A power transmission line condition monitoring method and system

Abstract

The application provides a power transmission line state monitoring method, which decouples and quantifies multi-section conduction and multi-physical quantity coupling by constructing an independent inter-section topological conduction parameter system and a single-section multi-physical quantity coupling parameter system; generates comprehensive degradation indexes and evolution trends after two-stage normalization of operation monitoring data, obtains a dynamic residual degradation barrier by combining weighted integration of adjacent section conduction influence and local degradation rate, determines a synergistic amplification factor according to the degradation degree, superimposes a gradual change base and an amplified transient impact to obtain a comprehensive risk value, and takes the ratio of the value to the residual degradation barrier as a risk ratio, and outputs a state type and a risk quantization result in combination with adjacent section constraints. The application solves the problem that in the prior art, it is difficult to comprehensively handle the multi-factor coupling influence and its dynamic evolution relationship in the operation of the power transmission line, resulting in insufficient accuracy and reliability of state evaluation.

Description

Technical Field

[0001] This invention relates to the field of power transmission line monitoring technology, and in particular to a method and system for monitoring the condition of power transmission lines. Background Technology

[0002] Transmission lines are the core infrastructure of the power system, responsible for long-distance power transmission, and their operational status directly affects the safety and stability of the power grid. With the continuous expansion of the power grid and the increasing demands for power supply reliability, precise and integrated online condition monitoring and assessment of transmission lines has become a key technical requirement for ensuring the safe operation of the power grid.

[0003] Transmission lines typically stretch for hundreds or even thousands of kilometers, traversing complex and ever-changing geographical and climatic environments, and enduring the alternating or superimposed effects of various factors such as icing, wind vibration, lightning strikes, pollution, and temperature variations over long periods. From an operational perspective, the state evolution of transmission lines exhibits significant multi-segment correlations, multi-physical quantity coupling, and a coexistence of long-period gradual changes and short-term sudden events. On the one hand, the various tower sections of the line are mechanically connected through structures such as conductors, ground wires, and insulator strings, and local state changes may propagate and spread along the line direction to adjacent sections. On the other hand, physical quantities such as icing, stress, insulation pollution, and thermal effects interact through electromechanical and thermomechanical coupling mechanisms, jointly affecting the safety level of the equipment. Furthermore, from initial operation to final failure, equipment may undergo a slow process of material aging and damage accumulation, or it may be instantly triggered by sudden disturbances such as lightning strikes and strong winds; the deeper the aging, the lower the tolerance to disturbances.

[0004] In response to the aforementioned complex characteristics, existing transmission line condition monitoring methods still lack systematic solutions for handling multi-factor coupling, cross-segment transmission, and the correlation mechanisms between processes at different time scales. Traditional monitoring methods struggle to determine the interactive effects between various physical quantities and the chain reactions between segments within a unified quantitative framework. They also fail to effectively connect the dynamic relationship between long-term gradual accumulation and short-term sudden disturbances, resulting in insufficient comprehensive assessment of the actual condition of equipment and failing to fully meet the practical needs of precise operation and maintenance of long-distance transmission lines. Summary of the Invention

[0005] Based on this, the purpose of this invention is to provide a method and system for monitoring the condition of transmission lines, which aims to solve the problem in the prior art that it is difficult to comprehensively handle the coupled influence of multiple factors and their dynamic evolution relationship during the operation of transmission lines, resulting in insufficient accuracy and reliability of condition assessment.

[0006] A method for monitoring the condition of a transmission line according to an embodiment of the present invention includes: The design parameters and physical characteristic parameters of each section of the transmission line are obtained to construct independent inter-section topology transmission parameter systems and multi-physical quantity coupling parameter systems within a single section, and to determine the degradation gain factor and fusion weighting coefficient of each physical quantity. After normalizing the collected operational monitoring data, a comprehensive degradation index and degradation evolution trend information for a single section are generated based on the multi-physical quantity coupling parameter system. Using the aforementioned topological transmission parameter system, the degradation index of adjacent segments is transformed into a transmission degradation contribution to the current segment. The cumulative degradation loss is determined by weighted integration with the local degradation rate extracted from the degradation evolution trend information. The cumulative degradation loss is then deducted from the preset safety margin to obtain the dynamic residual degradation barrier of each segment. Short-term disturbance data is collected to generate transient impact quantities based on the topological transmission parameter system. The ratio of the degradation index to the preset safety margin is used to determine the synergistic amplification factor. The degradation index and the transient impact quantities amplified by the synergistic amplification factor are superimposed to obtain the comprehensive risk value of each section. The ratio of the comprehensive risk value to the dynamic residual degradation barrier is used as the current risk ratio. Based on the topological transmission parameter system, the risk ratio of neighboring areas is weighted to obtain the chain constraint quantity. The current risk ratio, the chain constraint quantity, and the dynamic residual degradation barrier are compared with their respective preset thresholds to output the state type and risk quantification assessment results of each segment.

[0007] In addition, the transmission line condition monitoring method according to the above embodiments of the present invention may also have the following additional technical features: Furthermore, the steps of obtaining the design parameters and physical characteristic parameters of each section of the transmission line to construct independent inter-section topology conduction parameter systems and single-section multi-physical quantity coupling parameter systems, and determining the degradation gain factor and fusion weighting coefficient of each physical quantity include: Obtain the total number of segments, the span of each segment, the set of adjacent segment numbers, and the disaster spread coefficient; for physically adjacent segments, calculate the topological correlation coefficient based on the ratio of the sum of the spans of the two segments to the smaller span, multiplied by the disaster spread coefficient; the correlation coefficient of non-adjacent segments is zero, thereby forming the topological transmission parameter system; Obtain the cross-coupling factors between the dimensions of each physical quantity within a single segment, calibrated by experiments or physical equations, and use the unit value as the coupling coefficient of each physical quantity itself to construct a multi-physical quantity coupling relationship matrix, thus forming the multi-physical quantity coupling parameter system. Based on the statistical relationship between the degree of deviation of each physical quantity from the normal range and the equipment degradation rate in historical operating data, the degradation gain factor corresponding to each physical quantity dimension is calibrated. Determine the safety saturation threshold and oversaturation acceleration penalty coefficient for each physical quantity dimension based on the equipment tolerance characteristic curve and operating procedures. Based on operational experience or the contribution ratio of local factors and neighboring cell transmission factors in historical fault samples, determine the local degradation contribution weight and the neighboring cell transmission contribution weight. The characteristic time constant is determined based on the statistical average of the duration of the local impact of neighboring cell degradation transmission, and is used to convert the impact of neighboring cell transmission into a time scale compatible with the local degradation rate. The physical quantity dimensions include at least four dimensions: icing, mechanical stress, insulation contamination, and thermal effect. The disaster spread coefficient is the icing spread coefficient.

[0008] Furthermore, the steps for generating a comprehensive degradation index and degradation evolution trend information for a single section based on the multi-physical quantity coupling parameter system after normalizing the collected operational monitoring data include: The ice thickness, conductor stress, insulator equivalent salt density, and conductor temperature are collected as the operational monitoring data. Physical limit normalization is performed based on the limit reference values ​​of each physical quantity, and then distribution equilibrium normalization is performed based on the historical statistical mean and variance to obtain the standardized state vector. A diagonal matrix is ​​constructed using each of the aforementioned degradation gain factors, and a similarity transformation is performed with the multi-physical quantity coupling relationship matrix to obtain the coupling degradation stiffness matrix. The quadratic form formed by the standardized state vector and the coupled deterioration stiffness matrix is ​​used as the multi-physical quantity cross-coupling deterioration effect term. For each physical quantity dimension, if the current value exceeds the corresponding safe saturation threshold, the square of the excess part is multiplied by the corresponding oversaturation acceleration penalty coefficient; otherwise, the term is zero, and the contributions of each dimension are summed as the over-limit acceleration degradation effect term. The degradation index is obtained by adding the multi-physical quantity cross-coupling degradation effect term to the over-limit accelerated degradation effect term. The degradation evolution rate is obtained by taking the partial derivatives of the degradation index with respect to each component of the standardized state vector, and the degradation evolution rate is allowed to be negative. The partial derivative of the multi-physical quantity cross-coupling degradation effect term is determined by multiplying the sum of the coupling degradation stiffness matrix and its transpose by the normalized state vector. The partial derivative of the over-limit acceleration degradation effect term is twice the product of the excess portion and the corresponding acceleration penalty coefficient when the threshold is exceeded, otherwise it is zero.

[0009] Furthermore, the steps of using the aforementioned topological conduction parameter system to transform the degradation index of adjacent segments into conduction degradation contributions to the current segment, and then using a weighted integral with the local degradation rate extracted from the degradation evolution trend information to determine the cumulative degradation loss, and finally subtracting the cumulative degradation loss from a preset safety margin to obtain the dynamic residual degradation barrier for each segment include: The square root of the sum of the squares of the components of the degradation evolution rate in each dimension is taken as the contribution of the local degradation rate. The degradation index of adjacent segments is weighted and summed using the topological correlation coefficient to obtain the transmission influence of neighboring segments, and then divided by the characteristic time constant to obtain the equivalent loss contribution. The total loss rate is obtained by weighting and summing the local degradation rate contribution and the equivalent loss contribution with the local degradation contribution weight and the neighbor cell conduction contribution weight, respectively. The cumulative degradation loss is obtained by integrating the total loss rate over a slow time scale. Subtracting the accumulated degradation loss from the preset safety margin yields the dynamic residual degradation barrier; The dynamic residual degradation barrier satisfies a non-negative constraint; when the total loss rate is negative, the dynamic residual degradation barrier is allowed to rise, but the value after the rise does not exceed the preset safety margin.

[0010] Furthermore, the steps of collecting short-term disturbance data to generate transient impact quantities based on the topological transmission parameter system, determining a collaborative amplification factor based on the ratio of the degradation index to the preset safety margin, and superimposing the degradation index with the transient impact quantities amplified by the collaborative amplification factor to obtain the comprehensive risk value of each segment include: The local disturbance impact amount is obtained by taking the inner product of the real-time collected lightning current intensity, instantaneous wind speed and condensation humidity as a disturbance vector and the preset response coefficient vector. The local disturbance impact quantities of adjacent segments are weighted and summed using topological correlation coefficients to obtain the disturbance propagation impact quantity. The local disturbance impact quantity and the disturbance propagation impact quantity together constitute the transient impact quantity. Divide the degradation index by the preset safety margin, multiply by the material fragility coefficient, and add one to obtain the synergistic amplification factor; The comprehensive risk value is obtained by adding the degradation index to the transient impact amount amplified by the synergistic amplification factor.

[0011] Furthermore, the steps of using the ratio of the comprehensive risk value to the dynamic residual degradation barrier as the current risk ratio, and weighting the neighboring risk ratios based on the topology transmission parameter system to obtain the chain constraint value, and comparing the current risk ratio, the chain constraint value, and the dynamic residual degradation barrier with their respective preset thresholds to output the state type and risk quantification assessment results of each segment include: The current risk ratio is obtained by dividing the comprehensive risk value by the dynamic residual deterioration barrier. The chain constraint quantity is obtained by weighting and summing the current risk ratios of adjacent segments using topological correlation coefficients; When the current risk ratio is less than the preset risk ratio warning threshold and the dynamic residual degradation barrier is greater than the preset barrier warning threshold, it is determined to be in a normal state. When the current risk ratio reaches or exceeds the risk ratio warning threshold and the dynamic remaining degradation barrier is less than or equal to the barrier warning threshold, it is determined to be a gradual degradation warning state. When the current risk ratio reaches or exceeds the preset fault determination threshold and the chain constraint amount is less than the preset chain contribution threshold, it is determined to be a local sudden fault state. When the current risk ratio reaches or exceeds the fault determination threshold and the cascading constraint reaches or exceeds the cascading contribution threshold, it is determined to be a cross-segment cascading sudden fault state.

[0012] Furthermore, the risk quantification assessment results include gradual degradation level, comprehensive risk value, and fault propagation path; The gradual degradation level is determined by the ratio of the cumulative degradation loss to the preset safety margin; The overall risk value is obtained by adding the current risk ratio to the chain constraint amount; The fault propagation path is determined by the segment with the largest product of topological correlation coefficient and current risk ratio among the adjacent segments of the current segment.

[0013] Another object of the present invention is to provide a transmission line condition monitoring system for implementing the above-described transmission line condition monitoring method, the system comprising: The parameter determination module is used to obtain the design parameters and physical characteristic parameters of each section of the transmission line, so as to construct an independent inter-section topology transmission parameter system and a multi-physical quantity coupling parameter system within a single section, and determine the degradation gain factor and fusion weighting coefficient of each physical quantity. The degradation index determination module is used to normalize the collected operation monitoring data and generate a single-segment comprehensive degradation index and degradation evolution trend information based on the multi-physical quantity coupling parameter system. The residual degradation barrier determination module is used to use the topological conduction parameter system to convert the degradation index of adjacent segments into conduction degradation contribution to the current segment, and to determine the cumulative degradation loss by weighted integral with the local degradation rate extracted from the degradation evolution trend information, and then deduct the cumulative degradation loss from the preset safety margin to obtain the dynamic residual degradation barrier of each segment. The comprehensive risk value determination module is used to collect short-term disturbance data to generate transient impact quantities according to the topological transmission parameter system, determine the synergistic amplification factor by the ratio of the degradation index to the preset safety margin, and superimpose the degradation index and the transient impact quantities amplified by the synergistic amplification factor to obtain the comprehensive risk value of each section. The monitoring result determination module uses the ratio of the comprehensive risk value to the dynamic residual degradation barrier as the current risk ratio, and obtains the chain constraint quantity by weighting the neighboring area risk ratio based on the topology transmission parameter system. The module then compares the current risk ratio, the chain constraint quantity, and the dynamic residual degradation barrier with their respective preset thresholds, and outputs the state type and risk quantification assessment results for each segment.

[0014] Another objective of this invention is to provide a storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described transmission line status monitoring method.

[0015] Another objective of this invention is to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the above-described transmission line condition monitoring method.

[0016] This invention first explicitly decouples the conduction relationships between multiple sections of a transmission line from the coupling relationships between multiple physical quantities within a single section by constructing independent topology conduction parameter systems and multi-physical quantity coupling parameter systems. This allows the cascading effects between sections and the interactive effects between physical quantities to be quantified and characterized separately. Second, by performing two-level normalization processing on operational monitoring data to generate a comprehensive degradation index, the dimensional differences and statistical distribution biases of different physical quantities are eliminated, enabling different physical quantities to objectively participate in condition assessment on a unified scale. Furthermore, by combining the conduction effects of neighboring areas with the local degradation rate on a unified time scale... The weighted integral dynamically deducts accumulated losses from a preset safety margin, quantifying the long-cycle gradual degradation process into a dynamically updatable residual degradation barrier, and determining the quantitative correlation between gradual degradation and fault criticality. Based on this, a synergistic amplification factor is determined by the ratio of the degradation index to the preset safety margin, enabling the risk synthesis of transient shocks to adaptively reflect the impact of equipment degradation on disturbance tolerance. Finally, by correlating the comprehensive risk value with the dynamic residual degradation barrier as a risk ratio, the critical threshold for fault identification is dynamically adjusted according to the actual state of the equipment, avoiding the defects of fixed thresholds that are prone to missed or delayed reporting in the later stages of equipment aging. Therefore, this invention solves the problem in the prior art of insufficient accuracy and reliability of condition assessment due to the difficulty in comprehensively handling the coupled influence of multiple factors and their dynamic evolution in transmission line operation. Attached Figure Description

[0017] Figure 1 This is a flowchart of the transmission line condition monitoring method in the first embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of the transmission line condition monitoring system in the second embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of the electronic device in the third embodiment of the present invention; The following detailed description, in conjunction with the accompanying drawings, will further illustrate the present invention. Detailed Implementation

[0018] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Several embodiments of the invention are illustrated in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0020] Example 1 Please see Figure 1 The figure shows a transmission line status monitoring method in the first embodiment of the present invention, which specifically includes S01-S05.

[0021] S01. Obtain the design parameters and physical characteristic parameters of each section of the transmission line to construct independent inter-section topology transmission parameter systems and multi-physical quantity coupling parameter systems within a single section, and determine the degradation gain factor and fusion weighting coefficient of each physical quantity.

[0022] In practical implementation, a topological transmission parameter system is constructed to quantify the physical laws that are transmitted between sections solely through adjacency into calculable correlation coefficients. This transforms the inter-section chain effects, which were originally only qualitatively described, into quantitative parameters that can be directly used in subsequent steps. Furthermore, by constructing a multi-physical quantity coupling parameter system, the objectively existing cross-coupling relationships between heterogeneous physical quantities such as icing, stress, contamination, and temperature are standardized in matrix form, avoiding the one-sidedness of isolated treatment of each physical quantity in subsequent condition assessments. Simultaneously, degradation gain coefficients, safe saturation thresholds, oversaturation acceleration penalty coefficients, and fusion weights are calibrated using historical data statistics and equipment characteristics. These parameters collectively determine the contribution intensity of each physical quantity to equipment degradation and the acceleration law after exceeding limits, providing a parameter basis that matches the actual operating characteristics of the equipment for subsequent generation of comprehensive degradation indices and calculation of dynamic residual degradation barriers. Moreover, the parameter system generated in this step only needs to be constructed once in the entire method and can be updated periodically based on equipment operating conditions and new historical data.

[0023] Specifically, the total number of segments, the span between each segment, the set of adjacent segment numbers, and the disaster spread coefficient are obtained. For physically adjacent segments, the topological correlation coefficient is calculated by multiplying the sum of the spans between the two segments by the smaller span by the disaster spread coefficient. The correlation coefficient for non-adjacent segments is zero, thus forming the topological transmission parameter system. The cross-coupling factors between the dimensions of each physical quantity within a single segment, calibrated by experiments or physical equations, are obtained, and the unit value is used as the coupling coefficient of each physical quantity itself to construct a multi-physical quantity coupling relationship matrix, forming the multi-physical quantity coupling parameter system. Based on the historical operational data of each physical quantity... The statistical relationship between the degree of deviation of quantities from the normal range and the equipment degradation rate is established, and the degradation gain factor corresponding to each physical quantity dimension is calibrated. The safety saturation threshold and oversaturation acceleration penalty coefficient corresponding to each physical quantity dimension are determined according to the equipment tolerance characteristic curve and operating procedures. The contribution ratio of local factors and neighboring area transmission factors in operation and maintenance experience or historical fault samples is used to determine the local degradation contribution weight and neighboring area transmission contribution weight. The characteristic time constant is determined according to the statistical average of the duration of the neighboring area degradation transmission on the local area, which is used to convert the neighboring area transmission effect into a time scale compatible with the local degradation rate. The physical quantity dimensions include at least four dimensions: icing, mechanical stress, insulation contamination, and thermal effect. The disaster spread coefficient is the icing spread coefficient.

[0024] In practical implementation, the first step is to construct a topology transmission parameter system to obtain the total number of transmission line segments. The span of each section, the set of adjacent section numbers, and the icing spread coefficient. Among them, the total number of segments and adjacent segment number set The data is obtained directly from the line tower list and maintenance log; the span distance for each section is obtained from the line design drawings or measured on-site with a rangefinder, in meters; the icing spread coefficient is also provided. Based on historical icing observation data and icing growth models of the micro-topographic region where this section is located, the value range is determined to be 0 < <1, typical areas with severe icing The value approaches 1. For any two segments i and k, the topological correlation coefficient is calculated according to the following rules. : If the k-th segment belongs to the set of adjacent segments of the i-th segment, that is This indicates that the two segments are physically adjacent segments, then: .

[0025] If the k-th segment is not part of the set of adjacent segments of the i-th segment, it indicates that the two segments are non-adjacent segments, and the correlation coefficient is zero. and Let i and k be the spans, respectively. The topological correlation coefficients between all segments constitute the topological transmission parameter system. The core characteristic of this system is that non-zero transmission coefficients exist only between physically adjacent segments, and the transmission strength is positively correlated with the degree of difference in span between the two segments, while also being modulated by the characteristics of icing spread. The greater the difference in span between two adjacent segments, the more significant the non-uniformity of structural mechanics and disaster evolution, and the stronger the transmission impact.

[0026] Secondly, a multi-physical quantity coupling parameter system is constructed to obtain the cross-coupling factor between any two types of physical quantities in a single segment across the four physical quantity dimensions of icing, mechanical stress, insulation contamination, and thermal effects. , All are indexes of physical quantity dimensions. The method for obtaining each cross-coupling factor can be: the coupling factor between icing and stress. and The coupling factor between icing and salt density was obtained by fitting measured data of conductor tension under different icing thicknesses. and The coupling factor between icing and temperature was calibrated through accelerated testing of icing-induced salt contamination or correlation analysis of historical data. and The coupling factor between stress and salt density was determined through theoretical derivation of a temperature-ice growth or ablation model and verified by field observations. and The coupling factor between stress and temperature was obtained through combined mechanical and pollution tests on long-term wire-mounted samples. and Derivation of physical equations based on the thermal expansion coefficient of conductors and the constitutive relationship between stress and strain; coupling factor between salt density and temperature. and The concentration rate was determined through comparative experiments on the surface contamination rate of insulators under different temperature conditions. A unit value of 1 was used as the coupling coefficient of each physical quantity, i.e., the diagonal element. Construct a multi-physical quantity coupling relationship matrix : .

[0027] This matrix forms a multi-physical quantity coupling parameter system. The purpose of taking unit values ​​for the diagonal elements is to establish a unified standardized benchmark, so that the cross-coupling factors on the off-diagonal lines are all measured relative to this benchmark, which facilitates subsequent similarity transformation with the degraded gain factor.

[0028] Then, the degradation gain factor is determined. Based on the statistical relationship between the degree of deviation of each physical quantity from the normal range and the equipment degradation rate in historical operating data, the degradation gain factor corresponding to each physical quantity dimension is calibrated. Specifically, historical monitoring data for each physical quantity over the past few years and corresponding equipment condition maintenance records are obtained. A statistical regression model is established between the deviation of each physical quantity from the normal range and the actual deterioration rate of the equipment. The slope of this regression model is used to determine... These coefficients constitute the degraded gain coefficient vector. It characterizes the contribution intensity of the shift in unit normalized physical quantities to the accumulation of degradation energy in a single segment.

[0029] Next, the safe saturation threshold and the oversaturation acceleration penalty coefficient are determined. Based on the equipment tolerance characteristic curve and operating procedures, the safe saturation threshold corresponding to each physical quantity dimension is determined, i.e. and oversaturation acceleration penalty coefficient .in, The determination method is as follows: the critical failure value of each physical quantity is determined through equipment type test, and 60% to 80% of the critical failure value is taken as the safety saturation threshold. The degradation acceleration was calibrated by analyzing the actual failure cases where each physical quantity exceeded its limit. When the normalized value of any physical quantity exceeds its safe saturation threshold, an additional nonlinear accelerated degradation penalty will be triggered.

[0030] Next, the fusion weighting coefficient is determined, and the local degradation contribution weight is determined based on the contribution ratio of local factors and neighboring cell transmission factors in operation and maintenance experience or historical fault samples. Contribution weight of transmission between neighboring regions The specific determination method is as follows: In historical failure cases, the proportions of failures caused by inherent defects in the current section and failures caused by transmission from adjacent sections are statistically analyzed, and the determination is based on this proportion. and The value satisfies 0 < , <1 and ≤1.

[0031] Finally, the characteristic time constant is determined, i.e. The characteristic time constant is determined based on the statistical average duration of the impact of adjacent area degradation on the local area. Specifically, it is determined by: analyzing historical data on the average time delay between the occurrence of an abnormal degradation state in an adjacent area and the generation of an observable response in the local area, as well as the average duration of that response. The above average duration is taken as the mean. The unit is days, and its function is to convert the energy influence transmitted from neighboring areas into a time scale compatible with the local degradation rate, ensuring the uniformity of dimensions in subsequent integration stages.

[0032] Among them, each physical quantity dimension includes at least four dimensions: icing, mechanical stress, insulation pollution and thermal effect, and the disaster spread coefficient is the icing spread coefficient.

[0033] S02, after normalizing the collected operation monitoring data, generate a single-segment comprehensive degradation index and degradation evolution trend information based on the multi-physical quantity coupling parameter system.

[0034] In practical implementation, a two-level normalization process is used. The first level, physical limit normalization, reduces each physical quantity to the relative range defined by its own physical limit, eliminating the pure dimensional differences between different physical quantities. The second level, distribution equilibrium normalization, further eliminates the inherent statistical distribution differences of different physical quantities in historical operation, such as the large fluctuation range of icing thickness and the relatively gentle change of insulator salt density, which may cause deviations in subsequent fusion evaluation, making each physical quantity have the same statistical scale. On this basis, the state vector after double normalization is combined with the coupled degradation stiffness matrix established in step S01 to generate a single-segment comprehensive degradation index. This index includes the mutual influence relationship between physical quantities within the normal operating range reflected by the multi-physical quantity cross-coupling degradation effect term, as well as the nonlinear accelerated degradation phenomenon that occurs when any physical quantity exceeds its safe saturation threshold, reflected by the over-limit accelerated degradation effect term. This over-limit accelerated degradation effect term is activated only when the physical quantity exceeds its safe saturation threshold, and contributes zero when it does not exceed it, which is consistent with the physical law that the degradation rate of equipment increases sharply under extreme operating conditions. Furthermore, the degradation evolution rate is obtained by taking the partial derivative of the degradation index, and negative values ​​are allowed, so that the subsequent barrier integration can truly reflect the equipment condition recovery process, such as ice melting and load reduction.

[0035] Specifically, the following data are collected for operation monitoring: icing thickness, conductor stress, insulator equivalent salt density, and conductor temperature. Physical limit normalization is performed based on the limit reference values ​​of each physical quantity, followed by distribution equilibrium normalization based on historical statistical mean and variance, resulting in a standardized state vector. A diagonal matrix is ​​constructed using each degradation gain factor, and a similarity transformation is performed with the multi-physical quantity coupling relationship matrix to obtain a coupled degradation stiffness matrix. The quadratic form formed by the standardized state vector and the coupled degradation stiffness matrix is ​​used as the multi-physical quantity cross-coupling degradation effect term. For each physical quantity dimension, if the current value exceeds the corresponding safe saturation threshold, the square of the excess is multiplied by the corresponding oversaturation acceleration penalty coefficient; otherwise, the term is zero, and the sum of the contributions from each dimension is used as the over-limit accelerated degradation effect term. The multi-physical quantity cross-coupling degradation effect term is added to the over-limit accelerated degradation effect term to obtain the degradation index. The partial derivatives of the degradation index with respect to each component of the standardized state vector are calculated to obtain the degradation evolution rate on each physical quantity dimension, where the degradation evolution rate is allowed to be negative. The partial derivative of the multi-physical quantity cross-coupling degradation effect term is determined by multiplying the sum of the coupling degradation stiffness matrix and its transpose by the normalized state vector. The partial derivative of the over-limit acceleration degradation effect term is twice the product of the excess portion and the corresponding acceleration penalty coefficient when the threshold is exceeded, otherwise it is zero.

[0036] In practice, the first step is to collect operational monitoring data, gathering real-time data for each section every 10 minutes. The specific data collection method is as follows: Ice thickness The equivalent icing thickness can be calculated by measuring conductor tension changes using tension sensors installed on representative towers in each section, combined with known parameters such as conductor weight and span, and back-calculated using the catenary equation or state equation. Alternatively, high-definition cameras installed on towers can capture images of conductor icing, and image recognition algorithms can be used to extract the icing contour and calculate the thickness.

[0037] conductor stress The axial strain of the conductor can be directly measured by a strain gauge or fiber optic grating sensor installed at the conductor clamp, and then multiplied by the elastic modulus of the conductor material to obtain the conductor stress; alternatively, the conductor tension measured by a tension sensor can be divided by the conductor cross-sectional area to obtain the stress indirectly.

[0038] Insulator equivalent salt density The leakage current amplitude and pulse frequency are collected by a leakage current sensor installed near the insulator string. Combined with environmental humidity data and insulator structural parameters, the equivalent salt density is calculated using a regression model. Alternatively, salt density testing can be performed by periodic manual sampling and combined with online data calibration.

[0039] conductor temperature : Measurement can be performed directly by thermocouples or distributed fiber optic temperature measurement systems installed on the surface of the conductor, or by non-contact measurement using infrared temperature sensors.

[0040] Secondly, a two-level normalization process is performed. The first level is physical limit normalization, which reduces various physical quantities to the relative range defined by their own physical limits: , , , .

[0041] in, The maximum permissible ice thickness of conductors as specified in the line design specifications is determined by the line design parameters and conductor type. The maximum allowable stress of the conductor is specified in the conductor mechanical properties manual and operating procedures. The salt density benchmark value for insulators is selected based on the pollution distribution map and operational experience of the region. The reference temperature for the conductor is usually taken as the annual average ambient temperature at the time of line design. This is the maximum allowable temperature of the conductor, determined by the conductor's heat resistance rating and operating procedures.

[0042] The second level is distribution equilibrium normalization, which is based on the global statistical mean of the data sequence after the first level of processing of historical data for each physical quantity. and variance Perform standardization transformation: , .

[0043] The standardized state vector is obtained: .

[0044] in, Let p be the value of the p-th physical quantity after two-level normalization. Let p be the value of the p-th physical quantity after passing through the first-level specification. Let be the standardized state vector of the i-th segment.

[0045] Then, the coupling degradation stiffness matrix is ​​generated. This is done using various degradation gain factors. Construct a diagonal matrix Γ=diag( Transforming the multi-physical coupling relationship matrix M and Γ, we obtain the coupling degradation stiffness matrix K: .

[0046] Next, the cross-coupling degradation effect term of multiple physical quantities is calculated, and the quadratic form formed by the normalized state vector and the coupling degradation stiffness matrix is ​​used as the cross-coupling degradation effect term of multiple physical quantities: .

[0047] Next, the over-limit acceleration degradation effect term is calculated. For each physical quantity dimension p, a unit step function H(·) is introduced to determine whether the current value exceeds the safe saturation threshold. The step function H(·) is defined as follows: it takes the value 1 when the independent variable is greater than 0, and takes the value 0 otherwise.

[0048] If the current value exceeds the safe saturation threshold, that is, when > When satisfied, the square of the excess portion is multiplied by the corresponding oversaturation acceleration penalty coefficient. If the limit is not exceeded, the contribution for this item is zero. Summing the contributions from each dimension yields the over-limit accelerated degradation effect term: .

[0049] Then, a comprehensive degradation index is generated by adding the degradation effect term of the cross-coupling of multiple physical quantities to the degradation effect term of the over-limit accelerated degradation term, thus obtaining a comprehensive degradation index for a single segment: .

[0050] The physical meaning of this degradation index is the equivalent degradation potential energy of a single segment at time t.

[0051] Finally, the degradation evolution rate is calculated by taking the partial derivatives of the degradation index with respect to each component of the standardized state vector, thus obtaining the degradation evolution rate in each physical quantity dimension. : .

[0052] The partial derivative of the multi-physical quantity cross-coupling degradation effect term is determined by multiplying the coupling degradation stiffness matrix K and its transpose by the normalized state vector.

[0053] Partial derivatives of the over-limit accelerated degradation effect term The components are determined according to the following rules: .

[0054] The rate of degradation evolution is allowed to be negative to truly reflect the reduction in the degree of degradation during the recovery process from conditions such as icing melting and load reduction.

[0055] S03, using the topological conduction parameter system, the degradation index of adjacent segments is transformed into a conduction degradation contribution to the current segment, and the cumulative degradation loss is determined by weighted integral with the local degradation rate extracted from the degradation evolution trend information. Then, the cumulative degradation loss is deducted from the preset safety margin to obtain the dynamic residual degradation barrier of each segment.

[0056] In practical implementation, because in existing technologies, gradual degradation is usually observed only as a qualitative trend, there is a lack of means to transform it into a quantitative indicator that can be used for fault diagnosis. Therefore, in this step, firstly, the topological correlation coefficient established in step S01 is used to quantify the transmission effect of the degradation state of adjacent sections on the local section, capturing the cascading degradation effect that is unique to transmission lines, where defects in adjacent sections may spread to the local section; secondly, by introducing a characteristic time constant, the transmission effects of adjacent sections with different time response characteristics and the local degradation rate are uniformly converted to the same time scale for weighted fusion, solving the problem that the two cannot be directly added because their response speeds are different in physical mechanism; then, the total loss rate is integrated on a slow time scale (in days) to obtain the cumulative degradation loss. This integration process gradually accumulates the small but continuous degradation in daily life into a considerable amount of loss, which is in line with the objective physical laws of equipment material aging and damage accumulation; finally, the cumulative degradation loss is deducted from the preset initial safety margin to obtain the dynamic residual degradation barrier. This potential barrier is dynamically updated, gradually decreasing over time and with accumulated wear; it is also directionally adaptive. When equipment condition is detected to have recovered, such as when ice melts and degradation indicators decrease, the integral result becomes negative, and the barrier can rise moderately, but the upper limit of the rise cannot exceed the initial value to maintain physical rationality. The dynamic residual degradation barrier serves as the dynamic critical threshold for fault identification in subsequent steps, naturally linking the long-term gradual change process with the short-term fault risk.

[0057] Specifically, the square root of the sum of the squares of the components of the degradation evolution rate in each dimension is taken as the local degradation rate contribution; the degradation indices of adjacent segments are weighted and summed using topological correlation coefficients to obtain the neighboring cell transmission influence, and then divided by the characteristic time constant to obtain the equivalent loss contribution; the local degradation rate contribution and the equivalent loss contribution are weighted and summed using the local degradation contribution weight and the neighboring cell transmission contribution weight, respectively, to obtain the total loss rate; the total loss rate is integrated on a slow time scale to obtain the cumulative degradation loss; the cumulative degradation loss is subtracted from the preset safety margin to obtain the dynamic residual degradation barrier; The dynamic residual degradation barrier satisfies a non-negative constraint; when the total loss rate is negative, the dynamic residual degradation barrier is allowed to rise, but the value after the rise does not exceed the preset safety margin.

[0058] It should be noted that the degradation evolution rate calculated in step S02... and its components The real-time monitoring time t (in minutes) is used as the independent variable. Before proceeding to the long-series integration in this step, these real-time data need to be processed according to a slow time scale. τ (Unit: days) The data is then aggregated. The aggregation method can be: take all data at time t within a day. The root mean square value or arithmetic mean is used as the representative value of τ for that day. All phrases in the following text using "…" τ The degradation rate and its components of the independent variable refer to the values ​​after the above aggregation process.

[0059] In practical implementation, firstly, the contribution of the local degradation rate is calculated, and then... The contribution of the local degradation rate is obtained by taking the square root of the sum of the squares of the components of each physical quantity dimension. : .

[0060] Secondly, the conduction influence of neighboring cells and its equivalent loss contribution are calculated using the topological correlation coefficient. The degradation index of adjacent sections Weighted summation yields the influence of neighboring cells. : .

[0061] in, Let be the topological correlation coefficient from segment i to segment k.

[0062] Then divide the influence of neighboring cell transmission by the characteristic time constant. This yields an equivalent loss contribution with the same time scale as the local degradation rate contribution, i.e. .

[0063] Then, the total loss rate is calculated, weighted by the local degradation contribution. Multiply by the local degradation rate contribution, and add the neighbor cell transmission contribution weight. Multiply by the equivalent loss contribution to obtain the total loss rate: .

[0064] Next, the cumulative degradation loss is calculated on a slow timescale. Integrating the total loss rate, the integration begins at the following time. The cumulative degradation loss over the past 6 months (gradual degradation assessment period) is as follows: .

[0065] Among them, the integral variable s The unit is days. For discrete data, numerical integration can be performed using the trapezoidal integral method or Simpson's integral method.

[0066] Finally, the dynamic residual degradation barrier is calculated, and the preset initial safety margin is applied. Subtracting the accumulated degradation loss, we obtain the dynamic remaining degradation barrier: .

[0067] Among them, the preset initial safety margin The determination method is as follows: based on the equipment's factory design life, type test data, and historical operating experience, determine the initial safety degradation barrier value for each section.

[0068] The potential barrier satisfies the nonnegativity constraint. ≥ 0. Specifically, when the total loss rate... When the value is negative (i.e., the equipment condition recovers, such as the degradation rate reversing due to ice melting), the dynamic residual degradation barrier allows the value to rise, but the value after the rise will not exceed the preset initial safety margin. This design allows the potential barrier to accurately reflect the recovery process of the device state, while ensuring the consistency of its physical meaning and the safety of engineering applications through non-negative constraints and upper bound constraints.

[0069] S04, collect short-term disturbance data to generate transient impact quantity according to the topological transmission parameter system, determine the cooperative amplification factor by the ratio of the degradation index to the preset safety margin, and superimpose the degradation index and the transient impact quantity amplified by the cooperative amplification factor to obtain the comprehensive risk value of each section.

[0070] In practical implementation, at the disturbance propagation level, the transient impact quantity is calculated based on the same topological correlation coefficient for neighboring cell propagation, maintaining consistency with the propagation mechanism of gradual degradation. This achieves a unified description of slow and fast-changing effects within the same topological framework. At the cooperative amplification level, the relationship between degradation degree and disturbance tolerance is quantified by defining a cooperative amplification factor. The larger the proportion of degradation index to the initial safety margin, the larger the cooperative amplification factor. The same disturbance impact will be amplified more in the comprehensive risk value, thus transforming the physical law that older equipment is more vulnerable to transient impacts into a calculable nonlinear relationship, rather than a simple linear superposition. The final comprehensive risk value uses the slow degradation base as the baseline term and the cooperatively amplified transient impact quantity as the superposition term, combining the two to form a unified comprehensive risk measurement index. This allows the method to handle multiple different failure modes simultaneously.

[0071] Specifically, the real-time collected lightning current intensity, instantaneous wind speed, and condensation humidity are used to construct a disturbance vector, which is then multiplied by a preset response coefficient vector to obtain the local disturbance impact. The local disturbance impact of adjacent sections is weighted and summed using topological correlation coefficients to obtain the conducted disturbance impact. The local disturbance impact and the conducted disturbance impact together constitute the transient impact. The degradation index is divided by the preset safety margin, multiplied by the material fragility coefficient, and then incremented by one to obtain the synergistic amplification factor. The degradation index is then added to the transient impact amplified by the synergistic amplification factor to obtain the comprehensive risk value.

[0072] In practice, the first step is to collect short-term disturbance data. This is done at a high sampling rate of once per second, collecting short-term sudden disturbance data for each segment in real time. The specific data collection method is as follows: Lightning current intensity The lightning current amplitude at the lightning strike point of the tower can be directly measured by a lightning current sensor installed on the top of the tower or on the ground wire, or by using a traveling wave ranging device at the outgoing end of the substation in conjunction with the recorded wave data to infer the lightning current amplitude at the lightning strike point of the tower.

[0073] Instantaneous wind speed Anemometers are used to collect wind speed and direction data in real time by installing ultrasonic anemometers or cup anemometers on representative towers. The installation location of the wind speed sensor should avoid the tower body from obstructing the airflow, and it is usually installed at the end of the tower crossarm or above the conductor.

[0074] Dew humidity The ambient relative humidity is collected by temperature and humidity sensors installed on the tower. When the relative humidity exceeds 90%, it is determined to be a condensation state. The collection frequency is synchronized with the wind speed.

[0075] The above three disturbance data constitute the disturbance vector. : .

[0076] Secondly, calculate the local disturbance impact amount and obtain the preset disturbance response coefficient vector. , The elements correspond to the response intensity of three types of disturbances: lightning strikes, strong winds, and condensation. This coefficient is determined by analyzing historical monitoring data of disturbance events and corresponding equipment damage, using statistical regression or expert experience.

[0077] The perturbation vector With the disturbance response coefficient vector By multiplying, we obtain the local disturbance impact: .

[0078] Next, the disturbance propagation impact is calculated using the topological correlation coefficient. The local disturbance impact quantities of adjacent sections are weighted and summed to obtain the disturbance propagation impact quantity: .

[0079] Local disturbance impact Impact quantity transmitted with disturbance Together they constitute the transient impact.

[0080] Then, the synergistic amplification factor is determined, and the material fragility coefficient is obtained. This coefficient is determined based on the aging characteristics of the equipment, and its value typically ranges from 0 to 3. It is calibrated by analyzing the differences in the response of equipment to disturbances of the same intensity under different degrees of degradation. Specifically, the calibration method involves comparing the actual damage levels of the same equipment after encountering disturbances of similar intensity at different stages of degradation, establishing a statistical relationship between the degree of degradation and the amplification factor of the disturbance response, and using the slope of this relationship as... The value of .

[0081] Deterioration indicators Divided by the preset initial safety margin Multiply by the material fragility coefficient Then add the unit value of 1 to obtain the co-amplification factor: .

[0082] The physical meaning of this factor is: when the degradation index Approaching the initial safety margin When the ratio approaches 1, the synergistic amplification factor... Approaching The value is significantly greater than 1, reflecting the physical law that the older the equipment, the more vulnerable it is to disturbances and shocks; when the equipment is in brand new condition, When it approaches 0, the co-amplification factor Approaching 1, the disturbance impact is not amplified.

[0083] Finally, a comprehensive risk value is synthesized, which includes the deterioration indicators. As a slowly changing base, plus a co-amplification factor The amplified transient impact value yields the comprehensive risk value for each section: .

[0084] in, This is a comprehensive risk measure. It integrates the long-term, gradually deteriorating base with the short-term, sudden shock, and reflects the nonlinear amplification effect between the two through a synergistic amplification factor.

[0085] S05, the ratio of the comprehensive risk value to the dynamic residual degradation barrier is used as the current risk ratio, and the risk ratio of the neighboring area is weighted based on the topology transmission parameter system to obtain the chain constraint quantity, so as to compare the current risk ratio, the chain constraint quantity and the dynamic residual degradation barrier with their respective preset thresholds, and output the state type and risk quantification assessment results of each segment.

[0086] In practical implementation, firstly, by using the ratio of the comprehensive risk value to the dynamic remaining degradation barrier as the current risk ratio, the judgment threshold is made dynamic. The same comprehensive risk value will generate a higher risk ratio when the equipment is severely degraded and the remaining barrier is low, making it easier to trigger an alarm. This dynamic threshold mechanism fundamentally solves the defect of traditional fixed threshold methods that are prone to missed or delayed alarms in the later stages of equipment aging. Secondly, by weighting the risk ratios of adjacent sections based on the topological correlation coefficient to obtain the cascading constraint, the fault risk of a single section is correlated with the risk level of adjacent sections for analysis. This allows the method to distinguish between isolated faults in a single section and cascading faults that may cause cross-section spread, which are two completely different situations in terms of operation and maintenance handling strategies. Finally, based on the four-quadrant comparison of the current risk ratio, cascading constraint, and dynamic remaining degradation barrier with their respective preset thresholds, four precise state types are output: normal state, gradual degradation warning state, local sudden fault state, and cross-section cascading sudden fault state. In addition to the status type, it also outputs three quantitative indicators: gradual degradation level, comprehensive risk value, and fault propagation path. These indicators provide quantitative decision-making basis for operation and maintenance personnel from three dimensions: the severity of degradation, the current comprehensive risk level, and the possible direction of fault propagation.

[0087] Specifically, the current risk ratio is obtained by dividing the comprehensive risk value by the dynamic residual degradation barrier; the chain constraint is obtained by weighting and summing the current risk ratios of adjacent segments using topological correlation coefficients; when the current risk ratio is less than a preset risk ratio warning threshold and the dynamic residual degradation barrier is greater than a preset barrier warning threshold, it is determined to be in a normal state; when the current risk ratio reaches or exceeds the risk ratio warning threshold and the dynamic residual degradation barrier is less than or equal to the barrier warning threshold, it is determined to be in a gradual degradation warning state; when the current risk ratio reaches or exceeds a preset fault determination threshold and the chain constraint is less than a preset chain contribution threshold, it is determined to be in a local sudden fault state; when the current risk ratio reaches or exceeds the fault determination threshold and the chain constraint reaches or exceeds the chain contribution threshold, it is determined to be in a cross-segment chain sudden fault state.

[0088] Furthermore, the risk quantification assessment results include gradual degradation level, comprehensive risk value, and fault propagation path; the gradual degradation level is determined by the ratio of the cumulative degradation loss to the preset safety margin; the comprehensive risk value is obtained by adding the current risk ratio to the cascading constraint; and the fault propagation path is determined by the segment with the largest product of the topological correlation coefficient and the current risk ratio among all adjacent segments of the current segment.

[0089] In practical implementation, firstly, calculate the current risk ratio and then combine the comprehensive risk value. Divided by the dynamic residual deterioration barrier The dimensionless current risk ratio is obtained: .

[0090] The physical meaning of this ratio is the proportion of the total equivalent shock currently experienced to the remaining tolerable margin. As equipment deteriorates, the remaining deterioration barrier... The ratio decreases and increases monotonically, thereby achieving a dynamic constraint on the threshold for judging sudden failures by gradual degradation: the more severe the equipment degradation, the smaller the external disturbance required to trigger a fault alarm.

[0091] Secondly, calculate the chain constraint quantity using the topological correlation coefficient. The current risk ratio of adjacent segments. Weighted summation yields the cross-segment cascading fault constraint: .

[0092] Then, obtain the preset threshold. The method for obtaining and setting the preset threshold is as follows: Risk ratio warning threshold The value ranges from 0 to 1 and is determined by analyzing the risk ratio boundary between normal and abnormal states that require attention in historical data. The typical value is 0.5.

[0093] Deterioration barrier warning threshold The unit is J, based on the initial safety margin. A certain proportion is usually set, usually taking 20%~30% of .

[0094] Fault determination threshold A typical value of 1.0 indicates that the total equivalent shock has reached or exceeded the remaining acceptable margin, and is in a critical state. When A value of 1.0 means that the remaining margin has just been exhausted. The specific value can be adjusted according to the conservatism of the operation and maintenance strategy. A conservative strategy can use 0.8 to 0.9, while a relatively lenient strategy can use 1.0 to 1.2.

[0095] cascading failure contribution threshold Its physical meaning is whether the risk level of adjacent areas in a certain section has reached the level that can trigger a chain reaction of failures. The typical value is 0.5, which is determined by analyzing the risk ratio level of adjacent sections in historical chain reaction failure cases before the failure propagation.

[0096] Next, the status type is determined based on the current risk ratio. Chain constraint quantity Dynamic residual deterioration barrier Compared with the above preset threshold, the state of each segment is determined to be one of the following four types: 1) Normal state, the discrimination condition is: < and > 2) Gradual degradation warning state, the criteria for which it is determined are: ≥ and ≤ 3) Local sudden fault status, the criteria for which it is determined are: ≥ and < 4) The criteria for determining a cross-section cascading sudden fault state are as follows: ≥ and ≥ .

[0097] The physical meaning of each state is as follows: Normal condition: The equipment is only slightly degraded, there is sufficient remaining safety margin, and the overall risk is within a controllable range.

[0098] Gradual deterioration warning status: Long-term operation has led to the depletion of margin to the warning level, and the risk ratio remains high, but has not yet reached the critical point of failure. Planned maintenance and repairs need to be arranged.

[0099] Local sudden failure status: The risk ratio of this section exceeds the failure threshold, but the risk level of adjacent sections is low, and the scope of the failure impact is relatively limited. It is judged as an isolated sudden impact failure.

[0100] Cross-section cascading sudden fault status: The risk ratio of this section exceeds the fault threshold, and the risk level of adjacent sections is also at a high level. The fault has the objective conditions to propagate across sections and is judged as a serious fault that is likely to trigger cascading trips.

[0101] Finally, the risk quantification assessment results are output, including the following in addition to the status type: Gradual degradation level It is determined by the ratio of accumulated degradation loss to the preset initial safety margin: .

[0102] The value ranges from 0 to 1, and can be divided into three levels: when When ∈[0, 0.3), it is judged as slightly deteriorated; when When ∈ [0.3, 0.7), it is judged as moderate degradation; when When ∈[0.7, 1], it is judged as severely degraded.

[0103] Overall Risk Value It is obtained by adding the current risk ratio to the chain constraint amount: .

[0104] The value range is ≥0, and it can be divided into three levels: when When <0.5, it is judged as low risk; when When ∈[0.5, 1), it is judged as medium risk; when A value of ≥1 indicates a high risk level.

[0105] Fault propagation path It is determined by the segment with the largest product of the topological correlation coefficient and the current risk ratio among all adjacent segments of the current segment: .

[0106] This path indicates the adjacent segment numbers most likely to be affected or serve as the source of the fault in a cascading failure scenario, providing maintenance personnel with accurate fault tracing information and emergency response directions.

[0107] In summary, this invention first explicitly decouples the conduction relationships between multiple sections of a transmission line from the coupling relationships between multiple physical quantities within a single section by constructing independent topology conduction parameter systems and multi-physical quantity coupling parameter systems. This allows the cascading effects between sections and the interactive effects between physical quantities to be quantified and characterized separately. Second, by performing two-level normalization processing on the operation monitoring data to generate a comprehensive degradation index, the dimensional differences and statistical distribution biases of different physical quantities are eliminated, enabling different physical quantities to objectively participate in the condition assessment on a unified scale. Furthermore, by applying the conduction effects of neighboring areas and the local degradation rate on a unified time scale... Weighted integration dynamically deducts accumulated losses from a preset safety margin, quantifying the long-cycle gradual degradation process into a dynamically updatable residual degradation barrier, and determining the quantitative correlation between gradual degradation and fault criticality. Based on this, a synergistic amplification factor is determined by the ratio of the degradation index to the preset safety margin, enabling the risk synthesis of transient shocks to adaptively reflect the impact of equipment degradation on disturbance tolerance. Finally, by correlating the comprehensive risk value with the dynamic residual degradation barrier as a risk ratio, the critical threshold for fault identification is dynamically adjusted according to the actual state of the equipment, avoiding the shortcomings of fixed thresholds that are prone to missed or delayed reporting in the later stages of equipment aging. Therefore, this invention solves the problem in existing technologies where it is difficult to comprehensively handle the coupled influence of multiple factors and their dynamic evolution in transmission line operation, leading to insufficient accuracy and reliability of condition assessment.

[0108] Example 2 Please see Figure 2The diagram shown is a structural block diagram of the transmission line condition monitoring system proposed in the second embodiment of the present invention. The transmission line condition monitoring system 200 includes: a parameter determination module 21, a degradation index determination module 22, a remaining degradation barrier determination module 23, a comprehensive risk value determination module 24, and a monitoring result determination module 25, wherein: The parameter determination module 21 is used to obtain the design parameters and physical characteristic parameters of each section of the transmission line, so as to construct an independent inter-section topology transmission parameter system and a multi-physical quantity coupling parameter system within a single section, and determine the degradation gain factor and fusion weighting coefficient of each physical quantity. The degradation index determination module 22 is used to normalize the collected operation monitoring data and generate a single-segment comprehensive degradation index and degradation evolution trend information based on the multi-physical quantity coupling parameter system. The residual degradation barrier determination module 23 is used to use the topological conduction parameter system to convert the degradation index of the adjacent segment into the conduction degradation contribution to the current segment, and to determine the cumulative degradation loss by weighted integral with the local degradation rate extracted from the degradation evolution trend information, and then deduct the cumulative degradation loss from the preset safety margin to obtain the dynamic residual degradation barrier of each segment. The comprehensive risk value determination module 24 is used to collect short-term disturbance data to generate transient impact quantities according to the topological transmission parameter system, determine the synergistic amplification factor by the ratio of the degradation index to the preset safety margin, and superimpose the degradation index and the transient impact quantities amplified by the synergistic amplification factor to obtain the comprehensive risk value of each section. The monitoring result determination module 25 uses the ratio of the comprehensive risk value to the dynamic residual degradation barrier as the current risk ratio, and obtains the chain constraint value by weighting the neighboring area risk ratio based on the topology transmission parameter system. The current risk ratio, the chain constraint value, and the dynamic residual degradation barrier are compared with their respective preset thresholds, and the state type and risk quantification assessment results of each segment are output.

[0109] Example 3 In another aspect, the present invention also proposes an electronic device, please refer to [link to relevant documentation]. Figure 3 The diagram shows an electronic device according to the third embodiment of the present invention, including a memory 20, a processor 10, and a computer program 30 stored in the memory and executable on the processor. When the processor 10 executes the computer program 30, it implements the transmission line status monitoring method as described above.

[0110] In some embodiments, the processor 10 may be a central processing unit (CPU), controller, microcontroller, microprocessor or other data processing chip, used to run program code stored in memory 20 or process data, such as executing access restriction programs.

[0111] The memory 20 includes at least one type of readable storage medium, such as flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 20 can be an internal storage unit of an electronic device, such as the hard disk of the electronic device. In other embodiments, the memory 20 can also be an external storage device of the electronic device, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. Furthermore, the memory 20 can include both internal and external storage units of the electronic device. The memory 20 can be used not only to store application software and various types of data of the electronic device, but also to temporarily store data that has been output or will be output.

[0112] It should be pointed out that, Figure 3 The structure shown does not constitute a limitation on the electronic device. In other embodiments, the electronic device may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0113] This invention also proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the power transmission line status monitoring method described above.

[0114] Those skilled in the art will understand that the logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can mean any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0115] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0116] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0117] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0118] The above embodiments merely illustrate several implementation methods of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this patent should be determined by the appended claims.

Claims

1. A method for monitoring the condition of a power transmission line, characterized in that, The method includes: The design parameters and physical characteristic parameters of each section of the transmission line are obtained to construct independent inter-section topology transmission parameter systems and multi-physical quantity coupling parameter systems within a single section, and to determine the degradation gain factor and fusion weighting coefficient of each physical quantity. After normalizing the collected operational monitoring data, a comprehensive degradation index and degradation evolution trend information for a single section are generated based on the multi-physical quantity coupling parameter system. Using the aforementioned topological transmission parameter system, the degradation index of adjacent segments is transformed into a transmission degradation contribution to the current segment. The cumulative degradation loss is determined by weighted integration with the local degradation rate extracted from the degradation evolution trend information. The cumulative degradation loss is then deducted from the preset safety margin to obtain the dynamic residual degradation barrier of each segment. Short-term disturbance data is collected to generate transient impact quantities based on the topological transmission parameter system. The ratio of the degradation index to the preset safety margin is used to determine the synergistic amplification factor. The degradation index and the transient impact quantities amplified by the synergistic amplification factor are superimposed to obtain the comprehensive risk value of each section. The ratio of the comprehensive risk value to the dynamic residual degradation barrier is used as the current risk ratio. Based on the topological transmission parameter system, the risk ratio of neighboring areas is weighted to obtain the chain constraint quantity. The current risk ratio, the chain constraint quantity, and the dynamic residual degradation barrier are compared with their respective preset thresholds to output the state type and risk quantification assessment results of each segment.

2. The method for monitoring the condition of transmission lines according to claim 1, characterized in that, The steps for obtaining the design parameters and physical characteristic parameters of each section of the transmission line to construct independent inter-section topology conduction parameter systems and single-section multi-physical quantity coupling parameter systems, and determining the degradation gain factor and fusion weighting coefficient of each physical quantity include: Obtain the total number of segments, the span of each segment, the set of adjacent segment numbers, and the disaster spread coefficient; for physically adjacent segments, calculate the topological correlation coefficient by multiplying the ratio of the sum of the spans of the two segments to the smaller of the spans of the two segments by the disaster spread coefficient; the correlation coefficient of non-adjacent segments is zero, thereby constituting the topological transmission parameter system; Obtain the cross-coupling factors between the dimensions of each physical quantity within a single segment, calibrated by experiments or physical equations, and use the unit value as the coupling coefficient of each physical quantity itself to construct a multi-physical quantity coupling relationship matrix, thus forming the multi-physical quantity coupling parameter system. Based on the statistical relationship between the degree of deviation of each physical quantity from the normal range and the equipment degradation rate in historical operating data, the degradation gain factor corresponding to each physical quantity dimension is calibrated. Determine the safety saturation threshold and oversaturation acceleration penalty coefficient for each physical quantity dimension based on the equipment tolerance characteristic curve and operating procedures. Based on operational experience or the contribution ratio of local factors and neighboring cell transmission factors in historical fault samples, determine the local degradation contribution weight and the neighboring cell transmission contribution weight. The characteristic time constant is determined based on the statistical average of the duration of the local impact of neighboring cell degradation transmission, which is used to convert the impact of neighboring cell transmission into a time scale compatible with the local degradation rate. The physical quantity dimensions include at least four dimensions: icing, mechanical stress, insulation contamination, and thermal effect. The disaster spread coefficient is the icing spread coefficient.

3. The method for monitoring the condition of transmission lines according to claim 2, characterized in that, After normalizing the collected operational monitoring data, the steps for generating a comprehensive degradation index and degradation evolution trend information for a single section based on the multi-physical quantity coupling parameter system include: The ice thickness, conductor stress, insulator equivalent salt density, and conductor temperature are collected as the operational monitoring data. Physical limit normalization is performed based on the limit reference values ​​of each physical quantity, and then distribution equilibrium normalization is performed based on the historical statistical mean and variance to obtain the standardized state vector. A diagonal matrix is ​​constructed using each of the aforementioned degradation gain factors, and a similarity transformation is performed with the multi-physical quantity coupling relationship matrix to obtain the coupling degradation stiffness matrix. The quadratic form formed by the standardized state vector and the coupled deterioration stiffness matrix is ​​used as the multi-physical quantity cross-coupling deterioration effect term. For each physical quantity dimension, if the current value exceeds the corresponding safe saturation threshold, the square of the excess part is multiplied by the corresponding oversaturation acceleration penalty coefficient; otherwise, the term is zero, and the contributions of each dimension are summed as the over-limit acceleration degradation effect term. The degradation index is obtained by adding the multi-physical quantity cross-coupling degradation effect term to the over-limit accelerated degradation effect term. The degradation evolution rate is obtained by taking the partial derivatives of the degradation index with respect to each component of the standardized state vector, and the degradation evolution rate is allowed to be negative. The partial derivative of the multi-physical quantity cross-coupling degradation effect term is determined by multiplying the sum of the coupling degradation stiffness matrix and its transpose by the normalized state vector. The partial derivative of the over-limit acceleration degradation effect term is twice the product of the excess portion and the corresponding acceleration penalty coefficient when the threshold is exceeded, otherwise it is zero.

4. The method for monitoring the condition of transmission lines according to claim 3, characterized in that, The steps of using the aforementioned topological conduction parameter system to transform the degradation index of adjacent segments into a conduction degradation contribution to the current segment, and then using a weighted integral with the local degradation rate extracted from the degradation evolution trend information to determine the cumulative degradation loss, and finally subtracting the cumulative degradation loss from a preset safety margin to obtain the dynamic residual degradation barrier for each segment include: The square root of the sum of the squares of the components of the degradation evolution rate in each dimension is taken as the contribution of the local degradation rate. The degradation index of adjacent segments is weighted and summed using the topological correlation coefficient to obtain the transmission influence of neighboring segments, and then divided by the characteristic time constant to obtain the equivalent loss contribution. The total loss rate is obtained by weighting and summing the local degradation rate contribution and the equivalent loss contribution with the local degradation contribution weight and the neighbor cell conduction contribution weight, respectively. The cumulative degradation loss is obtained by integrating the total loss rate over a slow time scale. Subtracting the accumulated degradation loss from the preset safety margin yields the dynamic residual degradation barrier; The dynamic residual degradation barrier satisfies a non-negative constraint; when the total loss rate is negative, the dynamic residual degradation barrier is allowed to rise, but the value after the rise does not exceed the preset safety margin.

5. The method for monitoring the condition of transmission lines according to claim 4, characterized in that, The steps of collecting short-term disturbance data to generate transient impact quantities based on the topological transmission parameter system, determining a collaborative amplification factor based on the ratio of the degradation index to the preset safety margin, and superimposing the degradation index and the transient impact quantities amplified by the collaborative amplification factor to obtain the comprehensive risk value of each segment include: The local disturbance impact amount is obtained by taking the inner product of the real-time collected lightning current intensity, instantaneous wind speed and condensation humidity as a disturbance vector and the preset response coefficient vector. The local disturbance impact quantities of adjacent segments are weighted and summed using topological correlation coefficients to obtain the disturbance propagation impact quantity. The local disturbance impact quantity and the disturbance propagation impact quantity together constitute the transient impact quantity. Divide the degradation index by the preset safety margin, multiply by the material fragility coefficient, and add one to obtain the synergistic amplification factor; The comprehensive risk value is obtained by adding the degradation index to the transient impact amount amplified by the synergistic amplification factor.

6. The method for monitoring the condition of transmission lines according to claim 4, characterized in that, The steps of using the ratio of the comprehensive risk value to the dynamic residual degradation barrier as the current risk ratio, and weighting the neighboring risk ratios based on the topological transmission parameter system to obtain the chain constraint value, and comparing the current risk ratio, the chain constraint value, and the dynamic residual degradation barrier with their respective preset thresholds to output the state type and risk quantification assessment results of each segment include: The current risk ratio is obtained by dividing the comprehensive risk value by the dynamic residual deterioration barrier. The chain constraint quantity is obtained by weighting and summing the current risk ratios of adjacent segments using topological correlation coefficients; When the current risk ratio is less than the preset risk ratio warning threshold and the dynamic residual degradation barrier is greater than the preset barrier warning threshold, it is determined to be in a normal state. When the current risk ratio reaches or exceeds the risk ratio warning threshold and the dynamic remaining degradation barrier is less than or equal to the barrier warning threshold, it is determined to be a gradual degradation warning state. When the current risk ratio reaches or exceeds the preset fault determination threshold and the chain constraint amount is less than the preset chain contribution threshold, it is determined to be a local sudden fault state. When the current risk ratio reaches or exceeds the fault determination threshold and the cascading constraint reaches or exceeds the cascading contribution threshold, it is determined to be a cross-segment cascading sudden fault state.

7. The method for monitoring the condition of transmission lines according to claim 6, characterized in that, The risk quantification assessment results include the gradual degradation level, the overall risk value, and the fault propagation path; The gradual degradation level is determined by the ratio of the cumulative degradation loss to the preset safety margin; The overall risk value is obtained by adding the current risk ratio to the chain constraint amount; The fault propagation path is determined by the segment with the largest product of topological correlation coefficient and current risk ratio among the adjacent segments of the current segment.

8. A transmission line condition monitoring system, characterized in that, The system for implementing the transmission line condition monitoring method according to any one of claims 1 to 7 comprises: The parameter determination module is used to obtain the design parameters and physical characteristic parameters of each section of the transmission line, so as to construct an independent inter-section topology transmission parameter system and a multi-physical quantity coupling parameter system within a single section, and determine the degradation gain factor and fusion weighting coefficient of each physical quantity. The degradation index determination module is used to normalize the collected operation monitoring data and generate a single-segment comprehensive degradation index and degradation evolution trend information based on the multi-physical quantity coupling parameter system. The residual degradation barrier determination module is used to use the topological conduction parameter system to convert the degradation index of adjacent segments into conduction degradation contribution to the current segment, and to determine the cumulative degradation loss by weighted integral with the local degradation rate extracted from the degradation evolution trend information, and then deduct the cumulative degradation loss from the preset safety margin to obtain the dynamic residual degradation barrier of each segment. The comprehensive risk value determination module is used to collect short-term disturbance data to generate transient impact quantities according to the topological transmission parameter system, determine the synergistic amplification factor by the ratio of the degradation index to the preset safety margin, and superimpose the degradation index and the transient impact quantities amplified by the synergistic amplification factor to obtain the comprehensive risk value of each section. The monitoring result determination module uses the ratio of the comprehensive risk value to the dynamic residual degradation barrier as the current risk ratio, and obtains the chain constraint quantity by weighting the neighboring area risk ratio based on the topology transmission parameter system. The module then compares the current risk ratio, the chain constraint quantity, and the dynamic residual degradation barrier with their respective preset thresholds, and outputs the state type and risk quantification assessment results for each segment.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the transmission line condition monitoring method as described in any one of claims 1 to 7.

10. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the transmission line condition monitoring method as described in any one of claims 1-7.

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