Ice Coating and Galloping Detection Method and System for Power Transmission and Transformation Lines Based on Optical Fiber Sensors

Abstract

This application relates to the technical field of transmission line sensor detection, specifically to an icing galloping detection method and system for power transmission and transformation lines based on optical fiber sensors. The method includes: obtaining the wind speed, wind direction, temperature, and relative humidity at each moment during each acquisition period in the environment where the power transmission and transformation line is located, as well as the tension data of the suspension insulator string connecting the power transmission and transformation line at each moment; calculating the trend change coefficient and wind force influence coefficient for each acquisition period to obtain the meteorological risk coefficient for each acquisition period; determining the galloping risk discrimination value for the current acquisition period; and detecting the icing galloping of the power transmission and transformation line based on the galloping risk discrimination value. This application can accurately evaluate the icing galloping state of the power transmission and transformation line, improve the accuracy of detecting the icing galloping of the power transmission and transformation line, and ensure the safe operation of the power transmission of the power transmission and transformation line.

Description

Technical Field

[0001] This application relates to the technical field of transmission line sensor detection, and specifically to a method and system for detecting ice accretion and galloping of power transmission and transformation lines based on optical fiber sensors. Background Art

[0002] The galloping of power transmission and transformation lines refers to a kind of low-frequency and large-amplitude self-excited vibration of the conductor generated by the wind on the non-circular cross-section conductor. Galloping mostly occurs on the power transmission lines with eccentric ice accretion in cold winters, which is likely to cause phase-to-phase flashover, damage to fittings, and even lead to the collapse of the tower, posing a great threat to the safe operation of the power grid.

[0003] The mechanism of power transmission line galloping is complex and there are many influencing factors. Among them, the conductor galloping caused by ice accretion and wind force factors is more significant. Therefore, the existing detection methods usually analyze the condensation height of ice accretion through the images of power transmission lines, or evaluate the galloping state by the change characteristics of the line tension measured by sensors. The influence of climate condition changes on ice accretion and galloping is not deeply considered, especially the risk influence of wind speed and wind direction on the ice accretion and galloping of conductors, resulting in inaccurate detection of the ice accretion and galloping state of power transmission and transformation lines, and thus unable to maintain the power transmission and transformation lines in time, affecting the power transmission of power transmission and transformation lines and being unfavorable to the safe operation of the power grid. Summary of the Invention

[0004] In order to solve the above technical problems, a method and system for detecting ice accretion and galloping of power transmission and transformation lines based on optical fiber sensors are provided to solve the existing problems.

[0005] The solution of this application to solve the technical problems is to provide a method and system for detecting ice accretion and galloping of power transmission and transformation lines based on optical fiber sensors, including the following steps:

[0006] In the first aspect, an embodiment of this application provides a method for detecting ice accretion and galloping of power transmission and transformation lines based on optical fiber sensors, and the method includes the following steps:

[0007] Obtain the wind speed, wind direction, temperature, and relative humidity at each moment in each acquisition period of the environment where the power transmission and transformation line is located, as well as the tension data of the suspension insulator string connecting the power transmission and transformation line at each moment through an intelligent sensing system;

[0008] Analyze the trend change characteristics of the temperature at all moments in each acquisition period, the trend change characteristics of the relative humidity at all moments, and the average level of the proportional relationship between the relative humidity and the temperature at each moment, and calculate the trend change coefficient of each acquisition period;

[0009] Determine the wind force influence coefficient of each acquisition period through the change situation of the angle between the wind direction and the power transmission and transformation line at all moments in each acquisition period and the change situation of the wind speed at all moments, and combine the trend change coefficient to obtain the meteorological risk coefficient of each acquisition period;

[0010] Analyze the relevant changes in the meteorological risk coefficients of the current collection period and its adjacent collection periods, and combine the discrete conditions of the tension data in the current collection period to determine the galloping risk discrimination value of the current collection period;

[0011] Based on the galloping risk discrimination value, detect the icing galloping of the transmission and transformation line.

[0012] Preferably, the further determination method of the trend change characteristic of the temperature is as follows:

[0013] Adopt a trend test algorithm to obtain the trend statistic of the temperature at all times in each collection period, denoted as the first trend quantity;

[0014] Perform a positive mapping on the first trend quantity, and use the result after the positive mapping as the trend change characteristic of the temperature.

[0015] Preferably, the further determination method of the trend change characteristic of the relative humidity is as follows:

[0016] Adopt a trend test algorithm to obtain the trend statistic of the relative humidity at all times in each collection period, denoted as the second trend quantity;

[0017] After performing a positive mapping on the second trend quantity, denote the result as the trend change characteristic of the relative humidity.

[0018] Preferably, calculating the trend change coefficient of each collection period includes:

[0019] Calculate the average value of the ratio of the relative humidity to the temperature at all times in each collection period;

[0020] Based on the trend change characteristic of the relative humidity and the trend change characteristic of the temperature, calculate the relative trend ratio;

[0021] The trend change coefficient is the fusion result of the relative trend ratio and the average value.

[0022] Preferably, the relative trend ratio is the ratio of the trend change characteristic of the relative humidity to the trend change characteristic of the temperature.

[0023] Preferably, determining the wind force influence coefficient of each collection period includes:

[0024] Calculate the sine value of the angle between the wind direction and the transmission and transformation line at each moment; calculate the ratio of the mean value to the variance of the sine values at all times in each collection period, denoted as the wind direction influence factor;

[0025] Calculate the ratio of the mean value to the variance of the wind speed at all times in each collection period, denoted as the wind speed influence factor;

[0026] The wind influence coefficient is the fusion result of the wind direction influence factor and the wind speed influence factor.

[0027] Preferably, the calculation formula of the meteorological risk coefficient for each collection period is: G t = α × E t + β × F t , where G t is the meteorological risk coefficient for the t-th collection period, E t is the trend change coefficient for the t-th collection period, F t is the wind influence coefficient for the t-th collection period, α is the preset first weight, β is the preset second weight, and α + β = 1, where the preset first weight is less than the preset second weight.

[0028] Preferably, determining the galloping risk discrimination value for the current collection period includes:

[0029] Calculating the autocorrelation coefficient of the meteorological risk coefficients for the current collection period and multiple collection periods before it;

[0030] Calculating the degree of dispersion of the tension data at all times within the current collection period;

[0031] The galloping risk discrimination value is the normalized result after the fusion of the autocorrelation coefficient and the degree of dispersion.

[0032] Preferably, detecting the icing galloping of the power transmission and transformation line includes: if the galloping risk discrimination value for the current collection period is greater than or equal to the preset threshold, there is a risk of icing galloping on the power transmission and transformation line; otherwise, there is no risk of icing galloping on the power transmission and transformation line.

[0033] In a second aspect, an embodiment of the present application also provides a power transmission and transformation line icing galloping detection system based on an optical fiber sensor, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of the above-mentioned power transmission and transformation line icing galloping detection method based on an optical fiber sensor are implemented.

[0034] The present application has at least the following beneficial effects:

[0035] By analyzing the trend changes of temperature and relative humidity in each collection period, the trend change coefficient of each collection period is calculated. The beneficial effect is to reflect the degree of influence of meteorological conditions on the icing degree of the transmission and transformation line, so as to reflect the possibility of icing on the conductor under the meteorological conditions of the corresponding collection period. Secondly, by analyzing the influence of wind direction and wind speed on the galloping of the transmission and transformation line, the wind force influence coefficient is calculated. The beneficial effect is to consider the influence of wind speed and wind direction on the galloping of the conductor, and to reflect the influence degree of the wind speed acting on the transmission and transformation line at different wind direction angles on the galloping of the conductor. Further, by combining the trend change coefficient and the wind force influence coefficient, the meteorological risk coefficient is calculated. The beneficial effect is to consider the galloping situation of the transmission and transformation line under the combined action of icing and wind excitation. By analyzing the consistency of the changes in the meteorological risk coefficients of multiple collection periods and the random fluctuations of the tension data, the galloping risk discrimination value is calculated to reflect the degree of consistency of the influence of climate conditions on the galloping of the transmission and transformation line between adjacent collection periods, and the longer the galloping duration of the transmission and transformation line, so as to illustrate the galloping risk degree of the transmission and transformation line. Based on the galloping risk discrimination value, the icing galloping of the transmission and transformation line is detected. The beneficial effect is to consider the influence of the changing factors of climate conditions in the environment, and can accurately evaluate the icing galloping state of the transmission and transformation line, improve the accuracy of the icing galloping detection of the transmission and transformation line, and ensure the safe operation of the power transmission of the transmission and transformation line. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The following further describes in detail the method for detecting icing galloping of a transmission and transformation line based on an optical fiber sensor according to the present application with reference to the accompanying drawings.

[0037] Figure 1 It is a flowchart of the steps of the method for detecting icing galloping of a transmission and transformation line based on an optical fiber sensor provided by an embodiment of the present application;

[0038] Figure 2 It is a flowchart of the steps of the method for obtaining the trend change coefficient of each collection period provided by an embodiment of the present application;

[0039] Figure 3 It is a flowchart of the steps of the method for obtaining the wind force influence coefficient of each collection period provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0040] In order to make the objectives, technical solutions and advantages of the present application clearer, the following further describes in detail the method and system for detecting icing galloping of a transmission and transformation line based on an optical fiber sensor proposed in the present application with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0041] 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 application belongs.

[0042] Please refer to Figure 1 , which shows a flowchart of the steps of a method for detecting icing galloping of a power transmission and transformation line based on an optical fiber sensor provided by an embodiment of this application. The method includes the following steps:

[0043] Step 1, obtain the wind speed, wind direction, temperature, and relative humidity of the environment where the power transmission and transformation line is located at each moment during each acquisition period through an intelligent sensing system, as well as the tension data of the suspension insulator string connecting the power transmission and transformation line at each moment.

[0044] The icing state of the line and meteorological factors will affect the occurrence of icing galloping of the power transmission and transformation line. If the conductor surface of the power transmission and transformation line is unevenly iced, irregular shapes such as fan-shaped, D-shaped, and crescent-shaped are likely to appear. These irregularly shaped icings change the aerodynamic shape of the conductor. Once affected by the action of wind excitation, conductor galloping will occur. And when the angle between the wind direction and the direction of the power transmission and transformation line is large, the action of wind excitation is large at this time, and the component of the wind force on the conductor in the direction perpendicular to the conductor increases, making it easier for the conductor to generate lateral swing, which has a serious impact on the galloping of the conductor.

[0045] Secondly, Brillouin Optical Time Domain Reflectometry (referred to as Brillouin OTDR for short) is a distributed optical fiber sensor technology that detects temperature and strain information by measuring the change in Brillouin frequency shift in the optical fiber. Temperature changes will cause changes in the refractive index and sound velocity of the optical fiber, thereby causing a linear change in Brillouin frequency shift. By detecting the amount of Brillouin frequency shift, the temperature and strain of the environment where the optical fiber is located can be inversely obtained. Therefore, Brillouin Optical Time Domain Reflectometry has the advantages of high spatial resolution, long measurement distance, and anti-electromagnetic interference, and can monitor the power transmission and transformation line in real time.

[0046] Obtain the temperature of the environment where the power transmission and transformation line is located and the tension data of the positions of the suspension insulator strings on both sides connected to the power transmission and transformation line through Brillouin OTDR technology;

[0047] Thus, integrate the Brillouin OTDR sensor, wind speed and direction sensor, and humidity sensor into an intelligent sensing system, and collect the wind speed data and wind direction data of the surrounding environment at the same height as the power transmission and transformation line through the wind speed and direction sensor in the intelligent sensing system;

[0048] Secondly, collect the relative humidity of the environment where the power transmission and transformation line is located through the humidity sensor in the intelligent sensing system;

[0049] Set the acquisition interval of the intelligent sensor to t seconds, and the intelligent sensing system transmits the acquired data through optical fiber.

[0050] Considering the changes in climatic conditions in winter, all moments within every 1 minute are recorded as each acquisition period, and the different degrees of conductor galloping caused by the possible large differences in climate in different acquisition periods are analyzed.

[0051] Thus, the tension data of the suspension insulator string connecting the power transmission and transformation line at each moment within each acquisition period, as well as the wind speed, wind direction, temperature, and relative humidity at each moment, are obtained, and the acquired tension data, wind speed, temperature, and relative humidity are normalized.

[0052] In this embodiment, the acquisition interval of the sensor is t = 1 second. As other implementation manners, the implementer can set it according to the actual situation, and the maximum-minimum normalization method is used for normalization processing. Among them, the maximum-minimum normalization method is a well-known technology and will not be elaborated here.

[0053] So far, the tension data of the suspension insulator string connecting the power transmission and transformation line at each moment, as well as the wind speed, wind direction, temperature, and relative humidity at each moment, are obtained.

[0054] Step 2: Analyze the trend change characteristics of the temperature at all moments within each acquisition period, the trend change characteristics of the relative humidity at all moments, and the average level of the proportional relationship between the relative humidity and the temperature at each moment, and calculate the trend change coefficient of each acquisition period.

[0055] Optical fiber composite overhead ground wire (OPGW) is a wire formed by using a power transmission line. OPGW includes a tubular structure containing one or more optical cables, and the outside is composed of steel and aluminum, which can achieve the purpose of grounding and communication. In practical applications, it is necessary to monitor the possible icing and galloping of OPGW optical cables to discover potential hidden dangers in time.

[0056] The main factors for the galloping of power transmission and transformation lines are three factors: conductor icing, wind excitation, and line structure parameters. Among them, the line structure parameters are the internal influencing factors leading to line galloping; however, large-area conductors are more likely to be covered by broken ice, and the longer the span of the conductor, the more likely it is to generate galloping. The conductor area and span are determined during the construction stage and will not change. Therefore, conductor icing and wind excitation are the external influencing factors that are more likely to cause line galloping. Due to the complex and changeable meteorological conditions, the ice-covered shape on the surface of the conductor presents various irregular shapes. Once affected by the wind force, it will cause the conductor to gallop, and the greater the wind force and the more perpendicular the wind direction is to the conductor direction, the greater the amplitude of the conductor galloping.

[0057] Based on the above analysis, the icing degree of the power transmission and transformation line is comprehensively affected by various factors. Under meteorological conditions of low temperature and high humidity, and when the temperature gradually decreases while the relative humidity of the air gradually increases, it is more likely to cause icing on the conductor. By analyzing the change trends of temperature and relative humidity at different time periods and calculating the trend change coefficient, the flowchart of the steps of the method for obtaining the trend change coefficient of each collection time period provided by the embodiments of the present application is as follows Figure 2 shown, specifically including:

[0058] Adopt a trend test algorithm to obtain the trend statistic of the temperature at all times within each collection time period, denoted as the first trend quantity;

[0059] Adopt a trend test algorithm to obtain the trend statistic of the relative humidity at all times within each collection time period, denoted as the second trend quantity;

[0060] In this embodiment, the Cox-Stuart algorithm is used to obtain the trend statistics of the temperature and relative humidity at all times within each collection time period respectively. Among them, the Cox-Stuart algorithm is a well-known technology and will not be elaborated here. As other implementation manners, implementers can adopt other methods of existing technologies, such as the Mann-Kendall trend test, etc. This embodiment does not make special restrictions on this.

[0061] It should be noted that if the trend statistic is a positive number, it indicates that the temperature or relative humidity at all times within the corresponding collection time period has an upward trend. If the trend statistic is a negative number, it indicates that the temperature or relative humidity at all times within the corresponding collection time period has a downward trend. If the trend statistic is 0, it indicates that the temperature or relative humidity at all times within the corresponding collection time period has no trend.

[0062] Perform a positive mapping on the first trend quantity, and denote the result after the positive mapping as the trend change feature of the temperature;

[0063] Perform a positive mapping on the second trend quantity, and denote the result after the positive mapping as the trend change feature of the relative humidity;

[0064] In this embodiment, an exponential function with the natural constant as the base and the first trend quantity as the exponent is used for positive mapping, so that the result of the trend change feature of the temperature is greater than 0; similarly, an exponential function with the natural constant as the base and the second trend quantity as the exponent is used for positive mapping, so that the result of the trend change feature of the relative humidity is greater than 0.

[0065] It should be noted that the smaller the value of the trend change feature of the temperature or relative humidity, that is, the value of the trend change feature of the temperature or relative humidity is less than 1, it indicates that the temperature or relative humidity at all times within the corresponding collection time period has a downward trend.

[0066] Calculate the average value of the ratio of relative humidity to temperature at all times within each collection period;

[0067] Calculate the ratio of the trend change characteristic of the temperature to the trend change characteristic of the relative humidity, denoted as the relative trend ratio;

[0068] Fuse the relative trend ratio with the average value to obtain the trend change coefficient for each collection period;

[0069] In this embodiment, the product of the relative trend ratio and the average value is used as the trend change coefficient for each collection period.

[0070] It should be noted that the larger the average value, the higher the relative humidity and the lower the temperature in the air corresponding to the collection period. The higher the trend change coefficient, the more likely the climate conditions at this time are to cause icing on the conductor.

[0071] Thus, the trend change coefficient for each collection period is obtained.

[0072] Step 3: Determine the wind force influence coefficient for each collection period based on the change in the angle between the wind direction and the power transmission and transformation line at all times within each collection period and the change in the wind speed at all times; combine the trend change coefficient to obtain the meteorological risk coefficient for each collection period.

[0073] Furthermore, if the climate environment remains in a high-humidity and low-temperature state for a long time, the ice layer covering the surface of the power transmission and transformation line will gradually thicken, and the weight of the ice layer will also increase accordingly. As a result, the tension of the suspension insulator string at both ends of the conductor generally shows a gradually increasing trend. And the trend change coefficient reflects the ease of icing on the surface of the conductor in the current climate environment. Based on this analysis, if the positive correlation between the trend change coefficients in different collection periods and the tension data is stronger, and the increasing characteristic of the tension data is more significant, the higher the icing degree of the power transmission and transformation line, and the greater the possible amplitude of conductor galloping.

[0074] Secondly, due to the complex and changeable meteorological conditions, the ice covering shape on the surface of the conductor presents various irregular shapes. Once affected by the wind force, it will cause the conductor to gallop. Moreover, the greater the wind force and the more perpendicular the wind direction is to the conductor direction, the greater the amplitude of the conductor galloping. Therefore, the wind force influence coefficient is calculated based on the wind direction and wind speed in each collection period. The flowchart of the method for obtaining the wind force influence coefficient for each collection period provided in this embodiment of the application is as Figure 3 shown, and specifically includes:

[0075] Calculate the sine value of the angle between the wind direction and the power transmission and transformation line at each moment;

[0076] It should be noted that the value range of the angle between the wind direction and the power transmission and transformation line is

[0077] Calculate the ratio between the mean value of the sine values at all moments within each acquisition period and the variance of the sine values at all moments, and denote it as the wind direction influence factor;

[0078] Calculate the ratio between the mean value of the wind speeds at all moments within each acquisition period and the variance of the wind speeds at all moments, and denote it as the wind speed influence factor;

[0079] Take the sum of the wind direction influence factor and the wind speed influence factor as the wind force influence coefficient for each acquisition period;

[0080] It should be noted that the larger the mean value of the sine values at all moments within each acquisition period and the smaller the variance, the more perpendicular the wind direction is to the transmission and transformation line. Secondly, the larger the mean value of the wind speeds at all moments within each acquisition period and the smaller the variance, the greater the wind speed and the smaller the variation fluctuation of the wind speed, that is, the longer the duration of the occurrence of a larger wind speed, the larger the obtained wind speed influence factor. At this time, the influence of the wind speed on the transmission and transformation line is greater, and the obtained wind force influence coefficient is greater, reflecting that the influence degree of the wind speed and wind direction on the conductor galloping is greater.

[0081] Furthermore, under the combined action of ice coating and wind excitation, different degrees of galloping of the transmission and transformation line will occur. Based on the trend change coefficient and the wind force influence coefficient, calculate the meteorological risk coefficient, specifically:

[0082] The calculation formula for the meteorological risk coefficient of each acquisition period is:

[0083] G t =α×E t +β×F t

[0084] where G t is the meteorological risk coefficient of the t-th acquisition period, E t is the trend change coefficient of the t-th acquisition period, F t is the wind force influence coefficient of the t-th acquisition period, α is the preset first weight, β is the preset second weight, and α + β = 1. Among them, since the galloping of the transmission and transformation line is relatively greatly affected by wind excitation, the preset first weight is less than the preset second weight.

[0085] In this embodiment, the preset first weight α takes a value of 0.4, and the preset second weight β takes a value of 0.6. As other implementation manners, the implementer can set them according to the actual situation.

[0086] It should be noted that the larger the meteorological risk coefficient, the greater the influence degree of the climate conditions of the corresponding acquisition period on the line galloping.

[0087] Thus, the meteorological risk coefficients of each acquisition period are obtained.

[0088] Step 4: Analyze the relevant changes in the meteorological risk coefficients of the current collection period and its adjacent collection periods, and combine the dispersion of the tension data within the current collection period to determine the galloping risk discrimination value for the current collection period; based on the galloping risk discrimination value, detect the ice galloping of the power transmission and transformation line.

[0089] Furthermore, if the galloping amplitude of the power transmission and transformation line is larger, under the action of wind excitation, the attitude of the conductor in the air may be distorted, resulting in more irregular swings of the power transmission and transformation line, so that the tension data shows the characteristics of random fluctuations. At the same time, the more consistent the influence degree of the climate conditions on the galloping of the power transmission and transformation line between adjacent collection periods, the longer the galloping duration of the power transmission and transformation line, indicating that the galloping risk of the power transmission and transformation line is higher.

[0090] Therefore, based on the meteorological risk coefficients of different collection periods and the fluctuation of the tension data at all times within different collection periods, determine the galloping risk discrimination value, specifically:

[0091] Calculate the autocorrelation coefficient of the meteorological risk coefficients of the current collection period and multiple collection periods before it;

[0092] In this embodiment, calculate the autocorrelation coefficient of the meteorological risk coefficients of the current collection period and the 10 collection periods before it. As other implementation manners, the implementer can set it by himself according to the actual situation; secondly, the time delay of the autocorrelation coefficient is 3. As other implementation manners, the implementer can set it by himself according to the actual situation.

[0093] It should be noted that the calculation of the autocorrelation coefficient is a well-known technology and will not be elaborated here.

[0094] Calculate the dispersion degree of the tension data at all times within the current collection period;

[0095] In this embodiment, the dispersion degree is measured by calculating the variance of the tension data at all times within the current collection period. As other implementation manners, the implementer can adopt other methods of the existing technology, such as the coefficient of variation, standard deviation, etc. This embodiment does not make special restrictions on this.

[0096] Take the normalized result of the product of the autocorrelation coefficient and the dispersion degree as the galloping risk discrimination value for the current collection period;

[0097] In this embodiment, the sigmoid function is used for normalization processing. Among them, the sigmoid function is a well-known technology and will not be elaborated here. As other implementation manners, the implementer can adopt other methods of the existing technology, such as the tanh function, etc. This embodiment does not make special restrictions on this.

[0098] It should be noted that the larger the autocorrelation coefficient is, the more consistent the influence degree of climate conditions on conductor galloping in multiple acquisition periods is. The larger the degree of dispersion is, the greater the random fluctuation degree of tension data is, and more irregular swings occur in the transmission and transformation line. The larger the obtained galloping risk discrimination value is, the more significant the galloping degree of the transmission and transformation line is, and the greater the risk of ice galloping existing in the transmission and transformation line is.

[0099] Based on the galloping risk discrimination value, the ice galloping of the transmission and transformation line is detected to judge the possible risk of ice galloping existing in the transmission and transformation line. Specifically:

[0100] If the galloping risk discrimination value in the current acquisition period is greater than or equal to the preset threshold, there is a risk of ice galloping in the transmission and transformation line; otherwise, there is no risk of ice galloping in the transmission and transformation line, so as to accurately evaluate the ice galloping of the transmission and transformation line and improve the accuracy of the ice galloping detection of the transmission and transformation line.

[0101] In this embodiment, the value of the preset threshold is 0.6. As other implementation manners, the implementer can set it according to the actual situation.

[0102] It should be noted that if there is a risk of ice galloping in the transmission and transformation line, the maintenance personnel can take corresponding maintenance measures to remove the ice on the transmission and transformation line in time to ensure the safe operation of the power transmission of the transmission and transformation line.

[0103] Based on the same inventive concept as the above method, the embodiment of the present application also provides an ice galloping detection system for a transmission and transformation line based on an optical fiber sensor, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of any one of the above methods for detecting ice galloping of a transmission and transformation line based on an optical fiber sensor are implemented.

[0104] It should be understood that although Figure 1 the steps in the flowchart of Figure 1 are displayed in sequence according to the indication of the arrows, these steps do not necessarily need to be executed in the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover,

[0105] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification.

[0106] The above-described embodiments merely represent several implementation manners of the present application. The description is relatively specific and detailed, but it should not be construed as a limitation to the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can be made. Therefore, any simple modifications, equivalent changes, and decorations made to the above embodiments based on the technical essence of the present application without departing from the content of the technical solution of the present application all fall within the protection scope of the technical solution of the present application.

Claims

1. A method for detecting icing galloping of power transmission and transformation lines based on optical fiber sensors, characterized in that The method includes the following steps: Obtain the wind speed, wind direction, temperature, and relative humidity at each moment during each collection period of the environment where the power transmission and transformation line is located through an intelligent sensing system, as well as the tension data of the suspension insulator string connecting the power transmission and transformation line at each moment; Analyze the trend change characteristics of the temperature at all moments during each collection period, the trend change characteristics of the relative humidity at all moments, and the average level of the proportional relationship between the relative humidity and the temperature at each moment, and calculate the trend change coefficient for each collection period; Determine the wind force influence coefficient for each collection period based on the change situation of the angle between the wind direction and the power transmission and transformation line at all moments during each collection period and the change situation of the wind speed at all moments. Combine the trend change coefficient to obtain the meteorological risk coefficient for each collection period; Analyze the relevant change situation of the meteorological risk coefficients of the current collection period and its adjacent collection periods, and combine the discrete situation of the tension data during the current collection period to determine the galloping risk discrimination value for the current collection period; Detect the ice galloping of the power transmission and transformation line based on the galloping risk discrimination value; The calculation of the trend change coefficient for each collection period includes: Calculate the average value of the ratio of the relative humidity to the temperature at all moments during each collection period; Calculate the relative trend ratio based on the trend change characteristics of the relative humidity and the trend change characteristics of the temperature; The trend change coefficient is the fusion result of the relative trend ratio and the average value; The relative trend ratio is the ratio of the trend change characteristics of the relative humidity to the trend change characteristics of the temperature; The determination of the wind force influence coefficient for each collection period includes: Calculate the sine value of the angle between the wind direction and the power transmission and transformation line at each moment; calculate the ratio between the mean value and the variance of the sine values at all moments during each collection period, denoted as the wind direction influence factor; Calculate the ratio between the mean value and the variance of the wind speed at all moments during each collection period, denoted as the wind speed influence factor; The wind force influence coefficient is the fusion result of the wind direction influence factor and the wind speed influence factor.

2. The ice accretion and galloping detection method for power transmission and transformation lines based on fiber optic sensors according to claim 1, wherein The further determination method for the trend change characteristics of the temperature is: Adopt a trend test algorithm to obtain the trend statistic of the temperature at all moments during each collection period, denoted as the first trend quantity; Perform a positive mapping on the first trend quantity, and use the result after the positive mapping as the trend change characteristics of the temperature.

3. The ice accretion and galloping detection method for power transmission lines based on optical fiber sensors according to claim 1, characterized in that, The further determination method for the trend change characteristics of the relative humidity is: Adopt a trend test algorithm to obtain the trend statistic of the relative humidity at all moments during each collection period, denoted as the second trend quantity; After performing a positive mapping on the second trend quantity, denote its result as the trend change characteristics of the relative humidity.

4. The ice accretion and galloping detection method for power transmission and transformation lines based on optical fiber sensors according to claim 1, wherein, The calculation formula for the meteorological risk coefficient of each collection period is as follows: , where is the meteorological risk coefficient of the th collection period, is the trend change coefficient of the th collection period, is the wind force influence coefficient of the th collection period, is the preset first weight, is the preset second weight, and , where the preset first weight is less than the preset second weight.

5. The ice accretion and galloping detection method for power transmission lines based on fiber optic sensors according to claim 1, characterized in that, The determination of the galloping risk discrimination value for the current collection period includes: Calculate the autocorrelation coefficient of the meteorological risk coefficients of the current collection period and multiple collection periods before it; Calculate the dispersion degree of the tension data at all moments during the current collection period; The galloping risk discrimination value is the normalized result after the fusion of the autocorrelation coefficient and the dispersion degree.

6. The method for detecting ice accretion and galloping of power transmission lines based on fiber optic sensors according to claim 1, characterized in that The detection of ice galloping on the power transmission and transformation line includes: if the ice galloping risk discrimination value in the current acquisition period is greater than or equal to the preset threshold, there is a risk of ice galloping on the power transmission and transformation line; otherwise, there is no risk of ice galloping on the power transmission and transformation line.

7. An ice accretion and galloping detection system for power transmission and transformation lines based on fiber optic sensors, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method for detecting ice galloping on the power transmission and transformation line based on an optical fiber sensor according to any one of claims 1-6.

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