An ice melting method and system for high-voltage power collection line of a power system
By analyzing the changes in humidity and wind speed at the endpoints, combined with the changes in temperature and ice thickness at intermediate monitoring points, the icing influencing factors of the power line were calculated, solving the problem of inaccurate assessment of icing conditions in high-voltage power collection lines and achieving efficient de-icing.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- 国网黑龙江省电力有限公司大庆供电公司
- Filing Date
- 2026-04-27
- Publication Date
- 2026-06-05
Smart Images
Figure CN122159123A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the technical field of line de-icing, specifically to a method and system for de-icing high-voltage power line collectors in a power system. Background Technology
[0002] High-voltage power collection lines are the core of power system energy transmission. They are laid out in various complex terrains, and the stability of high-voltage power collection lines directly affects the stability and security of power supply. They are a core component in ensuring power supply.
[0003] Under extreme weather conditions, ice easily forms on the surface of power transmission lines, increasing the load on these lines and potentially causing problems such as bending and sagging, tower tilting, and line breaks. It also affects the electrical parameters of the lines and reduces their insulation performance. However, by de-icing the surface ice on high-voltage collector lines in the power system, the stable operation of the power system can be effectively ensured, and the reliability of power supply under extreme weather conditions can be improved.
[0004] Currently, although there are various de-icing technologies available (such as DC de-icing, AC de-icing, and mechanical de-icing), the lack of effective processing of monitoring data from multiple monitoring points on high-voltage power collection lines makes it difficult to accurately determine the overall icing status of the line, which easily leads to problems such as "untimely de-icing" or "excessive de-icing".
[0005] In other words, the existing technology has a poor ice-melting effect. Summary of the Invention
[0006] The purpose of this invention is to provide a method and system for de-icing high-voltage power line collection lines in a power system, in order to solve the technical problem of poor de-icing effect in the prior art.
[0007] In a first aspect, one embodiment of the present invention provides a method for de-icing high-voltage power line collectors in a power system, the method comprising:
[0008] The humidity and wind speed changes at each endpoint of the target route are analyzed to determine the first characteristic value of each endpoint, wherein the first characteristic value is used to indicate the probability of abnormal icing at the corresponding endpoint.
[0009] Based on the first characteristic value of each endpoint, the temperature change and icing thickness change of each intermediate monitoring point on the target line are analyzed to obtain the line icing influence factor of each intermediate monitoring point. The intermediate monitoring point is located between two related endpoints. The line icing influence factor is used to indicate the importance of the corresponding intermediate monitoring point in monitoring the icing condition of the target line.
[0010] Obtain the predicted icing data for each intermediate monitoring point, and perform a weighted calculation on all predicted icing data based on the line icing influence factor of each intermediate monitoring point to obtain the line icing prediction data for the target line.
[0011] The icing prediction data of the target line is processed according to the preset icing treatment strategy to obtain the icing treatment information of the target line.
[0012] In some embodiments, the step of analyzing humidity and wind speed changes at each endpoint of the target route to determine a first characteristic value for each endpoint includes:
[0013] Multiple humidity features and multiple wind speed features are obtained for each endpoint on the target route. The multiple humidity features correspond one-to-one with multiple monitoring times, and the multiple wind speed features correspond one-to-one with the multiple monitoring times. The humidity feature is the ratio of the monitored humidity at the corresponding endpoint to the average monitored humidity of the target area at the corresponding monitoring time, and the wind speed feature is the ratio of the monitored wind speed at the corresponding endpoint to the average monitored wind speed of the target area at the corresponding monitoring time.
[0014] Analyze multiple humidity characteristics of each endpoint on the target route to obtain the humidity characteristic coefficient of each endpoint. The humidity characteristic coefficient is used to indicate the abnormal icing risk caused by humidity factors at the corresponding endpoint.
[0015] Analyze multiple wind speed characteristics at each endpoint of the target route to obtain the wind speed characteristic coefficient for each endpoint. The wind speed characteristic coefficient is used to indicate the abnormal icing risk caused by wind speed factors at the corresponding endpoint.
[0016] The first characteristic value of each endpoint is determined based on the humidity characteristic coefficient and wind speed characteristic coefficient of each endpoint.
[0017] In some embodiments, the step of determining a first characteristic value based on the humidity characteristic coefficient and wind speed characteristic coefficient of each endpoint includes:
[0018] Multiple electrical deviation characteristics are obtained for each endpoint on the target line. These multiple electrical deviation characteristics correspond one-to-one with multiple monitoring times. The electrical deviation characteristic is the ratio of the monitored electrical deviation amount to the rated electrical quantity at the corresponding endpoint at the corresponding monitoring time. The monitored electrical deviation amount is the absolute difference between the monitored electrical quantity and the rated electrical quantity at the corresponding endpoint at the corresponding monitoring time.
[0019] Correlation analysis is performed on multiple electrical deviation characteristics and multiple humidity characteristics at each endpoint of the target line to obtain the humidity calculation weight of each endpoint. Correlation analysis is also performed on multiple electrical deviation characteristics and multiple wind speed characteristics at each endpoint of the target line to obtain the wind speed calculation weight of each endpoint. The humidity calculation weight is used to indicate the degree of correlation between multiple electrical deviation characteristics and multiple humidity characteristics at the corresponding endpoint, and the wind speed calculation weight is used to indicate the degree of correlation between multiple electrical deviation characteristics and multiple wind speed characteristics at the corresponding endpoint.
[0020] Based on the humidity calculation weight and wind speed calculation weight of each endpoint, the humidity characteristic coefficient and wind speed characteristic coefficient of each endpoint are weighted and calculated to obtain the first characteristic value of each endpoint.
[0021] In some embodiments, the humidity characteristic coefficient is positively correlated with the first characteristic value of multiple humidity characteristics at the corresponding endpoint, and the humidity characteristic coefficient is positively correlated with the second characteristic value of multiple humidity characteristics at the corresponding endpoint. The first characteristic value is used to represent the central tendency of the corresponding multiple data, and the second characteristic value is used to represent the degree of numerical fluctuation of the corresponding multiple data.
[0022] The wind speed characteristic coefficient is positively correlated with the first characteristic value of multiple wind speed characteristics at the corresponding endpoint, and the wind speed characteristic coefficient is positively correlated with the second characteristic value of multiple wind speed characteristics at the corresponding endpoint.
[0023] In some embodiments, the step of analyzing the temperature change and icing thickness change at each intermediate monitoring point on the target line based on the first characteristic value of each endpoint, and obtaining the line icing influence factor at each intermediate monitoring point, includes:
[0024] Analyze the outlier degree of the monitored temperature of each intermediate monitoring point on the target line compared with the monitored temperatures of all intermediate monitoring points, and obtain the temperature outlier value of each intermediate monitoring point;
[0025] The degree of drastic change in icing thickness at each intermediate monitoring point along the target route was analyzed to obtain the icing specific value for each intermediate monitoring point.
[0026] Using the distance between each intermediate monitoring point and its associated endpoint as a weight, the first feature values of the two endpoints associated with each intermediate monitoring point are weighted and calculated to obtain the endpoint influence value of each intermediate monitoring point. The weight of the first feature value is negatively correlated with the corresponding distance.
[0027] Based on the temperature specificity, icing specificity, and endpoint influence value of each intermediate monitoring point, the line icing influence factor of each intermediate monitoring point is obtained.
[0028] In some embodiments, the step of analyzing the outlier degree of the monitored temperature of each intermediate monitoring point on the target line among the monitored temperatures of all intermediate monitoring points, and obtaining the temperature outlier value of each intermediate monitoring point, includes:
[0029] Obtain the temperature monitoring sequence of each intermediate monitoring point on the target route;
[0030] In the target route, analyze the sequence difference of temperature monitoring sequences at any two different intermediate monitoring points to obtain multiple sequence difference values corresponding to each intermediate monitoring point;
[0031] The average of multiple sequence difference values corresponding to each intermediate monitoring point is calculated to obtain the temperature specific value of each intermediate monitoring point.
[0032] In some embodiments, the step of analyzing the drastic change in icing thickness at each intermediate monitoring point on the target line and obtaining the icing anomaly value at each intermediate monitoring point includes:
[0033] Multiple icing thickness variation features and multiple environmental features are acquired for each intermediate monitoring point on the target route. The multiple icing thickness variation features correspond one-to-one with multiple monitoring times, and the multiple environmental features correspond one-to-one with the multiple monitoring times. The icing thickness variation features are used to indicate the rate of icing thickness change at the corresponding intermediate monitoring point at the corresponding monitoring time, and the environmental features are used to indicate the monitored environmental indicators at the corresponding intermediate monitoring point at the corresponding monitoring time. The environmental indicators include at least one of temperature, humidity, and wind speed.
[0034] Trend analysis was performed on multiple environmental characteristics of each intermediate monitoring point on the target route to obtain the environmental trend value of each intermediate monitoring point on the target route.
[0035] Correlation analysis was conducted on multiple environmental characteristics and multiple icing thickness variation characteristics of each intermediate monitoring point on the target route to obtain the environmental icing influence coefficient of each intermediate monitoring point on the target route.
[0036] Based on the environmental trend value and environmental icing impact coefficient of each intermediate monitoring point on the target route, the icing specific value of each intermediate monitoring point is determined. Among them, the environmental trend value and the icing specific value are positively correlated, and the environmental icing impact coefficient and the icing specific value are positively correlated.
[0037] In some embodiments, the step of obtaining the line icing influence factor for each intermediate monitoring point based on the temperature specificity, icing specificity, and endpoint influence value of each intermediate monitoring point includes:
[0038] Based on the temperature specificity, icing specificity, and endpoint influence value of each intermediate monitoring point, the second characteristic value of each intermediate monitoring point is obtained;
[0039] Calculate the sum of the second characteristic values of all intermediate monitoring points to obtain the characteristic sum value;
[0040] Calculate the ratio of the second characteristic value to the characteristic sum value for each intermediate monitoring point to obtain the line icing impact factor for each intermediate monitoring point.
[0041] In some embodiments, the temperature specific value is positively correlated with the second characteristic value, the icing specific value is positively correlated with the second characteristic value, and the endpoint influence value is positively correlated with the second characteristic value.
[0042] Secondly, another embodiment of the present invention provides an ice-melting system for high-voltage power line collectors in a power system, the system comprising:
[0043] The endpoint analysis module is used to analyze the humidity and wind speed changes at each endpoint of the target line to determine the first characteristic value of each endpoint, wherein the first characteristic value is used to indicate the probability of abnormal icing at the corresponding endpoint.
[0044] The monitoring point analysis module is used to analyze the temperature change and icing thickness change of each intermediate monitoring point on the target line based on the first characteristic value of each endpoint, and obtain the line icing influence factor of each intermediate monitoring point. The intermediate monitoring point is located between two related endpoints. The line icing influence factor is used to indicate the importance of the corresponding intermediate monitoring point in monitoring the icing condition of the target line.
[0045] The icing prediction module is used to acquire the predicted icing data of each intermediate monitoring point, and to perform weighted calculation on all the predicted icing data based on the line icing influence factor of each intermediate monitoring point to obtain the line icing prediction data of the target line.
[0046] The icing handling module is used to process the icing prediction data of the target line according to the preset icing handling strategy to obtain the icing handling information of the target line.
[0047] Thirdly, in another embodiment of the present invention, an electronic device is provided, including a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the method described in the first aspect.
[0048] Fourthly, in another embodiment of the present invention, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the method described in the first aspect.
[0049] The present invention has the following beneficial effects:
[0050] First, by analyzing the humidity and wind speed changes at each endpoint of the target line, the risk of abnormal icing at each endpoint is preliminarily assessed based on the environmental factors affecting abnormal icing conditions. Then, combining the abnormal icing risk at each endpoint, the temperature and ice thickness changes at each intermediate monitoring point on the target line are further analyzed to accurately assess the risk of sudden changes in icing conditions at each intermediate monitoring point in conjunction with environmental factors. This distinguishes the importance of different intermediate monitoring points in monitoring the line's icing conditions. Then, based on the importance of each intermediate monitoring point, the predicted icing data of each intermediate monitoring point are weighted to match the actual icing conditions at each monitoring point, resulting in line icing prediction data that more accurately reflects the overall icing condition of the target line. Based on this, corresponding icing measures can be taken to suppress the problems of untimely and excessive icing, enabling the target line to achieve better icing results under extreme weather conditions. Attached Figure Description
[0051] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0052] Figure 1 This is a schematic flowchart of a de-icing method for high-voltage collector lines in a power system provided by an embodiment of the present invention;
[0053] Figure 2 This is a schematic diagram of the structure of a de-icing system for a high-voltage power collection line provided in an embodiment of the present invention;
[0054] Figure 3 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation
[0055] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a de-icing method and system for high-voltage power line collectors according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.
[0056] 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.
[0057] The following description, in conjunction with the accompanying drawings, details a specific scheme for a de-icing method and system for high-voltage power line collection in a power system provided by the present invention.
[0058] In one embodiment, the present invention provides a method for de-icing high-voltage collector lines in a power system, such as... Figure 1 As shown, the method includes the following steps:
[0059] Step S1: Analyze the humidity and wind speed changes at each endpoint of the target route to determine the first characteristic value of each endpoint.
[0060] The first feature value is used to indicate the probability of an abnormal icing condition occurring at the corresponding endpoint.
[0061] In this invention, the target line is used to indicate any high-voltage collector line.
[0062] The target route includes multiple route segments. Each route segment includes two endpoints and several intermediate monitoring points located between the two endpoints (the distance between two adjacent intermediate monitoring points can be 1 meter). The intermediate monitoring points on the route segment are associated with the two endpoints of the route segment.
[0063] In application, the process described in this invention can be limited to the area where the target line is located meeting abnormal icing conditions (indicating extreme weather) before execution, so as to accurately monitor the icing status of the target line under extreme weather and take corresponding measures. When the area where the target line is located does not meet abnormal icing conditions, only the real-time icing thickness of each intermediate monitoring point on the target line is monitored and the average value is calculated. When the average icing thickness is greater than or equal to a set threshold (20 mm in this invention based on experience), a preset de-icing program is started (the AC current of the target line is increased to 1.3 times the rated current and lasts for 20 to 30 minutes to use the impedance of the target line itself to generate heat for de-icing).
[0064] The aforementioned abnormal icing conditions are defined as follows: the average temperature of the area where the target line is located is less than or equal to 0 degrees Celsius, the average humidity of the area where the target line is located is greater than or equal to 85%, and the average wind speed of the area where the target line is located is greater than 8 m / s.
[0065] In this embodiment, the step of analyzing the humidity and wind speed changes at each endpoint of the target route to determine the first characteristic value of each endpoint includes:
[0066] Multiple humidity features and multiple wind speed features are obtained for each endpoint on the target route. The multiple humidity features correspond one-to-one with multiple monitoring times, and the multiple wind speed features correspond one-to-one with the multiple monitoring times. The humidity feature is the ratio of the monitored humidity at the corresponding endpoint to the average monitored humidity of the target area at the corresponding monitoring time, and the wind speed feature is the ratio of the monitored wind speed at the corresponding endpoint to the average monitored wind speed of the target area at the corresponding monitoring time.
[0067] Analyze multiple humidity characteristics of each endpoint on the target route to obtain the humidity characteristic coefficient of each endpoint. The humidity characteristic coefficient is used to indicate the abnormal icing risk caused by humidity factors at the corresponding endpoint.
[0068] Analyze multiple wind speed characteristics at each endpoint of the target route to obtain the wind speed characteristic coefficient for each endpoint. The wind speed characteristic coefficient is used to indicate the abnormal icing risk caused by wind speed factors at the corresponding endpoint.
[0069] The first characteristic value of each endpoint is determined based on the humidity characteristic coefficient and wind speed characteristic coefficient of each endpoint.
[0070] The aforementioned target area specifically refers to the area where the target route is located. The multiple monitoring times mentioned above are continuous in the time domain, and their duration can be adaptively set based on experience (e.g., 30 minutes, 1 hour).
[0071] Line surface icing is a natural icing phenomenon under extreme weather conditions. The main environmental indicators affecting icing include humidity and wind speed. In the above setup, the ratio of the monitored humidity at the corresponding endpoint to the average monitored humidity (the average of the monitored humidity at multiple endpoints) and the ratio of the monitored wind speed at the corresponding endpoint to the average monitored wind speed (the average of the monitored wind speed at multiple endpoints) are calculated. This standardizes the monitored wind speed and humidity by fully preserving the data characteristics of humidity and wind speed data at each endpoint while effectively combining the actual weather conditions of the target line area. Based on this, multiple humidity characteristics and multiple wind speed characteristics of each endpoint are further analyzed to assess the risk of abnormal icing caused by humidity and wind force at each endpoint, thereby comprehensively quantifying the probability of abnormal icing at the corresponding endpoint from an environmental perspective.
[0072] In this invention, abnormal icing conditions can be understood as a situation where the rate of increase in ice thickness at the corresponding endpoint or intermediate monitoring point exceeds a set threshold. This abnormal icing condition can cause the effects of localized icing to spread rapidly throughout the entire line area, significantly impacting the power transmission performance of the target line.
[0073] Specifically, the humidity characteristic coefficient is positively correlated with the first characteristic value of multiple humidity characteristics at the corresponding endpoint, and the humidity characteristic coefficient is positively correlated with the second characteristic value of multiple humidity characteristics at the corresponding endpoint. The first characteristic value is used to represent the central tendency of the corresponding multiple data, and the second characteristic value is used to represent the degree of numerical fluctuation of the corresponding multiple data.
[0074] The wind speed characteristic coefficient is positively correlated with the first characteristic value of multiple wind speed characteristics at the corresponding endpoint, and the wind speed characteristic coefficient is positively correlated with the second characteristic value of multiple wind speed characteristics at the corresponding endpoint.
[0075] In this invention, the first characteristic value is specifically the average value of the corresponding multiple data, and the second characteristic value is specifically the variance of the corresponding multiple data.
[0076] The larger the first characteristic value of multiple humidity features of an endpoint, the higher the probability that the corresponding endpoint will exhibit high humidity characteristics at multiple monitoring times. Therefore, the probability that the corresponding endpoint will experience abnormal icing due to high humidity environment is also higher. Correspondingly, the larger the second characteristic value of multiple humidity features of an endpoint, the more unstable the monitored humidity value of the corresponding endpoint is at multiple monitoring times. When combined with the high humidity average value, it means that the corresponding endpoint is more likely to exhibit high humidity characteristics. Therefore, the probability that the corresponding endpoint will experience abnormal icing due to high humidity environment is also higher.
[0077] The larger the first characteristic value of multiple wind speed features of an endpoint, the higher the probability that the corresponding endpoint will exhibit high wind speed characteristics at multiple monitoring times. Therefore, the probability that the corresponding endpoint will experience abnormal icing due to strong wind conditions is also higher. Correspondingly, the larger the second characteristic value of multiple wind speed features of an endpoint, the more unstable the monitored wind speed values of the corresponding endpoint are at multiple monitoring times. Combined with the high wind speed average, this indicates that the corresponding endpoint is more likely to exhibit high wind speed characteristics. Therefore, the probability that the corresponding endpoint will experience abnormal icing due to strong wind conditions is also higher.
[0078] In the application, to ensure that the corresponding feature coefficients can fully reflect the first and second characteristic values of the corresponding multiple data, the wind speed feature coefficient is set as the product of the first and second characteristic values of the multiple wind speed features at the corresponding endpoint (after normalization), and the humidity feature coefficient is set as the product of the first and second characteristic values of the multiple wind speed features at the corresponding endpoint (after normalization).
[0079] Furthermore, the step of determining the first characteristic value based on the humidity characteristic coefficient and wind speed characteristic coefficient of each endpoint includes:
[0080] Multiple electrical deviation characteristics are obtained for each endpoint on the target line. These multiple electrical deviation characteristics correspond one-to-one with multiple monitoring times. The electrical deviation characteristic is the ratio of the monitored electrical deviation amount to the rated electrical quantity at the corresponding endpoint at the corresponding monitoring time. The monitored electrical deviation amount is the absolute difference between the monitored electrical quantity and the rated electrical quantity at the corresponding endpoint at the corresponding monitoring time.
[0081] Correlation analysis is performed on multiple electrical deviation characteristics and multiple humidity characteristics at each endpoint of the target line to obtain the humidity calculation weight of each endpoint. Correlation analysis is also performed on multiple electrical deviation characteristics and multiple wind speed characteristics at each endpoint of the target line to obtain the wind speed calculation weight of each endpoint. The humidity calculation weight is used to indicate the degree of correlation between multiple electrical deviation characteristics and multiple humidity characteristics at the corresponding endpoint, and the wind speed calculation weight is used to indicate the degree of correlation between multiple electrical deviation characteristics and multiple wind speed characteristics at the corresponding endpoint.
[0082] Based on the humidity calculation weight and wind speed calculation weight of each endpoint, the humidity characteristic coefficient and wind speed characteristic coefficient of each endpoint are weighted and calculated to obtain the first characteristic value of each endpoint.
[0083] In the above settings, the correlation between electrical quantities and corresponding environmental indicators (humidity and wind speed) is analyzed, and the calculation weight of the characteristic coefficient of the corresponding environmental indicators is set accordingly. Dynamic weight configuration is carried out in combination with the actual environmental factors at each endpoint to ensure that the calculated first characteristic value can accurately reflect the probability of abnormal icing at the corresponding endpoint.
[0084] In this process, the ratio of the monitored electrical deviation to the rated electrical quantity is used as the electrical deviation characteristic to participate in the similarity analysis process. This allows for the adaptive quantification of the impact of various environmental indicators on electrical quantity monitoring by extracting the numerical fluctuations generated during the electrical quantity monitoring process.
[0085] In one example, the humidity calculation weight is based on the absolute value of the Pearson correlation coefficient between multiple electrical deviation features of the corresponding endpoint and multiple humidity features (defined as the humidity-related absolute value), and the wind speed calculation weight is based on the absolute value of the Pearson correlation coefficient between multiple electrical deviation features of the corresponding endpoint and multiple wind speed features (defined as the wind speed-related absolute value). In this example, the humidity calculation weight is the ratio of the humidity-related absolute value to the cumulative absolute value (the sum of the humidity-related absolute value and the wind speed-related absolute value), and the wind speed calculation weight is the ratio of the wind speed-related absolute value to the cumulative absolute value.
[0086] In some implementations, multiple types of electrical quantities (such as current, voltage, power, etc.) can be used in the above weight calculation process. In this case, multiple electrical deviation characteristics corresponding to each type of electrical quantity at each endpoint can be obtained first. Then, correlation analysis is performed on multiple electrical deviation characteristics corresponding to each type of electrical quantity at each endpoint on the target line and multiple humidity characteristics to obtain the single-type humidity calculation weight for each type of electrical quantity at each endpoint. Correlation analysis is also performed on multiple electrical deviation characteristics corresponding to each type of electrical quantity at each endpoint on the target line and multiple wind speed characteristics to obtain the single-type wind speed calculation weight for each endpoint. Finally, the humidity calculation weight is calculated based on the average of the multiple single-type humidity calculation weights at each endpoint, and the wind speed calculation weight is calculated based on the average of the multiple single-type wind speed calculation weights at each endpoint.
[0087] Among them, the rated electrical quantity indicates the electrical quantity value of the corresponding terminal under ideal operating conditions.
[0088] For example, the first characteristic value of a certain endpoint in the target line It can be represented as:
[0089]
[0090] in, This indicates the humidity calculation weight for the corresponding endpoint. This represents the humidity characteristic coefficient at the corresponding endpoint. This indicates the wind speed calculation weight at the corresponding endpoint. This represents the wind speed characteristic coefficient at the corresponding endpoint.
[0091] Step S2: Based on the first characteristic value of each endpoint, analyze the temperature change and icing thickness change of each intermediate monitoring point on the target line to obtain the line icing influence factor of each intermediate monitoring point.
[0092] The intermediate monitoring point is located between two associated endpoints, and the line icing impact factor is used to indicate the importance of the corresponding intermediate monitoring point when monitoring the icing condition of the target line.
[0093] Specifically, the steps for analyzing the temperature and icing thickness changes at each intermediate monitoring point on the target line based on the first characteristic value of each endpoint, and obtaining the line icing influence factor at each intermediate monitoring point, include:
[0094] Analyze the outlier degree of the monitored temperature of each intermediate monitoring point on the target line compared with the monitored temperatures of all intermediate monitoring points, and obtain the temperature outlier value of each intermediate monitoring point;
[0095] The degree of drastic change in icing thickness at each intermediate monitoring point along the target route was analyzed to obtain the icing specific value for each intermediate monitoring point.
[0096] Using the distance between each intermediate monitoring point and its associated endpoint as a weight, the first feature values of the two endpoints associated with each intermediate monitoring point are weighted and calculated to obtain the endpoint influence value of each intermediate monitoring point. The weight of the first feature value is negatively correlated with the corresponding distance.
[0097] Based on the temperature specificity, icing specificity, and endpoint influence value of each intermediate monitoring point, the line icing influence factor of each intermediate monitoring point is obtained.
[0098] The larger the temperature outlier, the higher the outlier the monitoring temperature of the corresponding intermediate monitoring point is among all the monitoring temperatures of the intermediate monitoring points. In other words, the probability that the corresponding intermediate monitoring point is in a low-temperature environment is higher. Therefore, the probability that the corresponding intermediate monitoring point will experience abnormal icing is higher. The probability that the overall power transmission performance of the target line will be disturbed due to abnormal icing is higher. Thus, the importance of the corresponding intermediate monitoring point in the assessment of the icing condition of the target line is also higher.
[0099] The larger the icing outlier, the more drastic the change in icing thickness at the corresponding intermediate monitoring point. This means that the probability of the icing change at the corresponding intermediate monitoring point matching an abnormal icing condition is higher. Consequently, the probability of the overall power transmission performance of the target line being disturbed due to the abnormal icing condition is higher, and the importance of the corresponding intermediate monitoring point in the assessment of the icing condition of the target line is also higher.
[0100] The closer the intermediate monitoring point is to the corresponding endpoint, the higher the probability that the intermediate monitoring point and the corresponding endpoint are in the same environment, and the closer the probability that the intermediate monitoring point will experience abnormal icing due to environmental factors is to the corresponding endpoint.
[0101] For example, the endpoint impact value of an intermediate monitoring point in the target line. It can be represented as:
[0102]
[0103] in, This represents the ratio of the distance from a corresponding intermediate monitoring point to its associated endpoint to the length of its corresponding line segment. This represents the first feature value of the other endpoint associated with the corresponding intermediate monitoring point. This represents the ratio of the distance from a corresponding intermediate monitoring point to its associated other endpoint to the length of its corresponding line segment. This represents the first characteristic value of an endpoint associated with the corresponding intermediate monitoring point.
[0104] Specifically, the step of analyzing the outlier degree of the monitored temperature at each intermediate monitoring point on the target line compared to the monitored temperatures of all intermediate monitoring points, and obtaining the temperature outlier value at each intermediate monitoring point, includes:
[0105] Obtain the temperature monitoring sequence of each intermediate monitoring point on the target route;
[0106] In the target route, analyze the sequence difference of temperature monitoring sequences at any two different intermediate monitoring points to obtain multiple sequence difference values corresponding to each intermediate monitoring point;
[0107] The average of multiple sequence difference values corresponding to each intermediate monitoring point is calculated to obtain the temperature specific value of each intermediate monitoring point.
[0108] The temperature monitoring sequence is formed by arranging multiple monitoring temperatures at the corresponding intermediate monitoring points at multiple monitoring times in chronological order.
[0109] The sequence difference value is the dynamic time warping (DTW) distance between two different temperature monitoring sequences (after normalization).
[0110] The above-mentioned method of using sequence differences can effectively identify the degree of difference in temperature changes between two different intermediate monitoring points. Combined with the mean calculation method, it can accurately assess the degree of difference between the temperature changes of each intermediate monitoring point and the overall temperature changes shown by all intermediate monitoring points. Based on this, the degree of anomaly of the temperature change of each intermediate monitoring point in the target line can be accurately quantified.
[0111] In some implementations, the step of analyzing the drastic changes in icing thickness at each intermediate monitoring point along the target line to obtain an icing-specific value for each intermediate monitoring point includes:
[0112] Multiple icing thickness variation features and multiple environmental features are acquired for each intermediate monitoring point on the target route. The multiple icing thickness variation features correspond one-to-one with multiple monitoring times, and the multiple environmental features correspond one-to-one with the multiple monitoring times. The icing thickness variation features are used to indicate the rate of icing thickness change at the corresponding intermediate monitoring point at the corresponding monitoring time, and the environmental features are used to indicate the monitored environmental indicators at the corresponding intermediate monitoring point at the corresponding monitoring time. The environmental indicators include at least one of temperature, humidity, and wind speed.
[0113] Trend analysis was performed on multiple environmental characteristics of each intermediate monitoring point on the target route to obtain the environmental trend value of each intermediate monitoring point on the target route.
[0114] Correlation analysis was conducted on multiple environmental characteristics and multiple icing thickness variation characteristics of each intermediate monitoring point on the target route to obtain the environmental icing influence coefficient of each intermediate monitoring point on the target route.
[0115] Based on the environmental trend value and environmental icing impact coefficient of each intermediate monitoring point on the target route, the icing specific value of each intermediate monitoring point is determined. Among them, the environmental trend value and the icing specific value are positively correlated, and the environmental icing impact coefficient and the icing specific value are positively correlated.
[0116] The rate of change of ice thickness is the ratio of the change in ice thickness at the corresponding monitoring time (the absolute difference between the ice thickness monitored at the corresponding monitoring time and the ice thickness monitored at the previous monitoring time) to the interval time (the time difference between two adjacent monitoring times).
[0117] In this invention, the MK trend verification algorithm is used to perform trend analysis on multiple environmental features. In application, other trend analysis algorithms can also be selected to perform the above trend analysis operations according to actual needs.
[0118] The environmental trend value is specifically the Z statistic (after normalization) of multiple environmental features corresponding to intermediate monitoring points after processing by the MK trend verification algorithm.
[0119] The aforementioned environmental trend values are used to indicate the significance of the numerical change trend of environmental indicators at the corresponding intermediate monitoring points. The larger the environmental trend value, the more significant the numerical change trend of the environmental indicators at the corresponding intermediate monitoring point, the more likely abnormal icing conditions will occur, and thus the higher the importance of the corresponding intermediate monitoring point in the assessment of the icing conditions of the target line.
[0120] The larger the environmental icing influence coefficient, the higher the correlation between the rate of change of icing thickness at the corresponding intermediate monitoring point and environmental indicators. In other words, the more sensitive the corresponding intermediate monitoring point is to environmental factors (the more likely it is to produce abnormal icing conditions), and the more important the corresponding intermediate monitoring point is in the assessment of the icing conditions of the target line.
[0121] The environmental icing impact coefficient is specifically the absolute value of the Pearson correlation coefficients between multiple environmental characteristics and multiple icing thickness variation characteristics at the corresponding intermediate monitoring point. It should be noted that when the environmental indicators include at least two of temperature, humidity, and wind speed, the absolute value of the Pearson correlation coefficient for each environmental indicator can be calculated separately based on the above process, and then the corresponding environmental icing impact coefficient can be obtained by averaging. When there are two or more environmental indicators, the calculation of the environmental trend value is similar to the calculation method of the environmental icing impact coefficient. To avoid repetition, it will not be described again.
[0122] In applications, the product of the environmental trend value and the environmental icing influence coefficient at each intermediate monitoring point (after normalization) can be calculated to determine the icing specific value at each intermediate monitoring point.
[0123] Specifically, the steps for obtaining the line icing influence factor for each intermediate monitoring point based on the temperature specificity, icing specificity, and endpoint influence value of each intermediate monitoring point include:
[0124] Based on the temperature specificity, icing specificity, and endpoint influence value of each intermediate monitoring point, the second characteristic value of each intermediate monitoring point is obtained;
[0125] Calculate the sum of the second characteristic values of all intermediate monitoring points to obtain the characteristic sum value;
[0126] Calculate the ratio of the second characteristic value to the characteristic sum value for each intermediate monitoring point to obtain the line icing impact factor for each intermediate monitoring point.
[0127] Among them, the temperature specific value is positively correlated with the second characteristic value, the icing specific value is positively correlated with the second characteristic value, and the endpoint influence value is positively correlated with the second characteristic value.
[0128] The second characteristic value can be the product or sum of the temperature specific value, icing specific value, and endpoint influence value at the intermediate monitoring point.
[0129] Step S3: Obtain the predicted icing data for each intermediate monitoring point, and perform a weighted calculation on all predicted icing data based on the line icing influence factor of each intermediate monitoring point to obtain the line icing prediction data for the target line.
[0130] In this invention, the predicted icing data (including predicted icing thickness and predicted changes in icing thickness) is obtained based on model prediction, specifically:
[0131] The ice thickness sequence of the corresponding intermediate monitoring point (corresponding to multiple monitoring times) is input into the pre-trained thickness prediction model (LSTM model, loss function is cross-entropy loss function, optimizer is Adam optimizer). After processing by the thickness prediction model, the predicted ice thickness of the corresponding intermediate monitoring point is obtained (indicating the ice thickness of the corresponding intermediate monitoring point at a set future time).
[0132] The ice thickness change features of the corresponding intermediate monitoring point are input into the pre-trained rate of change prediction model (LSTM model, loss function is cross-entropy loss function, optimizer is Adam optimizer). After processing by the rate of change prediction model, the predicted ice thickness change value of the corresponding intermediate monitoring point is obtained (indicating the rate of change of ice thickness of the corresponding intermediate monitoring point at a set future time).
[0133] For example, the line icing prediction data includes the predicted line icing thickness value. It can be represented as:
[0134]
[0135] in, Indicates the first in the target route The impact factor of line icing at the intermediate monitoring points Indicates the first in the target route Predicted icing thickness at intermediate monitoring points This indicates the total number of intermediate monitoring points along the target route.
[0136] The data on line icing prediction includes the predicted thickness variation of line icing. It can be represented as:
[0137]
[0138] in, Indicates the first in the target route Predicted changes in icing thickness at intermediate monitoring points.
[0139] Based on the above settings, and combined with the probability of abnormal icing conditions occurring at each intermediate monitoring point, the actual icing conditions at each intermediate monitoring point in the target line are adapted to the overall icing prediction data of the target line.
[0140] Step S4: Process the line icing prediction data of the target line according to the preset icing treatment strategy to obtain the icing treatment information of the target line.
[0141] In this invention, the above-mentioned icing treatment strategy is specifically as follows:
[0142] If the predicted icing thickness of the line is less than 5 mm and the predicted icing thickness change is less than 0.5 mm / min, the target line is judged to be in a state of light icing. The corresponding icing handling information is as follows: send a line icing warning message to the operation and maintenance personnel through the monitoring center, temporarily suspend the de-icing process, and only shorten the prediction analysis cycle (i.e. shorten the duration of the time period indicated by multiple monitoring times) to strengthen the monitoring.
[0143] If the predicted icing thickness of the line is greater than or equal to 5 mm and less than 10 mm, and the predicted icing thickness change is greater than or equal to 0.5 mm / min and less than 2 mm / min, the target line is judged to be in a moderate icing state. The corresponding icing treatment information is as follows: activate AC de-icing technology for de-icing treatment (disconnect the line from the load side, then close the de-icing power switch, and adjust the output current to the set value through the voltage regulator). The de-icing current is set to 1.3 times the rated current of the line, and the de-icing treatment time is 20~30 minutes.
[0144] If the predicted icing thickness of the line is ≥10mm and / or the predicted icing thickness change is ≥2mm / min, the target line is determined to be in a state of severe icing. The corresponding icing treatment information is as follows: start the DC de-icing device (disconnect the line from the load side, start the converter to convert the AC power to DC power and connect it to the collector line, and adjust the output current to the set value through the voltage regulator), output DC current of 2000~3000A, maintain the conductor temperature at 5~10℃, and the de-icing treatment time is 5 minutes.
[0145] During the de-icing process, if the temperature at a certain intermediate monitoring point is greater than or equal to 15 degrees Celsius, a de-icing current suppression command is triggered to adaptively reduce the AC or DC current in order to avoid damage to the line under high current.
[0146] After the above de-icing operation is completed, the insulation performance of the conductors at each intermediate monitoring point in the target line is tested using insulation testing equipment, and the insulation resistance to ground is measured. If the measured resistance value is lower than the specified standard, for example, the resistance value of a 10kV line must be ≥0.5MΩ, then it is considered that there is residual icing or insulation damage in the line, and local de-icing treatment needs to be carried out again; if the measured resistance value is greater than or equal to the specified standard, the connection between the target line and the power grid is gradually restored.
[0147] It should be emphasized that the de-icing effect referred to in this invention can be understood as the degree of deviation between the actual overall impedance of the target line and the set impedance. The better the de-icing effect, the lower the degree of deviation between the two.
[0148] In this invention, the normalization measures used aim to eliminate the dimensional differences between different data, so that they are uniformly mapped within the 0-1 interval for subsequent use.
[0149] It should be noted that in this invention, data such as humidity, temperature, wind speed, ice thickness, current, voltage, and power are all collected using corresponding sensors, and all of the above sensors are capable of operating under low-temperature conditions (-30℃ to 50℃).
[0150] Multiple edge acquisition terminals are deployed along the target line to transmit data collected by sensors at various endpoints and intermediate monitoring points to these terminals. A layered transmission method is used to ensure stable real-time transmission of the sensor data. Specifically, data collected at each endpoint and intermediate monitoring point is first transmitted via industrial Ethernet to the edge computing node (edge acquisition terminal) at the bottom of the tower, with a transmission rate of 100Mbps and TCP / IP transmission protocol used to ensure the stability of the monitoring data transmission. For areas with weak signals, 5G modules are added for data transmission, with transmission latency controlled within 50ms. Beidou short message service is set up as an emergency backup channel for data transmission, ensuring accurate and timely data transmission even under extreme conditions. Between the edge computing node and the power system monitoring center, fiber optic dedicated line transmission is used as the primary transmission method (4G / 5G transmission is the backup method) for real-time data transmission.
[0151] Furthermore, during transmission, environmental and instrument noise interference can lead to poor data quality from sensor acquisition. Therefore, the edge computing nodes first preprocess the raw data during transmission, specifically removing outlier data based on the 3σ criterion and completing missing data using linear interpolation. The preprocessed data is then transmitted to the power system monitoring center, which stores the received data in the format of "device number-acquisition time-parameter type-value" for use in the aforementioned processes.
[0152] In some embodiments, the present invention also provides an ice-melting system 200 for high-voltage collector lines in a power system, such as... Figure 2 As shown, the system 200 includes:
[0153] Endpoint analysis module 201 is used to analyze the humidity and wind speed changes at each endpoint of the target line to determine the first characteristic value of each endpoint, wherein the first characteristic value is used to indicate the probability of abnormal icing at the corresponding endpoint.
[0154] The monitoring point analysis module 202 is used to analyze the temperature change and ice thickness change of each intermediate monitoring point on the target line according to the first characteristic value of each endpoint, and obtain the line icing influence factor of each intermediate monitoring point. The intermediate monitoring point is located between two related endpoints. The line icing influence factor is used to indicate the importance of the corresponding intermediate monitoring point in monitoring the icing condition of the target line.
[0155] The icing prediction module 203 is used to acquire the predicted icing data of each intermediate monitoring point, and to perform weighted calculation on all the predicted icing data based on the line icing influence factor of each intermediate monitoring point to obtain the line icing prediction data of the target line.
[0156] The icing treatment module 204 is used to process the line icing prediction data of the target line according to the preset icing treatment strategy to obtain the icing treatment information of the target line.
[0157] It should be noted that the system provided in the above embodiments is only an example of the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer device can be divided into different functional modules to complete all or part of the functions described above. In addition, the de-icing system for high-voltage power line collectors and the de-icing method for high-voltage power line collectors provided in the above embodiments belong to the same concept, and their specific implementation process can be found in the method embodiments, which will not be repeated here.
[0158] This invention also provides an electronic device. Please refer to [link to relevant documentation]. Figure 3 The electronic device may include a processor 301, a memory 302, and a program 3021 stored in the memory 302 and capable of running on the processor 301.
[0159] When program 3021 is executed by processor 301, it can achieve the following: Figure 1 Any steps in the corresponding method embodiments and the achievement of the same beneficial effects will not be repeated here.
[0160] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by hardware related to program instructions, and the program can be stored in a readable medium.
[0161] This invention also provides a readable storage medium storing a computer program, which, when executed by a processor, can perform the above-described functions. Figure 1 Any step in the corresponding method embodiment can achieve the same technical effect, and will not be repeated here to avoid repetition.
[0162] The computer-readable storage medium of this invention can be any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. For example, a computer-readable storage medium can be an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.
[0163] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.
[0164] The program code contained on the storage medium can be transmitted using any suitable medium, including but not limited to wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.
[0165] Computer program code for performing the operations of this invention can be written in one or more programming languages or a combination thereof, including object-oriented programming languages such as Java, Smalltalk, and C++, as well as conventional procedural programming languages such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or terminal. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).
[0166] This invention also provides a computer program product that, when run on a computer, causes the computer to perform the aforementioned steps to implement the de-icing method for high-voltage power line collection in the above embodiments.
[0167] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
[0168] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.
Claims
1. A method for de-icing high-voltage power line collectors in a power system, characterized in that, The method includes: The humidity and wind speed changes at each endpoint of the target route are analyzed to determine the first characteristic value of each endpoint, wherein the first characteristic value is used to indicate the probability of abnormal icing at the corresponding endpoint. Based on the first characteristic value of each endpoint, the temperature change and icing thickness change of each intermediate monitoring point on the target line are analyzed to obtain the line icing influence factor of each intermediate monitoring point. The intermediate monitoring point is located between two related endpoints. The line icing influence factor is used to indicate the importance of the corresponding intermediate monitoring point in monitoring the icing condition of the target line. Obtain the predicted icing data for each intermediate monitoring point, and perform a weighted calculation on all predicted icing data based on the line icing influence factor of each intermediate monitoring point to obtain the line icing prediction data for the target line. The icing prediction data of the target line is processed according to the preset icing treatment strategy to obtain the icing treatment information of the target line.
2. The de-icing method for high-voltage power line collectors in a power system according to claim 1, characterized in that, The steps for analyzing humidity and wind speed changes at each endpoint of the target route to determine the first characteristic value of each endpoint include: Multiple humidity features and multiple wind speed features are obtained for each endpoint on the target route. The multiple humidity features correspond one-to-one with multiple monitoring times, and the multiple wind speed features correspond one-to-one with the multiple monitoring times. The humidity feature is the ratio of the monitored humidity at the corresponding endpoint to the average monitored humidity of the target area at the corresponding monitoring time, and the wind speed feature is the ratio of the monitored wind speed at the corresponding endpoint to the average monitored wind speed of the target area at the corresponding monitoring time. Analyze multiple humidity characteristics of each endpoint on the target route to obtain the humidity characteristic coefficient of each endpoint. The humidity characteristic coefficient is used to indicate the abnormal icing risk caused by humidity factors at the corresponding endpoint. Analyze multiple wind speed characteristics at each endpoint of the target route to obtain the wind speed characteristic coefficient for each endpoint. The wind speed characteristic coefficient is used to indicate the abnormal icing risk caused by wind speed factors at the corresponding endpoint. The first characteristic value of each endpoint is determined based on the humidity characteristic coefficient and wind speed characteristic coefficient of each endpoint.
3. The de-icing method for high-voltage power line collectors according to claim 2, characterized in that, The steps for determining the first characteristic value based on the humidity characteristic coefficient and wind speed characteristic coefficient of each endpoint include: Multiple electrical deviation characteristics are obtained for each endpoint on the target line. These multiple electrical deviation characteristics correspond one-to-one with multiple monitoring times. The electrical deviation characteristic is the ratio of the monitored electrical deviation amount to the rated electrical quantity at the corresponding endpoint at the corresponding monitoring time. The monitored electrical deviation amount is the absolute difference between the monitored electrical quantity and the rated electrical quantity at the corresponding endpoint at the corresponding monitoring time. Correlation analysis is performed on multiple electrical deviation characteristics and multiple humidity characteristics at each endpoint of the target line to obtain the humidity calculation weight of each endpoint. Correlation analysis is also performed on multiple electrical deviation characteristics and multiple wind speed characteristics at each endpoint of the target line to obtain the wind speed calculation weight of each endpoint. The humidity calculation weight is used to indicate the degree of correlation between multiple electrical deviation characteristics and multiple humidity characteristics at the corresponding endpoint, and the wind speed calculation weight is used to indicate the degree of correlation between multiple electrical deviation characteristics and multiple wind speed characteristics at the corresponding endpoint. Based on the humidity calculation weight and wind speed calculation weight of each endpoint, the humidity characteristic coefficient and wind speed characteristic coefficient of each endpoint are weighted and calculated to obtain the first characteristic value of each endpoint.
4. The de-icing method for high-voltage power line collectors according to claim 2, characterized in that, The humidity characteristic coefficient is positively correlated with the first characteristic value of multiple humidity characteristics at the corresponding endpoint, and the humidity characteristic coefficient is positively correlated with the second characteristic value of multiple humidity characteristics at the corresponding endpoint. The first characteristic value is used to represent the central tendency of the corresponding multiple data, and the second characteristic value is used to represent the degree of numerical fluctuation of the corresponding multiple data. The wind speed characteristic coefficient is positively correlated with the first characteristic value of multiple wind speed characteristics at the corresponding endpoints. The wind speed characteristic coefficient is positively correlated with the second characteristic values of multiple wind speed characteristics at the corresponding endpoints.
5. The de-icing method for high-voltage power line collectors according to claim 1, characterized in that, The steps for analyzing the temperature and icing thickness changes at each intermediate monitoring point on the target line based on the first characteristic value of each endpoint, and obtaining the line icing influence factor at each intermediate monitoring point, include: Analyze the outlier degree of the monitored temperature of each intermediate monitoring point on the target line compared with the monitored temperatures of all intermediate monitoring points, and obtain the temperature outlier value of each intermediate monitoring point; The degree of drastic change in icing thickness at each intermediate monitoring point along the target route was analyzed to obtain the icing specific value for each intermediate monitoring point. Using the distance between each intermediate monitoring point and its associated endpoint as a weight, the first feature values of the two endpoints associated with each intermediate monitoring point are weighted and calculated to obtain the endpoint influence value of each intermediate monitoring point. The weight of the first feature value is negatively correlated with the corresponding distance. Based on the temperature specificity, icing specificity, and endpoint influence value of each intermediate monitoring point, the line icing influence factor of each intermediate monitoring point is obtained.
6. The de-icing method for high-voltage power line collectors in a power system according to claim 5, characterized in that, The steps for analyzing the outlier degree of the monitored temperature at each intermediate monitoring point on the target route compared to the monitored temperatures of all intermediate monitoring points, and obtaining the temperature outlier value at each intermediate monitoring point, include: Obtain the temperature monitoring sequence of each intermediate monitoring point on the target route; In the target route, analyze the sequence difference of temperature monitoring sequences at any two different intermediate monitoring points to obtain multiple sequence difference values corresponding to each intermediate monitoring point; The average of multiple sequence difference values corresponding to each intermediate monitoring point is calculated to obtain the temperature specific value of each intermediate monitoring point.
7. The de-icing method for high-voltage power line collectors according to claim 5, characterized in that, The steps for analyzing the drastic changes in icing thickness at each intermediate monitoring point along the target route and obtaining the icing anomaly value for each intermediate monitoring point include: Multiple icing thickness variation features and multiple environmental features are acquired for each intermediate monitoring point on the target route. The multiple icing thickness variation features correspond one-to-one with multiple monitoring times, and the multiple environmental features correspond one-to-one with the multiple monitoring times. The icing thickness variation features are used to indicate the rate of icing thickness change at the corresponding intermediate monitoring point at the corresponding monitoring time, and the environmental features are used to indicate the monitored environmental indicators at the corresponding intermediate monitoring point at the corresponding monitoring time. The environmental indicators include at least one of temperature, humidity, and wind speed. Trend analysis was performed on multiple environmental characteristics of each intermediate monitoring point on the target route to obtain the environmental trend value of each intermediate monitoring point on the target route. Correlation analysis was conducted on multiple environmental characteristics and multiple icing thickness variation characteristics of each intermediate monitoring point on the target route to obtain the environmental icing influence coefficient of each intermediate monitoring point on the target route. Based on the environmental trend value and environmental icing impact coefficient of each intermediate monitoring point on the target route, the icing specific value of each intermediate monitoring point is determined. Among them, the environmental trend value and the icing specific value are positively correlated, and the environmental icing impact coefficient and the icing specific value are positively correlated.
8. The de-icing method for high-voltage power line collectors in a power system according to claim 5, characterized in that, The steps for obtaining the line icing influence factor for each intermediate monitoring point based on the temperature specificity, icing specificity, and endpoint influence value of each intermediate monitoring point include: Based on the temperature specificity, icing specificity, and endpoint influence value of each intermediate monitoring point, the second characteristic value of each intermediate monitoring point is obtained; Calculate the sum of the second characteristic values of all intermediate monitoring points to obtain the characteristic sum value; Calculate the ratio of the second characteristic value to the characteristic sum value for each intermediate monitoring point to obtain the line icing impact factor for each intermediate monitoring point.
9. The de-icing method for high-voltage power line collectors in a power system according to claim 8, characterized in that, The temperature specific value is positively correlated with the second characteristic value, the icing specific value is positively correlated with the second characteristic value, and the endpoint influence value is positively correlated with the second characteristic value.
10. A de-icing system for high-voltage power line collectors in a power system, characterized in that, The system includes: The endpoint analysis module is used to analyze the humidity and wind speed changes at each endpoint of the target line to determine the first characteristic value of each endpoint, wherein the first characteristic value is used to indicate the probability of abnormal icing at the corresponding endpoint. The monitoring point analysis module is used to analyze the temperature change and icing thickness change of each intermediate monitoring point on the target line based on the first characteristic value of each endpoint, and obtain the line icing influence factor of each intermediate monitoring point. The intermediate monitoring point is located between two related endpoints. The line icing influence factor is used to indicate the importance of the corresponding intermediate monitoring point in monitoring the icing condition of the target line. The icing prediction module is used to acquire the predicted icing data of each intermediate monitoring point, and to perform weighted calculation on all the predicted icing data based on the line icing influence factor of each intermediate monitoring point to obtain the line icing prediction data of the target line. The icing handling module is used to process the icing prediction data of the target line according to the preset icing handling strategy to obtain the icing handling information of the target line.