Shaguo desert power transmission line capacity increasing method and system based on dynamic line rating value

Abstract

The application belongs to the field of power transmission line capacity increasing, and provides a Shaguo desert power transmission line capacity increasing method and system based on dynamic line rated value, which comprises the following steps: selecting key environmental parameters under extreme working conditions based on historical meteorological data, calculating the static rated value based on the key environmental parameters under the extreme working conditions by using a pre-constructed steady-state heat balance calculation model; calculating the conductor temperature change under real-time key environmental parameters based on a pre-constructed transient heat balance calculation model, and determining the dynamic rated value at any time step according to the conductor temperature change; and determining the capacity increasing margin at the current time step by using the dynamic rated value and the static rated value. The application particularly aims at the extreme climate conditions in the Shaguo desert area, and proposes adaptive correction suggestions for the factors such as sand dust adhesion, low humidity and high altitude which are not fully considered in the IEEE standard model, thereby improving the accuracy and practicability of the model under special environments.

Description

Technical Field

[0001] This invention belongs to the field of power transmission line capacity expansion technology, specifically relating to a method and system for expanding the capacity of the Shagohuang power transmission line based on dynamic line ratings. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] In existing technologies, increasing the capacity of transmission lines is a key technological direction for improving the power grid's transmission capacity and optimizing operational efficiency. Its core lies in scientifically assessing and fully utilizing the maximum allowable current carrying capacity of lines under specific environmental conditions. Traditional line capacity assessment mainly relies on the Static Line Rating (SLR) method. This method sets fixed limits based on extremely conservative meteorological parameters (such as maximum ambient temperature, minimum wind speed, and strongest solar radiation) to ensure the safe operation of lines under the worst conditions. However, the SLR method ignores the dynamic changes in meteorological conditions during actual operation, leading to a significant underestimation of the transmission capacity of lines under most normal operating conditions, resulting in substantial idleness and waste of transmission resources.

[0004] With the continuous expansion of the power grid and the large-scale integration of renewable energy, especially the construction of wind and solar power bases in the desert regions, the contradiction between the demand for power transmission and the transmission capacity of power lines is becoming increasingly prominent. To tap the potential of existing power lines, Dynamic Line Rating (DLR) technology has emerged. DLR dynamically calculates the maximum allowable current of the line under current meteorological conditions by real-time monitoring of environmental parameters (such as wind speed, ambient temperature, and solar radiation intensity) and combining this with a thermal balance model, thus achieving a balance between safety and efficiency. This method can significantly improve line utilization, and it has broad application prospects, especially in desert regions with abundant wind resources and large diurnal temperature variations.

[0005] However, the practical application of DLR technology in the desert region still faces many challenges. On the one hand, the extreme climatic conditions in this region (such as strong winds and sandstorms, large temperature differences, high altitude, and low humidity) place higher demands on the reliability of monitoring equipment, the condition of conductor surfaces, and the accuracy of heat dissipation models. On the other hand, the heat balance models used in existing international standards (such as IEEE 738-2023) are based on temperate climate conditions and do not fully consider the unique meteorological and geographical characteristics of the desert region, which may lead to deviations in current carrying capacity calculations. For example, strong winds and sandstorms can easily cause contamination of conductor surfaces, altering their absorptivity and emissivity, thus affecting the accuracy of solar radiation heat absorption and radiative heat dissipation calculations. Low humidity and high altitude will change the thermophysical properties of the air, affecting convective heat dissipation. If standard models are applied directly without regional corrections, the DLR evaluation results will deviate from reality.

[0006] Therefore, existing methods for increasing the capacity of transmission lines are difficult to establish more accurate and adaptable dynamic capacity increase models under extreme climatic conditions. They also fail to integrate factors such as regional climate characteristics, changes in conductor surface conditions, and corrections for air thermophysical parameters, resulting in reduced accuracy and reliability of dynamic capacity increase for transmission lines in the desert region. Summary of the Invention

[0007] To address the aforementioned issues, this invention proposes a method and system for increasing the capacity of transmission lines in the desert region based on dynamic line ratings. This invention constructs a thermal balance model based on the IEEE 738-2023 standard and accurately calculates the line current carrying capacity by considering the region's unique climatic characteristics. This method fully taps into the potential of transmission lines in the desert region, enhances transmission capacity, and possesses strong engineering applicability and accuracy, making it suitable for the optimized operation and planning of new energy transmission channels.

[0008] According to some embodiments, the first aspect of the present invention provides a method for increasing the capacity of the Shagohuang transmission line based on dynamic line ratings, employing the following technical solution: The capacity expansion method for the Shagohuang transmission line based on dynamic line ratings includes: Based on historical meteorological data, key environmental parameters under extreme conditions are selected, and static rated values ​​based on key environmental parameters under extreme conditions are calculated using a pre-built steady-state thermal balance calculation model. Based on a pre-built transient thermal balance calculation model, the conductor temperature change under real-time key environmental parameters is calculated, and the dynamic rated value at any time step is determined according to the conductor temperature change. The capacity margin for the current time step is determined by using dynamic and static ratings.

[0009] According to some embodiments, a second aspect of the present invention provides a capacity expansion system for the Shagohuang transmission line based on dynamic line ratings, employing the following technical solution: The Shagohuang transmission line capacity expansion system based on dynamic line ratings includes: The static module is used to select key environmental parameters under extreme conditions based on historical meteorological data, and to calculate the static rated values ​​based on key environmental parameters under extreme conditions using a pre-built steady-state thermal balance calculation model. The dynamic module is used to calculate the conductor temperature change under real-time key environmental parameters based on a pre-built transient thermal balance calculation model, and determine the dynamic rated value at any time step based on the conductor temperature change. The capacity expansion module is used to determine the capacity expansion margin for the current time step using dynamic and static rated values.

[0010] According to some embodiments, a third aspect of the present invention provides a computer-readable storage medium.

[0011] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the Shagohuang transmission line capacity expansion method and system based on dynamic line ratings as described in the first embodiment above.

[0012] According to some embodiments, a fourth aspect of the present invention provides a computer device.

[0013] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps in the method for increasing the capacity of the Shagohuang transmission line based on dynamic line ratings as described in the first embodiment above.

[0014] According to some embodiments, a fifth aspect of the present invention provides a computer program product or computer program.

[0015] A computer program product or computer program includes computer instructions stored in a computer-readable storage medium, wherein a processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the steps in the method for increasing the capacity of the Shagohuang transmission line based on dynamic line ratings as described in the first embodiment above.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention improves the climate adaptability of the desert region by making regional modifications to the heat balance equation in the IEEE 738-2023 standard and incorporating measured meteorological and material parameters from the desert region. It optimizes key parameters such as absorption coefficient, emissivity, and air thermophysical properties, specifically addressing the unique extreme environmental characteristics of the desert region. This significantly improves the accuracy and reliability of capacity calculation. Furthermore, to address issues such as missing historical meteorological data, insufficient data volume, or poor data quality, it combines machine learning (DBSCAN and KNNImputer) to clean and supplement the data, improving data quality for enhanced capacity calculation. Even with missing historical meteorological data, insufficient data volume, or poor data quality, it can still accurately predict capacity expansion.

[0017] This invention analyzes real-time meteorological data and combines it with machine learning to optimize data quality. Based on Dynamic Line Rating (DLR) technology, it integrates real-time data from multiple sources, such as wind speed, wind direction, ambient temperature, solar radiation intensity, and altitude, to establish a spatiotemporally continuous meteorological-thermal coupling model. This enables dynamic assessment and real-time adjustment of line capacity, fully tapping the potential for line capacity expansion. Attached Figure Description

[0018] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0019] Figure 1 This is a flowchart of the method for increasing the capacity of the Shagohuang transmission line based on dynamic line rating in an embodiment of the present invention. Detailed Implementation

[0020] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0021] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0022] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0023] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0024] Example 1 like Figure 1 As shown, this embodiment provides a method for increasing the capacity of the Shagohuang transmission line based on dynamic line ratings. This embodiment uses the application of this method to a server as an example for illustration. It can be understood that this method can also be applied to terminals, and can also be applied to a system including terminals, servers, and systems, and can be implemented through the interaction between terminals and servers. The server can be an independent physical server, a server cluster composed of multiple physical servers, or a distributed system. It can also be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network servers, cloud communication, middleware services, domain name services, CDN security services, and big data and artificial intelligence platforms. The terminal can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, etc., but is not limited to these. The terminal and server can be directly or indirectly connected through wired or wireless communication, which is not limited in this application. In this embodiment, the method includes the following steps: Step S1: Select key environmental parameters under extreme conditions based on historical meteorological data, and calculate the static rated values ​​based on key environmental parameters under extreme conditions using a pre-built steady-state thermal balance calculation model. Step S2: Based on the pre-built transient thermal balance calculation model, calculate the conductor temperature change under real-time key environmental parameters, and determine the dynamic rated value for any time step based on the conductor temperature change. Step S3: Determine the capacity margin for the current time step using dynamic and static ratings.

[0025] This embodiment utilizes a dynamic capacity expansion and thermal balance calculation technique for transmission lines based on the IEEE 738-2023 standard. This technique includes sensitivity analysis, steady-state thermal balance calculation, transient thermal balance calculation, and dynamic capacity expansion margin assessment. Sensitivity analysis assesses the impact of wind speed, wind direction, ambient temperature, solar radiation intensity, and altitude on current carrying capacity through multi-factor simulation, identifying key environmental parameters. Steady-state thermal balance calculation establishes a mathematical model of conductor temperature and current by balancing the four power factors: convective heat dissipation, radiative heat dissipation, solar radiation heat absorption, and resistive heating. Transient thermal balance calculation considers the heat capacity effect, simulating the dynamic response of conductor temperature under a step change in current. Dynamic capacity expansion margin assessment uses real-time meteorological data to drive the thermal balance model, calculates the dynamic line rating (DLR), and compares it with the static line rating (SLR) to determine the capacity expansion potential. This technique is specifically designed for the extreme climatic conditions of the Gobi Desert region, proposing adaptive corrections for factors such as dust accumulation, low humidity, and high altitude that are not fully considered in the IEEE standard model, improving the accuracy and practicality of the model in special environments.

[0026] Specifically, in step S1, key environmental parameters under extreme operating conditions are selected based on historical meteorological data. Using a pre-built steady-state thermal balance calculation model, the static rated values ​​based on these key environmental parameters under extreme operating conditions are calculated, including: Step S1.1: Based on the traditional heat balance equation, the relationship between each environmental factor and the line current carrying capacity is determined by using meteorological simulation data and the controlled variable method to obtain the key environmental parameters affecting the line capacity. The key environmental parameters include ambient temperature, wind speed, solar radiation intensity and wind direction.

[0027] In other words, based on the traditional heat balance equation, the relationship between each environmental factor and the line current carrying capacity is determined by using meteorological simulation data and the control variable method. The sensitivity between each environmental factor and the current carrying capacity is obtained, thereby determining which environmental factor is more important, in order to prepare for the construction of a thermal model.

[0028] Step S1.2: Select key environmental parameters under extreme conditions based on historical meteorological data. Key environmental parameters under extreme conditions include the highest ambient temperature, the lowest wind speed, the strongest solar radiation intensity, and the conservative condition of unfavorable wind direction. When studying historical data, problems such as missing data, small data volume, and poor data quality often arise. Such data is not conducive to computation. Therefore, machine learning algorithms are introduced to clean and impute historical data, optimizing data quality while ensuring data reliability, thus facilitating subsequent calculations. The specific operation process is as follows: First, all historical meteorological data were standardized. DBSCAN then detects and processes outliers. It can identify dense and sparse regions in the data, with outliers typically located in sparse regions. These values ​​are marked as noise points and corrected using smoothing methods.

[0029] Finally, missing values ​​are filled using KNNImputer. By calculating the Euclidean distance between the missing value sample and the complete sample, the k nearest neighbors are selected, and the missing values ​​are filled using their mean or weighted average.

[0030] Step S1.3: Considering the climatic characteristics of the Shagohuang region, construct a steady-state heat balance calculation model based on the principle of heat balance; By balancing the four power sources—convective heat dissipation, radiative heat dissipation, solar radiation heat absorption, and resistive heating—a steady-state thermal balance calculation model is established, as follows: ; in, It is the power of the conductor's resistance heating. It is the solar radiation heat absorption power of the conductor. It is the convective heat dissipation power of the conductor. It is the radiative heat dissipation power of the conductor; the equation shows that the convective heat dissipation power of the conductor is equal to the sum of its resistive heating power and the solar radiation heat absorption power. When the conductor reaches thermal equilibrium, its temperature remains stable.

[0031] Specifically, convection cooling is an important method of heat dissipation for power transmission lines, and the power of convection cooling... The calculations need to distinguish between natural convection and natural convection. and forced convection Finally, the maximum value of the two is taken.

[0032] Natural convection occurs under still air conditions (wind speed 0), where heat is dissipated through the natural flow of air. The calculation formula is as follows:

[0033] in: air density ( ), calculated from altitude and average membrane temperature; outer diameter of the conductor ( ); For conductor surface temperature ( ); Ambient air temperature ( ).

[0034] Forced convection is caused by wind. The results of two formulas need to be calculated and the larger value taken. This larger value is then compared with natural convection, and the maximum value is taken as the convective heat dissipation. The calculation formula is as follows: Low Reynolds number:

[0035] High Reynolds number:

[0036] in: Wind direction factor; The Reynolds number (divided into laminar and turbulent states; low Reynolds numbers usually correspond to laminar flow, generally referring to Reynolds numbers less than 1000; high Reynolds numbers usually correspond to turbulent flow, generally referring to Reynolds numbers greater than 1000). air thermal conductivity .

[0037] Final convection cooling :

[0038] Among them, Reynolds number To differentiate between laminar and turbulent flow, and considering that the inertia of dust particles in the sand dunes of the Gobi Desert consumes the kinetic energy of the wind, the actual effective wind speed acting on the conductor is lower than the measured wind speed. Therefore, an effective wind speed coefficient is added. =0.8~0.95. The higher the concentration of dust in the environment, The smaller the Reynolds number, the better; the formula for calculating the Reynolds number is as follows:

[0039] Average membrane temperature : The basis for calculating air density, viscosity, and thermal conductivity.

[0040]

[0041] Aerodynamic viscosity Due to the characteristics of the desert region—low humidity and high altitude—the aerodynamic viscosity is lower than the standard value; therefore, a low humidity correction factor is introduced. =0.95, then the aerodynamic viscosity is calculated as follows:

[0042] air density Considering altitude and average membrane temperature The calculation formula is as follows:

[0043] air thermal conductivity Due to the characteristics of the desert region—low humidity and high altitude—the air thermal conductivity is lower than the standard value. Therefore, a humidity correction term is introduced, and the air thermal conductivity is calculated as follows:

[0044] in, This is the measured relative humidity.

[0045] Wind direction factor Based on the angle between the wind and the conductor axis The wind direction factor is calculated as follows:

[0046] Vertical wind

[0047] In a further embodiment, the radiative heat dissipation power is calculated based on the Stefan-Boltzmann law, as follows:

[0048] in, emissivity of the conductor surface Temperature needs to be converted to Kelvin (K) and then raised to the fourth power.

[0049] The solar radiation heat absorption power is generated by the solar-irradiated conductor, and its standard calculation formula is:

[0050] The standard defines solar radiation intensity at midday on a clear summer day as... A value of 1000 W / m² can be used; conductor absorption coefficient Depending on the surface condition of the conductor, the value of a new bare aluminum conductor is approximately 0.2 to 0.3, while after oxidation it rises to 0.6 to 0.8. A value of 0.75 is generally recommended. The projected area of ​​the conductor (m² / m); The angle between the sunlight and the perpendicular direction of the conductor affects the amount of solar radiation projected onto the conductor.

[0051] Therefore, solar heat absorption needs to take into account the sun's position (elevation angle, azimuth angle), atmospheric attenuation, and altitude correction. The final formula for calculating solar radiation heat absorption power is as follows:

[0052] Among them, the projected area of ​​the conductor The calculation is as follows:

[0053] Sun's angle of incidence : By solar altitude angle Sun azimuth and line azimuth The calculation is as follows:

[0054] Altitude-corrected solar radiation intensity The calculation is as follows:

[0055] Solar radiation intensity at sea level The calculation is as follows: . Among them, A, B, C, D, E, F, and G are different powers of the solar altitude angle, which need to be adjusted according to the measured solar radiation intensity in different regions.

[0056] Altitude correction factor :

[0057] in, It refers to altitude. Different power coefficients , Adjustments need to be made based on the measured solar radiation intensity at different altitudes.

[0058] Specifically, the heat loss due to resistance is the heat loss rate caused by the current flowing in the conductor, and the calculation formula is as follows:

[0059] in, I The current in the conductor is (A). The resistance of the wire (Ω / m) It is not a fixed value, but rather varies with temperature and is calculated using linear interpolation.

[0060] For conductor temperature For temperatures less than or equal to 100°C, the low-temperature value of 25°C and the high-temperature value of 75°C should be used. For temperatures greater than 100°C... The low temperature value of 25°C and the high temperature value of 200°C should be used.

[0061] Step S1.4: Based on the key environmental parameters under extreme conditions, calculate the static rated values ​​under extreme conditions using a steady-state thermal balance calculation model.

[0062] Based on the key environmental parameters corresponding to the desert region, the static rated values ​​under extreme operating conditions were calculated using a steady-state thermal balance calculation model; static rated values The calculation formula is as follows:

[0063] When calculating the static stability values ​​of transmission lines, key environmental parameters under extreme operating conditions are determined based on a steady-state thermal balance calculation model and historical meteorological data. Specifically, the calculations are performed using key environmental parameters under extreme conditions in the measurement area. The static ratings of the line calculated under these conditions ensure safe operation even under the most severe weather conditions.

[0064] However, the probability of the worst weather conditions occurring is very small, and the maximum current carrying capacity of transmission lines is affected by various weather conditions, which are constantly changing over time. Therefore, the maximum current carrying capacity of transmission lines is not a static straight line like the static rating. To reduce the wasted capacity margin of the static rating, a transient thermal balance model of transmission lines was established, focusing on the changes in the current carrying capacity of transmission lines with environmental changes.

[0065] In step S2, based on the pre-built transient thermal balance calculation model, the conductor temperature change under real-time key environmental parameters is calculated. Based on the conductor temperature change, the dynamic rated value for any time step is determined, including: The initial dynamic ratings were obtained using a stable thermal equilibrium calculation model and current key environmental parameters. The measurement time range was divided into multiple time intervals; Assuming that the current and key environmental parameters remain constant within each time interval, the conductor temperature change within each time step is calculated based on a pre-built transient thermal balance calculation model. If the conductor temperature change does not exceed the maximum allowable current, the balance relationship of the four power components—convective heat dissipation, radiative heat dissipation, solar radiation heat absorption, and resistive heating—is obtained based on the conductor temperature change in each time step. By utilizing the balance relationship of four power components—convective heat dissipation, radiative heat dissipation, solar radiation heat absorption, and resistive heating—within each time step, the dynamic rated value change at each time step is determined. The dynamic rating for each time step is obtained based on the initial dynamic rating and the change in the rating at each time step.

[0066] The maximum permissible current here is a specification set by the conductor manufacturer, and different conductors have different maximum permissible currents.

[0067] Among them, based on a pre-built transient thermal equilibrium calculation model, the change in conductor temperature in each time step within the time interval is calculated, and the calculation formula is as follows: ; The formula for calculating the temperature change of the conductor described above is transformed as follows: ; in, It is the mass per unit length of the conductor. It is the specific heat capacity of the conductor material. It is the first Each time step.

[0068] Understandably, dynamic calculations divide the entire time span into multiple small time intervals (e.g., every 10 minutes), assuming that the current and weather parameters (wind speed, ambient temperature, solar radiation, etc.) remain constant within each interval (taking the average value for that period). For each small time step (e.g., 10 seconds or less), the change in conductor temperature is calculated using unsteady-state thermal balance equations. ; At the end of each time step, the conductor resistance, convective heat dissipation, radiative heat dissipation, and solar thermal increase are recalculated based on the new conductor temperature. These are typically updated every time interval.

[0069] In a further embodiment, step S3, determining the capacity margin for the current time step using dynamic and static ratings, includes: The difference between the dynamic and static ratings is determined using the following formula: ; in, These are static ratings. It is a dynamic rating. It is the difference between the dynamic rating and the static rating.

[0070] The capacity margin for the current time step is determined based on the percentage of the difference between the two values ​​relative to the static nominal value.

[0071] Transmission line capacity margin refers to the difference between the maximum load capacity that a line can currently accommodate and the existing load, provided that the line operates safely and stably. It is a crucial indicator of a line's carrying capacity potential and is of great significance for the planning, operation, and upgrading of power systems. Every transmission line has its maximum carrying capacity limit. Blindly increasing the load can lead to conductor overheating, accelerated insulation aging, and even safety accidents such as short circuits and fires. Calculating the capacity margin clarifies the upper limit of the load a line can withstand, preventing overload from impacting system safety. Accurately calculating the capacity margin and then reasonably increasing the load within that range can fully utilize the line's carrying capacity, avoid idle and wasted line resources, and improve the economic efficiency of the power system.

[0072] This embodiment constructs a thermal balance model based on the IEEE 738-2023 standard and accurately calculates the line current carrying capacity by considering the specific climatic characteristics of the region. The method includes: establishing steady-state and transient thermal balance equations; analyzing the impact of environmental factors such as wind speed, wind direction, ambient temperature, solar radiation intensity, and altitude on the line current carrying capacity; determining the weights of each factor through sensitivity analysis to guide sensor deployment strategies; calculating the dynamic line rating (DLR) based on real-time meteorological data and comparing it with the static line rating (SLR) to obtain the capacity expansion margin. This method fully taps the potential of transmission lines in the desert and Gobi areas, improves transmission capacity, and has strong engineering applicability and accuracy, making it suitable for the optimized operation and planning of new energy transmission channels.

[0073] Based on the line design standards and historical extreme weather data, the key environmental parameters affecting the line capacity are determined, including conservative conditions such as the highest ambient temperature, the lowest wind speed, the strongest solar radiation intensity, and the unfavorable wind direction. The maximum allowable current value under the above extreme operating conditions is calculated using the steady-state thermal balance equation specified in the standard, and the static rated value (SLR) is obtained.

[0074] For real-time meteorological and environmental parameters, a transient thermal balance calculation model is used to evaluate the allowable current carrying capacity of the line. First, the physical relationship between conductor temperature constraints and various heat dissipation and heat absorption factors is established according to the IEEE 738-2023 standard. Then, based on the established steady-state benchmark and real-time data such as wind speed, wind direction, ambient temperature, and solar radiation intensity, the maximum allowable current of the conductor under the current conditions is calculated to obtain the dynamic rating (DLR).

[0075] Based on the Dynamic Ratings (DLR) and Static Ratings (SLR), and according to the line capacity margin calculation formula, the current line capacity increase potential, expressed as a percentage, is finally calculated, i.e., the capacity margin.

[0076] Example 2 This embodiment provides a capacity expansion system for the Shagohuang transmission line based on dynamic line ratings, including: The static module is used to select key environmental parameters under extreme conditions based on historical meteorological data, and to calculate the static rated values ​​based on key environmental parameters under extreme conditions using a pre-built steady-state thermal balance calculation model. The dynamic module is used to calculate the conductor temperature change under real-time key environmental parameters based on a pre-built transient thermal balance calculation model, and determine the dynamic rated value at any time step based on the conductor temperature change. The capacity expansion module is used to determine the capacity expansion margin for the current time step using dynamic and static rated values.

[0077] The examples and application scenarios implemented by the above modules and corresponding steps are the same, but are not limited to the content disclosed in Embodiment 1 above. It should be noted that the above modules, as part of the system, can be executed in a computer system such as a set of computer-executable instructions.

[0078] The descriptions of each embodiment in the above embodiments have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0079] The proposed system can be implemented in other ways. For example, the system embodiments described above are merely illustrative, and the division of modules described above is only a logical functional division. In actual implementation, there may be other division methods. For example, multiple modules may be combined or integrated into another system, or some features may be ignored or not executed.

[0080] Example 3 This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in the Shagohuang transmission line capacity expansion method based on dynamic line ratings as described in Embodiment 1 above.

[0081] Example 4 This embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps in the method for increasing the capacity of the Shagohuang transmission line based on dynamic line ratings as described in Embodiment 1 above.

[0082] Example 5 This embodiment provides a computer program product or computer program, including computer instructions stored in a computer-readable storage medium. The processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the steps in the Shagohuang transmission line capacity expansion method based on dynamic line ratings described in Embodiment 1 above.

[0083] Those skilled in the art will understand that embodiments of the present invention can provide methods, systems, or computer program products. Therefore, the present invention can take the form of hardware embodiments, software embodiments, or embodiments combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0084] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0085] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0086] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0087] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0088] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A method for increasing the capacity of the Shagohuang transmission line based on dynamic line ratings, characterized in that, include: Based on historical meteorological data, key environmental parameters under extreme conditions are selected, and static rated values ​​based on key environmental parameters under extreme conditions are calculated using a pre-built steady-state thermal balance calculation model. Based on a pre-built transient thermal balance calculation model, the conductor temperature change under real-time key environmental parameters is calculated, and the dynamic rated value at any time step is determined according to the conductor temperature change. The capacity margin for the current time step is determined by using dynamic and static ratings.

2. The method for increasing the capacity of the Shagohuang transmission line based on dynamic line ratings as described in claim 1, characterized in that, Based on historical meteorological data, key environmental parameters under extreme operating conditions are selected. Using a pre-constructed steady-state thermal balance calculation model, static rated values ​​based on these key environmental parameters under extreme operating conditions are calculated, including: Based on the traditional heat balance equation, the relationship between each environmental factor and the line current carrying capacity is determined by using meteorological simulation data and the controlled variable method, thereby obtaining the key environmental parameters that affect the line capacity. Key environmental parameters under extreme working conditions were selected based on historical meteorological data. Considering the climatic characteristics of the Shagohuang region, a steady-state heat balance calculation model is constructed based on the principle of heat balance. Based on the key environmental parameters under extreme operating conditions, the static rated values ​​under extreme operating conditions are calculated using a steady-state thermal balance calculation model.

3. The method for increasing the capacity of the Shagohuang transmission line based on dynamic line ratings as described in claim 2, characterized in that, The static rated values ​​are calculated as follows: in, These are static ratings. It is the power of the conductor's resistance heating. It is the solar radiation heat absorption power of the conductor. It is the convective heat dissipation power of the conductor. It is the radiative heat dissipation power of the conductor. This represents the resistance of the wire.

4. The method for increasing the capacity of the Shagohuang transmission line based on dynamic line ratings as described in claim 1, characterized in that, Based on a pre-built transient thermal equilibrium calculation model, the conductor temperature change under real-time key environmental parameters is calculated. Based on the conductor temperature change, the dynamic rated value for any time step is determined, including: The initial dynamic ratings were obtained using a stable thermal equilibrium calculation model and current key environmental parameters. The measurement time range was divided into multiple time intervals; Assuming that the current and key environmental parameters remain constant within each time interval, the conductor temperature change within each time step is calculated based on a pre-built transient thermal balance calculation model. If the conductor temperature change does not exceed the maximum allowable current, the balance relationship of the four power components—convective heat dissipation, radiative heat dissipation, solar radiation heat absorption, and resistive heating—is obtained based on the conductor temperature change in each time step. By utilizing the balance relationship of four power components—convective heat dissipation, radiative heat dissipation, solar radiation heat absorption, and resistive heating—within each time step, the dynamic rated value change at each time step is determined. The dynamic rating for each time step is obtained based on the initial dynamic rating and the change in the rating at each time step.

5. The method for increasing the capacity of the Shagohuang transmission line based on dynamic line ratings as described in claim 4, characterized in that, Based on a pre-built transient thermal equilibrium calculation model, the change in conductor temperature at each time step within the time interval is calculated using the following formula: ； in, It is the mass per unit length of the conductor. It is the specific heat capacity of the conductor material. It is the first Each time step It is the power of the conductor's resistance heating. It is the solar radiation heat absorption power of the conductor. It is the convective heat dissipation power of the conductor. It is the radiative heat dissipation power of the conductor.

6. The method for increasing the capacity of the Shagohuang transmission line based on dynamic line ratings as described in claim 1, characterized in that, The process of determining the capacity margin for the current time step using dynamic and static ratings includes: The difference between the dynamic and static ratings is determined using the dynamic and static ratings. The capacity margin for the current time step is determined based on the percentage of the difference between the two values ​​relative to the static nominal value.

7. A capacity expansion system for the Shagohuang transmission line based on dynamic line ratings, characterized in that, include: The static module is used to select key environmental parameters under extreme conditions based on historical meteorological data, and to calculate the static rated values ​​based on key environmental parameters under extreme conditions using a pre-built steady-state thermal balance calculation model. The dynamic module is used to calculate the conductor temperature change under real-time key environmental parameters based on a pre-built transient thermal balance calculation model, and determine the dynamic rated value at any time step based on the conductor temperature change. The capacity expansion module is used to determine the capacity expansion margin for the current time step using dynamic and static rated values.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps in the method for increasing the capacity of the Shagohuang transmission line based on dynamic line ratings as described in any one of claims 1-6.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the method for increasing the capacity of the Shagohuang transmission line based on dynamic line ratings as described in any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps in the method for increasing the capacity of the Shagohuang transmission line based on dynamic line ratings as described in any one of claims 1-6.

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