High-voltage transmission line dynamic capacity increase risk prediction method, readable medium and equipment

Abstract

The invention relates to the technical field of power transmission and distribution of a power grid, in particular to a dynamic capacity increase risk prediction method for a high-voltage transmission line, a readable medium and equipment. The high-voltage power transmission line dynamic capacity increasing risk prediction method comprises the steps of collecting line temperature data of each point position of a corresponding power transmission line to form a data set; processing the data in the data set; calculating a rank correlation coefficient of the data; selecting the order of the fitting curve according to the absolute value of the rank correlation coefficient; obtaining a fitting approximate curve equation according to the order of the fitting curve; and performing prediction based on the fitting approximate curve equation, and outputting a prediction result. According to the scheme, the real-time temperature and the environment temperature of the temperature sensitive point position on the power transmission line and the current-carrying capacity data at the corresponding moment are measured, the corresponding relation algorithm of the difference value between the lead temperature and the environment temperature and the current-carrying capacity is constructed, and error feedback and adjustment are conducted at any time, so that the method has the advantages of being visual in calculation and rapid in adjustment; and the capacity increasing allowance and the safety in the subsequent short time can be predicted.

Description

Technical Field

[0001] The present invention relates to the field of power transmission and distribution technology for power grids, and in particular to a method, a readable medium and a device for predicting the risk of dynamic capacity increase of high-voltage transmission lines. Background Art

[0002] In the field of power transmission and distribution, as people's quality of life continues to improve, electricity consumption in various regions and industries continues to grow rapidly, the nature of electricity loads has become diversified, and the contradiction between power supply and demand continues to be prominent. At the same time, as a high proportion of new energy power generation has penetrated into the power grid, the unstable and fluctuating power of new energy power generation has also put forward higher requirements on the peak transmission capacity of transmission lines.

[0003] Heavy-loaded lines subject to static load limits are the "short board" that restricts the overall energy transmission of the power grid. Building new line corridors to make up for the "short board" not only requires huge investment and a long construction period, but is also constrained by the shortage of urban land. Therefore, increasing the load limit of transmission line operation is the key to improving the transmission capacity of the main grid. The dynamic capacity increase technology of transmission lines can help the operating department safely and dynamically increase the load limit of transmission line operation, break through the bottleneck of transmission capacity, and solve the problem of sudden peak load power transmission.

[0004] As a channel for power transmission, the load capacity limit of the transmission line is mainly based on the thermal stability limit (that is, the main factor limiting the capacity of the transmission line is the temperature of the line. The greater the current carrying capacity of the line, the greater the heat generation power, and the higher the line temperature. GB / 50545-2010 "Transmission Line Design Specification" stipulates a 70°C bare conductor operation limit). In order to ensure the safety of the line, the capacity design of the line is currently mainly based on static management and comprehensive consideration of local environmental temperature, wind speed, solar radiation and other influencing factors of the line. A fixed value is taken to calculate the limit, and finally a certain redundancy coefficient is set to calculate the static current limit of the line. However, the current limit calculated by the above design method takes into account the extreme environment. During normal operation, the line still has a considerable capacity increase margin. Therefore, through appropriate monitoring means and algorithms, while ensuring the safety of the line, to explore the dynamic capacity increase margin of the line, it is very helpful to meet the peak fluctuations and occasional supply and demand imbalances in the current power grid.

[0005] In addition, some prior arts are disclosed, as follows: The invention application with application number 202410917480.1 and patent name "A method for calculating the current carrying capacity of overhead transmission lines" discloses: by obtaining the environmental parameters, line structure parameters and conductor parameters of the overhead transmission lines, the temperature, stress and specific load of the overhead transmission lines under the installation conditions are calculated. Through multiple measurements before and after, the optimal parameter combination consisting of heat absorption coefficient, radiation heat dissipation coefficient, safety factor, etc. is obtained, and finally the optimal parameter combination is input into the current carrying capacity calculation model (static current carrying capacity calculation formula provided by the specification) to obtain the maximum allowable current carrying capacity of the overhead line. This method is based on multiple measurable data to deduce the coefficients that cannot be directly measured, and after obtaining the coefficients, it is brought into the calculation formula to calculate the real-time maximum current carrying capacity. On the one hand, there is a time difference between the data measured multiple times, and there is a problem of data asynchrony. On the other hand, the calculated data may have changed in environmental parameters when applied, and the maximum current carrying capacity has also changed accordingly, and there is a problem of timeliness. The invention application with application number 202410348241.9 and patent name "Dynamic Capacity Increase Method of Distribution Network Based on Insulated Wire Temperature Monitoring" discloses: by measuring the real-time temperature and current of the transmission line, a temperature-current model of the transmission line is constructed to calculate the capacity increase margin of the corresponding insulated line. The model includes measurement parameters, temperature coefficients, insulation coefficients and other conductor characteristic coefficients, and then integrates the thermal balance equation, heat loss formula, and heat dissipation resistance formula to construct it. The accuracy of the model needs to be verified, and based on the fitting method of various coefficients, when overfitting or underfitting occurs, it will cause large errors and impacts on the subsequent capacity increase margin prediction.

[0006] In summary, there is an urgent need for a dynamic capacity increase risk prediction method for high-voltage transmission lines that is effective and can predict the capacity increase margin and safety in a short period of time to solve the problems existing in the prior art. Summary of the invention

[0007] The purpose of the present invention is to provide a method for predicting the risk of dynamic capacity increase of high-voltage transmission lines. This scheme abandons the cumbersome work of direct measurement or indirect calculation of various parameters in the current carrying capacity design calculation formula of conventional transmission lines. By measuring the real-time temperature, ambient temperature and current carrying capacity data of temperature-sensitive points on the transmission line at the corresponding time, and by constructing an algorithm for the correspondence between the difference between the conductor temperature and the ambient temperature and the current carrying capacity, it directly focuses on the main contradiction between temperature and current carrying capacity. Based on statistical principles, it constructs a correlation between the two, and performs error feedback and adjustment at all times. It has the characteristics of intuitive calculation and rapid adjustment, and can predict the subsequent capacity increase margin and safety in a short period of time. It can provide a risk reference and early warning for other quantitative calculation dynamic capacity increase margin systems, and realize multiple guarantees for line safety. The specific technical scheme is as follows:

[0008] A method for predicting the risk of dynamic capacity increase of high-voltage transmission lines comprises the following steps:

[0009] The first step is to collect the line temperature data of each point of the corresponding transmission line according to the set frequency. The line temperature data includes the surface temperature T of the conductor. a,b and ambient temperature T′ a,b ; Record the current carrying capacity I of the transmission line corresponding to each measurement time point a,b ; Form a data set A, A = (T a,b -T′ a,b , I a,b ); where: subscript a represents the sequence number of the point, and subscript b represents the measurement time point;

[0010] Step 2: Select a smoothing window; select n consecutive raw data in data set A as a processing window for data processing, and smooth the subsequent data windows backward in sequence; calculate the rank correlation coefficient of the data by the following formula:

[0011]

[0012] Where: ρ is the Spearman rank correlation coefficient; n is the total amount of selected continuous original data, that is, the sample size; d j is the order difference of the jth pair of sample values, that is, the difference in the positions of the corresponding sample values ​​of the two variables after sorting;

[0013] Step 3: Select the order of the fitting curve according to the absolute value of the rank correlation coefficient obtained in step 2;

[0014] Step 4: Perform regression analysis using polynomial fitting according to the order of the fitting curve obtained in step 3, and obtain the fitting approximate curve equation based on the actual curve equation:

[0015] The actual equation of the curve: y = f(x);

[0016] Fitting approximate curve equation:

[0017] Where: y is the dependent variable, representing the value of the wire temperature; f(x) represents the function of the dependent variable y corresponding to the independent variable x; m is the fitting order of the polynomial, and M is the maximum value of m; w=[w0,w1,...,w M ] is the coefficient set of the polynomial; x is the independent variable, representing the difference between the surface temperature of the conductor and the ambient temperature;

[0018] Step 5: Make predictions based on the fitting approximate curve equation obtained in step 4 and output the prediction results.

[0019] Preferably, in the second step: the rank correlation coefficient ρ takes a value range of [-1,1], wherein: when ρ is positive, it indicates a positive correlation, and when ρ is negative, it indicates a negative correlation.

[0020] Preferably, in the third step, the order of the fitting curve is selected according to the absolute value of the rank correlation coefficient obtained in the second step as follows:

[0021] |ρ|>0.95, indicating significant correlation; the fitting order is M=2;

[0022] 0.8≤|ρ|≤0.95, indicating high correlation; the fitting order is M=3;

[0023] 0.5≤|ρ|<0.8, indicating moderate correlation; the fitting order is M=4;

[0024] 0.3≤|ρ|<0.5, indicating low correlation; the fitting order is M=5;

[0025] |ρ|<0.3, indicating weak correlation, and the fitting order is M=6.

[0026] Preferably, in the fourth step:

[0027] At a certain point i on the curve, the deviation value δ between the approximate curve and the actual curve i for:

[0028]

[0029] Where: y i represents the dependent variable corresponding to a point i on the curve; x i Represents the independent variable corresponding to a point i on the curve;

[0030] When the fitting order M is fixed and M≤3, the optimal fit is achieved by finding the minimum value of the following formula:

[0031]

[0032] When the fitting order M is fixed and M is greater than 3, a regular term is introduced to calculate the minimum value of the square sum of coefficients of each order. The expression is as follows:

[0033]

[0034] Where: λ is the regularization coefficient.

[0035] Preferably, the prediction in the fifth step based on the fitting approximate curve equation obtained in the fourth step is specifically: sequentially calculating whether the value of the wire temperature exceeds the maximum avoidance temperature T allowed by the wire when the current current is increased by 1.6 times, 1.4 times, and 1.2 times max; If the wire temperature exceeds the maximum avoidable temperature T allowed by the wire when the current is increased by 1.6 times, 1.4 times, or 1.2 times max , it means that under the current current carrying capacity, the risk of executing dynamic capacity increase measures is extremely high, and capacity increase operations are not recommended.

[0036] Preferably, when the current is increased by 1.6 times, the value of the wire temperature is calculated using the following formula:

[0037]

[0038] If it is satisfied, it means that dynamic capacity increase measures are implemented under the current current carrying capacity, and each state parameter is excellent, and there is no risk for the time being; if it is not satisfied, it is necessary to continue to calculate the value of the conductor temperature when the current current is increased by 1.4 times.

[0039] Preferably, when the current is increased by 1.4 times, the value of the wire temperature is calculated using the following formula:

[0040]

[0041] If it is satisfied, it means that under the current current carrying capacity, there is good potential for dynamic capacity increase measures and the line temperature is controllable; if it is not satisfied, it is necessary to continue to calculate the value of the conductor temperature when the current current is increased by 1.2 times.

[0042] Preferably, when the current is increased by 1.2 times, the value of the wire temperature is calculated using the following formula:

[0043]

[0044] If it is met, it means that under the current current carrying capacity, there is a certain risk in implementing dynamic capacity increase measures, and there is a risk that the line temperature may exceed the upper limit, which requires special attention.

[0045] The present invention also discloses a readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned method for predicting the dynamic capacity increase risk of high-voltage transmission lines is implemented.

[0046] The present invention also discloses an electronic device, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to execute the above-mentioned method for predicting the dynamic capacity increase risk of high-voltage transmission lines by executing the executable instructions.

[0047] In addition to the above-described objects, features and advantages, the present invention has other objects, features and advantages. The present invention will be further described in detail with reference to the embodiments below. DETAILED DESCRIPTION

[0048] The following is a detailed description of embodiments of the present invention, but the present invention can be implemented in many different ways as defined and covered by the claims.

[0049] Example:

[0050] A method for predicting the risk of dynamic capacity increase of high-voltage transmission lines comprises the following steps:

[0051] The first step is to collect the line temperature data of each point of the corresponding transmission line according to the set frequency. The line temperature data includes the surface temperature T of the conductor. a,b and ambient temperature T′ a,b ; Record the current carrying capacity I of the transmission line corresponding to each measurement time point a,b ; Form a data set A, A = (T a,b -T′ a,b , I a,b ), where subscript a represents the sequence number of the point, and subscript b represents the measurement time point.

[0052] In this embodiment, on the transmission line with capacity increase demand, a temperature-sensitive point is selected (i.e., the point where the line sag is the largest under the same temperature rise conditions and the safety distance is first broken), and the surface temperature and ambient temperature of the conductor are measured at a set time interval (generally 1 minute), as shown in Table 1:

[0053] Table 1 Statistics of the surface temperature, ambient temperature, current and temperature rise of the conductor at each point

[0054]

[0055]

[0056]

[0057] The second step is to select a smoothing window; select n consecutive original data in data set A as a processing window for data processing, and smooth the subsequent data windows backward in sequence.

[0058] In this embodiment, at a data sampling interval of 1 minute, n=60 is taken, that is, 1 hour is 1 data window. Generally, within a continuous time of one hour, various climate and environmental factors fluctuate and change little, and have continuity. The rules found by statistical methods are more in line with the actual situation. Subsequent data windows are smoothed backward in sequence in a self-increasing manner of 1. Analyze the correlation between ΔT and I in the data window. Since ΔT and I are not simply linearly correlated, the Spearman rank correlation coefficient is used for calculation (this method can realize the correlation analysis of nonlinear correlation data that does not satisfy the normal distribution). The specific expression is as follows:

[0059]

[0060] Where: ρ is the Spearman rank correlation coefficient; n is the total amount of selected continuous original data, that is, the sample size; d j It is the order difference of the jth pair of sample values, that is, the difference in the positions of the corresponding sample values ​​of the two variables after sorting.

[0061] Further preferably, the rank correlation coefficient ρ takes a value range of [-1,1], where: a positive ρ indicates a positive correlation, and a negative ρ indicates a negative correlation.

[0062] The Spearman rank correlation coefficient for the above data set is calculated as follows:

[0063] ρ is a positive number, indicating that there is a positive correlation between the two.

[0064] The third step is to select the order of the fitting curve according to the absolute value of the rank correlation coefficient obtained in the second step.

[0065] In this embodiment, the absolute value of the rank correlation coefficient ρ represents different correlations, and different orders of fitting coefficients M are used for data with different correlations. When the correlation is high, it means that there is a more obvious regularity between the data. At this time, the correlation trend can be obtained by fitting a curve with a lower order; when the correlation is low, it means that the regularity between the data is not obvious. At this time, more attention should be paid to the instantaneous data changes, so a higher fitting order is selected to achieve the best data fitting. Therefore, the absolute value of the rank correlation coefficient in this embodiment selects the order of the fitting curve as follows: |ρ|>0.95, representing significant correlation; the fitting order is M=2; 0.8≤|ρ|≤0.95, representing high correlation; the fitting order is M=3; 0.5≤|ρ|<0.8, representing moderate correlation; the fitting order is M=4; 0.3≤|ρ|<0.5, representing low correlation; the fitting order is M=5; |ρ|<0.3, representing weak correlation, and the fitting order is M=6.

[0066] In this embodiment, the rank correlation coefficient calculated through the data set is ρ=0.86, which is highly correlated, so the order of the subsequent polynomial fitting is selected as M=3.

[0067] Step 4: Perform regression analysis using polynomial fitting according to the order of the fitting curve obtained in step 3. Details are as follows:

[0068] Polynomial fitting is used to fit ΔT and I. The order M of the fitting is selected according to the calculation. The goal of the fitting is to minimize the error between the target value and the predicted value of each sample.

[0069] For a known data point A = (ΔT, I a,b ), the actual equation of the curve is

[0070] y = f(x);

[0071] The fitting approximate curve equation is as follows:

[0072]

[0073] Where: y is the dependent variable, representing the value of the wire temperature; f(x) represents the function of the dependent variable y corresponding to the independent variable x; m is the fitting order of the polynomial, and M is the maximum value of m; w=[w0,w1,...,w M ] is the set of polynomial coefficients; x is the independent variable, representing the difference between the surface temperature of the wire and the ambient temperature.

[0074] At a certain point i on the curve, the deviation value δ between the approximate curve and the actual curve i for:

[0075]

[0076] Where: y i represents the dependent variable corresponding to a point i on the curve; x i Represents the independent variable corresponding to a point i on the curve;

[0077] In order to achieve the best fitting effect, when the fitting order M is fixed and M≤3 (i.e., M=2 and M=3), the best fitting is achieved by finding the minimum value of the following formula:

[0078]

[0079] When the fitting order M is fixed and M is greater than 3 (i.e., when M=4, M=5, and M=6), in order to avoid overfitting that causes the data prediction to completely deviate from the true regular curve, a regular term is introduced into the above formula, and the square sum of the coefficients of each order is involved in the minimum value calculation to avoid overfitting. In the case of overfitting, the coefficients of each order are too large, resulting in a small change in the independent variable leading to a huge fluctuation in the dependent variable. The expression is as follows:

[0080]

[0081] Wherein: λ is the regularization coefficient. In this embodiment, λ can control the overfitting of the fitting curve and is adjusted according to the actual situation. Here, the preferred value is 0.005.

[0082] Therefore, in this embodiment, the binomial fitting order M=3 calculated based on the data set, so the following formula is selected to solve the coefficients:

[0083]

[0084] The coefficients of the third-order fitting polynomial [w0, = 102.32, w1 = -10.697, w2 = 2.8168, w3 = -0.0899] are calculated, and the fitting equation is:

[0085]

[0086] Step 5: Make predictions based on the fitting approximate curve equation obtained in step 4 and output the prediction results.

[0087] In this embodiment, the prediction based on the fitting approximate curve equation obtained in the fourth step is specifically: sequentially calculate whether the value of the wire temperature exceeds the maximum avoidance temperature T allowed by the wire when the current current is increased by 1.6 times, 1.4 times, and 1.2 times. max ; If the wire temperature exceeds the maximum avoidable temperature T allowed by the wire when the current is increased by 1.6 times, 1.4 times, or 1.2 times max , it means that under the current current carrying capacity, the risk of executing dynamic capacity increase measures is extremely high, and capacity increase operations are not recommended.

[0088] Further preferably, when the current is increased by 1.6 times, the value of the wire temperature is calculated using the following formula:

[0089]

[0090] If it is satisfied, it means that dynamic capacity increase measures are implemented under the current current carrying capacity, and each state parameter is excellent, and there is no risk for the time being; if it is not satisfied, it is necessary to continue to calculate the value of the conductor temperature when the current current is increased by 1.4 times.

[0091] Further preferably, when the current is increased by 1.4 times, the value of the wire temperature is calculated using the following formula:

[0092]

[0093] If it is satisfied, it means that under the current current carrying capacity, there is good potential for dynamic capacity increase measures and the line temperature is controllable; if it is not satisfied, it is necessary to continue to calculate the value of the conductor temperature when the current current is increased by 1.2 times.

[0094] Further preferably, when the current is increased by 1.2 times, the value of the wire temperature is calculated using the following formula:

[0095]

[0096] If it is met, it means that under the current current carrying capacity, there is a certain risk in implementing dynamic capacity increase measures, and there is a risk that the line temperature may exceed the upper limit, which requires special attention.

[0097] According to the data set, the value of the last data point is that the surface temperature of the wire is 32.5℃, the ambient temperature is 25.2℃, and the line current carrying capacity is 133A.

[0098] The maximum allowable surface temperature of the conductor is T max =70℃, the current increases by 1.6 times, and the calculation results are

[0099]

[0100] This formula is satisfied, so the results show that dynamic capacity increase measures are implemented under the current current carrying capacity, and each state parameter is excellent, and there is no risk for the time being.

[0101] In addition, this embodiment further discloses a readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned method for predicting the risk of dynamic capacity increase of high-voltage transmission lines is implemented.

[0102] In addition, this embodiment also discloses an electronic device, including: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to execute the above-mentioned high-voltage transmission line dynamic capacity increase risk prediction method by executing the executable instructions.

[0103] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for predicting the risk of dynamic capacity increase of high-voltage transmission lines, characterized in that: The following steps are involved: The first step is to collect the line temperature data of each point of the corresponding transmission line according to the set frequency. The line temperature data includes the surface temperature T of the conductor. a,b and ambient temperature Record the current carrying capacity I of the transmission line corresponding to each measurement time point a,b ; Forming data set A, Where: subscript a represents the serial number of the point, subscript b represents the measurement time point; Step 2: Select a smoothing window; select n consecutive raw data in data set A as a processing window for data processing, and smooth the subsequent data windows backward in sequence; calculate the rank correlation coefficient of the data by the following formula: Where: ρ is the Spearman rank correlation coefficient; n is the total amount of selected continuous original data, that is, the sample size; d j is the order difference of the jth pair of sample values, that is, the difference in the positions of the corresponding sample values ​​of the two variables after sorting; Step 3: Select the order of the fitting curve according to the absolute value of the rank correlation coefficient obtained in step 2; Step 4: Perform regression analysis using polynomial fitting according to the order of the fitting curve obtained in step 3, and obtain the fitting approximate curve equation based on the actual curve equation: The actual equation of the curve: y = f(x); Fitting approximate curve equation: Where: y is the dependent variable, representing the value of the wire temperature; f(x) represents the function of the dependent variable y corresponding to the independent variable x; m is the fitting order of the polynomial, and M is the maximum value of m; w=[w0,w1,...,w M ] is the coefficient set of the polynomial; x is the independent variable, representing the difference between the surface temperature of the conductor and the ambient temperature; Step 5: Make predictions based on the fitting approximate curve equation obtained in step 4 and output the prediction results.

2. The method for predicting the risk of dynamic capacity increase of high-voltage transmission lines according to claim 1 is characterized in that: In the second step: The rank correlation coefficient ρ ranges from [-1,1], where a positive ρ indicates a positive correlation and a negative ρ indicates a negative correlation.

3. The method for predicting the risk of dynamic capacity increase of high-voltage transmission lines according to claim 1 is characterized in that: In the third step, the order of the fitting curve is selected according to the absolute value of the rank correlation coefficient obtained in the second step as follows: |ρ|>0.95, indicating significant correlation; the fitting order is M=2; 0.8≤|ρ|≤0.95, indicating high correlation; the fitting order is M=3; 0.5≤|ρ|<0.8, indicating moderate correlation; the fitting order is M=4; 0.3≤|ρ|<0.5, indicating low correlation; the fitting order is M=5; |ρ|<0.3, indicating weak correlation, and the fitting order is M=6.

4. The method for predicting the risk of dynamic capacity increase of high-voltage transmission lines according to claim 1 is characterized in that: In the fourth step: At a certain point i on the curve, the deviation value δ between the approximate curve and the actual curve i for: Where: y i represents the dependent variable corresponding to a point i on the curve; x i Represents the independent variable corresponding to a point i on the curve; When the fitting order M is fixed and M≤3, the optimal fit is achieved by finding the minimum value of the following formula: When the fitting order M is fixed and M is greater than 3, a regular term is introduced to calculate the minimum value of the square sum of coefficients of each order. The expression is as follows: Where: λ is the regularization coefficient.

5. The method for predicting the risk of dynamic capacity increase of high-voltage transmission lines according to any one of claims 1 to 4, characterized in that: The prediction in the fifth step based on the fitting approximate curve equation obtained in the fourth step is specifically: sequentially calculating whether the value of the wire temperature exceeds the maximum avoidance temperature T allowed by the wire when the current current is increased by 1.6 times, 1.4 times, and 1.2 times. max ; If the wire temperature exceeds the maximum avoidable temperature T allowed by the wire when the current is increased by 1.6 times, 1.4 times, or 1.2 times max , it means that under the current current carrying capacity, the risk of executing dynamic capacity increase measures is extremely high, and capacity increase operations are not recommended.

6. The method for predicting the risk of dynamic capacity increase of high-voltage transmission lines according to claim 5 is characterized in that: When the current is increased by 1.6 times, the wire temperature is calculated using the following formula: If it is satisfied, it means that dynamic capacity increase measures are implemented under the current current carrying capacity, and each state parameter is excellent, and there is no risk for the time being; if it is not satisfied, it is necessary to continue to calculate the value of the conductor temperature when the current current is increased by 1.4 times.

7. The method for predicting the risk of dynamic capacity increase of high-voltage transmission lines according to claim 6 is characterized in that: When the current is increased by 1.4 times, the wire temperature is calculated using the following formula: If it is satisfied, it means that under the current current carrying capacity, there is good potential for dynamic capacity increase measures and the line temperature is controllable; if it is not satisfied, it is necessary to continue to calculate the value of the conductor temperature when the current current is increased by 1.2 times.

8. The method for predicting the risk of dynamic capacity increase of high-voltage transmission lines according to claim 7 is characterized in that: When the current is increased by 1.2 times, the wire temperature is calculated using the following formula: If it is met, it means that under the current current carrying capacity, there is a certain risk in implementing dynamic capacity increase measures, and there is a risk that the line temperature may exceed the upper limit, which requires special attention.

9. A readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for predicting the risk of dynamic capacity increase of high-voltage transmission lines according to any one of claims 1 to 8 is implemented.

10. An electronic device, characterized in that: include: processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to execute the method for predicting the risk of dynamic capacity increase of high-voltage transmission lines as described in any one of claims 1 to 8 by executing the executable instructions.

Images