Method and system for detecting icing galloping of power transmission and transformation line based on optical fiber sensor

Abstract

The invention relates to the technical field of power transmission line sensor detection, in particular to a power transmission and transformation line icing galloping detection method and system based on an optical fiber sensor, and the method comprises the steps: obtaining the wind speed, wind direction, temperature and relative humidity of the environment where a power transmission and transformation line is located at each moment in each collection time period; the tension data of a suspension insulator string connected with the power transmission and transformation line at each moment are obtained; calculating a trend change coefficient and a wind power influence coefficient of each acquisition time period to obtain a meteorological risk coefficient of each acquisition time period; determining a galloping risk discrimination value of the current acquisition time period; and detecting icing galloping of the power transmission and transformation line based on the galloping risk discrimination value. The icing galloping state of the power transmission and transformation line can be accurately evaluated, the accuracy of icing galloping detection of the power transmission and transformation line is improved, and safe operation of power transmission of the power transmission and transformation line is guaranteed.

Description

Technical Field

[0001] The present application relates to the technical field of power transmission line sensor detection, and in particular to a method and system for detecting ice dancing on power transmission and transformation lines based on optical fiber sensors. Background Art

[0002] The galloping of power transmission and transformation lines refers to a low-frequency, large-amplitude self-excited vibration of non-circular cross-section conductors caused by wind. Galloping often occurs on eccentrically iced transmission lines in cold winter, which can easily cause phase flashover, hardware damage, and even tower collapse, posing a great threat to the safe operation of the power grid.

[0003] The mechanism of transmission line dancing is complex and has many influencing factors, among which the conductor dancing caused by ice and wind factors is more significant. Therefore, existing detection methods usually analyze the condensation height of ice on the transmission line image, or evaluate the dancing state through the characteristics of line tension changes measured by sensors. Failure to deeply consider the impact of changing climate conditions on ice dancing, especially the risk of wind speed and wind direction on the ice dancing of conductors, leads to inaccurate detection of the ice dancing state of the transmission and transformation lines, and thus the inability to maintain the transmission and transformation lines in a timely manner, affecting the power transmission of the transmission and transformation lines, which is not conducive 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 dancing on power transmission and transformation lines based on optical fiber sensors are provided to solve the existing problems.

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

[0006] In a first aspect, an embodiment of the present application provides a method for detecting ice dancing of a power transmission and transformation line based on an optical fiber sensor, the method comprising the following steps:

[0007] The intelligent sensor system is used to obtain the wind speed, wind direction, temperature and relative humidity of the environment where the power transmission and transformation line is located at each time during each collection period, as well as the tension data of the suspension insulator string connecting the power transmission and transformation line at each time;

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

[0009] The wind force influence coefficient of each collection period is determined by the change of the angle between the wind direction and the power transmission and transformation line at all times in each collection period and the change of the wind speed at all times. The meteorological risk coefficient of each collection period is obtained by combining the trend change coefficient.

[0010] Analyze the relevant changes of the meteorological risk coefficients in the current collection period and its adjacent collection periods, and determine the dancing risk discrimination value of the current collection period in combination with the discrete situation of the tension data in the current collection period;

[0011] Based on the dancing risk judgment value, the dancing of ice on the power transmission and transformation line is detected.

[0012] Preferably, a method for further determining the trend change characteristics of the temperature is:

[0013] The trend test algorithm is used to obtain the trend statistics of the temperature at all times in each collection period, which is recorded as the first trend quantity;

[0014] The first trend quantity is positively mapped, and the result after the positive mapping is used as the trend change feature of the temperature.

[0015] Preferably, a method for further determining the trend change characteristics of the relative humidity is:

[0016] The trend test algorithm is used to obtain the trend statistics of the relative humidity at all times in each collection period, which is recorded as the second trend quantity;

[0017] After forward mapping is performed on the second trend quantity, the result is recorded as the trend change feature of relative humidity.

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

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

[0020] Calculating a relative trend ratio based on the trend change characteristics of the relative humidity and the trend change characteristics of the temperature;

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

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

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

[0024] 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 and variance of the sine value at all moments in each acquisition period, and record it as the wind direction influencing factor;

[0025] Calculate the ratio between the mean and variance of wind speed at all times in each collection period, and record it as the wind speed influencing factor;

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

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

[0028] Preferably, determining the dancing risk discrimination value of the current acquisition period includes:

[0029] Calculating the autocorrelation coefficient of the meteorological risk coefficient in the current collection period and the previous multiple collection periods;

[0030] Calculate the discrete degree of tension data at all times in the current acquisition period;

[0031] The dancing risk discrimination value is a normalized result of the fusion of the autocorrelation coefficient and the discrete degree.

[0032] Preferably, the detection of ice dancing of the power transmission and transformation line includes: if the dancing risk judgment value of the current collection period is greater than or equal to a preset threshold, there is a risk of ice dancing in the power transmission and transformation line; otherwise, there is no risk of ice dancing in the power transmission and transformation line.

[0033] In the second aspect, an embodiment of the present application also provides a power transmission and transformation line ice dancing detection system based on optical fiber sensors, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements any of the steps of the power transmission and transformation line ice dancing detection method based on optical fiber sensors when executing the computer program.

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

[0035] The present application calculates the trend change coefficient of each collection period by analyzing the trend changes of temperature and relative humidity in each collection period. The beneficial effect of this application is that the degree to which the icing degree of the transmission and transformation line is affected by meteorological conditions, so as to reflect the possibility of icing on the conductors due to the meteorological conditions in the corresponding collection period; secondly, by analyzing the influence of wind direction and wind speed on the dancing of the transmission and transformation line, the wind influence coefficient is calculated. The beneficial effect of this application is that the influence of wind speed and wind direction on the dancing of the conductor is taken into account, and it reflects the influence of wind speed on the dancing of the conductor when it acts on the transmission and transformation line at different wind direction angles. Furthermore, the meteorological risk coefficient is calculated by combining the trend change coefficient and the wind influence coefficient. The beneficial effect of this application is that the meteorological risk coefficient is taken into account under the common conditions of icing and wind excitation. The dancing of the power transmission and transformation line under the action of ice; by analyzing the consistency of changes in the meteorological risk coefficients of multiple collection periods and the random fluctuations of the tension data, the dancing risk discrimination value is calculated to reflect the consistency of the impact of climatic conditions on the dancing of the power transmission and transformation line between adjacent collection periods, and the longer the dancing of the power transmission and transformation line, the more it can illustrate the dancing risk degree of the power transmission and transformation line; based on the dancing risk discrimination value, the ice-covered dancing of the power transmission and transformation line is detected, which has the beneficial effect of taking into account the influence of the changing factors of climatic conditions in the environment, being able to accurately evaluate the ice-covered dancing state of the power transmission and transformation line, improving the accuracy of ice-covered dancing detection of the power transmission and transformation line, and ensuring the safe operation of power transmission of the power transmission and transformation line. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The optical fiber sensor-based ice dancing detection method for power transmission and transformation lines of the present application is further described in detail below in conjunction with the accompanying drawings.

[0037] Figure 1 A flowchart of the steps of a method for detecting ice dancing on a power transmission and transformation line based on an optical fiber sensor provided in an embodiment of the present application;

[0038] Figure 2 A flowchart of the steps of a method for obtaining a trend change coefficient of each acquisition period provided in an embodiment of the present application;

[0039] Figure 3 A flowchart of the steps of a method for obtaining the wind influence coefficient for each collection period provided in an embodiment of the present application. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical solutions and advantages of the present application more clearly understood, the following is a further detailed description of the optical fiber sensor-based ice dancing detection method and system for power transmission and transformation lines proposed in the present application in conjunction with the accompanying drawings and implementation examples. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application.

[0041] Unless defined otherwise, 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] See also Figure 1 , which shows a flowchart of a method for detecting ice dancing on a power transmission and transformation line based on an optical fiber sensor provided by an embodiment of the present application, the method comprising the following steps:

[0043] Step 1: Obtain wind speed, wind direction, temperature and relative humidity of the environment where the power transmission and transformation line is located at each moment in each collection period through an intelligent sensing system, as well as 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 ice dancing of the transmission and transformation line. If the surface of the conductor of the transmission and transformation line is unevenly covered with ice, it is easy to have irregular shapes such as fan-shaped, D-shaped, and crescent-shaped. These irregular shapes of ice change the aerodynamic shape of the conductor. Once affected by wind excitation, the conductor will dance. In addition, the angle between the wind direction and the direction of the transmission and transformation line is large. At this time, the effect of wind excitation is greater, and the component of the wind force on the conductor perpendicular to the direction of the conductor increases, which makes it easier for the conductor to swing horizontally, which has a serious impact on the dancing of the conductor.

[0045] Secondly, Brillouin Optical Time Domain Reflectometry (Brillouin OTDR) is a distributed fiber optic sensor technology that detects temperature and strain information by measuring the Brillouin frequency shift changes in the optical fiber. Temperature changes will cause changes in the refractive index and sound velocity of the optical fiber, thereby causing linear changes in the Brillouin frequency shift. By detecting the Brillouin frequency shift, the temperature and strain of the optical fiber environment can be inverted. Therefore, Brillouin Optical Time Domain Reflectometry has the advantages of high spatial resolution, long measurement distance and anti-electromagnetic interference, and can perform real-time monitoring of power transmission and transformation lines.

[0046] The Brillouin OTDR technology is used to obtain the temperature of the environment in which the power transmission and transformation line is located, as well as the tension data of the suspension insulator strings on both sides connected to the power transmission and transformation line;

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

[0048] Secondly, the relative humidity of the environment where the power transmission and transformation lines are located is collected by using humidity sensors in the intelligent sensing system;

[0049] The collection interval of the intelligent sensor is set to t seconds, and the intelligent sensor system transmits the collected data via optical fiber.

[0050] Taking into account the changing climate conditions in winter, all moments within each 1-minute interval are recorded as collection periods, and the different degrees of conductor galloping caused by the possible large differences in climate in different collection periods are analyzed.

[0051] Thus, the tension data of the suspension insulator string connecting the transmission and transformation lines at each moment in each collection period, as well as the wind speed, wind direction, temperature and relative humidity at each moment are obtained, and the collected tension data, wind speed, temperature and relative humidity are normalized.

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

[0053] At this point, the tension data of the suspension insulator string connecting the transmission and transformation lines 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 temperature at all times in each collection period, the trend change characteristics of relative humidity at all times, and the average level of the proportional relationship between relative humidity and temperature at each time, and calculate the trend change coefficient of each collection period.

[0055] Fiber-optic composite overhead ground wire (OPGW) is a wire constructed using transmission lines. OPGW consists of a tubular structure containing one or more optical cables, and the exterior is made of steel and aluminum. It can achieve the purpose of grounding and communication. In actual applications, it is necessary to monitor the possible icing and dancing of the OPGW optical cable to promptly discover potential hidden dangers.

[0056] The main factors causing the galloping of power transmission and transformation lines are conductor icing, wind excitation, and line structural parameters. Among them, line structural parameters are internal factors that cause line galloping; however, large-area conductors are more likely to be covered with broken ice, and the longer the span of the conductor, the easier it is to gallop. The conductor area and span are determined during the construction phase and will not change. Therefore, conductor icing and wind excitation are external factors that are more likely to cause line galloping. Due to the complex and changeable meteorological conditions, the ice on the surface of the conductor presents various irregular shapes. Once affected by 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 degree of icing of the transmission and transformation lines is affected by a combination of factors. Under low temperature and high humidity meteorological conditions, and when the temperature gradually decreases and the relative humidity of the air gradually increases, it is more likely to cause icing of the conductors. By analyzing the changing trends of temperature and relative humidity in different time periods, the trend change coefficient is calculated. The step flow chart of the method for obtaining the trend change coefficient of each collection time period provided in the embodiment of the present application is as follows: Figure 2 As shown, specifically including:

[0058] The trend test algorithm is used to obtain the trend statistics of the temperature at all times in each collection period, which is recorded as the first trend quantity;

[0059] The trend test algorithm is used to obtain the trend statistics of the relative humidity at all times in each collection period, which is recorded as the second trend quantity;

[0060] In this embodiment, the Cox-Stuart algorithm is used to obtain the trend statistics of temperature and relative humidity at all times in each collection period, wherein the Cox-Stuart algorithm is a well-known technology and will not be described here. As other implementation methods, the implementer can adopt other methods of the prior art, such as the Mann-Kendall trend test, etc., and this embodiment does not impose any special restrictions on this.

[0061] It should be noted that a positive trend statistic indicates that the temperature or relative humidity at all times in the corresponding collection period has an upward trend. A negative trend statistic indicates that the temperature or relative humidity at all times in the corresponding collection period has a downward trend. A trend statistic of 0 indicates that the temperature or relative humidity at all times in the corresponding collection period has no trend.

[0062] Performing positive mapping on the first trend quantity, and recording the result after the positive mapping as the trend change characteristic of temperature;

[0063] Performing positive mapping on the second trend quantity, and recording the result after the positive mapping as the trend change characteristic of relative humidity;

[0064] In this embodiment, an exponential function with a 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 characteristic of temperature is greater than 0; similarly, an exponential function with a 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 characteristic of relative humidity is greater than 0.

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

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

[0067] Calculating the ratio of the trend change characteristic of the temperature to the trend change characteristic of the relative humidity, and recording it as a relative trend ratio;

[0068] The relative trend ratio is merged with the average value to obtain the trend change coefficient of each acquisition period;

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

[0070] It should be noted that the larger the average value is, the higher the relative humidity and the lower the temperature in the air during the corresponding collection period is, and the higher the trend change coefficient is, which means that the climatic conditions at this time are more likely to cause ice on the wire.

[0071] At this point, the trend change coefficient of each collection period is obtained.

[0072] Step 3, determine the wind force influence coefficient of each collection period by the change of the angle between the wind direction and the transmission and transformation line at all times in each collection period and the change of the wind speed at all times; combine the trend change coefficient to obtain the meteorological risk coefficient of each collection period.

[0073] Furthermore, if the climate environment maintains high humidity and low temperature 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, which will cause the tension of the suspended insulator string at both ends of the conductor to show a gradually increasing characteristic in the overall trend. The trend change coefficient reflects the ease of ice covering the surface of the conductor under the current climate environment. Based on this analysis, if the trend change coefficient under different collection periods has a stronger positive correlation with the tension data, and the increase feature of the tension data is more significant, the higher the degree of ice covering the power transmission and transformation line, the greater the amplitude of the conductor dancing may be.

[0074] Secondly, due to the complex and changeable meteorological conditions, the ice on the surface of the conductor presents various irregular shapes. Once affected by the wind, it will cause the conductor to dance. The greater the wind force, the more perpendicular the wind direction is to the conductor direction, and the greater the amplitude of the conductor dance. Therefore, the wind influence coefficient is calculated by the wind direction and wind speed of each collection period. The step flow chart of the method for obtaining the wind influence coefficient of each collection period provided in the embodiment of the present application is as follows: Figure 3 As shown, specifically including:

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

[0076] It should be noted that the angle between wind direction and transmission line can be in the range of

[0077] Calculate the ratio between the mean of the sine values ​​at all times in each acquisition period and the variance of the sine values ​​at all times, and record it as the wind direction influencing factor;

[0078] Calculate the ratio between the mean of wind speed at all times in each collection period and the variance of wind speed at all times, and record it as the wind speed influencing factor;

[0079] The sum of the wind direction influence factor and the wind speed influence factor is used as the wind force influence coefficient of each collection period;

[0080] It should be noted that, the larger the mean of the sine values ​​at all times in each collection period and the smaller the variance, the more perpendicular the wind direction is to the power transmission and transformation line. Secondly, the larger the mean of the wind speed at all times in each collection period and the smaller the variance, the greater the wind speed and the smaller the fluctuation of the wind speed. That is, the longer the duration of the high wind speed, the greater the wind speed influence factor. At this time, the greater the influence of the wind speed on the power transmission and transformation line, the greater the wind force influence coefficient, which reflects that the influence of wind speed and wind direction on the conductor dancing is greater.

[0081] Furthermore, under the combined effects of icing and wind excitation, the transmission and transformation lines will experience different degrees of dancing. Based on the trend change coefficient and the wind force influence coefficient, the meteorological risk coefficient is calculated, specifically:

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

[0083] G t =α×E t +β×F t

[0084] Among them, G t is the meteorological risk coefficient of the tth collection period, E t is the trend change coefficient of the tth collection period, F t is the wind influence coefficient of the tth collection period, α is the preset first weight, β is the preset second weight, and α+β=1, wherein, since the dancing 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 value of the first weight α is 0.4, and the preset value of the second weight β is 0.6. As other implementation modes, the implementer can set them according to the actual situation.

[0086] It should be noted that the larger the meteorological risk coefficient is, the greater the impact of the climatic conditions in the corresponding collection period on the line dancing is.

[0087] At this point, the meteorological risk coefficient for each collection period is obtained.

[0088] Step 4: Analyze the relevant changes in the meteorological risk coefficient between the current collection period and the adjacent collection periods, and determine the dancing risk judgment value of the current collection period in combination with the discreteness of the tension data in the current collection period; based on the dancing risk judgment value, detect the dancing of ice on the transmission and transformation lines.

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

[0090] Therefore, based on the meteorological risk coefficients of different acquisition periods and the fluctuation of tension data at all times in different acquisition periods, the dancing risk discrimination value is determined, specifically:

[0091] Calculating the autocorrelation coefficient of the meteorological risk coefficient in the current collection period and the previous multiple collection periods;

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

[0093] It should be noted that the calculation of the autocorrelation coefficient is a well-known technique and will not be described in detail here.

[0094] Calculate the discrete degree of tension data at all times in the current acquisition period;

[0095] In this embodiment, the degree of discreteness is measured by calculating the variance of the tension data at all times in the current acquisition period. As other implementations, implementers may adopt other methods of the prior art, such as coefficient of variation, standard deviation, etc. This embodiment does not impose any special restrictions on this.

[0096] The normalized result of the product of the autocorrelation coefficient and the discrete degree is used as the dancing risk discrimination value of the current collection period;

[0097] In this embodiment, the sigmoid function is used for normalization processing, wherein the sigmoid function is a well-known technology and will not be described in detail here. As other implementation methods, the implementer may adopt other methods of the prior art, such as the tanh function, etc. This embodiment does not impose any special restrictions on this.

[0098] It should be noted that the larger the autocorrelation coefficient is, the more consistent the impact of climatic conditions on conductor dancing in multiple collection periods is; the larger the degree of discreteness is, the greater the random fluctuation of tension data is, and the more irregular swings occur in the power transmission and transformation lines; the larger the obtained dancing risk judgment value is, the more significant the dancing degree of the power transmission and transformation lines is, and the greater the risk of ice-covered dancing of the power transmission and transformation lines is.

[0099] Based on the dancing risk discrimination value, the dancing of ice on the power transmission and transformation line is detected to determine the possible risk of ice dancing on the power transmission and transformation line, specifically:

[0100] If the dancing risk judgment value of the current collection period is greater than or equal to the preset threshold, there is a risk of ice dancing in the transmission and transformation line; otherwise, there is no risk of ice dancing in the transmission and transformation line, thereby accurately evaluating the ice dancing of the transmission and transformation line and improving the accuracy of ice dancing detection of the transmission and transformation line.

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

[0102] It should be noted that if there is a risk of ice dancing on the transmission and transformation lines, maintenance personnel will take appropriate maintenance measures to promptly eliminate the ice on the transmission and transformation lines to ensure the safe operation of power transmission on the transmission and transformation lines.

[0103] Based on the same inventive concept as the above method, an embodiment of the present application also provides a power transmission and transformation line ice dancing detection system based on optical fiber sensors, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein when the processor executes the computer program, the steps of any one of the above-mentioned power transmission and transformation line ice dancing detection methods based on optical fiber sensors are implemented.

[0104] It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0105] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, 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, they should be considered to be within the scope of this specification.

[0106] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the present application. It should be pointed out that, for those of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application. Therefore, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present application without departing from the content of the technical solution of the present application, shall fall within the protection scope of the technical solution of the present application.

Claims

1. A method for detecting ice dancing on power transmission and transformation lines based on optical fiber sensors, characterized in that: The method comprises the following steps: The intelligent sensor system is used to obtain the wind speed, wind direction, temperature and relative humidity of the environment where the power transmission and transformation line is located at each time during each collection period, as well as the tension data of the suspension insulator string connecting the power transmission and transformation line at each time; Analyze the trend change characteristics of temperature at all times in each collection period, the trend change characteristics of relative humidity at all times, and the average level of the proportional relationship between relative humidity and temperature at each time, and calculate the trend change coefficient of each collection period; The wind force influence coefficient of each collection period is determined by the change of the angle between the wind direction and the power transmission and transformation line at all times in each collection period and the change of the wind speed at all times. The meteorological risk coefficient of each collection period is obtained by combining the trend change coefficient. Analyze the relevant changes of the meteorological risk coefficients in the current collection period and its adjacent collection periods, and determine the dancing risk discrimination value of the current collection period in combination with the discrete situation of the tension data in the current collection period; Based on the dancing risk judgment value, the dancing of ice on the power transmission and transformation line is detected.

2. The method for detecting ice dancing of power transmission and transformation lines based on optical fiber sensors according to claim 1, characterized in that: A further method for determining the trend change characteristics of the temperature is: The trend test algorithm is used to obtain the trend statistics of the temperature at all times in each collection period, which is recorded as the first trend quantity; The first trend quantity is positively mapped, and the result after the positive mapping is used as the trend change feature of the temperature.

3. The method for detecting ice dancing of power transmission and transformation lines based on optical fiber sensors according to claim 1, characterized in that: A further method for determining the trend change characteristics of the relative humidity is: The trend test algorithm is used to obtain the trend statistics of the relative humidity at all times in each collection period, which is recorded as the second trend quantity; After forward mapping is performed on the second trend quantity, the result is recorded as the trend change feature of relative humidity.

4. The method for detecting ice dancing of power transmission and transformation lines based on optical fiber sensors according to claim 1, characterized in that: The calculation of the trend change coefficient of each acquisition period includes: Calculate the average value of the ratio of relative humidity to temperature at all times in each collection period; Calculating a 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 a fusion result of the relative trend ratio and the average value.

5. The method for detecting ice dancing of power transmission and transformation lines based on optical fiber sensors according to claim 4, characterized in that: The relative trend ratio is the ratio of the trend change characteristics of the relative humidity to the trend change characteristics of the temperature.

6. The method for detecting ice dancing of power transmission and transformation lines based on optical fiber sensors according to claim 1, characterized in that: Determining the wind force influence coefficient of 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 and variance of the sine value at all moments in each acquisition period, and record it as the wind direction influencing factor; Calculate the ratio between the mean and variance of wind speed at all times in each collection period, and record it as the wind speed influencing factor; The wind force influence coefficient is a fusion result of the wind direction influence factor and the wind speed influence factor.

7. The method for detecting ice dancing of power transmission and transformation lines based on optical fiber sensors according to claim 1, characterized in that: The calculation formula of the meteorological risk coefficient of each collection period is: G t =α×E t +β×F t , where G t is the meteorological risk coefficient of the tth collection period, E t is the trend change coefficient of the tth collection period, F t is the wind influence coefficient of the tth collection period, α is the preset first weight, β is the preset second weight, and α+β=1, wherein the preset first weight is less than the preset second weight.

8. The method for detecting ice dancing of power transmission and transformation lines based on optical fiber sensors according to claim 1, characterized in that: Determining the dancing risk discrimination value of the current collection period includes: Calculating the autocorrelation coefficient of the meteorological risk coefficient in the current collection period and the previous multiple collection periods; Calculate the discrete degree of tension data at all times in the current acquisition period; The dancing risk discrimination value is a normalized result of the fusion of the autocorrelation coefficient and the discrete degree.

9. The method for detecting ice dancing of power transmission and transformation lines based on optical fiber sensors according to claim 1, characterized in that: The detecting of ice dancing of the power transmission and transformation line includes: if the dancing risk judgment value in the current collection period is greater than or equal to a preset threshold, there is a risk of ice dancing on the power transmission and transformation line; otherwise, there is no risk of ice dancing on the power transmission and transformation line.

10. A power transmission and transformation line ice dancing detection system based on optical fiber 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, the steps of the method for detecting ice dancing of power transmission and transformation lines based on optical fiber sensors as described in any one of claims 1 to 9 are implemented.

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