step, h n Indicates t n Ice thickness at the moment.

[0096] The calculation process of ice thickness is as follows:

[0097] First determine the values ​​of each parameter, the density of water ρ w , the thermal conductivity of ice k ice 、Latent heat of melting of ice L f etc. are known physical constants;

[0098] The water droplet capture efficiency η is calculated by the formula, where the initial water droplet capture efficiency η0, terrain influence coefficients β1 and β2 are pre-set according to the environment where the conductor is located, and the altitude H and terrain roughness τ are obtained from the geographical data collected by S1;

[0099] Wind speed v, precipitation phase φ p , current I in the wire, resistance R, critical temperature T of the wire c 、Ambient temperature T amb , conductor critical temperature T c , obtained from the data collected by S1 and the data corrected by S2, the heat loss Q loss is the preset value of the model;

[0100] According to the ambient temperature T amb and the critical temperature T of the conductor c The relationship between the temperature suppression function σ(T amb ,T c ) value, the function is in T amb When ≤0℃, the growth rate is close to exponential growth inhibition. amb When the temperature is >0℃, it decays rapidly to 0, forming a continuous, smooth and conductive inhibition mechanism.

[0101] Substitute the above-determined parameter values ​​into the ice thickness change rate model to calculate the change rate of ice thickness over time. In practical applications, it is assumed that the ice thickness at the initial time t0 is h0. Usually, when monitoring begins, if no ice occurs, h0 = 0. Using numerical integration methods, such as the Euler method, in each time interval Δt, according to the formula Calculate the ice thickness at subsequent moments step by step; the ice thickness prediction results can be found in Figure 3 .

[0102] S4, risk level assessment: Based on the ice thickness obtained in S3, the conductor operation status is graded using the preset risk assessment rules;

[0103] Preferably, in this embodiment, the preset risk assessment rules are:

[0104] Low risk: ice thickness ≤ 5mm, wind speed <10m / s, no freezing rain; under low risk, the most likely fault type is slight conductor swaying, no structural damage;

[0105] Medium risk: ice thickness >5mm and ≤10mm, or wind speed 10-15m / s, or freezing rain. Under medium risk, the following fault types are likely to occur: insulator flashover and increased conductor sag.

[0106] High risk: ice thickness > 10mm, or wind speed ≥ 15m / s, or freezing rain lasting more than 2 hours. In high-risk situations, the following types of faults are likely to occur: conductor breakage, tower overturning, and interlocking ice shedding tripping.

[0107] The presence or absence of freezing rain information is obtained through the weather forecast content, which is a common existing information collection technology and will not be described in detail here;

[0108] S5, Risk Alert: Based on the risk level obtained in S4, a prompt message is generated and notified to the operation and maintenance personnel.

[0109] Preferably, in this embodiment, in S5, the following response strategy is adopted according to the risk level of S4: when a low risk occurs, there is no need to remind the staff; when a medium risk occurs, an automatic text message reminder is sent to the staff, and the risk location and recommended measures are marked; when a high risk occurs, a buzzer alarm is sounded, and the staff is notified immediately by phone.

[0110] The above are merely preferred specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Substitutions may be partial structures, devices, or method steps, or they may be complete technical solutions. Any equivalent replacements or modifications based on the technical solution and inventive concept of the present invention shall be covered by the scope of protection of the present invention.

Claims

1. A method for predicting conductor icing faults taking into account meteorological factors, characterized in that: The steps include: S1. Multi-source data collection and fusion: Collect and fuse meteorological information, traverse information, and geographic data to construct a standardized fusion feature matrix; S2. Dynamic meteorological error compensation: Based on the error between the sensor's measured data and the weather forecast, and relying on the historical error values ​​of the past week, the correlation weight of the current error and the historical error is calculated. Finally, the weather forecast value is dynamically compensated based on the weight using a dynamic correction formula to generate corrected data. S3. Dynamic modeling of ice cover: integrating physical mechanisms with correction data, using a parameterized dynamic balance algorithm to calculate the rate of change of ice cover thickness, and using a numerical integration method to calculate ice cover thickness; S4, risk level assessment: Based on the ice thickness obtained in S3, the conductor operation status is graded using the preset risk assessment rules; S5, Risk Alert: Based on the risk level obtained in S4, a prompt message is generated and notified to the operation and maintenance personnel.

2. The method for predicting conductor icing faults considering meteorological factors according to claim 1, characterized in that: The meteorological information in S1 includes ambient temperature, wind speed and precipitation phase; the conductor information includes the current and resistance of the conductor; and the geographical environment data includes altitude and terrain roughness.

3. The method for predicting conductor icing faults considering meteorological factors according to claim 1, characterized in that: The fusion feature matrix is ​​specifically: X(t)=[T amb ,v,φ p ,I,R,H,τ]; Where: T amb Environment is temperature; v is wind speed; φ p is the precipitation phase; I is the current; R is the resistance; H is the altitude; τ is the terrain roughness.

4. The method for predicting conductor icing faults considering meteorological factors according to claim 1, characterized in that: The dynamic correction formula in S2 is specifically: Where: X sensor is the actual value measured by the sensor, X sensor (t) is the actual value measured by the sensor at time t, X NWP (t) is the weather forecast value at time t; α(t) is the dynamic weight based on historical errors, reflecting the credibility of the current correction; ΔX hist is the historical error between the sensor measured value and the weather forecast value at each moment in the past week, Var(ΔX hist ) is the variance of the historical error, and Y(t) is the revised forecast value.

5. The method for predicting conductor icing faults taking into account meteorological factors according to claim 1, characterized in that: The ice thickness change rate described in S3 is modeled by the following formula: in: is the rate of change of ice thickness over time, ρ w is the density of water, η represents the water droplet capture efficiency, v is the wind speed, φ p is the precipitation phase, A is the windward area of ​​the conductor in contact with the water droplets, k ice is the thermal conductivity of ice, σ(T amb ,T c ) is the temperature suppression function, I is the current in the wire, R is the resistance of the wire, Q loss is the heat loss of the wire, L f is the latent heat of melting of ice.

6. The method for predicting conductor icing faults taking into account meteorological factors according to claim 5, characterized in that: The temperature suppression function σ(T amb ,T c ) takes the following form: Where: T amb is the ambient temperature, T c is the critical temperature, T e is the equivalent temperature of the wire, λ is the temperature suppression coefficient, which specifically refers to the adjustment factor in the temperature suppression function and is used to control the decay rate of the exponential function.

7. The method for predicting conductor icing faults taking into account meteorological factors according to claim 5, characterized in that: The water droplet capture efficiency η is corrected as follows: η=η0(1+β1H+β2τ); Where: η0 is the initial water droplet capture efficiency; β1 and β2 are terrain influence coefficients, reflecting the influence of altitude H and terrain roughness τ on the water droplet capture efficiency, respectively; H is the altitude, and τ is the terrain roughness.

8. The method for predicting conductor icing faults taking into account meteorological factors according to claim 5, characterized in that: The ice thickness is calculated by the Euler integration method, specifically: Among them, n represents the sequence number of the time step, h n Indicates t n Ice thickness at the moment.

9. The method for predicting conductor icing faults taking into account meteorological factors according to claim 1, characterized in that: The preset risk assessment rules are: Low risk: ice thickness ≤ 5mm, wind speed <10m / s, no freezing rain; Medium risk: ice thickness >5mm and ≤10mm, or wind speed 10-15m / s, or freezing rain; High risk: ice thickness > 10 mm, or wind speed ≥ 15 m / s, or freezing rain lasting more than 2 hours.

10. The method for predicting conductor icing faults taking into account meteorological factors according to claim 1, characterized in that: In S5, the following response strategies are adopted according to the risk level of S4: when a low risk occurs, there is no need to remind the staff; when a medium risk occurs, an automatic text message reminder is sent to the staff, and the risk location and recommended measures are marked; when a high risk occurs, a buzzer alarm is sounded and the staff is notified immediately by phone.

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