CN117895483A - Method, equipment and medium for calculating residual power supply capacity of active overhead distribution line - Google Patents
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Abstract
The invention relates to a method, equipment and medium for calculating residual power supply capacity of an active overhead distribution line, wherein the method comprises the following steps: collecting historical load time series and historical photovoltaic processing time series of an active overhead distribution line in the past year, and conducting wire types of the overhead distribution line and weather forecast data of an area where the active overhead distribution line is located for 24 hours in the future; respectively predicting load active power, photovoltaic power generation active power and wire real-time transmission capacity of 24 hours in the future to obtain a power consumption load prediction result, a photovoltaic power generation power prediction result and a wire real-time transmission capacity prediction result of 24 hours in the future; and calculating the real-time load rate of the wire based on the electricity load prediction result, the photovoltaic power generation power prediction result and the wire real-time conveying capacity prediction result, so as to obtain the current residual power supply capacity of the active overhead distribution line. Compared with the prior art, the method has the advantage of high accuracy.
Description
Technical Field
The invention relates to the field of power systems, in particular to a method, equipment and medium for calculating residual power supply capacity of an active overhead distribution line.
Background
Along with flexible resources such as distributed photovoltaic, energy storage, electric vehicles and demand side response accessing to the power distribution network, the overhead power distribution network is converted from a passive network to a source network charge storage multi-element interactive active power distribution network, and the power supply capacity of the overhead power distribution line is also affected by external environments such as air temperature where the overhead power distribution line is located, and the power transmission capacity of the overhead power distribution line changes dynamically in load demand, distributed photovoltaic output capacity and the like.
Under the access of a distributed power supply, the load of a power line cannot truly reflect the power load, is completely different from the load characteristic of a pure load type in a traditional passive network, and the load of an active power distribution line is the result of balancing the power and the electric quantity under the action of balancing in situ by considering the participation of new energy.
The method for calculating the residual power supply capacity of the active overhead distribution line based on prediction improves the scheduling quota from static current-carrying capacity to dynamic current-carrying capacity through dynamic capacity increase, calculates the dynamic load rate of the line in real time, and further calculates the residual power supply capacity of the line.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a method, equipment and medium for calculating the residual power supply capacity of an active overhead distribution line.
The aim of the invention can be achieved by the following technical scheme:
according to a first aspect of the present invention, there is provided a method for calculating remaining power supply capacity of an active overhead distribution line, the method comprising:
acquiring a historical load time sequence and a historical photovoltaic processing time sequence of an active overhead distribution line in the past year, and conducting wire types of the overhead distribution line and weather forecast data of a future day of an area where the active overhead distribution line is located;
selecting historical data of the first k hours of a time point to be predicted and load active power of the same time of each day in the past 1 year as a sample data set of cluster analysis, and predicting the load active power of the next day by adopting a differential autoregressive moving average model ARIMA to obtain an electricity load prediction result of 24 hours of the next day;
selecting the load active power at the same moment of each day of the past 1 year of the time point to be predicted as a sample data set of clustering analysis, and predicting the photovoltaic power generation active power of the next day by adopting a differential autoregressive moving average model ARIMA to obtain a photovoltaic power generation power prediction result of 24 hours of the next day;
according to the wire type of the overhead distribution line and weather forecast data of a future day of a region where the overhead distribution line is located, adopting a cluster analysis and support vector machine algorithm, and calculating to obtain a wire real-time conveying capacity prediction result of 24 hours of the next day based on a wire real-time conveying capacity calculation model based on a thermal balance theory;
and calculating the real-time load rate of the wire based on the electricity load prediction result, the photovoltaic power generation power prediction result and the wire real-time conveying capacity prediction result, so as to obtain the current residual power supply capacity of the active overhead distribution line.
Preferably, the weather forecast data of the future day of the area where the active overhead distribution line is located comprises 24 hours of air temperature, air pressure, humidity, wind speed and sunlight intensity.
Preferably, for electrical load prediction, the historical data L (t-i) of the first 3 hours of the time t to be predicted and the load active power L (t-j) at the same time of day for the past 1 year are selected as sample data sets for cluster analysis, wherein i=1, 2,3; j=1, 2,3, …,365, and the load active power of the next day is predicted by using a differential autoregressive moving average model ARIMA to obtain the power load prediction result of 24 hours of the next day.
Preferably, for photovoltaic power generation power prediction, the active power G (t-j) of load at the same moment of each day of the past 1 year at the time t to be predicted is selected as a sample data set of cluster analysis, wherein j=1, 2,3, …,365, and the active power of photovoltaic power generation of the next day is predicted by adopting an autoregressive moving average model to obtain a photovoltaic power generation power prediction result G of 24 hours of the next day t 。
Preferably, the differential autoregressive moving average model ARIMA includes an autoregressive term AR, a differential term I, and a moving average term MA.
Preferably, the construction of a real-time wire transmission capacity calculation model based on a heat balance theory, adopts cluster analysis and a support vector machine algorithm to predict the real-time transmission capacity of a wire of the next day according to the wire type of an overhead distribution line and weather forecast data of the future day of a region to obtain a real-time wire transmission capacity prediction result of 24 hours of the next day, and specifically comprises the following substeps:
1) Aiming at a time point to be predicted, collecting data of relevant prediction influence factors, including whole point load, air temperature, air pressure, humidity and sunlight intensity, and forming a historical data set serving as an input sample set;
2) Performing k-means cluster analysis on the meteorological factors in the obtained data set, and selecting a historical data set which is the same as the meteorological factors of the points to be predicted as a training sample data set according to a clustering result;
3) Training and predicting the obtained training sample data set by using a support vector machine algorithm;
4) Based on the predicted meteorological factors, the real-time transmission capacity of the wire is calculated by adopting a real-time transmission capacity calculation model of the wire based on a heat balance theory.
Preferably, the real-time wire conveying capacity is calculated by using a real-time wire conveying capacity calculation model based on a heat balance theory based on the predicted meteorological factors, and the calculation expression is as follows:
R＝K×R 20 (1+α 20 (T c -20))
wherein K is the AC-DC resistance ratio, R 20 Is a direct current resistance, v, of a lead at 20 DEG C f D is the kinematic viscosity of air on the surface of the wire 0 Is the outer diameter of the wire, alpha is the absorptivity of the conductor to solar radiation, alpha 20 The temperature coefficient of resistance when the temperature of the wire is 20 ℃, epsilon is the emissivity of the surface of the wire, the data are parameters of the wire, and the wire is checked according to the model of the wire; s is Stoff-Bao Erci Mann constant; t (T) a At ambient air temperature, T c I is the temperature of the wire surface s Is sunlight intensity; τ is the wire temperature rise, i.e., the difference between the wire temperature and the ambient temperature.
Preferably, the ratio of the absolute value of the difference value between the electricity load prediction result and the photovoltaic power generation prediction result and the real-time transmission capacity prediction result of the wire is used as the real-time load rate of the wire.
According to a second aspect of the present invention there is provided an electronic device comprising a memory and a processor, the memory having stored thereon a computer program, the processor implementing the method of any one of the above when executing the program.
According to a third aspect of the present invention, there is provided a computer readable storage medium having stored thereon a computer program which when executed by a processor implements the method of any one of the above.
Compared with the prior art, the invention has the following beneficial effects:
1) According to the invention, the influence degree of the environment where the overhead distribution line is located on the line power transmission capacity is fully considered, a real-time power transmission capacity calculation model of the lead based on a heat balance theory is established, and the real-time transmission capacity of the lead in the future 24 hours is predicted by adopting 24-hour weather forecast data of weather forecast, lead model and other information.
2) According to the invention, the novel power system lower source load interaction characteristic taking new energy as a main body is fully considered, the overhead line load is decomposed into the electricity load and the distributed power output, the support vector machine method is adopted to predict the actual electricity load and the distributed power output on the line, and different prediction methods are selected according to different influence factors of the electricity load and the distributed power output, so that the prediction result can be ensured to be more in line with the actual situation of the active overhead distribution line, and the prediction accuracy is improved.
Drawings
Fig. 1 is a schematic flow chart of calculation of remaining power supply capacity of an active overhead distribution line in an embodiment;
FIG. 2 is a graph showing a maximum current curve for 2 months of the line load to be calculated in the embodiment;
FIG. 3 is a graph of the line distributed photovoltaic power generation power to be calculated in an embodiment;
FIG. 4 is a 24-hour weather forecast for an area where a line to be calculated is located in an embodiment;
FIG. 5 is a graph showing the real-time power supply capacity, the power load and the output power prediction result of the distributed power supply of the line to be calculated in the embodiment;
fig. 6 is a schematic diagram of a calculation result of the dynamic load rate and the residual power supply capacity of the line to be calculated in the embodiment.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
Examples
The embodiment provides a calculation method for residual power supply capacity of an active overhead distribution line, which utilizes historical power load active power of the overhead distribution line, accessed active power and date, air temperature, wind speed and sunlight intensity of distributed photovoltaic power generation under a normal operation mode to construct a data set, and respectively predicts the power load active power, the distributed photovoltaic power generation active power, net load active power of the line and real-time transmission capacity of a lead for 24 hours in future; predicting the load active power of 24 hours in the future by adopting the load active power of the same time every day in the past 1 year and the load active power of the first 3 hours of the time t to be predicted, so as to obtain a load prediction result of 24 hours in the future; the active power of photovoltaic power generation at the same moment of each day in the past 1 year is adopted to predict the active power of photovoltaic power generation for 24 hours in the future, and a photovoltaic power generation prediction result for 24 hours in the future is obtained; predicting the real-time transmission capacity of the lead in the future 24 hours by adopting the information such as the 24-hour air temperature, the air speed, the sunlight intensity, the lead model and the like of the weather forecast, so as to obtain the prediction result of the real-time transmission capacity of the lead in the future 24 hours; and taking the ratio of the absolute value of the difference value of the load prediction result and the photovoltaic power generation prediction result and the real-time transmission capacity of the lead as the real-time load rate of the lead, and further obtaining the current residual power supply capacity of the lead. Compared with the prior art, the method has the advantages of improving the calculation accuracy and the like.
As shown in fig. 1, the method specifically comprises the following steps:
step S1, collecting the active power of the time-by-time electric load of the line in the past year, and constructing a historical load data time sequence L ij Where i is the date number of one year (1 month 1 day 1,1 month 2 day 2, and so on, max 365), j is the time number of day (1 point 1,2 point 2, and so on, max 24).
Step S2, collecting the time-by-time photovoltaic active power of the line in the past year, and constructing a historical photovoltaic output time sequence G ij Wherein i is a date number in one year, and j is a time number in one day;
and S3, collecting the wire type of the overhead distribution line to be calculated and weather forecast data of the area for 1 day in the future, wherein the weather forecast data comprise 24-hour air temperature, air speed and sunlight intensity.
Step S4, selecting historical data L (t-i) of the first 3 hours of a time t to be predicted and active power L (t-j) of the load at the same time every day in the past 1 year as a sample data set of cluster analysis, wherein i=1, 2 and 3; j=1, 2,3, …,365, and the load active power of the next day is predicted by using a differential autoregressive moving average model ARIMA (p, q) to obtain the electric load prediction result of 24 hours of the next day.
Step S5, selecting the distributed photovoltaic power generation active power G (t-j) at the same moment every day of the past 1 year at the time t to be predicted as a sample data set of clustering analysis, wherein j=1, 2,3, …,365, predicting the photovoltaic power generation active power of the next day by adopting a differential autoregressive moving average model ARIMA (p, q) and using the illumination intensity (W/m) 2 ) As a feature vector (argument) x of the sample, distributed photovoltaic power generation (kW) is taken as an output (argument) y, and a photovoltaic power generation power prediction result of 24 hours on the next day is obtained.
The differential autoregressive moving average model ARIMA comprises an autoregressive term AR, a differential term I and a moving average term MA; ARIMA is a combination of ARMA and differential operation, and the ARMA model is expressed as follows:
in the middle of：X t Representing time-series samples; a, a j Coefficients representing autoregressive terms; x is X t-j A sample value representing the time t-j; b k Coefficients representing the running average term; e, e t-k Representing a white noise sequence;representing an autoregressive term; />Representing a running average term.
Time X of t t The value of (2) represents the sum of the recorded values of p times and q white noise added by a certain weight. p represents the number of history values; q represents the number of white noise and the analysis can be summarized by using ARMA (p, q) to represent the time series model.
And S6, predicting the real-time transmission capacity of the wire in the next day by adopting the information such as the 24-hour air temperature, the air speed, the sunlight intensity, the wire model and the like of the weather forecast, and obtaining a prediction result of the real-time transmission capacity of the wire in the 24-hour in the next day.
The wire transport capacity of the cluster analysis and support vector machine is predicted as follows:
a) Aiming at a time point to be predicted, collecting data of relevant prediction influence factors, including whole point load, air temperature, air pressure, humidity and sunlight intensity, and forming a historical data set serving as an input sample set;
b) Performing k-means cluster analysis on the meteorological factors (air temperature, air pressure and humidity) in the obtained data set, and selecting a historical data set which is the same as the meteorological factors of the points to be predicted as a training sample data set according to a clustering result;
the k-means cluster analysis process is as follows:
by giving the sample dataset x= { X 1 ,x 2 ,…,x m Dividing the sample data set into a plurality of clusters c= { C according to a k-means clustering algorithm 1 ,C 2 ,…,C k The calculation of the minimized square error (E) for each cluster is:
wherein:μ i is cluster C i Is a mean vector of (c). Equation (1) can describe to some extent how tightly each intra-cluster sample is around its mean vector, the smaller the E value, the higher the similarity of the intra-cluster samples.
c) And training and predicting the obtained data set by using a support vector machine algorithm.
The core idea of support vector machine nonlinear regression is that samples are mapped to a high-dimensional feature space, then linear regression is performed in the high-dimensional feature space, and the regression function is:
the function approximation problem is realized by minimizing the following formula:
in the formula, |w| 2 2 represents the smoothness, L ε (x, y-f (x)) is the epsilon insensitive loss function of Vapnik, and measures the risk of the SVM structure.
To find the coefficients w, b, two relaxation variables ζ, ζ are introduced * The above formula can be written as follows:
c (c > 0) is a penalty parameter representing the penalty for misclassification training instances. The lagrange multipliers beta, beta are introduced to obtain a support vector machine regression function:
wherein:
the nonlinear regression prediction of the support vector machine can be realized by controlling two parameters of c and epsilon and a kernel function. Where K (x, xi) is called a kernel function, and a gaussian kernel function is selected to satisfy Mercer conditions:
K(x,x i )＝exp(-||x-x i || 2 /2σ 2 ) (8)
constructing a real-time transmission capacity calculation model of the wire based on a heat balance theory, and calculating to obtain the real-time transmission capacity of the wire by actually measuring current, wire parameters, air temperature, air speed and sunlight intensity data:
wherein:
R＝K×R 20 (1+α 20 (T c -20)) (10)
wherein K is the AC/DC resistance ratio; r is R 20 The resistor is a direct current resistor when the temperature of the lead is 20 ℃; v f The kinematic viscosity of air on the surface of the wire; d (D) 0 (m) is the outer diameter of the wire; alpha is the absorptivity of the conductor to solar radiation, the value is 0.23-0.91, alpha 20 The temperature coefficient of resistance is the temperature coefficient of resistance when the temperature of the lead is 20 ℃; epsilon is the emissivity of the surface of the wire, the value depends on the metal type and the aging and oxidation degree of the metal, the value is 0.23-0.43 in the bright state of the wire, and the value is 0.90-0.95 in the black state of the wire; the data are parameters of the wire, and are obtained by looking up a table according to the wire model. S is Stoff-Bao Erci Mann constant, S has a value of 5.67×10 -8 。T a (DEGC) is the ambient air temperature, T c (DEGC) is the temperature of the wire surface, I s The sunlight intensity is obtained through actual measurement; τ is the wire temperature rise, i.e., the difference between the wire temperature and the ambient temperature.
And S7, taking the ratio of the absolute value of the difference value between the load prediction result of the step S4 and the photovoltaic power generation prediction result of the step S5 and the real-time transmission capacity of the lead of the step S6 as the real-time load rate of the lead.
ρ＝|L t -G t |/W t (11)
And S8, calculating the current residual power supply capacity of the wire according to the real-time wire load rate of the step S7.
Fig. 2 to fig. 4 are respectively a maximum current curve of 2 months, a distributed photovoltaic power generation power curve and 24 hours weather forecast data of a region where the power load of the line to be calculated is located, and fig. 5 and fig. 6 are respectively schematic diagrams of calculation results of real-time power supply capacity, power load and distributed power supply output prediction results, dynamic load rate and residual power supply capacity of the line to be calculated.
Because distributed power sources in the active overhead distribution line can supply power nearby, the electric energy transmission quantity required by a part of load can be counteracted. Therefore, the difference value between the load and the power generation amount of the distributed power supply is the load which is actually required to be transmitted by the active distribution line, namely the net load, and the difference value can obtain the real-time dynamic load rate by dividing the dynamic power transmission capacity. The larger the value, the heavier the load born by the line.
The real-time dynamic load rate index can intuitively and truly reflect the load degree of the active circuit, and a dispatcher can quickly and accurately judge the power supply capacity of the circuit and perform corresponding dispatching work according to the real-time dynamic load rate. The dynamic current-carrying capacity is closer to the real situation, the judgment of the line bearing degree is more accurate, and compared with the static maximum load rate, the index is more accurate.
The electronic device of the present invention includes a Central Processing Unit (CPU) that can perform various appropriate actions and processes according to computer program instructions stored in a Read Only Memory (ROM) or computer program instructions loaded from a storage unit into a Random Access Memory (RAM). In the RAM, various programs and data required for the operation of the device can also be stored. The CPU, ROM and RAM are connected to each other by a bus. An input/output (I/O) interface is also connected to the bus.
A plurality of components in a device are connected to an I/O interface, comprising: an input unit such as a keyboard, a mouse, etc.; an output unit such as various types of displays, speakers, and the like; a storage unit such as a magnetic disk, an optical disk, or the like; and communication units such as network cards, modems, wireless communication transceivers, and the like. The communication unit allows the device to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processing unit performs the respective methods and processes described above, for example, the methods S1 to S8. For example, in some embodiments, methods S1-S8 may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device via the ROM and/or the communication unit. When the computer program is loaded into RAM and executed by the CPU, one or more steps of the methods S1 to S8 described above may be performed. Alternatively, in other embodiments, the CPU may be configured to perform methods S1-S8 by any other suitable means (e.g., by means of firmware).
The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a load programmable logic device (CPLD), etc.
Program code for carrying out methods of the present invention may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
While the invention has been described with reference to certain preferred embodiments, it will be understood by those skilled in the art that various changes and substitutions of equivalents may be made and equivalents will be apparent to those skilled in the art without departing from the scope of the invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (10)
1. The method for calculating the residual power supply capacity of the active overhead distribution line is characterized by comprising the following steps of:
acquiring a historical load time sequence and a historical photovoltaic processing time sequence of an active overhead distribution line in the past year, and conducting wire types of the overhead distribution line and weather forecast data of a future day of an area where the active overhead distribution line is located;
selecting historical data of the first k hours of a time point to be predicted and load active power of the same time of each day in the past 1 year as a sample data set of cluster analysis, and predicting the load active power of the next day by adopting a differential autoregressive moving average model ARIMA to obtain an electricity load prediction result of 24 hours of the next day;
selecting the load active power at the same moment of each day of the past 1 year of the time point to be predicted as a sample data set of clustering analysis, and predicting the photovoltaic power generation active power of the next day by adopting a differential autoregressive moving average model ARIMA to obtain a photovoltaic power generation power prediction result of 24 hours of the next day;
according to the wire type of the overhead distribution line and weather forecast data of a future day of a region where the overhead distribution line is located, adopting a cluster analysis and support vector machine algorithm, and calculating to obtain a wire real-time conveying capacity prediction result of 24 hours of the next day based on a wire real-time conveying capacity calculation model based on a thermal balance theory;
and calculating the real-time load rate of the wire based on the electricity load prediction result, the photovoltaic power generation power prediction result and the wire real-time conveying capacity prediction result, so as to obtain the current residual power supply capacity of the active overhead distribution line.
2. The method for calculating the remaining power supply capacity of an active overhead power distribution line according to claim 1, wherein weather forecast data of a future day of the area where the active overhead power distribution line is located comprises 24 hours of air temperature, air pressure, humidity, wind speed and sunlight intensity.
3. The method for calculating the remaining power supply capacity of an active overhead distribution line according to claim 2, wherein for electric load prediction, historical data L (t-i) of the first 3 hours of a time t to be predicted and active power L (t-j) of the load at the same time every day of the past 1 year are selected as sample data sets of cluster analysis, wherein i=1, 2,3; j=1, 2,3, …,365, and the load active power of the next day is predicted by using a differential autoregressive moving average model ARIMA to obtain the power load prediction result of 24 hours of the next day.
4. An active overhead distribution line residual power capability calculation as claimed in claim 2The method is characterized in that for photovoltaic power generation power prediction, load active power G (t-j) at the same moment of day of the past 1 year at a time t to be predicted is selected as a sample data set of cluster analysis, wherein j=1, 2,3, … and 365, and an autoregressive moving average model is adopted to predict the photovoltaic power generation active power of the next day to obtain a photovoltaic power generation power prediction result G of 24 hours of the next day t 。
5. A method of calculating the remaining power supply capacity of an active overhead distribution line according to claim 3 or 4, wherein the differential autoregressive moving average model ARIMA includes an autoregressive term AR, a differential term I, and a moving average term MA.
6. The method for calculating the remaining power supply capacity of the active overhead distribution line according to claim 2, wherein the constructing of the wire real-time power transmission capacity calculation model based on the heat balance theory predicts the wire real-time transmission capacity of the next day by adopting a cluster analysis and support vector machine algorithm according to the wire model of the overhead distribution line and weather forecast data of the future day of the area, and obtains the wire real-time power transmission capacity prediction result of 24 hours of the next day, and specifically comprises the following substeps:
1) Aiming at a time point to be predicted, collecting data of relevant prediction influence factors, including whole point load, air temperature, air pressure, humidity and sunlight intensity, and forming a historical data set serving as an input sample set;
2) Performing k-means cluster analysis on the meteorological factors in the obtained data set, and selecting a historical data set which is the same as the meteorological factors of the points to be predicted as a training sample data set according to a clustering result;
3) Training and predicting the obtained training sample data set by using a support vector machine algorithm;
4) Based on the predicted meteorological factors, the real-time transmission capacity of the wire is calculated by adopting a real-time transmission capacity calculation model of the wire based on a heat balance theory.
7. The method for calculating the remaining power supply capacity of an active overhead distribution line according to claim 6, wherein the real-time wire real-time power supply capacity is calculated by using a real-time wire power supply capacity calculation model based on a thermal balance theory based on a predicted meteorological factor, and the calculation expression is as follows:
R＝K×R 20 (1+α 20 (T c -20))
wherein K is the AC-DC resistance ratio, R 20 Is a direct current resistance, v, of a lead at 20 DEG C f D is the kinematic viscosity of air on the surface of the wire 0 Is the outer diameter of the wire, alpha is the absorptivity of the conductor to solar radiation, alpha 20 The temperature coefficient of resistance when the temperature of the wire is 20 ℃, epsilon is the emissivity of the surface of the wire, the data are parameters of the wire, and the wire is checked according to the model of the wire; s is Stoff-Bao Erci Mann constant; t (T) a At ambient air temperature, T c I is the temperature of the wire surface s Is sunlight intensity; τ is the wire temperature rise, i.e., the difference between the wire temperature and the ambient temperature.
8. The method for calculating the residual power supply capacity of the active overhead distribution line according to claim 1, wherein the ratio of the absolute value of the difference between the power load prediction result and the photovoltaic power generation prediction result to the real-time transmission capacity prediction result of the wire is used as the real-time load rate of the wire.
9. An electronic device comprising a memory and a processor, the memory having stored thereon a computer program, characterized in that the processor, when executing the program, implements the method according to any of claims 1-8.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method according to any one of claims 1-8.
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