A probabilistic prediction method for dynamic capacity increase of overhead transmission lines

Abstract

The present invention discloses a probabilistic prediction method for dynamic capacity increase of overhead transmission lines, which is used to improve the power transmission capacity of existing transmission lines. Based on the heat balance equation considering the rainfall cooling effect, the present invention quantitatively analyzes the influence of various meteorological factors on the heat capacity of transmission lines, selects the most important meteorological factors for probabilistic prediction of dynamic capacity increase; establishes a spatio-temporal regression prediction model of the main meteorological factors according to all meteorological data sources, and obtains the expected value and standard deviation of the hourly prediction of each meteorological factor one day in advance; based on the heat balance equation and Taylor series expansion, first obtains the expected value and standard deviation of the heat capacity prediction of each span of the transmission line, and then obtains the distribution of the heat capacity of the final whole line and the corresponding percentile values based on the multivariate truncated Gaussian distribution, providing a high-confidence hourly dynamic capacity increase prediction value one day in advance for the safe and stable operation of the power transmission system, and having a unique advantage of larger capacity and higher safety compared with the existing methods, and having good engineering practical value.

Description

Technical Field

[0001] The present invention provides a probabilistic prediction method for dynamic capacity increase of overhead transmission lines, belonging to the technical field of power system automation. Background Art

[0002] Many wind and solar power stations located in remote areas require transmission lines to transmit electricity to the consumption areas. At present, static transmission line ratings with relatively conservative values are generally adopted, which limits the power transmission capacity of existing lines and may lead to transmission line congestion. Building new transmission lines not only consumes a large amount of resources but also has a negative impact on the environment. The dynamic capacity increase technology of transmission lines adjusts the transmission capacity of transmission lines in a timely manner according to the measured or predicted meteorological conditions, usually providing a higher transmission capacity without affecting the safety of the system. High-precision dynamic capacity increase prediction can be combined with problems such as unit commitment and power flow analysis in the day-ahead market of the power system to improve economic benefits. Therefore, it is of great significance to conduct probabilistic dynamic capacity increase prediction of transmission lines with a high percentage value or confidence level. In the past few decades, many research progresses have been made in the field of dynamic capacity increase technology of transmission lines. However, the uncertainty of meteorological prediction and the multi-span topological structure of long-distance transmission lines increase the complexity of dynamic capacity increase, often resulting in the estimated dynamic transmission capacity exceeding the actual thermal capacity of the line and causing power system insecurity and thermal overload. Summary of the Invention

[0003] The present invention provides a probabilistic prediction method for dynamic capacity increase of overhead transmission lines, which can effectively solve the key problems in the background art. Based on the influence level analysis, the main meteorological factors affecting the thermal capacity of transmission lines are selected, and a spatio-temporal regression prediction model for meteorological prediction based on multiple meteorological data is established. By modeling the line rating as a random variable, that is, the minimum value of the thermal capacity of the selected span, the problems of spatial topology and meteorological uncertainty of transmission lines are solved. Then, the distribution of dynamic transmission capacity and the corresponding percentile values are obtained with low computational requirements and high modeling accuracy. The numerical test results of short-distance and long-distance transmission lines consistently show that this method provides a higher and safer rated transmission capacity for overhead transmission lines, and is expected to bring considerable economic benefits to grid-related enterprises.

[0004] To achieve the above object, the present invention adopts the following technical solutions:

[0005] A probabilistic prediction method for dynamic capacity increase of overhead transmission lines, comprising the following steps:

[0006] Step 1: Based on the heat balance equation considering the rainfall cooling effect, use the influence level method to quantitatively analyze the influence of each meteorological factor on the transmission capacity of the transmission line, and select the main meteorological factors for dynamic capacity increase prediction, including ambient temperature, wind speed, wind direction angle, solar radiation heat density, and precipitation rate;

[0007] Step 2: Based on the main meteorological factors determined in Step 1, collect and obtain the historical measurement values and hourly prediction values of each meteorological factor through the meteorological sensors installed on the transmission line and the meteorological stations near the line;

[0008] Step 3: Based on the meteorological factor data obtained in Step 2, establish a spatio-temporal regression prediction model of meteorological factors, and calculate the expected value and standard deviation of the hourly prediction data of each meteorological factor at each span position of the transmission line;

[0009] Step 4: Based on the predicted values of the main meteorological factors obtained in Step 3, calculate the hourly prediction expected value and standard deviation of the heat capacity of each span of the transmission line through the heat balance equation and Taylor series expansion;

[0010] Step 5: Based on the predicted expected value and standard deviation of the heat capacity of each span obtained in Step 4, considering the transmission line topology and multi-variable truncated Gaussian distribution, calculate the distribution of the predicted heat capacity of the entire line and the corresponding high confidence percentile values.

[0011] As a further preferred solution, the specific steps in Step 1 are as follows:

[0012] 1) The calculation formula of the wire heat balance equation considering the rainfall cooling effect is:

[0013]

[0014] where I 2 R(T c ) is the Joule heat generated by the current-carrying capacity I, the resistance R is a function of the wire temperature T c , the heat absorbed by the wire from solar radiation Q s is calculated according to the solar radiation heat density Q se , the heat dissipated by the wire through radiation Q r is determined by the wire temperature T c and the ambient temperature T a , the heat dissipated by air convection Q c is a function of T c , T a , wind speed V, and wind direction angle φ, the heat dissipated by rainfall, snowfall, and evaporation Q e is a function of the precipitation rate P r , relative humidity RH, and atmospheric pressure Pa, MC p is the heat capacity of the wire;

[0015] Calculate the thermal capacity of the transmission line under stable conditions, i.e., assume that T c reaches equilibrium and dT c / dt is zero. Through the meteorological data (Q se , T a , V, φ, P r , RH, P a ) of a given span and the maximum allowable temperature of the conductor The maximum allowable current I max The calculation formula is:

[0016]

[0017] 2) Quantitatively analyze the influence level of various meteorological factors on the transmission capacity of the span transmission line, which is defined as the relative percentage change of the maximum transmission capacity and the minimum transmission capacity of the overhead line when a meteorological factor changes. The calculation formula is:

[0018]

[0019] Select the meteorological factors with more significant influence levels and substitute them into the heat balance equation for the next step of prediction.

[0020] As a further optimization scheme, the calculation formula of the spatio-temporal regression prediction model in step three is:

[0021]

[0022] Among them, the exponent m represents the meteorological factors considered, including temperature, adjusted wind speed, wind angle, solar radiation intensity, and precipitation rate. The exponent k represents the selected span position, β represents the synergy coefficient, and ε is the residual;

[0023] To determine the meteorological factor w k,m (t) of the target span k at time t, regard the historical measurement values w k,m (t - 24) of the three adjacent weather stations n1(1, 2, 3) as part of the explanatory variables in the regression model; is the predicted value or interpolation of the meteorological factor of the target span k at time t. To better capture the spatial characteristics and achieve prediction where there are no measurement points, a set of geographical covariates s n2 is used for prediction, including 4 explanatory variables of n2(1, 2, 3, 4), namely latitude, longitude, distance to the valley, and distance to the coast;

[0024] In the spatio-temporal regression prediction model, the wind speed at the nearby weather station The measurement point height h0 often does not match the tower height of the transmission line. The wind speed at the tower height h is calculated as:

[0025]

[0026] Among them, the parameter a is obtained by fitting historical wind speed data;

[0027] In the spatio-temporal regression prediction model, if the meteorological prediction value of the target location cannot be obtained from the meteorological monitoring equipment Then the inverse distance squared weighting method is used to interpolate the prediction data of nearby meteorological stations. The calculation formula is:

[0028]

[0029] where N is the number of meteorological stations considered, w n,m is the prediction data of the mth meteorological factor of the nth nearby meteorological station, and d n,k is the distance between the nth nearby meteorological station and the kth location;

[0030] The coefficients in the spatio-temporal regression prediction model can be calculated by least squares estimation; when new data is input into the model, the expected values and standard deviations of the predictions of each meteorological factor can be directly calculated.

[0031] As a further preferred solution, the span heat capacity I in step four k is assumed to be a truncated Gaussian distribution, expressed as where μ k is the mean value, σ k is the standard deviation, and the parameters a k and b k are the upper and lower limits determined in extreme cases; based on the first-order expansion of the Taylor series at the expected value point of the meteorological factor prediction k to obtain an approximate expression of the heat capacity I at span k, the calculation formula is: to obtain an approximate expression of the heat capacity I at span k, the calculation formula is: k to obtain an approximate expression of the heat capacity I at span k, the calculation formula is:

[0032]

[0033] where is the derivative of the function f, and R k is the remainder term;

[0034] Furthermore, an approximate value of the expected span E(I k ) is obtained, and the calculation formula is:

[0035]

[0036] The approximate value calculation formula of the span standard deviation Var(I k ) is:

[0037]

[0038] where Var(wm,k ) is the variance of the meteorological factor m at span k obtained from the spatio-temporal meteorological regression prediction model, Cov(w i,k , w j,k ) represents the covariance of two meteorological factors.

[0039] As a further preferred solution, for the entire transmission line considering multiple spans, the thermal capacity rating of power transmission is determined by the thermal capacity of the critical span, and the calculation formula is:

[0040]

[0041] where I k is the thermal capacity at span k, Y is the thermal capacity of the entire overhead transmission line, and K is the total number of spans considered; the distribution function F Y of the thermal capacity of the entire overhead transmission line is calculated as follows:

[0042]

[0043] where is the cumulative density function of the truncated Gaussian distribution, and the remaining terms are the cumulative density functions of the joint distribution of truncated Gaussian variables;

[0044] The percentile value (p) of the thermal capacity prediction of the transmission line is calculated as:

[0045]

[0046] Beneficial effects

[0047] Based on the quantitative analysis of the influence of each meteorological factor on the thermal capacity of the transmission line, the present invention selects the most important meteorological factors for dynamic capacity increase probabilistic prediction. A spatio-temporal regression prediction model of meteorological factors is established based on multiple meteorological data sources, and the meteorological prediction values and standard deviations considering the spatio-temporal characteristics of the transmission line are calculated. Based on the heat balance equation and Taylor series expansion, the expected values and standard deviations of the thermal capacity prediction data for each span of the transmission line can be obtained. By modeling the line rating as a random variable, that is, the minimum value of the selected span thermal capacity, the distribution of the thermal capacity of the entire transmission line and the corresponding percentile are obtained based on the multivariate truncated Gaussian distribution, and at the same time, the important problems of the spatial topology of the transmission line and the uncertainty of meteorological factors are solved. The distribution of the dynamic transmission capacity and the corresponding high-confidence percentile values are calculated with low computational requirements and high modeling accuracy. The numerical test results of short-distance and long-distance transmission lines show that this method provides higher and safer thermal capacity prediction values for overhead transmission lines, has unique advantages compared with other methods, provides guarantee for the safe and stable operation of the power transmission system, has good engineering practical value, and is expected to bring significant economic benefits to power transmission enterprises. Description of the drawings

[0048] Figure 1 Flow chart of dynamic capacity increase prediction for overhead transmission lines;

[0049] Figure 2 Influence of each meteorological factor on the transmission capacity of the span;

[0050] Figure 3 Meteorological data sources related to overhead transmission lines;

[0051] Figure 4 Percentile prediction result chart of the experimental transmission line. Specific implementation manner

[0052] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present invention.

[0053] Step 1: Based on the heat balance equation considering the rainfall cooling effect, use the influence level method to quantitatively analyze the influence of each meteorological factor on the transmission capacity of the transmission line, and select the most important meteorological factors for dynamic capacity increase prediction, including ambient temperature, wind speed, wind direction angle, solar radiation heat density, and precipitation rate;

[0054] Step 2: Based on the main meteorological factors determined in Step 1, collect the historical measurement values and hourly prediction values of each meteorological factor obtained through the meteorological sensors installed on the transmission line and the meteorological stations near the line;

[0055] Step 3: Use the multiple meteorological data sources obtained in Step 2 to establish a spatio-temporal regression prediction model of meteorological factors, and obtain the expected value and standard deviation of the hourly prediction of each meteorological factor at each span position of the transmission line;

[0056] Step 4: Based on the predicted values of the main meteorological factors obtained in Step 3, calculate the expected value and standard deviation of the hourly prediction of the heat capacity of each span of the transmission line through the heat balance equation and Taylor series expansion;

[0057] Step 5: Based on the expected value and standard deviation of the heat capacity prediction of each span obtained in Step 4, obtain the distribution of the transmission heat capacity prediction of the entire line and the corresponding high-confidence percentile values by considering the transmission line topology and multi-variable truncated Gaussian distribution.

[0058] The specific steps in Step 1 are as follows:

[0059] 1) The calculation formula of the wire heat balance equation considering the rainfall cooling effect is:

[0060]

[0061] Where I2 R(T c ) is the Joule heat generated by the current-carrying capacity I, and the resistance R is a function of the wire temperature T c The wire absorbs the solar radiation heat Q s According to the solar radiation heat density Q se Calculate, the wire radiation heat dissipation Q r Is determined by the wire temperature T c And the ambient temperature T a Decided, the air convection heat dissipation Q c Is T c 、T a 、The function of the wind speed V and the wind direction angle φ, the rainfall and snow evaporation heat dissipation Q e Is the precipitation rate P r 、The function of the relative humidity RH and the air pressure Pa, MC p Is the heat capacity of the wire;

[0062] Under stable conditions, calculate the heat capacity of the transmission line, that is, assume that T c Reaches equilibrium, dT c / dt is zero, through the meteorological data (Q se ,T a ,V, φ,P r ,RH,P a ) and the maximum allowable temperature T of the wire c max , the maximum allowable current I max The calculation formula is:

[0063]

[0064] 2) Quantitatively analyze the influence level of various meteorological factors on the transmission capacity of the span transmission line, and define it as the relative percentage change of the maximum transmission capacity and the minimum transmission capacity of the overhead line when a meteorological factor changes. The calculation formula is:

[0065]

[0066] The change relationship between each meteorological factor and the heat capacity of the transmission line is as Figure 2As shown. When analyzing a meteorological factor, the rest are fixed at the static capacity calculation reference values of the transmission line shown in Table 1. Among all the given meteorological factors, wind speed is the meteorological factor with the greatest impact. Therefore, Table 2 calculates the influence levels of other meteorological factors on the transmission line capacity under five different wind speeds. It can be seen that the wind direction angle, ambient temperature, solar radiation, and precipitation rate are the main factors affecting the thermal capacity of the transmission line. The two factors of relative humidity and atmospheric pressure, which have less impact on the thermal capacity of the transmission line, can take fixed reference values, and the uncertainties of these factors will not be considered in the dynamic capacity increase system. Thus, the meteorological factors with more significant influence levels are selected and brought into the heat balance equation for the next prediction.

[0067] Table 1. Reference Values of Each Meteorological Factor

[0068]

[0069] Table 2. Influence Levels of Each Meteorological Factor on the Thermal Capacity Calculation of the Transmission Line (%)

[0070]

[0071] In Step 2, through the meteorological sensors installed on the transmission line and the meteorological stations near the line as shown in Figure 3 , the data of each meteorological factor are collected and obtained.

[0072] The calculation formula of the spatio-temporal regression prediction model in Step 3 is:

[0073]

[0074] Among them, the exponent m represents the meteorological factors considered, including temperature, adjusted wind speed, wind angle, solar radiation intensity, and precipitation rate, the exponent k represents the selected span position, β represents the synergy coefficient, and ε is the residual;

[0075] To determine the meteorological factor w k,m (t) of the target span k at time t, the historical measurement values w k,m (t - 24) of the three adjacent meteorological stations n1(1, 2, 3) are regarded as part of the explanatory variables in the regression model; is the predicted value or interpolation of the meteorological factor of the target span k at time t; In order to better capture the spatial characteristics and achieve predictions in places without measurement points, a set of geographical covariates s n2 are used, including 4 explanatory variables of n2(1, 2, 3, 4), namely latitude, longitude, distance to the valley, and distance to the coast;

[0076] In the spatio-temporal regression prediction model, the wind speed of the nearby meteorological station The height h0 of the measurement point often does not match the height of the transmission line tower. The wind speed calculation formula at the tower height h is:

[0077]

[0078] Among them, the parameter a is obtained by fitting historical wind speed data;

[0079] In the spatio-temporal regression prediction model, if the meteorological prediction value of the target location cannot be obtained from the meteorological monitoring equipment Then the inverse distance squared weighting method is used to interpolate the data of nearby meteorological stations. The calculation formula is:

[0080]

[0081] Among them, N is the number of meteorological stations considered, w n,m is the predicted data of the m-th meteorological factor at the n-th nearby meteorological station, d n,k is the distance between the n-th nearby meteorological station and the k-th location;

[0082] The coefficients in the spatio-temporal regression prediction model can be calculated by least squares estimation. When new input data arrives, the expected value and standard deviation of the prediction of each meteorological factor can be directly obtained.

[0083] In step four, the span heat capacity I k is assumed to be a truncated Gaussian distribution, expressed as Among them, μ k is the mean value, σ k is the standard deviation, and the parameters a k and b k are the upper and lower limits determined in the extreme case; based on the first-order expansion of the Taylor series at the expected value point of the meteorological factor prediction k the approximate expression of the heat capacity I at the span k is obtained. The calculation formula is: The approximate expression of the heat capacity I at the span k is obtained. The calculation formula is: k The calculation formula is:

[0084]

[0085] Among them is the derivative of the function f, and R k is the remainder term.

[0086] The partial derivative of the convective cooling effect Q c cannot be directly calculated because Q c involves a max function:

[0087] Q C = max(q c1 , q c2 )

[0088] To calculate the partial derivative, the maximum function is rewritten in the form of the absolute value function as:

[0089]

[0090] Then, for Q c After taking the derivative, we can obtain:

[0091]

[0092] Furthermore, the approximate value calculation formula for obtaining the expected value E(I k ) of the span is:

[0093]

[0094] The approximate value calculation formula for the span standard deviation Var(I k ) is:

[0095]

[0096] Where Var(w m,k ) is the variance of the weather factor m at span k obtained from the spatio-temporal regression model. Since the weather factors can be regarded as independent random variables, the covariance Cov(w i,k , w j,k ) between every two weather factors is zero.

[0097] In step five, considering the entire transmission line with multiple spans, the thermal capacity rating of power transmission is determined by the thermal capacity of the most critical span, and the calculation formula is:

[0098]

[0099] Where I k is the thermal capacity at span k, Y is the thermal capacity of the entire overhead transmission line, and K is the number of spans considered. The distribution function F Y of the thermal capacity of the entire overhead transmission line is calculated as:

[0100]

[0101] Where, is the cumulative density function of the truncated Gaussian distribution, and the calculation formula is:

[0102]

[0103] Where Φ is the cumulative density function of the standard Gaussian distribution.

[0104] F Y The remaining terms of are the cumulative density function of the joint distribution of truncated Gaussian variables. To reduce the computational complexity, only the correlation between the thermal capacities of continuous spans is considered, and the calculation formula is:

[0105]

[0106]

[0107] As described above, the cumulative density function of the transmission line heat capacity prediction is calculated. Furthermore, the calculation formula for the percentile value (p) of the transmission line heat capacity prediction is obtained as follows:

[0108]

[0109] Generally, low percentages, such as the predicted values of the transmission line heat capacity at the 2nd, 1st, and 0.1st percentiles, are used as safety limits, which means that the probability that the actual transmission line heat capacity does not exceed the predicted value is 98%, 99%, and 99.9%.

[0110] In the present invention, a spatio-temporal meteorological prediction model and a multi-span transmission line capacity probability model are adopted to predict dynamic capacity increase. Based on the heat balance equation considering the rainfall cooling effect, the influence of each meteorological factor on the transmission line heat capacity is quantitatively analyzed, and the main meteorological factors are selected for probabilistic prediction of dynamic capacity increase; based on various meteorological data, a spatio-temporal regression prediction model of the main meteorological factors is established to obtain the expected value and standard deviation of the prediction of each meteorological factor per hour on the day before; based on the heat balance equation and Taylor series expansion, first, the expected value and standard deviation of the heat capacity prediction of each span of the transmission line are obtained, and then based on the multivariate truncated Gaussian distribution, the distribution of the heat capacity of the entire line and the corresponding percentile values are obtained, providing a high-confidence prediction value of the dynamic capacity increase per hour on the day before for the safe and stable operation of the power transmission system. The numerical experimental results show that the present invention has high prediction accuracy and calculation efficiency. For example, in the experiment, the present invention is applied to a 230 kV transmission line between a substation and a power plant in Colorado, USA. This line consists of 6 sections and a total of 68 spans. As Figure 4 shown, the predicted value of the transmission capacity at the 2nd percentile is higher than the currently used static transmission capacity and does not exceed the transmission capacity that the wire can actually bear. Obviously, the present invention can provide accurate, effective, safe, and high-quality predicted values of dynamic capacity increase for transmission lines.

[0111] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, makes equivalent substitutions or changes, and all should be covered within the protection scope of the present invention.

Claims

1. A probabilistic prediction method for dynamic capacity increase of overhead transmission lines, characterized in that: It includes the following steps: Step 1: Based on the heat balance equation considering the rainfall cooling effect, define the influence level index to quantitatively analyze the influence of each meteorological factor on the transmission capacity of the transmission line, and select the main meteorological factors for dynamic capacity increase prediction, including ambient temperature, wind speed, wind direction angle, solar radiation heat density, and precipitation rate; Step 2: Based on the main meteorological factors determined in Step 1, collect and obtain the historical measured values and hourly predicted values of each meteorological factor through the meteorological sensors installed on the transmission line and the meteorological stations near the line; Step 3: Based on the meteorological factor data obtained in Step 2, establish a spatio-temporal regression prediction model of meteorological factors, and calculate the expected value and standard deviation of the hourly predicted data of each meteorological factor at each span position of the transmission line; The calculation formula of the spatio-temporal regression prediction model is: Among them, the exponent m represents the meteorological factors considered, including temperature, adjusted wind speed, wind angle, solar radiation intensity, and precipitation rate, the exponent k represents the selected span position, β k,m,0 represents the intercept of the prediction model, β k,m,p β k,m,4 β k,m,4+q represents the coefficient of the corresponding dependent variable factor w k,m,p (t - 24), s q and ε is the residual; To determine the meteorological factors w of the target span k at time t k,m (t), the measured values of the meteorological factors w of the span k provided by the adjacent meteorological station p at the moment one day before the prediction date, i.e., at time t - 24 k,m，n1 (t - 24) are regarded as part of the explanatory variables in the regression model; is the predicted value or interpolation of the meteorological factors provided by the meteorological station for the target span k at time t; In order to better capture the spatial characteristics and achieve predictions in places where there are no measurement points, a set of geographical variables s representing space q are used for prediction, including 4 explanatory variables, namely the latitude and longitude of the span k, the distance from the span to the valley, and the distance to the coast; In the spatio-temporal regression prediction model, the wind speed of nearby weather stations The height h0 of the measurement point is inconsistent with the height of the transmission line tower. The wind speed calculation formula at the tower height h is: Among them, the parameter a is obtained by fitting the historical wind speed data; In the spatio-temporal regression prediction model, if the meteorological prediction value of the target location cannot be obtained from the meteorological monitoring equipment then the inverse distance squared weighting method is used to interpolate the prediction data of nearby meteorological stations. The calculation formula is as follows: where N is the number of weather stations considered, w n,m is the predicted data of the m-th meteorological factor of the n-th nearby weather station, d n,k is the distance between the n-th nearby weather station and the k-th location; Coefficients β in the spatio-temporal regression prediction model k,m,0 , β k,m,p β k,m,4 β k,m,4+q Are obtained by least squares estimation using the historical dataset. At the same time, the variance and covariance of β are obtained. When new dependent variable data is input into the model, the expected values of the independent variable's meteorological factors are calculated from the β values, and the standard deviations of the predicted values of each meteorological factor are calculated from the variance and covariance of β; Step 4: Based on the predicted values of the main meteorological factors obtained in Step 3, calculate the hourly predicted expected value and standard deviation of the heat capacity of each span of the transmission line through the heat balance equation and Taylor series expansion; Span heat capacity I k Assumed to be a truncated Gaussian distribution, expressed as where μ k is the mean value, σ k is the standard deviation, and the parameters a k and b k are the upper and lower limits determined in the extreme cases of I k ; based on the Taylor series, a first-order expansion is performed at the expected value point of meteorological factors to obtain an approximate expression for the heat capacity I at span k, and the calculation formula is: k ​ where is the derivative of the function f, and f(θ k ) is the value of the function f at the expected value point θ k , and R k is the residual term; Furthermore, an approximate value of the expected span E(I k ) is obtained, and the calculation formula is as follows: Approximate value calculation formula for the standard deviation of span Var(I k ) is as follows: where Var(w m,k ) is the variance of the meteorological factor m at the span k obtained from the spatio-temporal meteorological regression prediction model, and Cov(w i,k , w j,k ) represents the covariance of the two meteorological factors; Step 5: Based on the predicted expected value and standard deviation of the heat capacity of each span obtained in Step 4, use the multivariate truncated Gaussian distribution to calculate the distribution of the predicted heat capacity of the entire transmission line and the percentile values at corresponding confidence levels; For the entire transmission line considering multiple spans in Step 5, the rated value of the transmission heat capacity is determined by the critical span heat capacity, and the calculation formula is: Among them, I k is the heat capacity at span k, Y is the heat capacity of the entire overhead transmission line, and K is the total number of spans considered; the distribution function F Y (y) of the heat capacity of the entire overhead transmission line is calculated as follows: where P() represents the probability of an event occurring, is the cumulative density function of the truncated Gaussian distribution, and the remaining terms are the cumulative density functions of the joint distribution of truncated Gaussian variables; The predicted value y of the transmission line heat capacity prediction at the p-th percentile p The calculation formula for p is as follows: Indicates that there is a probability of p% that the thermal capacity of the entire overhead transmission line is less than y p .

2. The probabilistic prediction method for dynamic capacity increase of overhead transmission lines according to claim 1, characterized in that: The specific steps in Step 1 are as follows: 1) The calculation formula of the conductor heat balance equation considering the rainfall cooling effect is: Among which I 2 R(T c ) is the Joule heat generated by the current-carrying capacity I, and the resistance R is a function of the wire temperature T c The heat absorbed by the wire from solar radiation is Q s Calculated according to the solar radiation heat density Q se The heat dissipated by the wire through radiation is Q r Determined by the wire temperature T c and the ambient temperature T a The heat dissipated by air convection is Q c is a function of T c , T a , the wind speed V and the wind direction angle φ. The heat dissipated by rainfall, snowfall and evaporation is Q e is a function of the precipitation rate P r , the relative humidity RH and the atmospheric pressure P a MC p is the heat capacity of the wire; Calculate the thermal capacity of the transmission line under steady conditions, i.e., assume that T c reaches equilibrium and dT c / dt is zero. Through the meteorological data (Q se , T a , V, φ, P r , RH, P a ) of a given span and the maximum allowable temperature of the conductor The maximum allowable current I max The calculation formula is as follows: 2) Quantitatively analyze the influence level of various meteorological factors on the transmission capacity of the span transmission line, and define it as the relative percentage change of the maximum transmission capacity and the minimum transmission capacity of the overhead line when a meteorological factor changes. The calculation formula is: Select the meteorological factors with an influence level greater than the set threshold and substitute them into the heat balance equation for the next prediction.

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