Power transmission channel risk early warning assessment method and system

Abstract

The invention discloses a power transmission channel risk early warning evaluation method and system, and relates to the technical field of power transmission channel abnormal condition early warning, and the method comprises the steps: analyzing the current waveform distortion degree caused by a nonlinear load in a power transmission line through monitoring the voltage harmonic distortion rate, and evaluating the influence of harmonic waves on the insulation aging and heating loss of the line and equipment; the real-time gradient of the temperature of the wire is analyzed, abnormal heating conditions of different sections of the wire are judged, and the abnormal rising degree of the temperature of the wire and potential thermal damage accumulation caused by overload and poor contact factors are evaluated; by monitoring and analyzing multi-dimensional indexes such as the voltage harmonic distortion rate, the lead temperature real-time gradient, the sag dynamic deviation rate and the corona discharge intensity, the defect of a traditional monitoring means in the aspect of abnormal heating risk assessment is overcome.

Description

Technical Field

[0001] The present invention belongs to the technical field of early warning of abnormal situations in power transmission channels, and in particular to a method and system for early warning assessment of risks in power transmission channels. Background Art

[0002] During the operation of power transmission channels, abnormal heating is a significant threat to power grid security. On the one hand, with the widespread integration of nonlinear loads such as industrial equipment and electronic devices, voltage harmonic distortion in transmission lines has become increasingly severe. Harmonics not only accelerate the aging of line and equipment insulation but also increase heat losses, posing a safety hazard. On the other hand, abnormal temperatures caused by factors such as conductor overload and poor contact can degrade the conductor's mechanical properties, accumulate thermal damage, and even cause disconnection. Furthermore, changes in conductor sag and increased corona discharge also pose threats to transmission channel safety. Traditional monitoring methods struggle to comprehensively and accurately assess these risks. Summary of the Invention

[0003] The purpose of the present invention is to provide a transmission channel risk early warning assessment method and system, which solves the technical problem in the prior art that traditional monitoring means are insufficient in abnormal heating risk assessment.

[0004] A transmission channel risk early warning assessment method, comprising: By monitoring the voltage harmonic distortion rate, the current waveform distortion caused by nonlinear loads in the transmission line is analyzed, and the impact of harmonics on insulation aging and heat loss of the line and equipment is evaluated; Analyze the real-time conductor temperature gradient to determine abnormal heating conditions in different sections of the conductor and assess the degree of abnormal conductor temperature increase and potential thermal damage accumulation caused by overload and poor contact. Determine whether there is a risk of abnormal heating in the transmission line based on the impact of the voltage harmonic distortion rate on equipment insulation aging and heat loss and the abnormal conductor temperature reflected by the real-time conductor temperature gradient. When there is a risk of abnormal heating in the transmission line: analyze the sag dynamic deviation rate to evaluate the sag changes of the conductor due to temperature changes and mechanical stress factors, and determine whether the distance between the conductor and the ground meets safety standards. Monitor the corona discharge intensity to evaluate the intensification of corona discharge on the conductor surface due to heating and damage factors, and determine the degree of insulation performance attenuation. Comprehensively analyze the spatial position changes of the conductor reflected by the sag dynamic deviation rate and the degree of insulation performance attenuation reflected by the corona discharge intensity to assess the risk level of transmission channel operation and issue early warnings.

[0005] As a further solution of the present invention: the monitoring of voltage harmonic distortion rate, analysis of the degree of current waveform distortion caused by nonlinear loads in transmission lines, and assessment of the impact of harmonics on insulation aging and heat loss of lines and equipment include: Collect three-phase voltage signals in real time, calculate the harmonic content through spectrum analysis, and calculate the voltage harmonic distortion rate according to the standard formula; Establish a correlation model between voltage harmonic distortion rate and insulation aging rate to evaluate the damage to insulation life caused by harmonics; Calculate the additional heating losses caused by harmonics and evaluate the impact on the current carrying capacity of the wires.

[0006] As a further solution of the present invention, the real-time temperature gradient of the conductor is analyzed to determine the abnormal heating conditions in different sections of the conductor, and to assess the degree of abnormal temperature increase and potential thermal damage accumulation caused by overload and poor contact. Specifically, the following are included: Distributed temperature measurement points are set at intervals on the conductor, and the temperature difference and distance ratio of adjacent temperature measurement points are calculated as ΔT; A thermal damage accumulation model was established to evaluate the degree of thermal damage accumulation based on the duration that ΔT exceeded the warning threshold and the thermal damage accumulation rate. When the ΔT of a certain section exceeds the warning threshold and the thermal damage accumulation rate is greater than the first preset value, it is determined that there is an abnormal heating risk and continuous monitoring is initiated; When ΔT in a certain section is greater than a second preset value for a continuous preset time, it is directly determined that there is a serious fever risk.

[0007] As a further solution of the present invention, judging whether there is a risk of abnormal heating of the transmission line based on the impact of the voltage harmonic distortion rate on the insulation aging and heat loss of the equipment and the abnormal conductor temperature reflected by the real-time conductor temperature gradient is specifically including: Construct a risk matrix, with the horizontal axis representing the voltage harmonic distortion rate level and the vertical axis representing the conductor temperature real-time gradient level; When the risk matrix falls into the warning area or above, the abnormal heating warning is triggered and the sag and corona monitoring is started.

[0008] As a further solution of the present invention, the analysis of the sag dynamic deviation rate to evaluate the sag changes caused by temperature changes and mechanical stress factors of the conductor and determine whether the distance between the conductor and the ground meets the safety standards specifically includes: Obtain the conductor's spatial coordinates through drone lidar scanning or image recognition, and calculate the real-time sag value; Calculate the sag dynamic deviation rate; Based on the mapping relationship between the sag dynamic deviation rate and the distance to the ground, evaluate whether it is below the safety threshold.

[0009] As a further solution of the present invention, the method of monitoring the intensity of corona discharge, evaluating the aggravation of corona discharge caused by heating and damage factors on the surface of the conductor, and determining the degree of insulation performance attenuation specifically includes: Use ultraviolet imager or electric field sensor to measure the number of corona discharge photons or electric field intensity and convert it into discharge intensity; A correlation model between corona discharge intensity and insulation aging is established. When the discharge intensity exceeds a preset multiple of the corona inception voltage and the remaining insulation life is less than half, the insulation performance is judged to be severely degraded.

[0010] As a further solution of the present invention, the comprehensive analysis of the conductor spatial position change reflected by the sag dynamic deviation rate and the insulation performance attenuation degree reflected by the corona discharge intensity specifically includes: normalizing the spatial risk coefficient corresponding to the sag dynamic deviation rate and the insulation risk coefficient corresponding to the corona discharge intensity, respectively, and calculating the normalized spatial risk coefficient and insulation risk coefficient; A comprehensive risk coefficient threshold is preset, and the comprehensive risk coefficient is compared with the comprehensive risk coefficient threshold: when the comprehensive risk coefficient is less than or equal to the comprehensive risk coefficient threshold, it indicates that the operation risk of the transmission channel is low; when the comprehensive risk coefficient is greater than the comprehensive risk coefficient threshold, it indicates that the operation risk of the transmission channel is high.

[0011] As a further solution of the present invention: the spatial risk coefficient is determined by the following method: Based on the acquired sag dynamic deviation rate data, the degree of deviation from the historical normal operation data is calculated, and the degree of deviation is converted into a spatial risk coefficient through a preset mapping function. The mapping function is a monotonically increasing function. The greater the degree of deviation, the greater the spatial risk coefficient.

[0012] As a further solution of the present invention: the insulation risk coefficient is determined by: based on the acquired corona discharge intensity data, the ratio thereof to the corona inception voltage is calculated, and the ratio is converted into the insulation risk coefficient by a preset mapping function, wherein the mapping function is a monotonically increasing function, and the larger the ratio, the greater the insulation risk coefficient.

[0013] In a second aspect, the present application provides a transmission channel risk early warning and assessment system, the system comprising: The monitoring module monitors the voltage harmonic distortion rate, analyzes the degree of current waveform distortion caused by nonlinear loads in the transmission line, and evaluates the impact of harmonics on insulation aging and heat loss of the line and equipment; The analysis module analyzes the real-time temperature gradient of the conductor, determines the abnormal heating conditions in different sections of the conductor, and evaluates the degree of abnormal conductor temperature increase and potential thermal damage accumulation caused by overload and poor contact. The judgment module determines whether there is a risk of abnormal heating in the transmission line based on the impact of the voltage harmonic distortion rate on equipment insulation aging and heat loss and the abnormal conductor temperature reflected by the real-time conductor temperature gradient. The assessment module, when there is a risk of abnormal heating in the transmission line: by analyzing the sag dynamic deviation rate, evaluates the sag changes of the conductor caused by temperature changes and mechanical stress factors, and determines whether the distance between the conductor and the ground meets safety standards; by monitoring the corona discharge intensity, evaluates the intensification of corona discharge on the conductor surface caused by heating and damage factors, and determines the degree of insulation performance attenuation. The early warning module conducts a comprehensive analysis of the spatial position changes of the conductor reflected by the sag dynamic deviation rate and the degree of insulation performance attenuation reflected by the corona discharge intensity, evaluates the risk level of transmission channel operation and issues early warnings.

[0014] Compared with the prior art, the present invention has the following beneficial effects: This invention helps address the shortcomings of traditional monitoring methods in assessing abnormal heating risks by monitoring and analyzing multi-dimensional indicators such as voltage harmonic distortion rate, real-time conductor temperature gradient, sag dynamic deviation rate, and corona discharge intensity. From harmonic impact assessment and determination of conductor temperature anomalies to in-depth analysis of sag and insulation performance under abnormal heating risk, and finally to risk level assessment and early warning, a complete risk assessment system has been established. This system accurately identifies potential risks during transmission channel operation, issues early warnings, and provides operators with a scientific basis for decision-making, effectively reducing the incidence of transmission line failures and ensuring the safe and stable operation of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 Schematic diagram of the framework structure of the method of the present invention. DETAILED DESCRIPTION

[0016] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0017] First aspect: please refer to Figure 1 , this application provides a transmission channel risk early warning assessment method, including: By monitoring the voltage harmonic distortion rate, the current waveform distortion caused by nonlinear loads in the transmission line is analyzed, and the impact of harmonics on insulation aging and heat loss of the line and equipment is evaluated; Analyze the real-time conductor temperature gradient to determine abnormal heating conditions in different conductor sections and assess the degree of abnormal conductor temperature rise and potential thermal damage accumulation caused by overload and poor contact. Determine whether there is a risk of abnormal heating on the transmission line based on the impact of voltage harmonic distortion on equipment insulation aging and heat loss, as well as abnormal conductor temperature conditions reflected by the real-time conductor temperature gradient. Abnormal conductor temperature refers to the degree of abnormal conductor temperature rise and potential accumulation of thermal damage. When a transmission line is at risk of abnormal heating, the following measures are taken: By analyzing the dynamic sag deviation rate, changes in conductor sag due to temperature changes and mechanical stress are assessed, and the conductor's distance to the ground is determined to meet safety standards. By monitoring the corona discharge intensity, the intensification of corona discharge on the conductor surface due to heating and damage is assessed, and the degree of insulation degradation is determined. A comprehensive analysis of the changes in the conductor's spatial position as reflected by the dynamic sag deviation rate and the degree of insulation degradation as reflected by the corona discharge intensity is conducted to assess the risk level of transmission channel operation and issue early warnings.

[0018] In actual operation, nonlinear loads (such as inverters and arc furnaces) inject large amounts of harmonic currents into the power grid, causing voltage waveform distortion. When the voltage harmonic distortion rate exceeds a certain threshold, the harmonic currents will cause additional losses in transmission lines and equipment, accelerating the aging of insulation materials. Excessively high harmonic distortion rates significantly increase heat losses in lines and transformers, accelerate insulation aging, and severely shorten equipment life. Therefore, analyzing the real-time temperature gradient of the conductor can effectively determine the abnormal heating conditions of different sections of the conductor. Under normal operating conditions, the temperature of each section of the conductor is relatively uniform. If a section is overloaded (such as a sudden increase in line load) or has poor contact (such as a loose wire clamp), the local temperature will rise, forming an obvious temperature gradient. By collecting temperature data at each point on the conductor through distributed temperature sensors and constructing a temperature gradient model, the conductor temperature distribution can be intuitively presented. For example, during the peak load period of a certain section of transmission line, the temperature gradient analysis found that the conductor temperature near a tension clamp was 15°C higher than that of other sections. It was determined that the wire clamp had a poor contact problem. If not handled in time, as thermal damage accumulates, it may cause a line break accident; Based on the impact of voltage harmonic distortion on equipment insulation aging and heat loss, as well as the abnormal conductor temperature reflected by the real-time conductor temperature gradient, a comprehensive assessment is made of whether there is a risk of abnormal heating in the transmission line. When the voltage harmonic distortion is too high and the conductor temperature shows obvious abnormalities, it indicates that there is a high risk of abnormal heating in the transmission line. For example, if the voltage harmonic distortion of a transmission line reaches 7% and the temperature gradient of a certain section of the conductor is abnormal, it is likely that the heat loss caused by harmonics is superimposed on the abnormal heating of the conductor itself. At this time, the possibility of line failure increases significantly, and immediate measures must be taken to conduct a risk assessment. When there is a risk of abnormal heating in the transmission line, further in-depth assessment work should be carried out. By analyzing the dynamic deviation rate of sag, the changes in the sag of the conductor caused by temperature changes and mechanical stress factors are evaluated. As the temperature of the conductor increases, its thermal expansion will increase the sag. If the sag exceeds the safe range, the distance between the conductor and the ground will decrease, which may cause a safety accident. At the same time, by monitoring the intensity of corona discharge, the intensification of corona discharge on the surface of the conductor due to heating and damage factors is evaluated. Corona discharge will accelerate the oxidation of the surface of the conductor and reduce the insulation performance. When the intensity of corona discharge increases, it means that the insulation performance of the conductor is decaying. For example, when a section of transmission line is abnormally heated, the dynamic deviation rate of sag reaches 10%, which exceeds the normal range, and the intensity of corona discharge is significantly enhanced, indicating that the distance between the conductor and the ground may not meet the safety standards and the insulation performance is also declining, which requires special attention; A comprehensive analysis of the dynamic sag deviation rate, as reflected by the spatial position changes of conductors, and the degree of insulation degradation, as reflected by the corona discharge intensity, is performed to assess the risk level of transmission channel operations and issue early warnings. Based on a pre-defined risk assessment model, the risk levels are categorized as low, medium, and high. When the sag deviation is small and the corona discharge intensity is within the normal range, the risk is assessed as low; when the sag deviation is large or the corona discharge intensity is significantly increased, the risk is assessed as medium; and when the sag exceeds the standard and the corona discharge intensity is extremely high, the risk is assessed as high, and an early warning of the corresponding level is immediately issued. For example, if a transmission channel is assessed as high risk, the system will quickly send an early warning to operations and maintenance personnel, prompting them to take immediate action, such as adjusting the line load or replacing damaged conductors, to ensure safe operation of the transmission channel.

[0019] By monitoring and analyzing multi-dimensional indicators such as voltage harmonic distortion, real-time conductor temperature gradients, sag dynamic deviation rate, and corona discharge intensity, this system addresses the shortcomings of traditional monitoring methods in assessing abnormal heating risks. From harmonic impact assessment and determination of conductor temperature anomalies to in-depth analysis of sag and insulation performance under abnormal heating risks, and ultimately to risk level assessment and early warning, a comprehensive risk assessment system has been established. This system accurately identifies potential risks during transmission channel operation, issues early warnings, and provides operators with a scientific basis for decision-making, effectively reducing the incidence of transmission line failures and ensuring the safe and stable operation of the power grid.

[0020] As an optional embodiment, by monitoring the voltage harmonic distortion rate, analyzing the degree of current waveform distortion caused by nonlinear loads in the transmission line, and evaluating the impact of harmonics on insulation aging and heat loss of the line and equipment, the following are included: Collect three-phase voltage signals in real time, calculate the harmonic content through spectrum analysis, and calculate the voltage harmonic distortion rate according to the standard formula; Establish a correlation model between voltage harmonic distortion rate and insulation aging rate to evaluate the damage to insulation life caused by harmonics; Calculate the additional heating losses caused by harmonics and evaluate the impact on the current carrying capacity of the wires.

[0021] Specifically, in modern power systems, with the widespread use of nonlinear devices such as industrial frequency converters and new energy inverters, the problem of harmonic pollution in transmission lines is becoming increasingly serious. Traditional harmonic monitoring only focuses on content detection and is unable to quantify the actual harm of harmonics to lines and equipment: on the one hand, the high-frequency electromagnetic fields generated by harmonic currents accelerate the electrical aging of insulation materials, shortening the insulation life of equipment, but the lack of quantitative assessment methods makes it difficult to predict the risk of insulation failure; on the other hand, the additional heat loss caused by harmonics can reduce the current carrying capacity of conductors and even cause conductor overheating failures. Existing methods cannot accurately calculate the extent of this impact, thereby increasing the safety risks of power grid operation. Therefore, by collecting three-phase voltage signals in real time and using spectrum analysis techniques such as Fast Fourier Transform (FFT), we can accurately calculate the content of each harmonic. For example, in a transmission line in a densely industrialized area, the monitoring device collects 1,000 sets of three-phase voltage data per second. Through spectrum analysis, it is found that the 5th harmonic content is 6% and the 7th harmonic content is 3.5%. Subsequently, according to the national standard formula ,in is the effective value of the hth harmonic voltage, is the effective value of the fundamental voltage, This value represents the square root of the sum of the squares of the effective values ​​of all harmonic voltages from the 2nd to the Hth order, reflecting the overall amplitude of the harmonic components. This value is used to calculate the voltage harmonic distortion rate. A larger value indicates a higher proportion of harmonic components in the voltage signal, worsening the power quality and significantly impacting insulation aging and heat loss in power equipment (such as transformers and motors). This ensures the real-time and accuracy of harmonic parameters, providing a reliable data foundation for subsequent analysis.

[0022] Among them, based on a large amount of experimental data and long-term on-site monitoring, a correlation model between voltage harmonic distortion rate and insulation aging rate was established. Studies have shown that when the harmonic distortion rate increases by 1%, the dielectric loss tangent of the oil-paper insulation will increase by 0.5%, accelerating the breakage of the molecular chain of the insulation material. For example, in the monitoring of a transformer in a certain substation, it was found through model calculation that when the voltage harmonic distortion rate reaches 8%, its insulation life is shortened by 30% compared with normal operating conditions. By quantifying the relationship between harmonics and insulation aging, this model helps operation and maintenance personnel predict the risk of insulation failure in advance and reasonably arrange equipment maintenance cycles; Specifically, the additional heat loss caused by harmonics can be calculated by establishing a multi-physics coupling model, taking into account factors such as the skin effect and proximity effect. For example, in a steel-core aluminum stranded conductor with a current carrying capacity of 800A, when the fifth harmonic content reaches 5%, it is calculated that the additional heat loss will increase the conductor temperature by 8°C, resulting in a decrease in current carrying capacity of approximately 6%. This quantitative analysis can assess the actual impact of harmonics on the current carrying capacity of conductors, providing a scientific basis for line load scheduling and equipment selection. By establishing a quantitative relationship between harmonic distortion rate and insulation aging, the risk of insulation life loss is transformed from a fuzzy assessment to an accurate prediction. Multi-physics field models are then used to quantify harmonic heating losses and clarify their impact on the current-carrying capacity of conductors. This complements other monitoring indicators of transmission lines (such as conductor temperature and sag changes), helping operation and maintenance personnel to fully understand the hazards of harmonics, adjust operating strategies in a timely manner, reduce the risk of equipment insulation failure and conductor overheating, and significantly improve the operational reliability of transmission channels.

[0023] As an optional embodiment, the real-time conductor temperature gradient is analyzed to determine the abnormal heating conditions in different sections of the conductor, and to assess the degree of abnormal conductor temperature increase and potential thermal damage accumulation caused by overload and poor contact. Specifically, the following steps are performed: Distributed temperature measurement points are set at intervals on the conductor, and the temperature difference and distance ratio of adjacent temperature measurement points are calculated as ΔT; A thermal damage accumulation model was established to evaluate the degree of thermal damage accumulation based on the duration that ΔT exceeded the warning threshold and the thermal damage accumulation rate. When the ΔT of a certain section exceeds the warning threshold and the thermal damage accumulation rate is greater than the first preset value, it is determined that there is an abnormal heating risk and continuous monitoring is initiated; When ΔT in a certain section is greater than a second preset value for a continuous preset time, it is directly determined that there is a serious fever risk.

[0024] Specifically, in the operation of transmission lines, the problem of accumulated thermal damage to conductors has long faced the situation of blind spots in monitoring and misjudgment of risks. Traditional single-point temperature measurement cannot capture the temperature gradient changes in the conductor section. For example, when the tension clamp has poor contact, the local high temperature may only change significantly within a few meters, and single-point temperature measurement is prone to miss detection; and the static threshold warning does not take into account the cumulative effect of thermal damage. For example, if the temperature gradient in a section continues to slightly exceed the standard but does not trigger an alarm, long-term operation may cause the mechanical strength of the conductor to decrease. In addition, the lack of quantitative analysis of the duration of temperature gradient anomalies makes it difficult to distinguish between instantaneous load fluctuations and persistent defects, resulting in mismatch of operation and maintenance resources; Therefore, distributed temperature measurement points are first placed at intervals along the conductor (e.g., a fiber optic temperature measurement node is set up every 50 meters). After real-time temperature data is collected, the ratio of the temperature difference between adjacent temperature measurement points to the distance is calculated (unit: °C / m). For example, if the temperatures at two adjacent points on a certain section of conductor are 65°C and 50°C, respectively, and the distance between them is 100 meters, then ΔT = (65-50) / 100 = 0.15°C / m. This parameter directly reflects the spatial rate of change of conductor temperature. During normal operation, ΔT is typically less than 0.1°C / m, while in sections with poor contact or overload, ΔT can reach over 0.3°C / m. A thermal damage accumulation model was established based on the Arrhenius equation, coupling the duration (t) during which ΔT exceeds a warning threshold (e.g., 0.2°C / m) with the thermal damage accumulation rate (k). Experimental data showed that when ΔT = 0.25°C / m, the creep damage rate (k) of the conductor aluminum alloy material is 0.01 / h. If this continues for 24 hours, the thermal damage accumulation level (D) is 0.24 (the maximum value is 1). The model calculates the D value through real-time integration. When D ≥ 0.5, the conductor's tensile strength drops to 80% of its original value, necessitating maintenance intervention. When the ΔT of a certain section exceeds the warning threshold (0.2°C / m) and the thermal damage accumulation rate k>0.005 / h (the first preset value), it is determined that there is an abnormal heating risk. For example, if ΔT=0.22°C / m and k=0.008 / h, the system automatically starts high-frequency monitoring every 10 minutes to track the damage development trend; If the ΔT in a certain section exceeds 0.3°C / m (the second preset value) for four consecutive hours (preset time), it is directly judged as a serious risk. At this time, the conductor temperature may exceed 120°C, the oxide layer between the strands will intensify, and the contact resistance will increase exponentially. A power outage and maintenance must be arranged within 2 hours. This conductor temperature gradient analysis solution adopts a complete technical path of "distributed temperature measurement-gradient quantification-damage modeling-risk classification". First, through distributed temperature measurement points and temperature gradient ΔT calculation, the spatial monitoring accuracy of local thermal anomalies in conductors is improved to a preset interval, which improves the anomaly recognition rate and solves the problem that traditional single-point temperature measurement is difficult to capture local thermal hazards. Second, the thermal damage accumulation model is used to quantify the continuous impact of temperature gradient anomalies. Combined with classification measures, the early warning accuracy is improved, and fault omissions caused by ignoring the cumulative effect of thermal damage are avoided. It also complements modules such as harmonic impact assessment and sag change monitoring, which can help operation and maintenance personnel to promptly grasp the thermal safety status of conductors and reasonably arrange maintenance strategies, which is conducive to reducing the risks of line breakage and insulation failure caused by conductor overheating, and significantly improving the safety and reliability of transmission channel operation.

[0025] As an optional embodiment, based on the impact of voltage harmonic distortion on equipment insulation aging and heat loss and the abnormal conductor temperature reflected by the real-time conductor temperature gradient, it is determined whether there is a risk of abnormal heating of the transmission line, specifically including: Construct a risk matrix, with the horizontal axis representing the voltage harmonic distortion rate level and the vertical axis representing the conductor temperature real-time gradient level; When the risk matrix falls into the warning area or above, the abnormal heating warning is triggered and the sag and corona monitoring is started.

[0026] Specifically, in the operation monitoring of transmission channels, the use of a single indicator to assess the risk of abnormal heating has significant limitations. On the one hand, relying solely on the voltage harmonic distortion rate assessment may overlook temperature anomalies caused by poor contact or overload of the conductor itself; on the other hand, judging solely based on the conductor temperature gradient may easily miss the problems of insulation aging and increased heat loss caused by harmonics. In addition, when the harmonic distortion rate slightly exceeds the standard but the conductor temperature gradient is significantly abnormal, it is difficult to quickly determine the risk level, which can easily lead to delayed warnings or misjudgments, and cannot meet the efficient decision-making needs for the safe operation of transmission lines. Therefore, in order to solve the above problems, a risk matrix is ​​constructed to quantify and grade the two key indicators, voltage harmonic distortion rate and conductor temperature real-time gradient. The horizontal axis represents the voltage harmonic distortion rate level, which is divided into three levels: low (THD_V < 5%), medium (5% ≤ THD_V < 8%), and high (THD_V ≥ 8%) according to national standards and equipment tolerance. The vertical axis represents the conductor temperature real-time gradient level, which is divided into three levels: normal (ΔT < 0.1°C / m), warning (0.1°C / m ≤ ΔT < 0.2°C / m), and severe (ΔT ≥ 0.2°C / m) according to the temperature difference and distance ratio ΔT of adjacent temperature measurement points. In this way, continuous monitoring data is converted into discrete risk dimensions, which facilitates intuitive analysis and quick judgment. When a data point in the risk matrix falls into the warning zone or above, the system triggers an abnormal heating warning. For example, if the voltage harmonic distortion rate is "medium" and the conductor temperature real-time gradient is "warning," corresponding to the high-risk zone in the risk matrix, the system immediately issues a warning signal. This avoids the limitations of traditional single-indicator alarms and comprehensively considers the synergistic effects of harmonics and temperature anomalies. Furthermore, after the warning is triggered, the system automatically initiates sag and corona monitoring, conducting in-depth analysis of the conductor's spatial position changes and insulation degradation, providing more dimensional data support for subsequent risk assessment. This helps address the shortcomings of traditional monitoring's multi-factor coupled risk assessment. By quantifying and grading these two key indicators into a matrix, the system overcomes the one-sidedness of single-indicator judgments and makes risk assessment more comprehensive and accurate. The judgment rules for warning and above zones enable rapid identification of the synergistic effects of harmonics and temperature anomalies, helping to avoid delayed warnings or misjudgments caused by single indicators. Furthermore, the linkage mechanism between sag and corona monitoring after the warning is triggered further enhances the risk assessment system.

[0027] As an optional embodiment, by analyzing the sag dynamic deviation rate, evaluating the sag changes caused by temperature changes and mechanical stress factors of the conductor, and judging whether the distance between the conductor and the ground meets the safety standards, specifically including: Obtain the conductor's spatial coordinates through drone lidar scanning or image recognition, and calculate the real-time sag value; Calculate the sag dynamic deviation rate; Based on the mapping relationship between the sag dynamic deviation rate and the distance to the ground, evaluate whether it is below the safety threshold.

[0028] Specifically, in the operation and maintenance of transmission lines, changes in conductor sag directly threaten the safe operation of the line. Traditional monitoring methods face many challenges. Existing technologies lack quantitative correlation analysis between sag and distance to the ground, making it difficult for operators to quickly determine whether the conductor is in a safe operating state, which may lead to safety accidents such as electric shock and discharge. The spatial coordinates of the conductors are obtained by using drone-mounted laser radar scanning or image recognition technology. Laser radar can scan the contours of the conductors with millimeter-level accuracy to obtain three-dimensional point cloud data; image recognition uses a high-definition camera to capture images of the conductors and extracts the coordinates of the conductor feature points through computer vision algorithms. Based on the acquired spatial coordinates and combined with the information of the suspension points at both ends of the conductor, the parabola model or catenary equation is used to calculate the real-time sag value. For example, in the monitoring of a cross-river power transmission line, after the drone laser radar scan, it was calculated that the current sag was 12.5 meters, which was 1.2 meters higher than the design value. Calculate the dynamic sag deviation rate, which reflects the degree of change in conductor sag relative to the design value or historical stable value. The calculation formula is: Dynamic sag deviation rate = (real-time sag value - sag reference value) / sag reference value × 100%. The sag reference value can be the line design sag or the sag value during recent stable operation. For example, if the design sag of a certain conductor section is 10 meters and the current real-time sag is 11 meters, then the dynamic sag deviation rate = (11-10) / 10 × 100% = 10%. This indicator can be used to intuitively quantify the magnitude of conductor sag changes. Based on the pre-established mapping relationship between the sag dynamic deviation rate and the distance to the ground, the system assesses whether the conductor's distance to the ground is below the safety threshold. This mapping relationship is based on line design parameters, terrain data, and mechanical models. For example, when the sag dynamic deviation rate exceeds 8%, the corresponding conductor's distance to the ground may be lower than the 7 meters required by safety standards. Once the system detects that the sag dynamic deviation rate has triggered the threshold, it immediately issues an early warning, alerting operators to the safety risks of the conductor and the need to promptly adjust the sag or take other protective measures. In summary, the use of drone lidar and image recognition technology has overcome the limitations of low efficiency and poor accuracy of manual inspections, and achieved real-time and high-precision acquisition of conductor spatial coordinates; the calculation of the sag dynamic deviation rate quantifies the sag change, avoiding the ambiguity of empirical judgment alone; the safety assessment based on the mapping relationship establishes a direct connection between the sag change and the distance to the ground, solving the problem that traditional methods cannot quickly judge the safety status. In addition, this solution works in conjunction with other transmission line monitoring modules, which is conducive to significantly improving the perception and early warning capabilities of conductor sag anomalies, effectively reducing the risk of safety accidents caused by sag changes, and ensuring the safe and stable operation of transmission lines.

[0029] As an optional embodiment, by monitoring the intensity of corona discharge, evaluating the aggravation of corona discharge caused by heating and damage factors on the conductor surface, and judging the degree of insulation performance attenuation, specifically including: Use ultraviolet imager or electric field sensor to measure the number of corona discharge photons or electric field intensity and convert it into discharge intensity; A correlation model between corona discharge intensity and insulation aging is established. When the discharge intensity exceeds a preset multiple of the corona inception voltage and the remaining insulation life is less than half, the insulation performance is judged to be severely degraded.

[0030] Specifically, during the operation of transmission lines, insulation degradation is a potential threat to power grid security. On the one hand, relying solely on manual inspections to observe surface damage on conductors makes it difficult to detect the aging of internal insulation materials, such as cracks inside insulators or electrical corrosion of the conductor's outer insulation. On the other hand, there is a lack of quantitative analysis of corona discharge, a key indicator of insulation degradation. Corona discharge not only accelerates the aging of insulation materials but can also cause power loss and electromagnetic interference. However, traditional methods cannot accurately determine the relationship between discharge intensity and insulation degradation, resulting in the inability to detect insulation degradation in a timely manner, which can easily lead to equipment failures and even power outages. First, use an ultraviolet imager or electric field sensor to measure the intensity of the corona discharge. The ultraviolet imager can capture the ultraviolet photons generated by the corona discharge and quantify the discharge intensity by counting the number of photons per unit time; the electric field sensor measures the electric field intensity by sensing the changes in the electric field generated by the discharge and converts it into the corresponding discharge intensity. For example, during the inspection of a high-voltage transmission line, the ultraviolet imager detects that the number of corona discharge photons per unit time reaches 1000, and the conversion algorithm derives the current corona discharge intensity as 20μA; or the electric field sensor measures the change in electric field intensity, and the discharge intensity is converted to 18μA. These measurement methods realize real-time, non-contact monitoring of the corona discharge intensity; Next, a correlation model between corona discharge intensity and insulation aging was established, which was constructed based on a large amount of experimental data and field operation experience. The model sets key judgment conditions: when the corona discharge intensity exceeds a preset multiple of the corona inception voltage (such as 1.5 times), and the model calculates that the remaining insulation life is less than half, it is determined that the insulation performance has severely degraded. The corona inception voltage is the critical voltage at which the conductor begins to generate corona discharge. Exceeding this multiple means that the discharge phenomenon intensifies and the erosion of the insulation material is accelerated. For example, the corona inception voltage of a certain section of conductor is 30kV. When the voltage corresponding to the monitored corona discharge intensity reaches 45kV (i.e. 1.5 times the corona inception voltage), and the model calculates that the remaining insulation life is only 40%, the system immediately issues an early warning of severe insulation degradation; In summary, the use of ultraviolet imagers and electric field sensors to accurately measure corona discharge intensity overcomes the limitations of manual inspections. The established model correlating corona discharge intensity with insulation aging transforms abstract insulation degradation into quantifiable criteria, changing the ambiguity of previous empirical judgments. By setting dual thresholds for the corona onset voltage multiple and the remaining insulation life, it is possible to accurately identify instances of severe insulation degradation, avoiding equipment failures and power outages caused by insulation problems. This solution, in conjunction with modules such as conductor sag monitoring and harmonic impact assessment, provides comprehensive protection for the safe operation of transmission lines and significantly improves the reliability and stability of power grid operations.

[0031] As an optional embodiment, a comprehensive analysis is performed on the conductor spatial position change reflected by the sag dynamic deviation rate and the insulation performance attenuation degree reflected by the corona discharge intensity, specifically including: normalizing the spatial risk coefficient corresponding to the sag dynamic deviation rate and the insulation risk coefficient corresponding to the corona discharge intensity, and calculating the normalized spatial risk coefficient and insulation risk coefficient; A comprehensive risk coefficient threshold is preset, and the comprehensive risk coefficient is compared with the comprehensive risk coefficient threshold: when the comprehensive risk coefficient is less than or equal to the comprehensive risk coefficient threshold, it indicates that the operation risk of the transmission channel is low; when the comprehensive risk coefficient is greater than the comprehensive risk coefficient threshold, it indicates that the operation risk of the transmission channel is high.

[0032] Specifically, in the operation and management of transmission channels, a single-dimensional risk assessment cannot fully reflect the true safety status of the line. On the one hand, judging the spatial position risk of the conductor based solely on the dynamic sag deviation rate may ignore the potential threats brought by the attenuation of insulation performance; on the other hand, simply assessing the insulation risk through the corona discharge intensity makes it difficult to take into account the safety hazards caused by the insufficient distance between the conductor and the ground. In addition, different risk indicators have different dimensions and numerical ranges (such as the sag deviation rate is measured in percentages, and the corona discharge intensity is measured in units of current or electric field strength). Traditional independent analysis methods cannot achieve unified quantification and comprehensive assessment of risks, making it difficult for operation and maintenance personnel to quickly locate high-risk areas, which can easily lead to risk misjudgment or delayed handling. To solve the above problems, the spatial risk coefficient corresponding to the sag dynamic deviation rate and the insulation risk coefficient corresponding to the corona discharge intensity are first normalized. Since the original numerical ranges of the two risk coefficients are different, direct calculation can easily lead to an imbalance in the weights of some indicators. Normalization eliminates dimensional differences by mapping the data to the [0,1] interval, making different risk dimensions comparable. For example, the original value of the spatial risk coefficient is 0.7, and the insulation risk coefficient is 0.6. After normalization, the two are converted to 0.7 / (0.7+0.3)=0.7 and 0.6 / (0.6+0.4)=0.6, respectively, to ensure the scientificity and accuracy of subsequent calculations; After normalization, the two risk factors are calculated to obtain a comprehensive risk factor. This calculation can be done using a weighted sum (e.g., assigning weights to spatial risk and insulation risk based on historical accident data) or a product model. A threshold for the comprehensive risk factor is also preset, based on transmission line design standards, historical operating data, and safety regulations. The calculated comprehensive risk factor is then compared with the threshold: It should be noted that the calculation formula can also be used: Calculation is performed, where R is the comprehensive risk coefficient; δ is the spatial risk coefficient; is the insulation risk factor; are the weight parameters of the spatial risk factor and the insulation risk factor, respectively, and ; When the comprehensive risk coefficient is less than or equal to the comprehensive risk coefficient threshold (for example, the threshold is set to 0.6 and the calculated value is 0.5), the transmission channel operation risk is determined to be low and the regular inspection frequency can be maintained; When the comprehensive risk coefficient is greater than the comprehensive risk coefficient threshold (such as the calculated value is 0.8), it indicates that the operation risk of the transmission channel is high, and the emergency response mechanism needs to be immediately activated and special maintenance needs to be arranged.

[0033] In summary, normalization eliminates the dimensional differences between spatial risk and insulation risk, resolving the issue of multiple indicators being unable to be directly compared. A comprehensive risk coefficient calculation model is then constructed, integrating single-dimensional risks into a unified quantitative indicator to avoid risk misjudgments caused by one-sided assessments. Finally, a threshold-based comparison enables rapid classification of risk levels, improving the efficiency of identifying high-risk areas compared to traditional decentralized analysis models. This solution, in conjunction with modules such as harmonic impact assessment and conductor temperature monitoring, forms an intelligent assessment system covering the multi-dimensional risks of transmission channels. This provides a scientific basis for operation and maintenance decisions, significantly reducing the probability of line failures caused by complex risks and ensuring the safe and stable operation of the power grid.

[0034] As an optional embodiment, the spatial risk coefficient is determined by: Based on the acquired sag dynamic deviation rate data, the degree of deviation from the historical normal operation data is calculated, and the deviation degree is converted into a spatial risk coefficient through a preset mapping function. The mapping function is a monotonically increasing function. The greater the deviation degree, the greater the spatial risk coefficient.

[0035] As an optional embodiment, the insulation risk coefficient is determined in the following manner: based on the acquired corona discharge intensity data, the ratio thereof to the corona inception voltage is calculated, and the ratio is converted into the insulation risk coefficient through a preset mapping function. The mapping function is a monotonically increasing function. The larger the ratio, the greater the insulation risk coefficient.

[0036] Specifically, in transmission line operational safety assessments, traditional technologies struggle to identify potential risks associated with changes in conductor spatial position and insulation degradation. On the one hand, the lack of quantitative assessment of dynamic sag changes prevents direct reflection of the risk of spatial collisions caused by insufficient conductor-to-ground distance. On the other hand, the relationship between corona discharge intensity and insulation aging is only qualitative, making it difficult to accurately measure the likelihood of insulation failure. Consequently, ignoring potential risks can lead to safety incidents. Based on the acquired sag dynamic deviation rate data, the degree of deviation between the data and the historical normal operation data is first calculated. For example, the sag dynamic deviation rate of a certain section of conductor is stable within ±2% during historical normal operation, and the current detection value is 8%, so the degree of deviation is 6%. Subsequently, the degree of deviation is converted into a spatial risk coefficient through a preset monotonically increasing mapping function. Due to the characteristics of the mapping function, the greater the degree of deviation, the higher the converted spatial risk coefficient. Assume that when the degree of deviation is 2%, the spatial risk coefficient is 0.2; when the degree of deviation reaches 8%, the spatial risk coefficient increases to 0.7. This allows the risk of conductor sag changes to be presented intuitively, making it easier for operation and maintenance personnel to quickly identify the risk level; For the corona discharge intensity data, calculate its ratio to the corona inception voltage. For example, if the corona inception voltage of a conductor is 40kV and the voltage corresponding to the current corona discharge intensity is 60kV, the ratio is 1.5. Then, use the preset monotonically increasing mapping function to convert the ratio into an insulation risk coefficient. As the ratio increases, the insulation risk coefficient also increases accordingly. For example, when the ratio is 1.2, the insulation risk coefficient is 0.3; when the ratio reaches 1.5, the insulation risk coefficient increases to 0.6. Through this quantitative conversion, the impact of corona discharge on insulation performance is converted into a specific value, providing an accurate basis for insulation risk assessment; This risk factor determination scheme effectively addresses the core issues in traditional transmission line risk assessment by quantitatively analyzing the dynamic deviation rate of sag and corona discharge intensity. The deviation of sag changes from historical data and the ratio of corona discharge intensity to corona inception voltage are converted into spatial risk coefficients and insulation risk coefficients, respectively, using monotonically increasing mapping functions. This enables accurate quantification of the conductor's spatial position risk and insulation performance risk. Compared to traditional fuzzy assessment methods, this quantification method makes the degree of risk clear at a glance, helping operations and maintenance personnel quickly locate high-risk areas and develop maintenance plans. This helps avoid safety accidents caused by spatial collisions or insulation failures, and significantly improves the accuracy and effectiveness of transmission line operation safety assessments.

[0037] In a second aspect, the present invention further proposes a transmission channel risk early warning and assessment system, the system comprising the following steps: The monitoring module monitors the voltage harmonic distortion rate, analyzes the degree of current waveform distortion caused by nonlinear loads in the transmission line, and evaluates the impact of harmonics on insulation aging and heat loss of the line and equipment; The analysis module analyzes the real-time temperature gradient of the conductor, determines the abnormal heating conditions in different sections of the conductor, and evaluates the degree of abnormal conductor temperature increase and potential thermal damage accumulation caused by overload and poor contact. The judgment module determines whether there is a risk of abnormal heating in the transmission line based on the impact of the voltage harmonic distortion rate on equipment insulation aging and heat loss and the abnormal conductor temperature reflected by the real-time conductor temperature gradient. The assessment module, when there is a risk of abnormal heating in the transmission line: by analyzing the sag dynamic deviation rate, evaluates the sag changes of the conductor caused by temperature changes and mechanical stress factors, and determines whether the distance between the conductor and the ground meets safety standards; by monitoring the corona discharge intensity, evaluates the intensification of corona discharge on the conductor surface caused by heating and damage factors, and determines the degree of insulation performance attenuation. The early warning module conducts a comprehensive analysis of the spatial position changes of the conductor reflected by the sag dynamic deviation rate and the degree of insulation performance attenuation reflected by the corona discharge intensity, evaluates the risk level of transmission channel operation and issues early warnings.

[0038] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A transmission channel risk early warning assessment method, characterized in that: include: By monitoring the voltage harmonic distortion rate, the current waveform distortion caused by nonlinear loads in the transmission line is analyzed, and the impact of harmonics on insulation aging and heat loss of the line and equipment is evaluated; Analyze the real-time conductor temperature gradient to determine abnormal heating conditions in different sections of the conductor and assess the degree of abnormal conductor temperature increase and potential thermal damage accumulation caused by overload and poor contact. Determine whether there is a risk of abnormal heating in the transmission line based on the impact of the voltage harmonic distortion rate on equipment insulation aging and heat loss and the abnormal conductor temperature reflected by the real-time conductor temperature gradient. When there is a risk of abnormal heating in the transmission line: analyze the sag dynamic deviation rate to evaluate the sag changes of the conductor due to temperature changes and mechanical stress factors, and determine whether the distance between the conductor and the ground meets safety standards. Monitor the corona discharge intensity to evaluate the intensification of corona discharge on the conductor surface due to heating and damage factors, and determine the degree of insulation performance attenuation. Comprehensively analyze the spatial position changes of the conductor reflected by the sag dynamic deviation rate and the degree of insulation performance attenuation reflected by the corona discharge intensity to assess the risk level of transmission channel operation and issue early warnings.

2. A transmission channel risk early warning assessment method according to claim 1, characterized in that: By monitoring the voltage harmonic distortion rate, analyzing the degree of current waveform distortion caused by nonlinear loads in the transmission line, and evaluating the impact of harmonics on insulation aging and heat loss of the line and equipment, the following are included: Collect three-phase voltage signals in real time, calculate the harmonic content through spectrum analysis, and calculate the voltage harmonic distortion rate according to the standard formula; Establish a correlation model between voltage harmonic distortion rate and insulation aging rate to evaluate the damage to insulation life caused by harmonics; Calculate the additional heating losses caused by harmonics and evaluate the impact on the current carrying capacity of the wires.

3. A transmission channel risk early warning assessment method according to claim 1, characterized in that: Analyze the real-time conductor temperature gradient to determine abnormal heating conditions in different conductor sections and assess the degree of abnormal conductor temperature rise and potential thermal damage accumulation caused by overload and poor contact. Specifically, the following are included: Distributed temperature measurement points are set at intervals on the conductor, and the temperature difference and distance ratio of adjacent temperature measurement points are calculated as ΔT; A thermal damage accumulation model was established to evaluate the degree of thermal damage accumulation based on the duration that ΔT exceeded the warning threshold and the thermal damage accumulation rate. When the ΔT of a certain section exceeds the warning threshold and the thermal damage accumulation rate is greater than the first preset value, it is determined that there is an abnormal heating risk and continuous monitoring is initiated; When ΔT in a certain section is greater than a second preset value for a continuous preset time, it is directly determined that there is a serious fever risk.

4. A transmission channel risk early warning assessment method according to claim 1, characterized in that: Based on the impact of voltage harmonic distortion on equipment insulation aging and heat loss, and the abnormal conductor temperature reflected by the real-time conductor temperature gradient, determine whether there is an abnormal heating risk on the transmission line. Specifically, the following are included: Construct a risk matrix, with the horizontal axis representing the voltage harmonic distortion rate level and the vertical axis representing the conductor temperature real-time gradient level; When the risk matrix falls into the warning area or above, the abnormal heating warning is triggered and the sag and corona monitoring is started.

5. A transmission channel risk early warning assessment method according to claim 1, characterized in that: By analyzing the sag dynamic deviation rate, we can evaluate the sag changes caused by temperature changes and mechanical stress factors, and determine whether the distance between the conductor and the ground meets safety standards. Specifically, we can: Obtain the conductor's spatial coordinates through drone lidar scanning or image recognition, and calculate the real-time sag value; Calculate the sag dynamic deviation rate; Based on the mapping relationship between the sag dynamic deviation rate and the distance to the ground, evaluate whether it is below the safety threshold.

6. The transmission channel risk early warning assessment method according to claim 1, characterized in that: By monitoring the intensity of corona discharge, we can evaluate the aggravation of corona discharge caused by heating and damage on the conductor surface and determine the degree of insulation degradation. Specifically, we can: Use ultraviolet imager or electric field sensor to measure the number of corona discharge photons or electric field intensity and convert it into discharge intensity; A correlation model between corona discharge intensity and insulation aging is established. When the discharge intensity exceeds a preset multiple of the corona inception voltage and the remaining insulation life is less than half, the insulation performance is judged to be severely degraded.

7. A transmission channel risk early warning assessment method according to claim 1, characterized in that: Comprehensively analyze the conductor spatial position change reflected by the sag dynamic deviation rate and the insulation performance degradation degree reflected by the corona discharge intensity. Specifically, the spatial risk coefficient corresponding to the sag dynamic deviation rate and the insulation risk coefficient corresponding to the corona discharge intensity are normalized, and the normalized spatial risk coefficient and insulation risk coefficient are calculated. A comprehensive risk coefficient threshold is preset, and the comprehensive risk coefficient is compared with the comprehensive risk coefficient threshold: when the comprehensive risk coefficient is less than or equal to the comprehensive risk coefficient threshold, it indicates that the operation risk of the transmission channel is low; when the comprehensive risk coefficient is greater than the comprehensive risk coefficient threshold, it indicates that the operation risk of the transmission channel is high.

8. A power transmission channel risk early warning assessment method according to claim 7, characterized in that: The spatial risk factor is determined by: Based on the acquired sag dynamic deviation rate data, the degree of deviation from the historical normal operation data is calculated, and the degree of deviation is converted into a spatial risk coefficient through a preset mapping function. The mapping function is a monotonically increasing function. The greater the degree of deviation, the greater the spatial risk coefficient.

9. A power transmission channel risk early warning assessment method according to claim 7, characterized in that: The insulation risk coefficient is determined in the following way: based on the acquired corona discharge intensity data, the ratio of the data to the corona inception voltage is calculated, and the ratio is converted into the insulation risk coefficient through a preset mapping function. The mapping function is a monotonically increasing function. The larger the ratio, the greater the insulation risk coefficient.

10. A transmission channel risk early warning assessment system, applicable to a transmission channel risk early warning assessment method according to any one of claims 1 to 9, characterized in that: The system comprises: The monitoring module monitors the voltage harmonic distortion rate, analyzes the degree of current waveform distortion caused by nonlinear loads in the transmission line, and evaluates the impact of harmonics on insulation aging and heat loss of the line and equipment; The analysis module analyzes the real-time temperature gradient of the conductor, determines the abnormal heating conditions in different sections of the conductor, and evaluates the degree of abnormal conductor temperature increase and potential thermal damage accumulation caused by overload and poor contact. The judgment module determines whether there is a risk of abnormal heating in the transmission line based on the impact of the voltage harmonic distortion rate on equipment insulation aging and heat loss and the abnormal conductor temperature reflected by the real-time conductor temperature gradient. The assessment module, when there is a risk of abnormal heating in the transmission line: by analyzing the sag dynamic deviation rate, evaluates the sag changes of the conductor caused by temperature changes and mechanical stress factors, and determines whether the distance between the conductor and the ground meets safety standards; by monitoring the corona discharge intensity, evaluates the intensification of corona discharge on the conductor surface caused by heating and damage factors, and determines the degree of insulation performance attenuation. The early warning module conducts a comprehensive analysis of the spatial position changes of the conductor reflected by the sag dynamic deviation rate and the degree of insulation performance attenuation reflected by the corona discharge intensity, evaluates the risk level of transmission channel operation and issues early warnings.

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