An overhead transmission line ice thickness and tension coupling monitoring method

Abstract

This application discloses a method for coupled monitoring of icing thickness and tension in overhead transmission lines, relating to the field of power line tension monitoring technology. The method includes acquiring abnormal tension signals of the transmission line; obtaining confidence levels based on the abnormal tension signals; obtaining multiple first component signals from the abnormal tension signals based on empirical mode decomposition; obtaining significance percentage values ​​corresponding one-to-one with each first component signal based on the confidence levels; obtaining at least one second component signal based on each significance percentage value; and obtaining icing assessment values ​​based on each second component signal. This application obtains icing assessment values ​​based on core components, effectively distinguishing transmission line tension changes caused by different operating conditions such as uniform icing growth, ice jumping, and galloping. It overcomes the problem of existing technologies being unable to decouple multi-source changes and prone to false alarms and missed alarms, achieving precise coupled monitoring of icing thickness and tension, and improving the accuracy and reliability of transmission line icing risk monitoring.

Description

Technical Field

[0001] This application relates to the field of power line tension monitoring technology, specifically a method for coupled monitoring of ice thickness and tension in overhead transmission lines. Background Technology

[0002] Overhead transmission lines are the core infrastructure of the power system. Winter icing disasters can cause complex changes in the mechanical state of transmission lines: uniform icing growth will cause the tension of transmission lines to rise slowly, while uneven distribution of icing, local ice shedding, ice jumping and galloping can cause instantaneous and violent fluctuations in tension signals. In severe cases, this can lead to major power grid accidents such as line breakage and tower collapse. Therefore, accurate coupled monitoring of line icing thickness and tension changes is a core requirement for the safe operation and maintenance of the power grid in winter.

[0003] Existing technologies generally assess the amplitude of tension fluctuations by analyzing the tension values ​​of wires in isolation and limiting the fluctuations to a preset band. This approach cannot effectively decouple the coupled multi-source change components and makes it difficult to distinguish whether the tension change is caused by uniform icing growth or by sudden events such as ice jumping or dancing. This can easily lead to false alarms and missed alarms. Summary of the Invention

[0004] The purpose of this application is to provide a method for monitoring the coupling of icing thickness and tension on overhead transmission lines, so as to solve the technical problems of false alarms and missed alarms when monitoring the icing risk of transmission lines in the existing technology.

[0005] To achieve the above objectives, this application provides the following technical solution: A method for monitoring the coupling of icing thickness and tension on overhead transmission lines includes: The abnormal tension signal of the transmission line is acquired; the tension signal is a sequence of multiple time-series tension values ​​of the transmission line collected in real time; the abnormal tension signal is an arbitrary tension signal containing a first time-series tension value; the first time-series tension value is any tension value greater than a first preset value. Based on the abnormal tension signal, a confidence level value is obtained; the confidence level value is used to characterize at least the probability that the abnormal tension signal is a valid signal; The abnormal tension signal is decomposed based on empirical mode decomposition to obtain multiple first component signals; Based on the confidence level value, a significance percentage value corresponding to each first component signal is obtained; the significance percentage value is at least used to characterize the energy of the corresponding first component signal as a proportion of the energy in the abnormal tension signal. Based on each significance percentage value, at least one second component signal is obtained; the second component signal is the component signal whose corresponding significance percentage value among each first component signal meets a preset condition; Based on each second component signal, an icing assessment value is obtained; the icing assessment value is at least used to characterize the probability that the icing state of the transmission line will cause tension imbalance and line fault risk in the transmission line.

[0006] As a specific solution in this application, the step of obtaining the confidence level value based on the abnormal tension signal includes: Based on the abnormal tension signal, an abnormal time-series tension value is obtained; the abnormal time-series tension value is any time-series tension value in the abnormal tension signal that is greater than a first preset value. Based on the abnormal time-series tension value, a first average value and a second average value are obtained; the first average value is the average value of each time-series tension value in a preset window; the preset window is centered on the abnormal time-series tension value, and the length of the preset window is equal to a second preset value; the second average value is the average value of each time-series tension value in the abnormal tension signal. A confidence level value is obtained based on the first average value and the second average value; the confidence level value is positively correlated with the first average value and negatively correlated with the second average value.

[0007] As a specific solution in this application, the step of obtaining the confidence level value based on the first average value and the second average value includes: A first ratio is obtained based on the first average and the second average; the first ratio is used to characterize the magnitude of the ratio between the first average and the second average. Based on the abnormal tension signal, a first slope is obtained; the first slope is the absolute value of the slope corresponding to the position of the abnormal temporal tension value in the abnormal tension signal; A second ratio is obtained based on the first slope and the preset maximum slope; the second ratio is used to characterize the magnitude of the ratio between the first slope and the preset maximum slope. The confidence level value is obtained based on the first ratio and the second ratio; the confidence level value is positively correlated with the first ratio and negatively correlated with the second ratio.

[0008] As a specific solution in this application, the third component signal is any component signal among the various first component signals; obtaining the significance ratio of the third component signal includes: Based on the third component signal, the amplitudes of multiple component signals are obtained; the amplitudes of the component signals correspond one-to-one with the time-series tension values ​​in the abnormal tension signal. A third average value is obtained based on the amplitude of each component signal; the third average value is the average of the absolute values ​​of the amplitudes of each component signal. Based on the confidence level value, the third average value, and the second average value, the significance ratio of the third component signal is obtained; the second average value is the average value of each time-series tension value in the abnormal tension signal; the significance ratio value is positively correlated with both the confidence level value and the third average value, and negatively correlated with the second average value.

[0009] As a specific solution in this application, the preset conditions include: if the saliency percentage is greater than or equal to a third preset value, then the first component signal corresponding to the saliency percentage is used as the second component signal; if all saliency percentages are less than the third preset value, then the saliency percentages are sorted from largest to smallest, and the first component signal corresponding to the saliency percentage with a sequence number less than or equal to a fourth preset value is used as the second component signal; the fourth preset value is a positive integer, and the fourth preset value is not greater than the total number of first component signals.

[0010] As a specific solution in this application, the fourth preset value is a dynamic value; the method for obtaining the fourth preset value includes: Based on the significance percentage values, a sequence of percentage values ​​is obtained; the sequence of percentage values ​​is obtained by sorting the significance percentage values ​​from largest to smallest. Based on the percentage value sequence, a difference sequence is obtained; the difference sequence is a sequence formed by the absolute values ​​of the differences between each adjacent significance percentage value in the percentage value sequence; Based on the difference sequence, a significant difference is obtained; the significant difference is the maximum value in the difference sequence. Based on the significant difference, a target significance percentage is obtained; the target significance percentage is the minuend of the significant difference obtained from the percentage sequence. The sequence number corresponding to the target saliency percentage in the percentage value sequence is taken as the fourth preset value.

[0011] As a specific solution in this application, the step of obtaining the icing assessment value based on each second component signal includes: Based on each second component signal, a first frequency value and a second frequency value are obtained; the first frequency value is the frequency value of the component signal corresponding to the largest amplitude value among the second component signals; the second frequency value is the average value of the frequency values ​​of the second component signals. Based on the first frequency value and the second frequency value, a third ratio is obtained; the third ratio is used to characterize the magnitude of the ratio between the first frequency value and the second frequency value. Based on the abnormal tension signal, the signal difference degree between it and the historical tension signal is obtained; the historical tension signal is located before the time sequence of the abnormal tension signal, and the time sequence of the historical tension signal is adjacent to that of the abnormal tension signal. An abnormal event intensity value is obtained based on the signal difference degree and the third ratio; the abnormal event intensity value is positively correlated with both the signal difference degree and the third ratio; The icing assessment value is obtained based on the intensity value of the abnormal event.

[0012] As a specific solution in this application, the step of obtaining the abnormal event intensity value based on the signal difference degree and the third ratio includes: Based on each second component signal, obtain the signal envelope of the maximum component signal curve; Based on the signal envelope, a first time and a second time are obtained; the first time is the time it takes for the signal envelope to rise from the baseline to the peak value; the second time is the time it takes for the signal envelope to decay from the peak value back to the baseline. A fourth ratio is obtained based on the first time and the second time; the fourth ratio is used to characterize the magnitude of the ratio between the first time and the second time. The abnormal event intensity value is obtained based on the signal difference, the third ratio, and the fourth ratio; the abnormal event intensity value is also positively correlated with the fourth ratio.

[0013] As a specific solution in this application, after obtaining the abnormal event intensity value based on the signal difference, the third ratio, and the fourth ratio, the method further includes: The environmental signals corresponding to the abnormal tension signal of the transmission line are acquired; the environmental signals include multiple time-series temperature values, multiple time-series wind speed values, and multiple time-series humidity values ​​of the transmission line collected in real time. Based on the environmental signal, a first rate of change, a second rate of change, and an environmental humidity value are obtained; the first rate of change is the average rate of change of temperature in the environmental signal; the second rate of change is the average rate of change of wind speed in the environmental signal; and the environmental humidity value is the average of the humidity values ​​at various time intervals in the environmental signal. The intensity value of the abnormal event is corrected based on the first rate of change, the second rate of change, and the ambient humidity value.

[0014] As a specific solution in this application, obtaining the icing assessment value based on the abnormal event intensity value includes: Obtain a fourth average value; the fourth average value is the average of the intensity values ​​of each historical event corresponding to the ice shedding on the transmission line. The icing assessment value is obtained based on the abnormal event intensity value and the fourth average value; the icing assessment value is positively correlated with the abnormal event intensity value and negatively correlated with the fourth average value.

[0015] Compared with the prior art, the beneficial effects of this application are: This application first screens abnormal tension signals and calculates confidence levels to accurately determine signal validity, eliminating interference signals at the source. Then, through empirical mode decomposition, the non-stationary tension signals are decoupled into multi-scale components. Combining the confidence level values, the significance ratio is calculated to screen out the components that carry the core characteristics of icing risk. Finally, based on the core components, an icing assessment value is obtained, which can effectively distinguish the tension changes of transmission lines caused by different operating conditions such as uniform icing growth, ice jumping, and galloping. This solves the problem that existing technologies cannot decouple multi-source changes and are prone to false alarms and missed alarms, achieving precise coupled monitoring of icing thickness and tension, and improving the accuracy and reliability of transmission line icing risk monitoring. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating a method for monitoring the coupling of icing thickness and tension in overhead transmission lines, as proposed in an embodiment of this application. Detailed Implementation

[0017] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0018] The terms "first," "second," etc., in the specification and accompanying drawings of the embodiments of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. For example, the first preset value and the second preset value mentioned below are different preset values. It should be understood that such names can be used interchangeably where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or modules is not necessarily limited to those steps or modules explicitly listed, but may include other steps or modules not explicitly listed or inherent to these processes, methods, products, or devices. The division of modules in the embodiments of this application is merely a logical division. In actual applications, there may be other division methods. For example, multiple modules may be combined into or integrated into another system, or some features may be ignored or not performed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be through some interface, and the indirect coupling or communication connection between modules may be electrical or other similar forms. None of these are limited in the embodiments of this application. Furthermore, the modules or sub-modules described as separate components may or may not be physically separated, may or may not be physical modules, or may be distributed among multiple circuit modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the embodiments of this application.

[0019] To address the technical problems of false alarms and missed alarms in existing technologies for monitoring the risk of icing on transmission lines, this application proposes a method for monitoring the coupling of icing thickness and tension on overhead transmission lines. Figure 1 The method for monitoring the coupling of icing thickness and tension on overhead transmission lines includes steps 100 to 600.

[0020] Step 100: Obtain the abnormal tension signal of the transmission line.

[0021] In this embodiment, the tension signal is a sequence of multiple time-series tension values ​​of the transmission line acquired in real time; the abnormal tension signal is an arbitrary tension signal containing a first time-series tension value; the first time-series tension value is an arbitrary tension value greater than a first preset value.

[0022] In this embodiment, multiple time-series tension values ​​of the transmission line can be obtained in real time using any reasonable method. For example, a tension sensor (e.g., any one of a fiber optic tension sensor, a resistance strain gauge tension sensor, or a magnetostrictive tension sensor) can be installed at the tension clamp, suspension clamp, insulator string end, or connection point between the tower crossarm and the transmission line of the overhead transmission line. The axial tension of the transmission line can be continuously sampled in real time at a preset sampling frequency (e.g., 0.1 seconds / time or 2 seconds / time, etc.). The tension values ​​corresponding to each sampling moment are recorded sequentially to form a continuous time-series tension value sequence. The time-series tension values ​​collected by the sensor can be uploaded to the power grid monitoring master station in real time via wireless communication methods such as 4G / 5G, LoRa, and BeiDou short message. Alternatively, they can be temporarily stored in a local data acquisition terminal and then uploaded in batches at fixed time intervals to ensure the continuity of time-series tension value acquisition, data integrity, and real-time transmission, providing reliable raw data for subsequent abnormal tension signal screening.

[0023] In this embodiment, the core function of step 100 is to complete the real-time acquisition of transmission line tension data and the preliminary screening of abnormal signals, providing a basic data source for subsequent icing coupling monitoring and analysis. The first round of signal filtering is completed by using a preset threshold (i.e., the first preset value), eliminating normal tension data without fluctuations, greatly reducing the scope of subsequent data processing, and improving the execution efficiency of the monitoring method.

[0024] In this embodiment, the first preset value is a pre-set tension anomaly judgment threshold, used to distinguish between normal tension fluctuations and abnormal tension changes in the transmission line. The setting of the first preset value can be determined based on the design parameters and historical operating data of the transmission line. Specifically, it can be set to 1.2 to 1.5 times the static tension value of the transmission line under standard operating conditions (e.g., no icing, no wind, rated temperature); or it can be based on the normal operating tension data of the transmission line over the past year, taking 1.1 times the maximum normal operating tension value as the first preset value, taking into account both the detection sensitivity and anti-interference capability of abnormal signals.

[0025] In this embodiment, the specific method for acquiring the abnormal tension signal is as follows: each time-series tension value in the real-time acquired tension signal is judged one by one. If the value of any time-series tension value is greater than a first preset value, it is determined that a tension abnormality has occurred at that moment. The time-series tension value sequence centered on the abnormal time-series tension value and within a preset time range is extracted as the abnormal tension signal for this monitoring and analysis. The preset time range can be set according to the sampling frequency and the line tension fluctuation characteristics. For example, it can be set to 5 seconds before and after the abnormal moment to ensure that the extracted abnormal tension signal can completely cover the entire process of the rise, peak and attenuation of the tension abnormal event.

[0026] Step 200: Based on the abnormal tension signal, obtain the confidence level value.

[0027] In this embodiment, the confidence level value is used at least to characterize the probability that the abnormal tension signal is a valid signal.

[0028] In this embodiment, the probability of a valid signal refers to the probability that the time-series tension value (i.e., the first time-series tension value) in the abnormal tension signal is greater than the first preset value due to actual line conditions such as line icing, de-icing, and galloping.

[0029] It is important to note that the core function of step 200 is to determine the validity of the abnormal tension signals obtained from the initial screening. This quantitatively characterizes the probability that the abnormal tension signal is a valid signal caused by real line conditions such as line icing, de-icing, and galloping, rather than an invalid interference signal caused by factors such as sensor measurement noise, electromagnetic interference, and data acquisition errors. This reduces false alarms caused by interference signals from the source and improves the reliability of monitoring results.

[0030] In a specific embodiment of this application, step 200, obtaining a confidence level value based on the abnormal tension signal, includes steps 210 to 230.

[0031] Step 210: Based on the abnormal tension signal, obtain the abnormal time-series tension value.

[0032] In this embodiment, the abnormal timing tension value is any timing tension value in the abnormal tension signal that is greater than a first preset value.

[0033] In this embodiment, the abnormal temporal tension value is the core sampling point that triggers the anomaly determination in the abnormal tension signal, and it is the core anchor point for subsequent calculation of local and global tension features. When there are multiple temporal tension values ​​in the abnormal tension signal that are greater than a first preset value, the temporal tension value with the largest value can be selected as the core abnormal temporal tension value, or all temporal tension values ​​greater than the first preset value can be used as abnormal temporal tension values, and the average value can be taken after subsequent calculations to ensure the stability of the calculation results.

[0034] Step 220: Based on the abnormal time-series tension value, obtain the first average value and the second average value.

[0035] In this embodiment, the first average value is the average value of each time-series tension value within a preset window. The preset window is centered on the abnormal time-series tension value, and the length of the preset window is equal to a second preset value. The second average value is the average value of each time-series tension value in the abnormal tension signal.

[0036] In this embodiment, the first average value is the average tension value within a preset window containing the abnormal temporal tension value, used to characterize the local tension level during the period of the abnormal event. The second average value is the global tension average value of the entire abnormal tension signal, used to characterize the overall tension level throughout the entire abnormal event. By comparing the local average value and the global average value, it is possible to effectively distinguish between locally concentrated real tension abrupt changes and globally uniformly distributed random interference noise.

[0037] In this embodiment, the second preset value is the length of the preset window, which is the number of sampling points. It can be set according to the sampling frequency of the tension signal. For example, when the sampling frequency is 100Hz, the second preset value can be set to 21, that is, the preset window covers 10 sampling points before and after the abnormal time-series tension value, ensuring that the preset window can completely characterize the local tension characteristics near the abnormal change point, while avoiding the introduction of too much interference data from non-abnormal time periods due to an excessively long preset window.

[0038] Step 230: Obtain the confidence level value based on the first average value and the second average value.

[0039] In this embodiment, the confidence level value is positively correlated with the first average value and negatively correlated with the second average value. That is, in this embodiment, any reasonable method can be used to obtain the confidence level value based on the first average value and the second average value, as long as the confidence level value is positively correlated with the first average value and negatively correlated with the second average value. For example, the confidence level value can be the difference or ratio between the first average value and the second average value.

[0040] In this embodiment, a higher confidence level indicates that the tension mutation is concentrated in a local area near the abnormal time-series tension value, which is consistent with the characteristics of tension mutation caused by real icing, de-icing, galloping and other working conditions, and the corresponding confidence level is higher; conversely, a lower confidence level indicates that the tension fluctuation is evenly distributed throughout the entire signal period, which is more consistent with the characteristics of random interference noise, and the corresponding confidence level is lower.

[0041] It should be noted that while the confidence level value obtained based on the difference or ratio between the first average value and the second average value can identify the validity of abnormal tension signals, it may still have insufficient accuracy in practical engineering applications. On the one hand, for slowly varying tension anomalies caused by uniform icing growth on the line, the difference between local and global tension amplitudes is small, which can easily lead to an underestimation of the confidence level of valid abnormal tension signals, resulting in missed detections. On the other hand, for spike-type noise signals caused by electromagnetic interference, instantaneous sensor failures, etc., single-point amplitude abrupt changes can easily be misjudged as high-confidence valid signals, leading to false alarms. To effectively solve the above technical problems and simultaneously improve the detection rate and anti-interference capability of abnormal tension signals, in one embodiment of this application, step 230, obtaining a confidence level value based on the first average value and the second average value, includes steps 231 to 234.

[0042] Step 231: Obtain the first ratio based on the first average value and the second average value.

[0043] In this embodiment, the first ratio is used to characterize the magnitude of the ratio between the first average and the second average. That is, the first ratio can be directly equal to the ratio of the first average to the second average. It is important to note that if the first ratio is equal to the ratio of the first average to the second average, and the second average could potentially be zero, a mathematical calculation anomaly (denominator equal to zero) is highly likely to occur, causing the first ratio to fail and interfering with the accurate calculation of subsequent confidence levels. To completely avoid this computational risk, a near-zero coefficient can be introduced in the denominator. This coefficient is a very small positive number (e.g., 0.001 or 0.01), which neither changes the numerical trend of the first ratio nor compromises the stability and feasibility of the division operation. Subsequent second to fourth ratios can also be processed in this way, and will not be elaborated further.

[0044] In this embodiment, the first ratio directly quantifies the difference between the local tension level at the abnormal point in the abnormal tension signal and the global tension level in the abnormal tension signal, and is one of the core indicators for determining the validity of the signal. For example, in a specific embodiment, if the first average value is 18kN and the second average value is 12kN, then the first ratio is 1.5, which indicates that the local tension level is significantly higher than the global average level, consistent with the characteristics of a real abnormal event.

[0045] Step 232: Obtain the first slope based on the abnormal tension signal.

[0046] In this embodiment, the first slope is the absolute value of the slope corresponding to the position of the abnormal temporal tension value in the abnormal tension signal.

[0047] In this embodiment, the first slope is used to characterize the rate of tension change at the location of the abnormal temporal tension value. Tension abrupt changes caused by real-world conditions (e.g., ice jumps caused by ice shedding) typically have extremely rapid rates of increase, resulting in a large absolute slope value. However, this rate of tension change will not exceed the maximum rate of tension change (i.e., the first slope will not be greater than the preset maximum slope mentioned below). In contrast, the rate of change of sensor interference noise is usually irregular, and the rate of tension change almost always exceeds the maximum rate of tension change (i.e., the first slope is always greater than the preset maximum slope mentioned below). The first slope can be calculated using the difference method, that is, taking the difference between the abnormal temporal tension value and the temporal tension value at the previous sampling time, dividing it by the sampling time interval, and then taking the absolute value of the slope at that location.

[0048] Step 233: Obtain the second ratio based on the first slope and the preset maximum slope.

[0049] In this embodiment, the second ratio is used to characterize the magnitude of the ratio between the first slope and the preset maximum slope.

[0050] In this embodiment, the preset maximum slope is the maximum tension change rate that can be achieved under actual operating conditions of the transmission line. It can be determined based on line mechanical simulation and historical fault data, and is used to normalize the first slope. The closer the second ratio is to 1, the closer the tension change rate is to the limit of the actual physical operating conditions; if the second ratio is greater than 1, it indicates that the slope exceeds the range that the physical characteristics of the line can reach, and it is likely an interference signal.

[0051] Step 234: Obtain the confidence level value based on the first ratio and the second ratio.

[0052] In this embodiment, the confidence level value is positively correlated with the first ratio and negatively correlated with the second ratio.

[0053] In this embodiment, any reasonable method can be used to obtain the confidence level value based on the first ratio and the second ratio, as long as the confidence level value is positively correlated with the first ratio and negatively correlated with the second ratio. For example, in one embodiment of this application, step 234, the formula for calculating the confidence level value based on the first ratio and the second ratio, can be as follows: in, Indicates the confidence level value; Indicates the first weight; Indicates the first ratio; Indicates the second weight; This represents the second ratio; the first and second weights can be set according to requirements, for example: the first weight can be equal to 0.8 and the second weight can be equal to 0.5; or, the first weight can be equal to 0.7 and the second weight can be equal to 0.6, etc.

[0054] In another embodiment of this application, step 234, based on the first ratio and the second ratio, the formula for calculating the confidence level value can be as follows: in, Indicates the confidence level value; Indicates the first ratio; Indicates the second ratio; Represents the natural constant.

[0055] This embodiment achieves dual cross-validation by simultaneously fusing the local-to-global tension amplitude ratio (i.e., the first ratio) and the abrupt change slope as a dual-dimensional feature. The first ratio anchors the local concentration of anomalous events, while the second ratio quantifies the steepness of tension abrupt changes. This effectively avoids the shortcomings of a single amplitude ratio in identifying slowly varying anomalies, accurately eliminates the interference of instantaneous spike noise, improves the accuracy of effective signal identification, and provides a reliable weighting basis for subsequent component signal selection.

[0056] Step 300: Decompose the abnormal tension signal based on empirical mode decomposition to obtain multiple first component signals.

[0057] It is important to understand that the core function of step 300 is to adaptively decompose the nonlinear and non-stationary abnormal tension signal through Empirical Mode Decomposition (EMD), breaking down the complex original tension signal into multiple intrinsic mode function (IMF) component signals with different time scale characteristics, i.e., the first component signal. This decouples the transmission line tension change components caused by different factors in the tension signal, and solves the technical pain point that existing technologies cannot effectively separate coupled multi-source tension change components.

[0058] It is important to note that Empirical Mode Decomposition (EMD) is an adaptive decomposition method applicable to nonlinear and non-stationary signals. Its core principle is to decompose a complex signal into a series of intrinsic mode function (EMF) components arranged from high to low frequency, and a residual component. Each EMF component corresponds to a fluctuation component at a different characteristic scale in the signal, accurately characterizing the variation features of different inducing factors in the original signal. Specifically, the EMD process for abnormal tension signals is as follows: Identify all local maxima and minima in the abnormal tension signal sequence, and fit the maxima and minima using a cubic spline interpolation function to obtain the upper and lower envelopes of the signal. Calculate the average value of the upper and lower envelopes to obtain the mean envelope of the signal. Subtract this mean envelope from the original abnormal tension signal to obtain a new signal sequence. Repeat the envelope fitting and mean removal steps above for the new signal sequence until the obtained signal sequence satisfies the condition of the intrinsic mode function, and take the signal sequence as the first first component signal; The original abnormal tension signal is subtracted from the first first component signal to obtain the residual signal. The above decomposition steps are repeated on the residual signal to obtain the second, third, ..., nth first component signal, until the residual signal becomes a monotonic function or a constant, at which point the decomposition process terminates. Empirical mode decomposition is a mature technique and will not be elaborated upon here.

[0059] In this embodiment, the multiple first component signals obtained from the decomposition can be arranged sequentially from high to low frequency. The high-frequency first component signal typically corresponds to instantaneous tension fluctuations caused by sudden conditions such as line icing, galloping, and wind-induced vibration. The low-frequency first component signal typically corresponds to slow tension changes caused by uniform icing growth and temperature variations. The residual component signal corresponds to the static tension baseline of the line. This decomposition process effectively decouples the tension change components of abnormal tension signals from different causes and time scales, providing a data foundation for accurately distinguishing icing-related risks from other interference factors.

[0060] Step 400: Based on the confidence level value, obtain the significance ratio value corresponding to each of the first component signals.

[0061] In this embodiment, the saliency percentage is used at least to characterize the energy of the corresponding first component signal relative to the energy of the abnormal tension signal.

[0062] It is important to understand that the core function of step 400 is to combine the confidence level value calculated in the previous step to perform a significance quantification assessment on the first component signal obtained from each decomposition, calculate the energy proportion and significance level of each component signal in the abnormal tension signal, and provide a quantitative basis for the subsequent screening of core effective component signals.

[0063] In this embodiment, the significance percentage value can be normalized to the range of 0 to 1. The higher the significance percentage value, the higher the energy percentage of the corresponding first component signal in the abnormal tension signal, the greater its contribution to the abnormal tension event, and the more effective the component signal that carries the core characteristics of the abnormal event. Conversely, the lower the significance percentage value, the lower the energy percentage of the corresponding first component signal, and the more likely it is a noise component signal or an irrelevant interference component signal.

[0064] Since the saliency percentage values ​​corresponding to all first component signals in this application follow the same calculation logic and implementation steps, in order to clearly and completely explain the specific acquisition method of the saliency percentage values ​​corresponding to each first component signal in this application, in the embodiments below this application, any one of the first component signals (that is, the third component signal mentioned below) is selected as an example to explain the specific acquisition steps of the saliency percentage value in detail.

[0065] In this embodiment, obtaining the saliency percentage of the third component signal includes steps 410 to 430.

[0066] Step 410: Based on the third component signal, obtain the amplitude values ​​of multiple component signals.

[0067] In this embodiment, the amplitude of the component signal is the numerical value of the third component signal at each sampling moment, which corresponds one-to-one with the temporal tension value of the abnormal tension signal on the time axis, fully characterizing the amplitude change characteristics of the component signal throughout the entire abnormal event. That is to say, in this embodiment, the amplitude of the component signal corresponds one-to-one with each temporal tension value in the abnormal tension signal.

[0068] Step 420: Obtain the third average value based on the amplitude of each component signal.

[0069] In this embodiment, the third average value is the average of the absolute values ​​of the amplitudes of each component signal. In this embodiment, the larger the third average value, the greater the overall energy of the third component signal and the higher its contribution to the abnormal tension signal.

[0070] Step 430: Based on the confidence level value, the third average value, and the second average value, obtain the significance ratio of the third component signal.

[0071] In this embodiment, the second average value is the average value of each time-series tension value in the abnormal tension signal. The significance ratio is positively correlated with both the confidence level value and the third average value, and negatively correlated with the second average value. Specifically, in step 430, the calculation formula for obtaining the significance ratio of the third component signal based on the confidence level value, the third average value, and the second average value can be as follows: in, This represents the significance percentage of the third component signal; Indicates the confidence level value; This represents the second average value; This represents the third average value; This represents the preset baseline tension offset, which can be set based on empirical values, such as 0.1N or 0.01N. In this embodiment, the ratio of the third average value to the second average value is weighted by a confidence level value, achieving a deep fusion of the validity of the abnormal tension signal and the significance of the component signals. When the confidence level value of the abnormal tension signal is high, the overall significance ratio of each component signal increases, ensuring that the core component signal of the valid abnormal tension signal can be fully identified; when the confidence level value is low, the overall significance ratio of each component signal decreases, preventing interference signal components from being mistakenly selected as valid component signals.

[0072] Step 500: Based on each significance percentage value, obtain at least one second component signal.

[0073] In this embodiment, the second component signal is the component signal whose corresponding saliency percentage among each of the first component signals meets a preset condition.

[0074] It is important to understand that the core function of step 500 is to select the second component signal carrying the core characteristics of the abnormal event from multiple first component signals based on the significance ratio value, eliminate low significance noise component signals and irrelevant interference component signals, further reduce the data dimensions of subsequent analysis, and at the same time retain the core feature component signals related to icing risk, solve the problem that existing technologies cannot accurately separate effective feature component signals, and improve the accuracy of icing risk assessment.

[0075] In this application, any reasonable condition can be used as the preset condition. For example, in a specific embodiment, the preset condition may include: if the saliency percentage is greater than or equal to a third preset value, then the first component signal corresponding to the saliency percentage is used as the second component signal; if all saliency percentages are less than the third preset value, then the saliency percentages are sorted from largest to smallest, and the first component signal corresponding to the saliency percentage with a sequence number less than or equal to a fourth preset value is used as the second component signal; the fourth preset value is a positive integer, and the fourth preset value is not greater than the total number of first component signals.

[0076] In this embodiment, the third preset value is the threshold for determining the saliency of the component signals. This threshold can be set according to monitoring requirements, for example, it can be set to 0.1. That is, first component signals with a saliency percentage greater than or equal to 0.1 are all determined to be valid component signals carrying core features and included in the range of second component signals. This preset condition adaptively filters out all highly significant component signals, ensuring no core features are missed. When the saliency percentage of all component signals is lower than the third preset value, it indicates that the saliency differences between the component signals are small. In this case, the top N highly significant component signals are selected using a fixed sorting method to avoid the situation where no valid component signals are filtered out, ensuring the integrity of subsequent analysis processes. The fourth preset value can be set to a fixed value, for example, it can be set to 3. That is, the top 3 first component signals with the highest saliency percentages are selected as second component signals, balancing feature integrity and computational efficiency.

[0077] It should be noted that while setting the fourth preset value to a fixed value ensures the integrity of the process, it does not consider the actual distribution characteristics of the significant differences in the signals of each component after decomposition of different abnormal tension signals. This can easily lead to problems such as selecting too many second component signals, introducing noise, or selecting too few, resulting in the loss of core features. Therefore, in one embodiment of this application, the fourth preset value can be a dynamic value. The method for obtaining the fourth preset value may include steps 510 to 550.

[0078] Step 510: Obtain the percentage value sequence based on each significance percentage value.

[0079] In this embodiment, the percentage value sequence is a sequence obtained by sorting the various salience percentage values ​​from largest to smallest.

[0080] In this embodiment, the percentage value sequence is arranged from high to low according to the significance percentage value, that is, the percentage value sequence fully presents the significance (i.e., significance percentage value) distribution characteristics of each component signal. For example, if the decomposition yields 6 first component signals with significance percentage values ​​of 0.25, 0.20, 0.18, 0.05, 0.03, and 0.02 respectively, then the sorted percentage value sequence is [0.25, 0.20, 0.18, 0.05, 0.03, 0.02].

[0081] Step 520: Obtain the difference sequence based on the percentage value sequence.

[0082] In this embodiment, the difference sequence is a sequence formed by the absolute values ​​of the differences between adjacent saliency percentage values ​​in the percentage value sequence. In this embodiment, the difference sequence quantifies the degree of significant abrupt change between adjacent component signals by calculating the difference between adjacent saliency percentage values ​​in the percentage value sequence. The larger the difference, the more pronounced the saliency of the component signal before and after that position is. This position is the boundary between the core effective component signal and the noise component signal. Continuing the above example, the absolute values ​​of the differences between adjacent saliency percentage values ​​in the percentage value sequence are 0.05, 0.02, 0.13, 0.02, and 0.01, respectively, which corresponds to the difference sequence [0.05, 0.02, 0.13, 0.02, 0.01].

[0083] Step 530: Based on the difference sequence, obtain significant differences.

[0084] In this embodiment, the significant difference is the maximum value in the difference sequence, corresponding to the position where the significance decreases most drastically in the proportion sequence, i.e., the optimal boundary between the core component signal and the noise component signal. Continuing with the above example, the maximum value in the difference sequence is 0.13, that is, the significant difference is 0.13.

[0085] Step 540: Based on the significant difference, obtain the target significance percentage.

[0086] In this embodiment, the target significance percentage is the minuend of the significant difference obtained from the percentage value sequence. In this embodiment, the significant difference is obtained by subtracting the next value (i.e., the subtrahend) from the preceding value (i.e., the minuend) in the percentage value sequence. The corresponding minuend is the significance percentage of the last core effective component signal before the dividing point. Continuing the above example, the significant difference of 0.13 is obtained by subtracting 0.05 from 0.18, and the corresponding minuend is 0.18, meaning the target significance percentage is 0.18.

[0087] Step 550: Use the sequence number corresponding to the target saliency percentage in the percentage value sequence as the fourth preset value.

[0088] In this embodiment, the sequence number is the sorting number starting from 1 in the proportion value sequence. Continuing from the above example, the sequence number of the target significance proportion value of 0.18 in the proportion value sequence is 3, which is the fourth preset value of 3. At this time, the first component signal corresponding to the first 3 significance proportion values ​​in the proportion value sequence is selected as the second component signal, realizing the adaptive dynamic adjustment of the fourth preset value. There is no need to manually preset a fixed threshold. It can adapt to the component significance distribution characteristics under different abnormal events, ensuring that the selected second component signal can completely cover the core features of the abnormal event, while eliminating noise components to the maximum extent.

[0089] Step 600: Obtain the icing assessment value based on each second component signal.

[0090] In this embodiment, the icing assessment value is used at least to characterize the probability that the icing state of the transmission line will cause tension imbalance and line fault risk in the transmission line.

[0091] It is important to understand that the core function of step 600 is to extract key features related to risks such as line icing, de-icing, and galloping based on the core second component signal obtained through screening, and to quantitatively calculate the icing assessment value. This enables accurate quantitative assessment of the risks of faults such as tension imbalance, line breakage, and tower collapse caused by the icing state of transmission lines, solving the technical problem that existing technologies cannot distinguish the causes of tension changes and are prone to false alarms and missed alarms, and providing accurate decision-making basis for power grid operation and maintenance during winter icing.

[0092] In this embodiment, the icing assessment value can be normalized to the range of 0 to 1. The closer the icing assessment value is to 1, the higher the risk of tension imbalance and line fault caused by the current icing state of the line, and emergency measures such as de-icing and current limiting should be taken immediately. The closer the icing assessment value is to 0, the more it indicates that the current tension abnormality of the line is not related to the risk of icing, or the risk of fault caused by icing is extremely low, and no special treatment is required.

[0093] In this embodiment, any reasonable method can be used to obtain the icing assessment value based on each second component signal. For example, the sum of the significance percentage values ​​corresponding to each second component signal can be used as the icing assessment value. It should be noted that the above method of calculating the icing assessment value solely by the sum of significance percentage values ​​can achieve basic risk quantification, but it can only characterize the overall energy percentage of the second component signals after screening. It cannot accurately distinguish the specific causes of abnormal tension signals, and it is difficult to identify whether the abnormal tension is caused by icing-related risk conditions such as line icing growth, icing detachment, and ice dancing, or by non-icing factors such as wind vibration, sudden temperature changes, and slight sensor drift. At the same time, this method does not combine the time-domain and frequency-domain characteristics of abnormal events, the historical operating conditions of the line, and the field environmental parameters for multi-dimensional cross-verification, and cannot achieve a refined quantitative assessment of the fault risk level. There is still a possibility of risk misjudgment and missed early warning, which is difficult to meet the core requirements of power grid winter icing operation and maintenance for monitoring accuracy and reliability. To further overcome the aforementioned technical deficiencies and achieve accurate identification of icing-related risk factors and refined quantitative assessment of line tension imbalance and fault risks, in one embodiment of this application, step 600 involves obtaining icing assessment values ​​based on each second component signal, including steps 610 to 650.

[0094] Step 610: Based on each second component signal, obtain the first frequency value and the second frequency value.

[0095] In this embodiment, the first frequency value is the frequency value of the component signal corresponding to the largest amplitude among all the second component signals. The second frequency value is the average of the frequency values ​​of all the second component signals. In this embodiment, each second component signal corresponds to a fixed characteristic frequency, which can be obtained through spectral analysis methods such as Hilbert transform and fast Fourier transform. The first frequency value is the characteristic frequency corresponding to the core component with the highest energy and largest amplitude, which directly determines the core vibration characteristics of the tension anomaly event; the second frequency value is the average of the frequencies of all the second component signals, representing the overall frequency level of the anomaly event. It is important to understand that fault conditions such as ice jumping and galloping caused by line icing have their typical characteristic frequency ranges. By comparing the first and second frequency values, the type of tension anomaly event can be accurately identified, distinguishing between icing-related risks and tension fluctuations caused by other factors such as wind vibration and equipment vibration.

[0096] Step 620: Obtain a third ratio based on the first frequency value and the second frequency value.

[0097] In this embodiment, the third ratio is used to characterize the magnitude of the ratio between the first frequency value and the second frequency value. In this embodiment, the third ratio quantifies the degree of deviation between the core component frequency and the overall average frequency. When the third ratio is close to 1, it indicates that the frequencies of each second component signal are concentrated, the vibration characteristics of the abnormal event are singular, and it conforms to the tension change characteristics caused by icing and de-icing. When the third ratio is far from 1, it indicates that the frequencies of each component are dispersed, and the abnormal event is more consistent with the random vibration characteristics caused by multi-source interference.

[0098] Step 630: Based on the abnormal tension signal, obtain the signal difference degree with the historical tension signal.

[0099] In this embodiment, the historical tension signal is located before the time sequence of the abnormal tension signal, and the historical tension signal is adjacent to the time sequence of the abnormal tension signal.

[0100] In this embodiment, the historical tension signal is the normal operating tension signal of the same duration before the occurrence of the abnormal tension signal. For example, if the abnormal tension signal is the signal 2.5 seconds before and after the abnormal moment, then the historical tension signal is the tension signal from 10 seconds to 5 seconds before the abnormal moment. The signal difference degree is used to characterize the waveform difference between the abnormal tension signal and the normal tension signal before the abnormality, and can be calculated using algorithms such as Dynamic Time Warping (DTW), cosine similarity, and correlation coefficient. Preferably, this embodiment uses the DTW distance to calculate the degree of difference between the two signals, i.e., the signal difference degree. The higher the signal difference degree, the more drastic the change in the tension waveform caused by the abnormal event, and the higher the severity of the event; conversely, the lower the signal difference degree, the smaller the change in the tension waveform, and the lower the severity of the event.

[0101] Step 640: Based on the signal difference and the third ratio, obtain the abnormal event intensity value.

[0102] In this embodiment, the abnormal event intensity value is positively correlated with both the signal difference degree and the third ratio. That is, in this embodiment, any reasonable method can be used to obtain the abnormal event intensity value based on the signal difference degree and the third ratio, as long as the abnormal event intensity value is positively correlated with both the signal difference degree and the third ratio. For example, the abnormal event intensity value can be a weighted sum or product of the signal difference degree and the third ratio.

[0103] It is important to note that while the abnormal event intensity values ​​obtained based on signal difference and the third ratio can provide a basic quantitative assessment of the severity of abnormal events, they may still suffer from insufficient accuracy in practical engineering applications. Firstly, this calculation method only integrates frequency domain concentration characteristics and overall waveform difference characteristics, failing to cover the core temporal dynamic characteristics of tension anomalies. It cannot accurately capture the unique tension signal envelope variation patterns of icing-related fault conditions such as ice shedding, ice jumping, and dancing, easily leading to insufficient differentiation in the intensity quantification results of fault events at different risk levels, and hindering refined classification of fault severity. Secondly, this method cannot differentiate the impact intensity and duration of abnormal events, making it difficult to distinguish between instantaneous tension fluctuations caused by non-icing factors such as wind vibration and minor sensor drift, and the persistent high-risk tension anomalies caused by icing faults. This can easily lead to inaccurate event intensity assessments, thereby reducing the reliability and early warning accuracy of subsequent icing risk assessments. Based on this, in one embodiment of this application, step 640, obtaining the abnormal event intensity value based on the signal difference degree and the third ratio, may include steps 641 to 644.

[0104] Step 641: Based on each second component signal, obtain the signal envelope of the maximum component signal curve.

[0105] In this embodiment, the maximum component signal curve is the time-series curve corresponding to the component signal with the largest amplitude among the various second component signals, which carries the core fluctuation characteristics of the abnormal event; the signal envelope can be obtained through Hilbert transform (a mature technology, which will not be elaborated here), and is used to characterize the trend of the amplitude of the maximum component signal curve changing over time, fully presenting the entire process of the abnormal event from occurrence, rising to the peak, and then decaying to the baseline.

[0106] Step 642: Based on the signal envelope, obtain the first time and the second time.

[0107] In this embodiment, the first time is the time it takes for the signal envelope to rise from the baseline to the peak value. The second time is the time it takes for the signal envelope to decay from the peak value back to the baseline. That is, in this embodiment, the baseline is the static tension baseline of the line before the abnormal event occurs, i.e., the average value of historical tension signals. The first time is the duration of the rising edge of the abnormal event, and the second time is the duration of the falling edge of the abnormal event. Ice jumping events caused by icing detachment typically have a "steep rise and slow fall" envelope characteristic, i.e., the rising edge duration is extremely short, and the falling edge duration is relatively long. By comparing the first time and the second time, this type of high-risk icing-related event can be accurately identified.

[0108] Step 643: Based on the first time and the second time, obtain the fourth ratio.

[0109] In this embodiment, the fourth ratio is used to characterize the ratio between the first time and the second time. In this embodiment, the smaller the fourth ratio, the more obvious the "steep rise and slow fall" characteristic of the signal envelope, the more it conforms to the typical characteristics of icing and de-icing events, and the higher the degree of danger of the event; conversely, the closer the fourth ratio is to 1, the closer the rise and fall times of the envelope are, the more it conforms to the characteristics of conventional vibrations such as wind vibration, and the lower the degree of danger of the event.

[0110] Step 644: Based on the signal difference, the third ratio, and the fourth ratio, obtain the intensity value of the abnormal event.

[0111] In this embodiment, the intensity value of the abnormal event is also negatively correlated with the fourth ratio.

[0112] In a specific embodiment, step 644: Based on the signal difference, the third ratio, and the fourth ratio, the formula for calculating the intensity value of the abnormal event can be as follows: in, This indicates a correction to the previous anomaly event strength value; Indicates the degree of signal difference; Indicates the third ratio; This represents the fourth ratio.

[0113] It is important to understand that after the initial calculation of the abnormal event intensity value, considering that the generation, development, and induction of tension anomalies in transmission lines are closely related to environmental factors such as on-site temperature, wind speed, and humidity, the abnormal event intensity value calculated solely based on the time-domain and frequency-domain characteristics of the tension signal may not fully reflect the actual environmental conditions and could deviate from the actual icing risk. To further improve the accuracy and reliability of the abnormal event intensity value and make it more closely match the actual operating environment of the line, environmental signals can be introduced to correct the abnormal event intensity value obtained in step 644. Based on this, in one embodiment of this application, after obtaining the abnormal event intensity value based on the signal difference, the third ratio, and the fourth ratio in step 644, the method may further include steps 645 to 647.

[0114] Step 645: Obtain the environmental signal corresponding to the transmission line and the abnormal tension signal.

[0115] In this embodiment, the environmental signals include multiple time-series temperature values, multiple time-series wind speed values, and multiple time-series humidity values ​​of the transmission line, which are collected in real time.

[0116] In this embodiment, environmental signals are collected synchronously by miniature weather stations deployed on transmission line towers, at the same time and sampling frequency as the abnormal tension signals, providing environmental basis for subsequent determination of the causes of abnormal events and intensity correction. The formation and detachment of line icing are closely related to ambient temperature, humidity, and wind speed, and usually occur under meteorological conditions of below 0°C, high humidity, and wind. By fusing environmental signals, it is possible to further distinguish whether the abnormal tension is caused by icing-related operating conditions.

[0117] Step 646: Based on the environmental signal, obtain the first rate of change, the second rate of change, and the environmental humidity value.

[0118] In this embodiment, the first rate of change is the average rate of change of temperature in the environmental signal. The second rate of change is the average rate of change of wind speed in the environmental signal. The environmental humidity value is the average of the humidity values ​​over various time periods in the environmental signal.

[0119] In this embodiment, the first rate of change is the rate of temperature change during the abnormal event period. A rapid rise in temperature is usually the core cause of ice shedding. The second rate of change is the rate of wind speed change during the abnormal event period. Sudden changes in wind speed can cause line galloping and ice shedding. The ambient humidity value is used to determine whether the high humidity conditions for ice formation are met.

[0120] Step 647: Based on the first rate of change, the second rate of change, and the ambient humidity value, correct the intensity value of the abnormal event.

[0121] Specifically, in step 647, the calculation formula for correcting the intensity value of the abnormal event based on the first rate of change, the second rate of change, and the ambient humidity value can be as follows: in, This indicates the adjusted severity value of the abnormal event. This indicates a correction to the previous anomaly event strength value; Indicates the first rate of change; This indicates the preset temperature change rate (which can be equal to the historical maximum temperature change rate). Indicates the second rate of change; This indicates the preset wind speed change rate (which can be equal to the historical maximum wind speed change rate). Represents the natural constant; Indicates the ambient humidity value; This indicates the preset humidity value (which can be equal to the historical maximum humidity value). This indicates that the absolute value is being calculated.

[0122] Step 650: Obtain the icing assessment value based on the abnormal event intensity value.

[0123] In one embodiment of this application, the intensity value of the abnormal event can be directly used as the icing assessment value.

[0124] In another embodiment of this application, step 650, obtaining the icing assessment value based on the abnormal event intensity value, may include steps 651 and 652.

[0125] Step 651: Obtain the fourth average.

[0126] In this embodiment, the fourth average value is the average of the intensity values ​​of each historical event corresponding to the ice shedding on the transmission line. That is, the fourth average value is the average of the intensity values ​​of abnormal events corresponding to ice shedding events that have occurred in the past and have been confirmed by operation and maintenance for the transmission line. As a benchmark value for the icing risk assessment of the line, it can be adapted to the design parameters, geographical environment and operating characteristics of different lines, and avoid the assessment deviation caused by a uniform threshold.

[0127] In this embodiment, the historical event intensity value refers to the corrected abnormal event intensity value calculated by steps 610 to 647 of this method for historical abnormal tension events caused by actual icing-related conditions such as ice shedding / ice jumping / dancing, as confirmed by on-site operation and maintenance of the transmission line. That is, the calculation method of the historical event intensity value is completely consistent with the calculation process of the corrected abnormal event intensity value, and will not be described in detail here.

[0128] Step 652: Obtain the icing assessment value based on the abnormal event intensity value and the fourth average value.

[0129] In this embodiment, the icing assessment value is positively correlated with the abnormal event intensity value and negatively correlated with the fourth average value.

[0130] In this embodiment, the formula for calculating the icing assessment value can be set as follows: in, Indicates the icing assessment value; This indicates the adjusted severity value of the abnormal event. This represents the fourth average. This represents the natural constant. In this embodiment, the icing assessment value is within the range of 0 to 1. When the value is close to 1, it indicates that the severity of this abnormal event reaches or exceeds the average level of historical icing detachment events, and the risk of line failure is extremely high; when the icing assessment value is close to 1, it indicates that the severity of this abnormal event reaches or exceeds the average level of historical icing detachment events. When the value is close to 0, it indicates that the severity of this event is lower than the average level of historical icing events, and the risk is relatively low.

[0131] It should be noted that the icing assessment value in this application is a quantitative indicator used to characterize the severity of icing on transmission lines. This assessment value is positively correlated with the actual physical icing thickness. In practical applications, the icing assessment value can be converted into a specific icing thickness value (e.g., millimeters) using a pre-calibrated mapping table or function, or directly used as an indicator of icing thickness level (e.g., light icing, medium icing, heavy icing). Therefore, the process of obtaining the icing assessment value based on the core components is essentially a process of indirectly monitoring and assessing icing thickness.

[0132] In one optional embodiment of this application, multiple risk warning levels can be set based on the icing assessment value: when the icing assessment value is greater than or equal to 0 and less than 0.3, it is determined to be low risk and no warning is required; when the icing assessment value is greater than or equal to 0.3 and less than 0.7, it is determined to be medium risk and a yellow warning is issued, prompting maintenance personnel to strengthen line inspections; when the icing assessment value is greater than or equal to 0.7 and less than 1.0, it is determined to be high risk and an orange warning is issued, prompting maintenance personnel to prepare for emergency response; when the icing assessment value is equal to 1.0, it is determined to be extremely high risk and a red warning is issued, immediately initiating emergency response measures such as line de-icing and load transfer, providing a graded and implementable decision-making basis for power grid operation and maintenance.

[0133] The embodiment of the overhead transmission line icing thickness and tension coupling monitoring method proposed in this application first screens abnormal tension signals and calculates confidence level values ​​to accurately determine the validity of the signals and eliminate interference signals at the source; then, through empirical mode decomposition, the non-stationary tension signals are decoupled into multi-scale components, and the significance ratio is calculated in combination with the confidence level values ​​to screen out the components that carry the core characteristics of icing risk; finally, based on the core components, the icing assessment value is obtained, which can effectively distinguish the transmission line tension changes caused by different operating conditions such as uniform icing growth, ice jumping, and galloping, and solve the problem that the existing technology cannot decouple multi-source changes and is prone to false alarms and missed alarms, so as to achieve accurate coupling monitoring of icing thickness and tension and improve the accuracy and reliability of transmission line icing risk monitoring.

[0134] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0135] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the methods, apparatuses, and devices described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0136] In the several embodiments provided in this application, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some interfaces; indirect couplings or communication connections between devices or modules may be electrical, mechanical, or other forms.

[0137] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0138] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium.

[0139] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product.

[0140] The computer program product includes one or more computer instructions. When the computer program is loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., digital video optical disc), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0141] Although embodiments of this application have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles of this application.

Claims

1. A method for monitoring the coupling of icing thickness and tension on overhead transmission lines, characterized in that, include: Acquire abnormal tension signals of transmission lines; The tension signal is a sequence of multiple time-series tension values ​​of the transmission line acquired in real time; The abnormal tension signal is any tension signal that includes a first time-series tension value; the first time-series tension value is any tension value that is greater than a first preset value. Based on the abnormal tension signal, a confidence level value is obtained; the confidence level value is used to characterize at least the probability that the abnormal tension signal is a valid signal; The abnormal tension signal is decomposed based on empirical mode decomposition to obtain multiple first component signals; Based on the confidence level value, a significance percentage value corresponding to each first component signal is obtained; the significance percentage value is at least used to characterize the energy of the corresponding first component signal as a proportion of the energy in the abnormal tension signal. Based on each significance percentage value, at least one second component signal is obtained; the second component signal is the component signal whose corresponding significance percentage value among each first component signal meets a preset condition; Based on each second component signal, the icing assessment value is obtained; The icing assessment value is used at least to characterize the probability that the icing state of the transmission line will cause tension imbalance and line fault risk in the transmission line.

2. The method for coupled monitoring of icing thickness and tension on overhead transmission lines according to claim 1, characterized in that, The process of obtaining a confidence level value based on the abnormal tension signal includes: Based on the abnormal tension signal, an abnormal time-series tension value is obtained; the abnormal time-series tension value is any time-series tension value in the abnormal tension signal that is greater than a first preset value. Based on the abnormal time-series tension value, a first average value and a second average value are obtained; the first average value is the average value of each time-series tension value in a preset window; the preset window is centered on the abnormal time-series tension value, and the length of the preset window is equal to a second preset value; the second average value is the average value of each time-series tension value in the abnormal tension signal. A confidence level value is obtained based on the first average value and the second average value; the confidence level value is positively correlated with the first average value and negatively correlated with the second average value.

3. The method for coupled monitoring of icing thickness and tension on overhead transmission lines according to claim 2, characterized in that, The step of obtaining the confidence level value based on the first average and the second average includes: A first ratio is obtained based on the first average and the second average; the first ratio is used to characterize the magnitude of the ratio between the first average and the second average. Based on the abnormal tension signal, a first slope is obtained; the first slope is the absolute value of the slope corresponding to the position of the abnormal temporal tension value in the abnormal tension signal; A second ratio is obtained based on the first slope and the preset maximum slope; the second ratio is used to characterize the magnitude of the ratio between the first slope and the preset maximum slope. The confidence level value is obtained based on the first ratio and the second ratio; the confidence level value is positively correlated with the first ratio and negatively correlated with the second ratio.

4. A method for coupled monitoring of icing thickness and tension on overhead transmission lines according to any one of claims 1 to 3, characterized in that, The third component signal is any component signal among the various first component signals; Obtaining the saliency percentage of the third component signal includes: Based on the third component signal, the amplitudes of multiple component signals are obtained; the amplitudes of the component signals correspond one-to-one with the time-series tension values ​​in the abnormal tension signal. A third average value is obtained based on the amplitude of each component signal; the third average value is the average of the absolute values ​​of the amplitudes of each component signal. Based on the confidence level value, the third average value, and the second average value, the significance ratio of the third component signal is obtained; the second average value is the average value of each time-series tension value in the abnormal tension signal; the significance ratio value is positively correlated with both the confidence level value and the third average value, and negatively correlated with the second average value.

5. A method for coupled monitoring of icing thickness and tension on overhead transmission lines according to any one of claims 1 to 3, characterized in that, The preset conditions include: if the saliency percentage is greater than or equal to a third preset value, then the first component signal corresponding to the saliency percentage is used as the second component signal; if all saliency percentages are less than the third preset value, then the saliency percentages are sorted from largest to smallest, and the first component signal corresponding to the saliency percentage with a sequence number less than or equal to a fourth preset value is used as the second component signal; the fourth preset value is a positive integer, and the fourth preset value is not greater than the total number of first component signals.

6. The method for coupled monitoring of icing thickness and tension on overhead transmission lines according to claim 5, characterized in that, The fourth preset value is a dynamic value; The method for obtaining the fourth preset value includes: Based on the significance percentage values, a sequence of percentage values ​​is obtained; the sequence of percentage values ​​is obtained by sorting the significance percentage values ​​from largest to smallest. Based on the percentage value sequence, a difference sequence is obtained; the difference sequence is a sequence formed by the absolute values ​​of the differences between each adjacent significance percentage value in the percentage value sequence; Based on the difference sequence, a significant difference is obtained; the significant difference is the maximum value in the difference sequence. Based on the significant difference, a target significance percentage is obtained; the target significance percentage is the minuend of the significant difference obtained from the percentage sequence. The sequence number corresponding to the target saliency percentage in the percentage value sequence is taken as the fourth preset value.

7. A method for monitoring the coupling of icing thickness and tension on overhead transmission lines according to any one of claims 1 to 3, characterized in that, The process of obtaining icing assessment values ​​based on each second component signal includes: Based on each second component signal, a first frequency value and a second frequency value are obtained; the first frequency value is the frequency value of the component signal corresponding to the largest amplitude value among the second component signals; the second frequency value is the average value of the frequency values ​​of the second component signals. Based on the first frequency value and the second frequency value, a third ratio is obtained; the third ratio is used to characterize the magnitude of the ratio between the first frequency value and the second frequency value. Based on the abnormal tension signal, the signal difference degree between it and the historical tension signal is obtained; the historical tension signal is located before the time sequence of the abnormal tension signal, and the time sequence of the historical tension signal is adjacent to that of the abnormal tension signal. An abnormal event intensity value is obtained based on the signal difference degree and the third ratio; the abnormal event intensity value is positively correlated with both the signal difference degree and the third ratio; The icing assessment value is obtained based on the intensity value of the abnormal event.

8. The method for coupled monitoring of icing thickness and tension on overhead transmission lines according to claim 7, characterized in that, The process of obtaining the abnormal event intensity value based on the signal difference and the third ratio includes: Based on each second component signal, obtain the signal envelope of the maximum component signal curve; Based on the signal envelope, a first time and a second time are obtained; the first time is the time it takes for the signal envelope to rise from the baseline to the peak value; the second time is the time it takes for the signal envelope to decay from the peak value back to the baseline. A fourth ratio is obtained based on the first time and the second time; the fourth ratio is used to characterize the magnitude of the ratio between the first time and the second time. The abnormal event intensity value is obtained based on the signal difference, the third ratio, and the fourth ratio; the abnormal event intensity value is also positively correlated with the fourth ratio.

9. The method for coupled monitoring of icing thickness and tension on overhead transmission lines according to claim 8, characterized in that, After obtaining the abnormal event intensity value based on the signal difference, the third ratio, and the fourth ratio, the method further includes: The environmental signals corresponding to the abnormal tension signal of the transmission line are acquired; the environmental signals include multiple time-series temperature values, multiple time-series wind speed values, and multiple time-series humidity values ​​of the transmission line collected in real time. Based on the environmental signal, a first rate of change, a second rate of change, and an environmental humidity value are obtained; the first rate of change is the average rate of change of temperature in the environmental signal; the second rate of change is the average rate of change of wind speed in the environmental signal; and the environmental humidity value is the average of the humidity values ​​at various time intervals in the environmental signal. The intensity value of the abnormal event is corrected based on the first rate of change, the second rate of change, and the ambient humidity value.

10. The method for coupled monitoring of icing thickness and tension on overhead transmission lines according to claim 7, characterized in that, The process of obtaining the icing assessment value based on the intensity value of the abnormal event includes: Obtain a fourth average value; the fourth average value is the average of the intensity values ​​of each historical event corresponding to the ice shedding on the transmission line. The icing assessment value is obtained based on the abnormal event intensity value and the fourth average value; the icing assessment value is positively correlated with the abnormal event intensity value and negatively correlated with the fourth average value.

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