Overhead transmission line state monitoring method and system based on multiple regression model

Abstract

The invention relates to the technical field of electric power engineering, and discloses an overhead transmission line state monitoring method and system based on a multiple regression model, which considers the influence of seasonal characteristics on current-carrying capacity prediction, and matches a corresponding target regression coefficient through the seasonal characteristics to which real-time operation data belongs. A multiple regression current-carrying capacity prediction model adaptive to seasonal characteristics is dynamically constructed, and the nonlinear correlation between environmental parameters and current-carrying capacity under different seasonal meteorological conditions is accurately captured. By combining the dynamically calculated sag safety threshold value, the linkage verification current-carrying capacity safety margin, the target sag and the sag safety threshold value, the rigid judgment limitation of the traditional single parameter and static threshold value is broken through, the comprehensive and dynamic monitoring of the state of the power transmission line is realized, the monitoring accuracy of the state of the power transmission line is improved, and the potential risk is effectively identified.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power engineering, and in particular to a method and system for monitoring the status of an overhead transmission line based on a multiple regression model. Background Art

[0002] As critical infrastructure for power transmission, transmission lines play a vital role in the modern energy supply system. With rapid socioeconomic development and the continued growth of electricity demand, ensuring the safe, stable, and efficient operation of transmission lines has become a core task for the power industry. Numerous transmission lines span vast territories, facing complex and diverse natural environments and operating conditions. Accurately assessing their operating status is crucial for preventing accidents and ensuring power supply reliability.

[0003] Condition assessment and early warning are core tasks in the operational management of power transmission lines. Real-time monitoring and analysis of transmission line parameters and operating conditions can promptly identify potential safety hazards, enabling proactive measures to address them and prevent accidents. Currently, transmission line condition assessment technology is continuously evolving, transitioning from traditional manual inspections to intelligent, automated monitoring to accommodate the growing scale and complex operating environments of power transmission networks.

[0004] At present, traditional transmission line status monitoring focuses only on the monitoring of a single parameter, and performs status monitoring by adopting a static threshold judgment method based on a single current carrying capacity. This rigid boundary judgment method is difficult to cope with the operating characteristics under different seasonal conditions, ignores the comprehensive impact of other key operating parameters on the line status, and cannot accurately reflect the real-time safety status and potential risks of the line. Summary of the Invention

[0005] The present invention provides a method and system for monitoring the status of an overhead power transmission line based on a multiple regression model, which solves the technical problem of how to improve the accuracy of monitoring the status of a power transmission line.

[0006] A first aspect of the present invention provides a method for monitoring the status of an overhead transmission line based on a multiple regression model, comprising:

[0007] Obtain real-time operating data, target sag and maximum current carrying capacity of the target line;

[0008] Performing a dynamic safety assessment using the real-time operating data to obtain a sag safety threshold;

[0009] Analyzing the seasonal characteristics of the real-time operating data, and matching the target regression coefficient associated with the seasonal characteristics based on a preset regression coefficient database;

[0010] Dynamically constructing a multivariate regression current carrying capacity prediction model based on the target regression coefficient, and determining a current carrying capacity prediction value using the real-time operation data;

[0011] Performing a difference calculation between the predicted current carrying capacity value and the maximum current carrying capacity of the line to obtain a current carrying capacity safety margin;

[0012] The current carrying capacity safety margin is compared with the associated minimum safety margin, the target sag is compared with the sag safety threshold, and the operating status of the target line is generated according to the comparison result.

[0013] Optionally, analyzing the seasonal characteristics of the real-time operating data and matching target regression coefficients associated with the seasonal characteristics based on a preset regression coefficient database includes:

[0014] Extracting and parsing the timestamp of the real-time operation data to obtain seasonal characteristics associated with the real-time operation data;

[0015] Generating a corresponding target key using the seasonal feature;

[0016] Using the target key to search the preset regression coefficient database, output the corresponding target value;

[0017] The initial regression coefficient associated with the target value is used as the target regression coefficient.

[0018] Optionally, the real-time operation data includes conductor axial temperature field distribution data, linear expansion coefficient, and reference sag parameter, and the use of the real-time operation data to perform dynamic safety assessment to obtain the sag safety threshold includes:

[0019] Performing a difference operation on the conductor axial temperature field distribution data and a preset first sag coefficient to obtain a first difference;

[0020] performing a multiplication operation on the first difference and the linear expansion coefficient to obtain a first product value;

[0021] Performing a sum operation using the first product value and a preset second sag coefficient to obtain a first sum value;

[0022] The first sum is multiplied by the reference sag parameter to obtain a sag safety threshold corresponding to the target line.

[0023] Optionally, the real-time operating data includes ambient temperature and wind speed, and the dynamically constructing a multivariate regression current carrying capacity prediction model based on the target regression coefficient and determining the current carrying capacity prediction value using the real-time operating data includes:

[0024] Substituting the target regression coefficient into a preset multiple regression equation to obtain a multiple regression current carrying capacity prediction model;

[0025] The ambient temperature and the wind speed are input into the multivariate regression current carrying capacity prediction model to solve the problem and obtain the current carrying capacity prediction value corresponding to the target line.

[0026] Optionally, comparing the current carrying capacity safety margin with an associated minimum safety margin, comparing the target sag with the sag safety threshold, and generating the operating status of the target line according to the comparison results includes:

[0027] When the current carrying capacity safety margin is greater than or equal to the minimum safety margin, and the target sag is less than or equal to the sag safety threshold, it is determined that the target line is in a safe operating state;

[0028] When the current carrying capacity safety margin is less than the minimum safety margin, and the target sag is less than or equal to the sag safety threshold, it is determined that the target line is in a current carrying capacity warning state;

[0029] When the current carrying capacity safety margin is greater than or equal to the minimum safety margin, and the target sag is greater than the sag safety threshold, it is determined that the target line is in a sag warning state;

[0030] When the current carrying capacity safety margin is less than the minimum safety margin and the target sag is greater than the sag safety threshold, it is determined that the target line is in a dangerous operating state.

[0031] Optionally, it also includes:

[0032] When the operating state of the target line is in the current carrying capacity warning state, the sag warning state, or the dangerous operating state, the interruptible load of the target line is adjusted until the operating state of the target line is in the safe operating state.

[0033] Optionally, it also includes:

[0034] Acquiring historical operation data of the target line;

[0035] Dividing the historical operation data into seasonal cycles to obtain multiple operation subsets;

[0036] The operation subset is the historical operation data collected in each season within the historical operation data;

[0037] Inputting each of the running subsets into a preset multiple regression equation and solving it using the least squares method to obtain a plurality of corresponding initial regression coefficients;

[0038] Establish a data structure template for the regression coefficient database;

[0039] Importing each of the initial regression coefficients into the data structure template to generate a regression coefficient database.

[0040] A second aspect of the present invention provides an overhead transmission line status monitoring system based on a multiple regression model, comprising:

[0041] Acquisition module, used to obtain the real-time operation data, target sag and maximum current carrying capacity of the target line;

[0042] An evaluation module, configured to perform a dynamic safety evaluation using the real-time operating data to obtain a sag safety threshold;

[0043] A retrieval module, configured to analyze the seasonal characteristics of the real-time operation data and match target regression coefficients associated with the seasonal characteristics based on a preset regression coefficient database;

[0044] A construction module is used to dynamically construct a multivariate regression current carrying capacity prediction model based on the target regression coefficient and determine the current carrying capacity prediction value using the real-time operation data;

[0045] A calculation module, configured to perform a difference calculation between the predicted current carrying capacity value and the maximum current carrying capacity of the line to obtain a current carrying capacity safety margin;

[0046] A comparison module is used to compare the current carrying capacity safety margin with the associated minimum safety margin, compare the target sag with the sag safety threshold, and generate the operating status of the target line according to the comparison result.

[0047] A third aspect of the present invention provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the overhead transmission line status monitoring method based on a multivariate regression model as described in any one of the above items.

[0048] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed, implements the overhead transmission line state monitoring method based on the multivariate regression model as described in any one of the above items.

[0049] A fifth aspect of the present invention provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions, wherein when the program instructions are executed by a computer, the computer executes the overhead transmission line status monitoring method based on the multivariate regression model as described in any one of the above items.

[0050] It can be seen from the above technical solutions that the present invention has the following advantages:

[0051] This invention takes into account the impact of seasonal characteristics on current carrying capacity prediction. By matching the seasonal characteristics of real-time operating data with the corresponding target regression coefficient, a multivariate regression current carrying capacity prediction model that adapts to seasonal characteristics is dynamically constructed, accurately capturing the nonlinear relationship between environmental parameters and current carrying capacity under different seasonal meteorological conditions. Combined with a dynamically calculated sag safety threshold, the current carrying capacity safety margin, target sag, and sag safety threshold are jointly verified, breaking through the rigid judgment limitations of traditional single parameters and static thresholds. This allows for comprehensive and dynamic monitoring of transmission line status, improves the accuracy of transmission line status monitoring, effectively identifies potential risks, and addresses the shortcomings of traditional monitoring methods that cannot accurately reflect the real-time safety status and potential risks of the line. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0053] Figure 1 A flowchart of a method for monitoring the status of an overhead transmission line based on a multiple regression model provided in the first embodiment of the present invention;

[0054] Figure 2 A flowchart of a method for monitoring the status of an overhead transmission line based on a multiple regression model provided in the second embodiment of the present invention;

[0055] Figure 3 A structural block diagram of an overhead transmission line status monitoring system based on a multiple regression model provided in the third embodiment of the present invention;

[0056] Figure 4 This is a structural block diagram of a computer device provided in Example 4 of the present invention. DETAILED DESCRIPTION

[0057] The embodiments of the present invention provide a method and system for monitoring the status of an overhead power transmission line based on a multiple regression model, which are used to solve the technical problem of how to improve the accuracy of monitoring the status of a power transmission line.

[0058] In order to make the purpose, features, and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0059] See also Figure 1 , Figure 1 This is a flowchart of the steps of a method for monitoring the status of an overhead transmission line based on a multiple regression model provided in Example 1 of the present invention.

[0060] The present invention provides a method for monitoring the status of an overhead transmission line based on a multiple regression model, comprising:

[0061] Step 101: Acquire real-time operating data, target sag, and maximum current carrying capacity of a target line.

[0062] The target line refers to the overhead transmission line that needs to be monitored.

[0063] Real-time operation data refers to a set of key parameters collected in real time under the current operating state, which is used to reflect the operating conditions of the line, including but not limited to environmental parameters and electrical parameters.

[0064] Target sag refers to the conductor sag value during the actual operation of the target line. Sag is the degree of droop caused by the weight of an overhead conductor after being suspended. It reflects the conductor's mechanical state and affects the safe distance of the line. It is measured using sag sensors, laser rangefinders, or drone inspections.

[0065] The maximum current-carrying capacity of a line refers to the maximum continuous current-carrying capacity allowed for the target line under the design / safety operation criteria. It is a key threshold for safe line operation and is determined through design specifications, thermal stability calculations, or historical test data. It is used to determine whether the current-carrying capacity exceeds the limit.

[0066] In an embodiment of the present invention, operation and maintenance personnel select an overhead transmission line to be monitored as a target line based on an inspection plan, and then collect real-time operating data through monitoring equipment deployed on the target line. The monitoring equipment includes but is not limited to a meteorological monitoring station (for measuring wind speed, ambient temperature, etc.), a current transformer / smart meter (for measuring electrical parameters such as conductor current and power), and an Internet of Things sensor (for collecting conductor vibration, tension, and other data). To ensure that the equipment is online and communication is normal, the target sag is collected using a drone equipped with a laser ranging module and a conductor inclination sensor, or a sag observation instrument and a measuring rope for manual measurement. The original design data of the target line (including a conductor thermal stability calculation report and a maximum current carrying capacity design value) is retrieved from the line design document management system and the operation and maintenance ledger, or the maximum continuous current carrying capacity allowed by the line is obtained through historical test data and an online current carrying capacity calculation model.

[0067] It is important to note that distributed sensor nodes (also known as monitoring nodes) are deployed at key locations, such as the center of the span and tension sections of the target transmission line, to monitor the operating status and environmental parameters of the target conductors in real time. Conductor temperature sensors, tightly bonded to the conductor surface with thermally conductive silicone, continuously collect conductor temperature data with an accuracy of ±0.5°C. A multifunctional meteorological monitoring station operates simultaneously, collecting data at least every five minutes to ensure the timeliness and integrity of environmental parameters. All sensor nodes transmit data using low-power wireless transmission technology, providing high-density, multi-dimensional data support for subsequent dynamic current-carrying capacity prediction and safety assessment.

[0068] Step 102: Perform a dynamic safety assessment using real-time operating data to obtain a sag safety threshold.

[0069] Dynamic safety assessment refers to the process of dynamically analyzing and judging the current safety status of the target line based on real-time operating data.

[0070] The sag safety threshold refers to the maximum sag critical value allowed for the target line under the current real-time operating conditions.

[0071] In an embodiment of the present invention, real-time operating data of a target line is received. Based on the line type (e.g., ordinary steel-core aluminum stranded wire, heat-resistant conductor), corresponding basic parameters such as reference sag parameters and linear expansion coefficient are retrieved from a database. The real-time conductor current is substituted into a thermal balance formula. Combined with the ambient temperature and wind speed, the axial temperature field distribution data of the conductor is calculated. The calculated axial temperature field distribution data of the conductor is then combined with basic parameters such as the reference sag parameters and linear expansion coefficient to calculate a sag safety threshold.

[0072] Step 103: Analyze the seasonal characteristics of the real-time operation data, and match the target regression coefficient associated with the seasonal characteristics based on a preset regression coefficient database.

[0073] Seasonal characteristics refer to the seasonal classification identifier (such as spring, summer, autumn, and winter) of the target line operation, which is obtained by parsing the time information associated with the real-time operation data (such as timestamp and collection date). It is used as a "key" to retrieve the preset regression coefficient database and match the target regression coefficient of the corresponding season to adapt to the impact of different seasonal meteorological conditions on the carrying capacity prediction.

[0074] The preset regression coefficient database refers to a pre-built regression coefficient key-value pair database, which is established based on the association between the season to which the real-time operation data belongs and the regression coefficient, wherein the seasonal characteristics serve as the key and the target regression coefficient serves as the value.

[0075] The target regression coefficient refers to the key parameter used to construct a multivariate regression current carrying capacity prediction model, which is used to quantify the linear correlation between "environmental parameters (wind speed, ambient temperature, etc.) and current carrying capacity".

[0076] In an embodiment of the present invention, the season to which the real-time operation data belongs is analyzed according to the timestamp associated with the real-time operation data, and the season is used as a feature identifier to retrieve the preset regression coefficient database, and accurately match the target regression coefficient for dynamically constructing a multivariate regression current carrying capacity prediction model.

[0077] Step 104: dynamically construct a multiple regression current carrying capacity prediction model based on the target regression coefficient, and use real-time operation data to determine the current carrying capacity prediction value.

[0078] The multivariate regression current carrying capacity prediction model refers to a mathematical model constructed based on the principle of multivariate linear regression. It predicts the current carrying capacity of transmission lines by fitting the mapping relationship between "environmental parameters-current carrying capacity".

[0079] The current carrying capacity prediction value refers to the current instantaneous current carrying capacity prediction value of the target transmission line obtained by substituting real-time operation data into the multivariate regression model.

[0080] In an embodiment of the present invention, a multivariate regression current carrying capacity prediction model associated with corresponding seasonal characteristics is established based on the target regression coefficient obtained by matching, and then real-time operation data is input into the constructed multivariate regression current carrying capacity prediction model for solution to obtain the current instantaneous current carrying capacity prediction value of the target line.

[0081] Step 105: Perform a difference calculation between the predicted current carrying capacity and the maximum current carrying capacity of the line to obtain a current carrying capacity safety margin.

[0082] The ampacity safety margin refers to the difference between the predicted instantaneous ampacity of the target line and the maximum allowable ampacity of the line. It is used to quantify the safety redundancy of the line's operating status.

[0083] In an embodiment of the present invention, by calculating the difference between the predicted current carrying capacity and the maximum current carrying capacity of the line, the remaining capacity space of the target line from the critical overload state under the current operating conditions, that is, the current carrying capacity safety margin, is obtained.

[0084] Step 106 : Compare the current carrying capacity safety margin with the associated minimum safety margin, compare the target sag with the sag safety threshold, and generate the operating status of the target line according to the comparison results.

[0085] The minimum safety margin refers to the minimum allowable safety margin threshold set based on line voltage level, importance, environmental conditions, etc.

[0086] In the embodiment of the present invention, the current carrying capacity safety margin is compared with the associated minimum safety margin, the target sag is compared with the sag safety threshold, and the operating status of the target line is generated according to the comparison result.

[0087] This invention takes into account the impact of seasonal characteristics on current carrying capacity prediction. By matching the seasonal characteristics of real-time operating data with the corresponding target regression coefficient, a multivariate regression current carrying capacity prediction model that adapts to seasonal characteristics is dynamically constructed, accurately capturing the nonlinear relationship between environmental parameters and current carrying capacity under different seasonal meteorological conditions. Combined with a dynamically calculated sag safety threshold, the current carrying capacity safety margin, target sag, and sag safety threshold are jointly verified, breaking through the rigid judgment limitations of traditional single parameters and static thresholds. This allows for comprehensive and dynamic monitoring of transmission line status, improves the accuracy of transmission line status monitoring, effectively identifies potential risks, and addresses the shortcomings of traditional monitoring methods that cannot accurately reflect the real-time safety status and potential risks of the line.

[0088] See also Figure 2 , Figure 2 A flowchart of the steps of a method for monitoring the status of an overhead transmission line based on a multiple regression model is provided in the second embodiment of the present invention.

[0089] The present invention provides a method for monitoring the status of an overhead transmission line based on a multiple regression model, comprising:

[0090] Step 201: Acquire real-time operating data, target sag, and maximum current carrying capacity of a target line.

[0091] In the embodiment of the present invention, the specific implementation process of step 201 is similar to that of step 101 and will not be repeated here.

[0092] Step 202: Perform a dynamic safety assessment using real-time operating data to obtain a sag safety threshold.

[0093] Furthermore, the real-time operation data includes the conductor axial temperature field distribution data, the linear expansion coefficient, and the reference sag parameter. Step 202 may include the following sub-steps:

[0094] S11. Perform a difference operation on the conductor axial temperature field distribution data and a preset first sag coefficient to obtain a first difference.

[0095] S12. Perform a multiplication operation on the first difference and the linear expansion coefficient to obtain a first product value.

[0096] S13. Perform a sum operation using the first product value and a preset second sag coefficient to obtain a first sum value.

[0097] S14. Perform a multiplication operation on the first sum value and the reference sag parameter to obtain a sag safety threshold corresponding to the target line.

[0098] In a specific implementation, in order to facilitate the implementation of the method, the above process can be converted into a formula encapsulation form, wherein the calculation method of the sag safety threshold can be as follows:

[0099]

[0100] Where, Indicates the sag safety threshold, Indicates the axial temperature field distribution data of the conductor, reflecting the axial temperature field distribution of the conductor during operation. Indicates the preset first sag coefficient, Indicates the linear expansion coefficient, specifically the linear expansion coefficient of the conductor at the reference temperature of 20°C. Indicates the preset second sag coefficient, It represents the reference sag parameter, which characterizes the reference sag parameter under the design conditions.

[0101] It should be noted that the conductor axial temperature field distribution data is calculated through the following process;

[0102] Real-time operating data also includes the conductor's real-time current, the conductor's DC resistance at 20°C (check the conductor specification table, e.g., LGJ-400 / 35 conductor Rzg ≈ 0.078Ω / km), the conductor's resistance temperature coefficient (about 0.003931 / °C for aluminum conductors), and the conductor's surface area.

[0103] By inputting the real-time operation data into the heat balance equation, the axial temperature field distribution data of the conductor can be obtained.

[0104] In a specific implementation, in order to facilitate the implementation of the method, the above process can be converted into a formula encapsulation form, wherein the expression of the heat balance equation can be as follows:

[0105]

[0106]

[0107] Where, Indicates the real-time current of the conductor. Indicates the DC resistance of the wire at 20°C. represents the comprehensive heat dissipation coefficient, Indicates wind speed, represents the surface area of ​​the conductor, Indicates the axial temperature field distribution data of the conductor, Indicates the ambient temperature.

[0108] In an embodiment of the present invention, real-time operating data is first input into a heat balance equation to obtain the conductor axial temperature field distribution data, and then the conductor axial temperature field distribution data, linear expansion coefficient and reference sag parameter are combined to calculate the sag safety threshold.

[0109] Furthermore, before step 203, the following steps may be included:

[0110] A1. Obtain historical operation data of the target line.

[0111] In an embodiment of the present invention, historical operating data includes historical environmental parameters and historical conductor current-carrying data. The historical environmental parameters include historical wind speed and historical ambient temperature. The historical conductor current-carrying data is collected by current sensors installed on the conductors (or by using line gateway meters) at corresponding historical moments, and is precisely synchronized with the environmental parameter collection time.

[0112] A2. Divide the historical operating data into seasonal cycles to obtain multiple operating subsets;

[0113] The operation subset is the historical operation data collected in each season within the historical operation data.

[0114] In an embodiment of the present invention, first, based on the acquisition timestamps of the meteorological monitoring station and the current sensor, the historical environmental parameters are matched one by one with the historical conductor current carrying capacity data to form a "timestamp-wind speed-ambient temperature-current carrying capacity" four-tuple historical data set. Then, the historical data set is traversed and each piece of historical operation data is divided into a spring (March-May) operation subset, a summer (June-August) operation subset, an autumn (September-November) operation subset, and a winter (December-February) operation subset according to the timestamp of the data.

[0115] A3. Input each running subset into a preset multiple regression equation and solve it using the least squares method to obtain multiple corresponding initial regression coefficients.

[0116] The preset multiple regression equation is:

[0117]

[0118] Where, Represents historical conductor current carrying data, represents the historical wind speed, Indicates the historical ambient temperature, represents the initial regression coefficient.

[0119] In the embodiment of the present invention, each running subset is input into a preset multiple regression equation, and is solved using the least squares method to obtain the initial regression coefficient corresponding to each running subset.

[0120] A4. Establish a data structure template for the regression coefficient database.

[0121] In an embodiment of the present invention, based on template data, the template data includes seasonal characteristics and initial regression coefficients, the seasonal characteristics include spring, summer, autumn and winter, the initial regression coefficients include intercept term A (basic offset), wind speed coefficient B (coefficient of influence of wind speed on current carrying capacity) and temperature coefficient C (coefficient of influence of temperature on current carrying capacity), and a multi-level relationship is formed using horizontal pointers and subordinate pointers to construct a data structure template, specifically, the pointer pointing to the horizontal structure, using the horizontal pointer, refers to the horizontal classification (seasonal characteristics and initial regression coefficient), the pointer pointing to the following structure, using the subordinate pointer, refers to the subordinate classification (the subordinate of the seasonal characteristics includes spring, summer, autumn and winter, and the subordinate of the initial regression coefficient includes the intercept term A, wind speed coefficient B and temperature coefficient C).

[0122] The data structure template is shown in Table 1 below:

[0123]

[0124] A5. Import each initial regression coefficient into the data structure template to generate a regression coefficient database.

[0125] The initial regression coefficients in the following table are only sample data. They are imported according to the actual solution. The regression coefficient database is shown in Table 2 below:

[0126]

[0127] Step 203: Analyze the seasonal characteristics of the real-time operation data, and match the target regression coefficient associated with the seasonal characteristics based on a preset regression coefficient database.

[0128] Furthermore, step 203 may include the following sub-steps:

[0129] S21. Extract and parse the timestamp of the real-time operation data to obtain seasonal characteristics associated with the real-time operation data.

[0130] For example, the timestamp 2020-07-15 14:30:00 is the month 07 and the season is summer.

[0131] In an embodiment of the present invention, the timestamp of the real-time operation data is extracted and parsed to obtain the seasonal features associated with the real-time operation data. The seasonal features use fixed strings to identify the seasons, such as spring, summer, autumn, and winter.

[0132] S22. Generate corresponding target keys using seasonal features.

[0133] S23. Use the target key to search the preset regression coefficient database and output the corresponding target value.

[0134] S24. The initial regression coefficient associated with the target value is used as the target regression coefficient.

[0135] In an embodiment of the present invention, seasonal characteristics are used to generate corresponding target keys, the target keys are used to retrieve a preset regression coefficient database, the corresponding target value is output, and the initial regression coefficient associated with the target value is used as the target regression coefficient.

[0136] Furthermore, the real-time operating data includes ambient temperature and wind speed.

[0137] Step 204: Substitute the target regression coefficient into a preset multiple regression equation to obtain a multiple regression current carrying capacity prediction model.

[0138] In an embodiment of the present invention, the target regression coefficient is substituted into a preset multiple regression equation. For example, the initial regression coefficient in summer is matched as the target regression coefficient. Then, the expression of the multiple regression current carrying capacity prediction model is specifically:

[0139]

[0140] The same is true for other seasons, so I will not go into details here.

[0141] It is worth mentioning that if a unified regression coefficient is used for modeling, mixed training of summer "high temperature-low capacity" data and winter "low temperature-high capacity" data will cause the model parameters to be averaged. Therefore, the training subsets are divided by season through historical data (spring / summer / autumn / winter), and the least squares method is used to solve the season-specific regression coefficients. The preset multiple regression equations also incorporate the historical correlations of wind speed, temperature, and capacity, and quantify the nonlinear effects of seasonal environmental parameters on capacity, so as to accurately match seasonal characteristics and improve the accuracy of capacity prediction.

[0142] Step 205: Use the ambient temperature and wind speed as inputs to solve the multivariate regression current carrying capacity prediction model to obtain the current carrying capacity prediction value corresponding to the target line.

[0143] In the embodiment of the present invention, the ambient temperature and wind speed are input into a multivariate regression current carrying capacity prediction model to solve the problem and obtain the current carrying capacity prediction value corresponding to the target line.

[0144] Step 206: Perform a difference calculation between the predicted current carrying capacity and the maximum current carrying capacity of the line to obtain a current carrying capacity safety margin.

[0145] In a specific implementation, in order to facilitate the implementation of the method, the above process can be converted into a formula encapsulation form, wherein the expression of the current carrying safety margin can be as follows:

[0146]

[0147] Where, Indicates the current carrying capacity safety margin, Indicates the maximum current carrying capacity of the line. Indicates the predicted current carrying capacity.

[0148] Step 207 : Compare the current carrying capacity safety margin with the associated minimum safety margin, compare the target sag with the sag safety threshold, and generate the target line operation status according to the comparison results.

[0149] For ease of understanding, Indicates the current carrying capacity safety margin, represents the minimum safety margin, represents the target sag, Indicates the sag safety threshold.

[0150] Furthermore, step 207 may include the following sub-steps:

[0151] S31. When the current carrying capacity safety margin is greater than or equal to the minimum safety margin, and the target sag is less than or equal to the sag safety threshold, it is determined that the target line is in a safe operating state.

[0152] First current carrying capacity dimension:

[0153] First sag dimension:

[0154] In an embodiment of the present invention, when the first current carrying capacity dimension and the first sag dimension are satisfied, it is determined that the target line is in a safe operating state.

[0155] S32. When the current carrying capacity safety margin is less than the minimum safety margin and the target sag is less than or equal to the sag safety threshold, it is determined that the target line is in the current carrying capacity warning state.

[0156] Second current carrying capacity dimension:

[0157] Second sag dimension:

[0158] In an embodiment of the present invention, when the second current carrying capacity dimension and the second sag dimension are met, it means that the current carrying capacity is approaching the safety boundary and the sag is not significantly affected. The current carrying capacity warning needs to be triggered and the target line is determined to be in the current carrying capacity warning state.

[0159] S33. When the current carrying capacity safety margin is greater than or equal to the minimum safety margin, and the target sag is greater than the sag safety threshold, it is determined that the target line is in a sag warning state.

[0160] The third dimension of current carrying capacity:

[0161] Third sag dimension:

[0162] In an embodiment of the present invention, when the third current-carrying capacity dimension and the third sag dimension are met, it indicates that the sag exceeds the limit. The current-carrying capacity appears to be safe, but in reality, there may be hidden risks due to the influence of sag (such as conductor relaxation causing resistance changes, indirectly changing the current-carrying capacity characteristics). A sag warning needs to be triggered to determine that the target line is in a sag warning state.

[0163] S34. When the current carrying capacity safety margin is less than the minimum safety margin and the target sag is greater than the sag safety threshold, it is determined that the target line is in a dangerous operating state.

[0164] The fourth dimension of current carrying capacity:

[0165] Fourth sag dimension:

[0166] In an embodiment of the present invention, when the fourth current carrying capacity dimension and the fourth sag dimension are met, it indicates that the target line is in a high-risk operating state, and emergency regulation needs to be triggered immediately to determine that the target line is in a dangerous operating state.

[0167] Furthermore, it also includes:

[0168] When the operating state of the target line is in the current carrying capacity warning state, the sag warning state, or the dangerous operating state, the interruptible load of the target line is adjusted until the operating state of the target line is in the safe operating state.

[0169] In an embodiment of the present invention, when the operating state of the target line is in the current carrying capacity warning state, the sag warning state, or the dangerous operating state, the interruptible load of the target line is disconnected, the line current carrying capacity is reduced, and the process jumps to step 201 until the operating state of the target line is in the safe operating state.

[0170] This invention takes into account the impact of seasonal characteristics on current carrying capacity prediction. By matching the seasonal characteristics of real-time operating data with the corresponding target regression coefficient, a multivariate regression current carrying capacity prediction model that adapts to seasonal characteristics is dynamically constructed, accurately capturing the nonlinear relationship between environmental parameters and current carrying capacity under different seasonal meteorological conditions. Combined with a dynamically calculated sag safety threshold, the current carrying capacity safety margin, target sag, and sag safety threshold are jointly verified, breaking through the rigid judgment limitations of traditional single parameters and static thresholds. This allows for comprehensive and dynamic monitoring of transmission line status, improves the accuracy of transmission line status monitoring, effectively identifies potential risks, and addresses the shortcomings of traditional monitoring methods that cannot accurately reflect the real-time safety status and potential risks of the line.

[0171] See also Figure 3, Figure 3 This is a structural block diagram of an overhead transmission line status monitoring system based on a multiple regression model provided in the third embodiment of the present invention.

[0172] The present invention provides an overhead transmission line status monitoring system based on a multiple regression model, comprising:

[0173] An acquisition module 301 is used to acquire real-time operating data, target sag, and maximum current carrying capacity of a target line;

[0174] An evaluation module 302 is configured to perform a dynamic safety evaluation using real-time operating data to obtain a sag safety threshold;

[0175] A retrieval module 303 is used to analyze the seasonal characteristics of the real-time operation data and match the target regression coefficient associated with the seasonal characteristics based on a preset regression coefficient database;

[0176] A construction module 304 is used to dynamically construct a multivariate regression current carrying capacity prediction model based on the target regression coefficient and determine the current carrying capacity prediction value using real-time operation data;

[0177] A calculation module 305 is used to perform a difference calculation between the current carrying capacity prediction value and the maximum current carrying capacity of the line to obtain a current carrying capacity safety margin;

[0178] The comparison module 306 is used to compare the current carrying capacity safety margin with the associated minimum safety margin, compare the target sag with the sag safety threshold, and generate the operating status of the target line according to the comparison results.

[0179] Furthermore, the retrieval module 303 includes:

[0180] Seasonal feature submodule, used to extract and parse the timestamp of real-time operation data to obtain seasonal features associated with the real-time operation data;

[0181] The target key submodule is used to generate the corresponding target key using seasonal features;

[0182] The target value submodule is used to retrieve the preset regression coefficient database using the target key and output the corresponding target value;

[0183] The target regression coefficient submodule is used to take the initial regression coefficient associated with the target value as the target regression coefficient.

[0184] Furthermore, the real-time operation data includes the conductor axial temperature field distribution data, linear expansion coefficient and reference sag parameter, and the evaluation module 302 includes:

[0185] A first difference submodule is configured to perform a difference operation using the conductor axial temperature field distribution data and a preset first sag coefficient to obtain a first difference;

[0186] A first multiplication submodule is configured to perform a multiplication operation using the first difference and the linear expansion coefficient to obtain a first product value;

[0187] A first sum submodule is configured to perform a sum operation using the first product value and a preset second sag coefficient to obtain a first sum;

[0188] The sag safety threshold submodule is used to perform a multiplication operation on the first sum value and the reference sag parameter to obtain a sag safety threshold corresponding to the target line.

[0189] Furthermore, the real-time operating data includes ambient temperature and wind speed, and the construction module 304 includes:

[0190] The multiple regression current carrying capacity prediction model submodule is used to substitute the target regression coefficient into the preset multiple regression equation to obtain the multiple regression current carrying capacity prediction model;

[0191] The current carrying capacity prediction value submodule is used to solve the multivariate regression current carrying capacity prediction model using ambient temperature and wind speed as input to obtain the current carrying capacity prediction value corresponding to the target line.

[0192] Furthermore, the comparison module 306 includes:

[0193] The safe operation state submodule is used to determine that the target line is in a safe operation state when the current carrying capacity safety margin is greater than or equal to the minimum safety margin and the target sag is less than or equal to the sag safety threshold;

[0194] The current carrying capacity warning state submodule is used to determine that the target line is in the current carrying capacity warning state when the current carrying capacity safety margin is less than the minimum safety margin and the target sag is less than or equal to the sag safety threshold;

[0195] The sag warning state submodule is used to determine that the target line is in the sag warning state when the current carrying capacity safety margin is greater than or equal to the minimum safety margin and the target sag is greater than the sag safety threshold;

[0196] The dangerous operation state submodule is used to determine that the target line is in a dangerous operation state when the current carrying capacity safety margin is less than the minimum safety margin and the target sag is greater than the sag safety threshold.

[0197] Furthermore, it also includes:

[0198] When the operating state of the target line is in the current carrying capacity warning state, the sag warning state, or the dangerous operating state, the interruptible load of the target line is adjusted until the operating state of the target line is in the safe operating state.

[0199] Furthermore, it also includes:

[0200] Historical operation data module, used to obtain historical operation data of the target line;

[0201] The operation subset module is used to divide the historical operation data into seasonal cycles to obtain multiple operation subsets;

[0202] Among them, the operation subset is the historical operation data collected in each season within the historical operation data;

[0203] The initial regression coefficient module is used to input each running subset into the preset multiple regression equation and solve it using the least squares method to obtain multiple corresponding initial regression coefficients;

[0204] Data structure template module, used to establish the data structure template of the regression coefficient database;

[0205] The regression coefficient database module is used to import each initial regression coefficient into the data structure template to generate a regression coefficient database.

[0206] This invention takes into account the impact of seasonal characteristics on current carrying capacity prediction. By matching the seasonal characteristics of real-time operating data with the corresponding target regression coefficient, a multivariate regression current carrying capacity prediction model that adapts to seasonal characteristics is dynamically constructed, accurately capturing the nonlinear relationship between environmental parameters and current carrying capacity under different seasonal meteorological conditions. Combined with a dynamically calculated sag safety threshold, the current carrying capacity safety margin, target sag, and sag safety threshold are jointly verified, breaking through the rigid judgment limitations of traditional single parameters and static thresholds. This allows for comprehensive and dynamic monitoring of transmission line status, improves the accuracy of transmission line status monitoring, effectively identifies potential risks, and addresses the shortcomings of traditional monitoring methods that cannot accurately reflect the real-time safety status and potential risks of the line.

[0207] See also Figure 4 , Figure 4 This is a structural block diagram of a computer device provided in Example 4 of the present invention.

[0208] An electronic device according to an embodiment of the present invention includes: a memory 401 and a processor 402, wherein the memory 401 stores a computer program; when the computer program is executed by the processor 402, the processor 402 executes the overhead transmission line status monitoring method based on the multivariate regression model as in any of the above embodiments.

[0209] Memory 401 may be an electronic memory such as flash memory, EEPROM (Electrically Erasable Programmable Read-Only Memory), EPROM, hard disk, or ROM. Memory 401 has storage space 403 for program code 413 for executing any of the method steps described above. For example, storage space 403 for program code may include individual program codes 413 for implementing various steps in the method described above. These program codes may be read from or written to one or more computer program products. These computer program products include program code carriers such as hard disks, compact disks (CDs), memory cards, or floppy disks. The program codes may be compressed, for example, in a suitable format. When executed by a processing device, these codes cause the processing device to execute the various steps in the method described above. These program codes may be read from or written to one or more computer program products. These computer program products include program code carriers such as hard disks, compact disks (CDs), memory cards, or floppy disks. The program codes may be compressed, for example, in a suitable format. When these codes are executed by a computing and processing device, they cause the computing and processing device to execute the various steps of the overhead transmission line condition monitoring method based on the multivariate regression model described above.

[0210] The fifth embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for monitoring the state of an overhead transmission line based on a multivariate regression model as described in any of the above embodiments is implemented.

[0211] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0212] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0213] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0214] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0215] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the method of the present invention. The aforementioned storage medium includes various media that can store program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0216] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for monitoring the status of overhead transmission lines based on a multiple regression model, characterized in that: include: Obtain real-time operating data, target sag and maximum current carrying capacity of the target line; Performing a dynamic safety assessment using the real-time operating data to obtain a sag safety threshold; Analyzing the seasonal characteristics of the real-time operating data, and matching the target regression coefficient associated with the seasonal characteristics based on a preset regression coefficient database; Dynamically constructing a multivariate regression current carrying capacity prediction model based on the target regression coefficient, and determining a current carrying capacity prediction value using the real-time operation data; Performing a difference calculation between the predicted current carrying capacity value and the maximum current carrying capacity of the line to obtain a current carrying capacity safety margin; The current carrying capacity safety margin is compared with the associated minimum safety margin, the target sag is compared with the sag safety threshold, and the operating status of the target line is generated according to the comparison result.

2. The method for monitoring the state of overhead transmission lines based on a multiple regression model according to claim 1, wherein: The analyzing the seasonal characteristics of the real-time operation data and matching the target regression coefficient associated with the seasonal characteristics based on a preset regression coefficient database includes: Extracting and parsing the timestamp of the real-time operation data to obtain seasonal characteristics associated with the real-time operation data; Generating a corresponding target key using the seasonal feature; Using the target key to search the preset regression coefficient database, output the corresponding target value; The initial regression coefficient associated with the target value is used as the target regression coefficient.

3. The method for monitoring the state of overhead transmission lines based on a multiple regression model according to claim 1, wherein: The real-time operation data includes the conductor axial temperature field distribution data, the linear expansion coefficient and the reference sag parameter. The dynamic safety assessment is performed using the real-time operation data to obtain the sag safety threshold, including: Performing a difference operation on the conductor axial temperature field distribution data and a preset first sag coefficient to obtain a first difference; performing a multiplication operation on the first difference and the linear expansion coefficient to obtain a first product value; Performing a sum operation using the first product value and a preset second sag coefficient to obtain a first sum value; The first sum is multiplied by the reference sag parameter to obtain a sag safety threshold corresponding to the target line.

4. The method for monitoring the state of overhead transmission lines based on a multiple regression model according to claim 1, wherein: The real-time operation data includes ambient temperature and wind speed, and the dynamically constructing a multivariate regression current carrying capacity prediction model based on the target regression coefficient and determining the current carrying capacity prediction value using the real-time operation data includes: Substituting the target regression coefficient into a preset multiple regression equation to obtain a multiple regression current carrying capacity prediction model; The ambient temperature and the wind speed are input into the multivariate regression current carrying capacity prediction model to solve the problem and obtain the current carrying capacity prediction value corresponding to the target line.

5. The method for monitoring the state of overhead transmission lines based on a multiple regression model according to claim 1, wherein: The comparing the current carrying capacity safety margin with the associated minimum safety margin, comparing the target sag with the sag safety threshold, and generating the operating status of the target line according to the comparison result includes: When the current carrying capacity safety margin is greater than or equal to the minimum safety margin, and the target sag is less than or equal to the sag safety threshold, it is determined that the target line is in a safe operating state; When the current carrying capacity safety margin is less than the minimum safety margin, and the target sag is less than or equal to the sag safety threshold, it is determined that the target line is in a current carrying capacity warning state; When the current carrying capacity safety margin is greater than or equal to the minimum safety margin, and the target sag is greater than the sag safety threshold, it is determined that the target line is in a sag warning state; When the current carrying capacity safety margin is less than the minimum safety margin and the target sag is greater than the sag safety threshold, it is determined that the target line is in a dangerous operating state.

6. The method for monitoring the state of overhead transmission lines based on a multiple regression model according to claim 1, wherein: Also includes: When the operating state of the target line is in the current carrying capacity warning state, the sag warning state, or the dangerous operating state, the interruptible load of the target line is adjusted until the operating state of the target line is in the safe operating state.

7. The method for monitoring the state of an overhead transmission line based on a multiple regression model according to any one of claims 1 to 6, characterized in that: Also includes: Acquiring historical operation data of the target line; Dividing the historical operation data into seasonal cycles to obtain multiple operation subsets; The operation subset is the historical operation data collected in each season within the historical operation data; Inputting each of the running subsets into a preset multiple regression equation and solving it using the least squares method to obtain a plurality of corresponding initial regression coefficients; Establish a data structure template for the regression coefficient database; Importing each of the initial regression coefficients into the data structure template to generate a regression coefficient database.

8. An overhead transmission line status monitoring system based on a multivariate regression model, characterized in that: include: Acquisition module, used to obtain the real-time operation data, target sag and maximum current carrying capacity of the target line; An evaluation module, configured to perform a dynamic safety evaluation using the real-time operating data to obtain a sag safety threshold; A retrieval module, configured to analyze the seasonal characteristics of the real-time operation data and match target regression coefficients associated with the seasonal characteristics based on a preset regression coefficient database; A construction module is used to dynamically construct a multivariate regression current carrying capacity prediction model based on the target regression coefficient and determine the current carrying capacity prediction value using the real-time operation data; A calculation module, configured to perform a difference calculation between the predicted current carrying capacity value and the maximum current carrying capacity of the line to obtain a current carrying capacity safety margin; A comparison module is used to compare the current carrying capacity safety margin with the associated minimum safety margin, compare the target sag with the sag safety threshold, and generate the operating status of the target line according to the comparison result.

9. An electronic device, characterized in that: It includes a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the steps of the overhead transmission line status monitoring method based on the multivariate regression model as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed, the method for monitoring the state of an overhead transmission line based on a multiple regression model as described in any one of claims 1 to 7 is implemented.

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