A method for identifying a power communication optical cable interruption

Abstract

This invention discloses a method for identifying power communication optical cable interruptions. By selecting m sampling points on both sides of the connection point between the OPGW and the tower mounting plate, the method focuses on areas prone to strain shift and high failure rates. It uses strain time-series curves of the m sampling points generated over time under conditions of no external load and only initial tension after installation to eliminate frequency shift data interference caused by this factor. Simultaneously, by deploying sensors along the micro-meteorological section of the OPGW to obtain measured temperatures at discrete points, including the m sampling points on both sides of the connection point between the OPGW and the tower mounting plate, calibration is provided to obtain more accurate temperature and strain shifts. This improves the power communication optical cable interruption identification technology and enhances identification accuracy.

Description

Technical Field

[0001] This invention relates to the field of power communication detection and control, and more specifically, to a method for identifying power communication optical cable interruptions. Background Technology

[0002] In actual maintenance of transmission lines, technicians have found that many faults mainly occur near the location where the OPGW is installed on the tower. The OPGW is connected to the tower hardware by hanging it on the PD mounting plate of the heart-shaped ring. The optical cable strips the pre-twisted wire at the splice point, and the pre-twisted wire is connected to the tower through the mounting plate. The internal optical cable is used as a jumper to cross the tower structure. This not only ensures good fixation of the OPGW, but also reduces the number of splice points of the optical cable and reduces losses. In some tower sections, the splice box can also be used to realize segmented communication control and realize the grounding wire of the tower point.

[0003] Optical cables subjected to tension or compression exceeding their design limits, such as improper construction, excessive icing, external impacts, or lightning strikes, can lead to the physical breakage of one or more internal optical fibers. Excessive bending during installation or operation, resulting in an excessively small bending radius, or poor splicing quality at fiber optic joints causing excessive loss and a sharp increase in signal attenuation, can also cause the aluminum-clad steel wire of the OPGW to break or loosen. The absence of a dedicated grounding wire or excessively high tower grounding resistance can generate continuous induced current discharge at grounding down conductors and other connections, leading to overheating and burning of components, damaging the optical cable structure, and in severe cases, even melting the cable. Furthermore, an abnormally increased current in the grounding down conductor can also cause overheating problems. Therefore, OPGW failures exhibit certain patterns and specific approximate locations.

[0004] Existing technologies for detecting faults in OPGW (Optical Fiber-Coated Wi-Fi) optical cables utilize Brillouin scattering, a process that occurs when light propagates through the fiber and is subjected to temperature and stress-related factors. This means that when the fiber experiences temperature changes or is subjected to tension or compression, causing stress deformation, the properties of the medium at that location change, leading to a frequency shift in the Brillouin scattered light. This shift is often very small, and its application in long-distance transmission, particularly in ultra-high voltage (UHV) lines, for fault detection is widespread. By leveraging the multi-scale analysis capabilities of wavelet transform, the frequency shift characteristics of the Brillouin scattering signal are extracted. The specific frequency shift is determined by calculating the center frequency difference between two sets of Brillouin spectral data. Then, the Lorentz curve is used to fit the frequency shift data, identifying the location of the peak value, which is the fault point. This system typically consists of a Brillouin Optical Time-Domain Reflectometer (BOTDR), which includes a laser source, pulse modulator, photodetector, and signal processing unit.

[0005] The obvious drawback of the above technology in actual operation is that it performs the same scattering data collection on optical cables at all locations to determine the degree of offset. However, as an OPGW optical cable, the typical stress and deformation characteristics at different locations have already undergone subtle changes during construction and installation, especially at the optical cable installation location near the tower. Therefore, the abnormal offset data collected may be normal data under normal operating conditions. The stress condition of the hardware connection points will change over time (wear and loosening at the mounting plate connection), and will also be significantly affected by the external environment or maintenance and construction (force applied by manual tools and inspection robots). Therefore, special marking cannot solve the problem of abnormal data interference.

[0006] Therefore, improving the identification technology for power communication optical cable interruptions, specifically for the actual situation of OPGW on elevated lines, is of significant practical importance. Summary of the Invention

[0007] To address the aforementioned problems in the existing technology, the purpose of this invention is to provide a method for identifying power communication optical cable interruptions, applicable to OPGW optical cable communication networks of high-voltage or ultra-high-voltage overhead lines.

[0008] To solve the above problems, the present invention adopts the following technical solution.

[0009] A method for identifying power communication optical cable interruptions includes the following steps:

[0010] This step employs a traditional Brillouin Distributed Fiber Analyzer (BOTDR / A), utilizing one or more optical fibers within the OPGW as the sensing medium. One end of the fiber is connected to the BOTDR / A analyzer within the station, while the other end extends along the transmission line, passing through all the towers, to the other end of the line (the end of the monitoring section). The back-end unit is used to receive, store, and analyze the data uploaded by the BOTDR / A in real time.

[0011] Set up n sampling points along the OPGW line and obtain the Brillouin frequency shift curve for each sampling point. Start the BOTDR / A analyzer to measure the entire OPGW line and obtain the original Brillouin frequency shift curve for each sampling point from the start to the end. On the original Brillouin curve, you will see some points where the frequency shift value changes abruptly. These abrupt changes usually correspond to the fixing points or splices of the OPGW on the tower and the hardware, because the zero-strain frequency shift may have slight differences between different fiber coils or different batches of fiber cores.

[0012] Based on the number and latitude / longitude coordinates of each tower of the transmission line, the identified abrupt change points are matched with the tower numbering sequence. For example, the first abrupt change point after the line starting point is highly likely to be the first tension tower or the tower at the splicing point, thus determining the location of key sampling points. Based on the original frequency shift curve and the actual tower location and number, sampling points are selected. m of the n sampling points are arranged on both sides of the connection point between the OPGW and the tower mounting plate, with at least one sampling point on each side of each connection point, where m ≤ n. The arrangement of the m sampling points on both sides of the connection point between the OPGW and the tower mounting plate, with at least one sampling point on each side of each connection point, includes: one sampling point at a preset position on each side of the pre-twisted wire stripping point closest to the connection point, with one sampling point located on the jumper wire.

[0013] The BOTDR / A analyzer can be set to automatically measure the BFS data of the entire line at regular intervals. From the newly measured curves, the BFS values ​​of the previously defined n sampling points are extracted to form the trend curves of these points changing over time. That is, based on the Brillouin frequency shift curve of the OPGW in the early stage of construction (no external load, only the tension effect after initial installation) and the Brillouin frequency shift curve at a preset time interval, the abnormal change characteristics of m sampling points are extracted to obtain the strain time series curves of m sampling points.

[0014] It should be noted that, under normal operating conditions, sampling data is collected at the same time points as the internal and external environments to eliminate the influence of temperature on frequency shift, so that the time series curve reflects the stress change trend of the sampling points under the influence of time changes and external force wear.

[0015] The advantage of the above steps is that by injecting probe light into the OPGW fiber, the frequency shift of the backscattered Brillouin light is detected. The axial strain of the fiber is linearly related to the Brillouin frequency shift. By using optical time-domain reflection localization, the strain distribution curve along the entire fiber can be obtained. This allows for the identification of any significant abnormal stress changes at the m sampling points over time, thus alerting maintenance personnel to check the reliability of power fitting connections or whether improper construction has caused abnormal stress. Furthermore, it identifies stress changes over time, providing a reference for daily monitoring and identification of fiber optic cable interruption risks and reducing data interference.

[0016] OPGW serves as both a communication optical cable and an overhead ground wire (lightning protection wire) for the power system. Under normal operation, almost no current flows through it; as a grounding conductor, the OPGW is at the same potential as the tower, and the induced voltage is directly conducted to the ground. However, in the event of a transmission line fault or lightning strike, it carries a huge short-circuit current, acting as a lightning protection wire to quickly divert the lightning current to the ground, protecting the transmission lines. The OPGW is laid parallel to the three-phase high-voltage conductors below. According to Faraday's law of electromagnetic induction, when an alternating load current flows through the three-phase conductors, an alternating electromagnetic field is generated in the surrounding space. This electromagnetic field cuts the overhead ground wire, inducing an electromotive force (EMF) on it. Although the ground wire is grounded on each tower, forming a closed loop, the induced voltage drives current to flow in the loop formed by the ground wire, tower, and ground, creating an induced circulating current. The greater the induced EMF on the ground wire, the larger the circulating current. When this circulating current flows through the resistance of the ground wire itself, it generates Joule heat.

[0017] Joule heating causes the OPGW temperature to rise, which in turn raises the fiber optic cable temperature. By acquiring real-time numerical weather grid data and current values ​​along the OPGW route, the actual temperature at each sampling point is determined. The numerical weather grid data is then interpolated using inverse distance weighting and assigned to each sampling point to obtain the grid-predicted temperature for that point. For temperature variations caused by factors other than ambient temperature, the calculated temperature at each OPGW sampling point is obtained by establishing and solving the OPGW heat balance equation. This can be achieved using a conventional heat balance equation to determine the OPGW temperature, and by using a specific variable coefficient to calculate the internal fiber optic cable temperature.

[0018] However, in some areas, particularly in key micro-meteorological sections along the optical cable route such as mountain passes, crossing points, and icing-prone areas, as well as near towers heavily affected by maintenance activities, temperature and stress influences are more frequent. To obtain more accurate temperature values, low-cost IoT weather sensors are deployed in these specific micro-meteorological sections to obtain more accurate OPGW and optical cable temperature values. This also provides calibration for the temperature input in the OPGW thermal balance equation. Specifically, sensors are deployed along the OPGW route in micro-meteorological sections to obtain measured temperatures at discrete points. These discrete points should ideally include m sampling points located on both sides of the connection point between the OPGW and the tower mounting plate. This allows for the combination of potentially slightly inaccurate physical model calculations with discrete measured data to obtain the closest approximation of the actual temperature value at each sampling point along the entire route.

[0019] Specifically, while the physical model can calculate the temperature at each point, errors in the model parameters can lead to systematic biases in the results. Sensors, on the other hand, provide more accurate measured data, but are only installed at a few points and cannot cover the entire line. Therefore, it is necessary to use discrete, sparse measured values ​​to correct the output of the entire physical model, while considering the uncertainties of both, to obtain an optimal estimate. That is, based on the calculated temperature at each sampling point and the measured temperature at discrete points, the optimal temperature estimate for each sampling point is obtained through Kalman filtering assimilation.

[0020] Kalman filtering is a recursive algorithm that predicts the temperature of all points at the current moment based on the physical model's heat balance equation and the optimal value at the previous moment. When new measured data is received from the sensor, the algorithm calculates a gain based on the difference between the measured and predicted values, as well as the covariance matrix of the two, as the confidence level. The gain is then used to correct the predicted values ​​across the entire line, ultimately obtaining the optimal estimate.

[0021] Temperature frequency shift and strain frequency shift are obtained according to the Brillouin frequency shift equation. It is known that the Brillouin frequency shift is affected by both temperature and strain. Using the obtained optimal temperature estimate, the frequency shift caused by temperature is calculated. The strain frequency shift is obtained by subtracting the frequency shift caused by temperature from the total frequency shift. Since the strain offset of the obtained n sampling points includes m sampling points arranged on both sides of the connection point between the OPGW and the tower mounting plate, in order to avoid the data of these m sampling points being misleading, the risk of optical cable interruption is identified based on the strain offset and the strain time series curve of the m sampling points, so as to determine whether the strain of the m sampling points has truly reached the strain alarm threshold.

[0022] Compared with the prior art, the advantages of this invention are:

[0023] This invention selects m sampling points on both sides of the connection point between the OPGW and the tower mounting plate, focusing on areas prone to strain shift and high failure rates. It uses strain time-series curves of these m sampling points, generated under conditions of no external load and only initial tension after installation, to eliminate frequency shift data interference caused by this. Simultaneously, it obtains measured temperatures at discrete points, including m sampling points on both sides of the connection point between the OPGW and the tower mounting plate, by deploying sensors along the micro-meteorological section of the OPGW, to provide calibration and obtain more accurate temperature and strain shifts. Attached Figure Description

[0024] Figure 1 This is a flowchart of the power communication optical cable interruption identification method of the present invention; Detailed Implementation

[0025] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0026] Example:

[0027] This embodiment is a method for identifying power communication optical cable interruptions, which is used to solve the identification error problem in the prior art.

[0028] The OPGW of the transmission line is installed on the tower through power fittings. The OPGW is connected to the tower fittings by hanging on the PD mounting plate of the heart ring. The optical cable strips the pre-twisted wire at the splice point and connects the pre-twisted wire to the tower through the mounting plate. The internal optical cable crosses the tower structure as a jumper. In some tower sections, segmented communication control can also be realized through splice boxes, and the tower point grounding wire can be realized. Optical cables subjected to tension or compression exceeding their design limits, such as improper construction, excessive icing, external impacts, or lightning strikes, can lead to the physical breakage of one or more internal optical fibers. Excessive bending during installation or operation, resulting in an excessively small bending radius, or poor splicing quality at fiber optic joints causing excessive loss and a sharp increase in signal attenuation, can also cause the aluminum-clad steel wire of the OPGW (Optical Plug-in Cable) to break or loosen. The absence of a dedicated grounding wire or excessively high tower grounding resistance can generate continuous induced current discharge at grounding down conductors and other connections, leading to overheating and burning of components, damaging the cable structure, and in severe cases, even melting the cable. Furthermore, an abnormally high current in the grounding down conductor can also cause overheating problems.

[0029] In existing technologies for detecting faults in OPGW optical cables, Brillouin scattering, which is temperature- and stress-dependent, occurs when light propagates through the fiber. Specifically, when the fiber experiences temperature changes or is subjected to tension or compression, causing stress deformation, the properties of the medium at that location change, leading to a frequency shift in the Brillouin scattered light. This shift is often relatively small. Especially for long-distance transmission lines like UHV lines, Brillouin scattering has been widely used for fault detection. By leveraging the multi-scale analysis capabilities of wavelet transform, the frequency shift characteristics of the Brillouin scattering signal are extracted. The specific frequency shift is determined by calculating the center frequency difference between two sets of Brillouin spectral data. Then, the frequency shift data is fitted using a Lorentz curve to find the location corresponding to the peak value, which is the fault point. This system typically consists of a Brillouin Optical Time-Domain Reflectometer (BOTDR), which includes a laser source, pulse modulator, photodetector, and signal processing unit.

[0030] When performing the same scattering data collection on optical cables at all locations to determine the degree of offset, as an OPGW optical cable, the typical stress and deformation characteristics at different locations have undergone subtle changes during construction and installation, especially at the optical cable installation location near the tower. Therefore, the abnormal offset data collected may be normal data under normal operating conditions. The stress condition of the hardware connection points will change over time (wear and loosening at the mounting plate connection), and will also be significantly affected by external environmental factors or maintenance and construction (force applied by manual tools and inspection robots). Therefore, special marking cannot solve the problem of abnormal data interference.

[0031] This embodiment addresses the actual situation of OPGW (Optical Power Wire Roofing) on ​​elevated power lines. During monitoring, m sampling points are selected on both sides of the connection point between the OPGW and the tower mounting plate. The focus is on areas prone to strain shift and frequent faults. Strain time-series curves of these m sampling points, generated under conditions of no external load and only initial tension after installation, are used to eliminate frequency shift interference caused by this factor. Simultaneously, measured temperatures at discrete points, including the m sampling points on both sides of the connection point between the OPGW and the tower mounting plate, are obtained by deploying sensors along the OPGW in micro-meteorological sections. This provides calibration for more accurate temperature and strain shifts. Furthermore, this improves the technology for identifying power communication fiber optic cable interruptions, solves practical technical problems, and enhances identification accuracy.

[0032] The steps in this embodiment include:

[0033] n sampling points are set along the OPGW line, and the Brillouin frequency shift curve of each sampling point is obtained. m of the n sampling points are arranged on both sides of the connection point between the OPGW and the tower mounting plate. There is at least one sampling point on each side of each connection point, where m≤n.

[0034] Based on the Brillouin frequency shift curves at the initial stage of OPGW construction and the Brillouin frequency shift curves at preset time intervals, the abnormal change characteristics of m sampling points are extracted to obtain the strain time series curves of m sampling points.

[0035] Real-time acquisition of numerical weather grid data and current values ​​covering the OPGW;

[0036] The numerical weather grid data is interpolated using inverse distance weighting and assigned to each sampling point to obtain the grid-predicted temperature for that sampling point.

[0037] The heat balance equation of OPGW was established and solved to obtain the calculated temperature of each sampling point of OPGW;

[0038] Sensors were deployed along the micro-meteorological sections of the OPGW to obtain the measured temperature at discrete points;

[0039] Based on the calculated temperature at each sampling point and the measured temperature at discrete points, the optimal temperature estimate for each sampling point is obtained by Kalman filtering assimilation.

[0040] The temperature frequency shift and strain frequency shift are obtained using the Brillouin frequency shift equation;

[0041] The risk of fiber optic cable interruption is identified based on strain offset and strain time-series curves at m sampling points.

[0042] This embodiment employs a Brillouin Distributed Fiber Analyzer (BOTDR / A), utilizing one or more optical fibers within the OPGW as the sensing medium. One end of the fiber is connected to the BOTDR / A analyzer within the station, while the other end extends along the transmission line, passing through all towers, to the other end of the line (the end of the monitoring section). The back-end unit is used to receive, store, and analyze the data uploaded by the BOTDR / A in real time.

[0043] First, n sampling points are set along the OPGW line to obtain the Brillouin frequency shift curve of each sampling point. m of the n sampling points are arranged on both sides of the connection point between the OPGW and the tower mounting plate. There is at least one sampling point on each side of each connection point, where m≤n.

[0044] Specifically, the BOTDR / A analyzer is activated to measure the entire OPGW line, obtaining the original Brillouin frequency shift curve for each sampling point from the start to the end. On the original Brillouin curve, you will see some points where the frequency shift values ​​change abruptly. These abrupt changes usually correspond to the location of the OPGW fixing point or splice on the tower, because the zero-strain frequency shift may have slight differences between different fiber coils or different batches of fiber cores.

[0045] Based on the tower number and latitude / longitude coordinates of each transmission line tower, the identified abrupt change points are matched with the tower numbering sequence. For example, the first abrupt change point after the line starting point is highly likely to be the first tension tower or the tower at the splicing point, thus determining the location of key sampling points. Based on the original frequency shift curve and the actual tower locations and numbers, sampling points are selected. The m sampling points are arranged on both sides of the connection point between the OPGW and the tower mounting plate, with at least one sampling point on each side of each connection point. As a preferred location for the m sampling points, one sampling point is set at a preset position on each side of the pre-twisted wire stripping point closest to the connection point, with one sampling point located on a jumper wire.

[0046] Determine the actual location of the pre-twisted wire stripping point, i.e., the distance L from the initial monitoring point. The distances between the left and right sampling points and the initial monitoring point are L-ΔL and L+ΔL, respectively. ΔL can be set according to the distribution of abrupt change points in the original frequency shift curve. Usually, it covers the locations of these abrupt change points with greater stress influence. That is, these abrupt change points located on both sides of the pre-twisted wire stripping point are within the selection range of m sampling points.

[0047] The BOTDR / A analyzer can be set to automatically measure the BFS data of the entire line at regular intervals. From the newly measured curves, the BFS values ​​of the previously defined n sampling points are extracted to form trend curves of these points changing over time. Based on the Brillouin frequency shift curve Vb(0) of the OPGW in the early stage of construction (no external load, only tension influence after initial installation) (the curve needs to be recorded in the early stage), and the Brillouin frequency shift curve Vb(t) at a preset time interval, the abnormal change characteristics of m sampling points are extracted to obtain the strain time series curve Vbm(t) of m sampling points. The strain time series curve Vbm(t) of each of the m sampling points reflects the strain change trend of each sampling point. In this step, sampling data is collected at time points with the same internal and external operating environments to eliminate the influence of temperature on frequency shift, so that the time series curve reflects the stress change trend of the sampling points under the influence of time changes and their own external force wear.

[0048] By introducing probe light into the OPGW optical fiber, the frequency shift of the backscattered Brillouin light is detected. The axial strain of the fiber is linearly related to the Brillouin frequency shift. Through optical time-domain reflectometry, the strain distribution curve along the entire fiber can be obtained. This allows for two main methods: firstly, identifying any significant stress anomalies at the m sampling points over time, thus alerting maintenance personnel to check the reliability of electrical fitting connections or whether improper construction has caused abnormal stress; secondly, identifying stress changes over time, providing a reference for daily monitoring and identification of optical cable interruption risks, and reducing data interference.

[0049] OPGW serves as both a communication optical cable and an overhead ground wire (lightning protection wire) for the power system. Under normal operation, almost no current flows through it; as a grounding conductor, the OPGW is at the same potential as the tower, and the induced voltage is directly conducted to the ground. However, in the event of a transmission line fault or lightning strike, it carries a huge short-circuit current, acting as a lightning protection wire to quickly divert the lightning current to the ground, protecting the transmission lines. The OPGW is laid parallel to the three-phase high-voltage conductors below. According to Faraday's law of electromagnetic induction, when an alternating load current flows through the three-phase conductors, an alternating electromagnetic field is generated in the surrounding space. This electromagnetic field cuts the overhead ground wire, inducing an electromotive force (EMF) on it. Although the ground wire is grounded on each tower, forming a closed loop, the induced voltage drives current to flow in the loop formed by the ground wire, tower, and ground, creating an induced circulating current. The greater the induced EMF on the ground wire, the larger the circulating current. When this circulating current flows through the resistance of the ground wire itself, it generates Joule heat.

[0050] Joule heating causes the OPGW temperature to rise, which in turn raises the fiber optic cable temperature. By acquiring real-time numerical weather grid data and current values ​​along the OPGW route, the actual temperature at each sampling point is determined. The numerical weather grid data is then interpolated using inverse distance weighting and assigned to each sampling point to obtain the grid-predicted temperature for that point. For temperature variations caused by factors other than ambient temperature, the calculated temperature at each OPGW sampling point is obtained by establishing and solving the OPGW heat balance equation. This can be achieved using a conventional heat balance equation to determine the OPGW temperature, and by using a specific variable coefficient to calculate the internal fiber optic cable temperature.

[0051] Real-time acquisition of numerical weather prediction grid data covering the OPGW: parameters such as temperature, wind speed, and sunshine duration, as well as real-time conductor current values. Using inverse distance weighted interpolation, for each sampling point, the nearest several meteorological grid points are found based on its latitude and longitude coordinates, and the grid-predicted temperature φd for that point is calculated by weighting the values ​​according to the inverse distance. The heat balance equation for the OPGW is established.

[0052]

[0053] Among them, 𝑞 𝑠 To absorb heat from sunlight, 𝐼 2 T c ) is the Joule heating of the electric current (which varies with temperature), 𝑞 𝑐 For convection cooling, 𝑞 𝑟 For radiative heat dissipation, The rate of change of heat stored in a conductor, i.e., the thermal inertia term or transient term, is given by ρ, the density of the conductor material, and c. p The specific heat capacity under constant pressure of a conductor material. The rate of change of conductor temperature over time is given by substituting weather forecast grid data and real-time conductor current values ​​into the OPGW heat balance equation to obtain the calculated temperature T at each sampling point. c .

[0054] Sensors were deployed along the OPGW (Operating Power Grid) in micro-meteorological zones to obtain measured temperatures at discrete points. Wireless temperature sensors or fiber optic grating sensors were deployed in typical micro-meteorological zones along the OPGW, such as long crossings, mountain passes, heavy icing areas, and areas near towers significantly affected by maintenance activities, to acquire measured temperatures at discrete points. 𝑜𝑏𝑠 Low-cost IoT weather sensors are deployed in the aforementioned specific micrometeorological sections to obtain more accurate OPGW and fiber optic cable temperature values, and to provide calibration for the temperature input of the OPGW heat balance equation. Specifically, sensors are deployed along the OPGW line in micrometeorological sections to obtain measured temperatures at discrete points. These discrete points should ideally include m sampling points located on both sides of the connection point between the OPGW and the tower mounting plate. This allows for the combination of potentially slightly inaccurate physical model calculations with discrete measured data to obtain the closest approximation of the actual temperature value at each sampling point along the entire line.

[0055] While the physical model can calculate the temperature at each point, errors in the model parameters can lead to systematic biases in the results. Sensors provide more accurate measured data, but are only installed at a few points, failing to cover the entire line. Therefore, it is necessary to use discrete, sparse measured values ​​to correct the output of the entire physical model, while considering the uncertainties of both, to obtain an optimal estimate. Specifically, based on the calculated temperature at each sampling point and the measured temperature at discrete points, the optimal temperature estimate for each sampling point is obtained through Kalman filtering assimilation.

[0056] Kalman filtering is a recursive algorithm that predicts the temperature of all points at the current moment based on the physical model's heat balance equation and the optimal value at the previous moment. When new measured data is received from the sensor, the algorithm calculates a gain based on the difference between the measured and predicted values, as well as the covariance matrix of the two, as the confidence level. The gain is then used to correct the predicted values ​​across the entire line, ultimately obtaining the optimal estimate.

[0057] Based on the calculated temperature at each sampling point and the measured temperature at discrete points, the optimal temperature estimate for each sampling point is obtained through Kalman filtering assimilation. The steps include:

[0058] Using the temperature at each sampling point as a state variable, the temperature φ obtained from the above steps is calculated. 𝑐 As the system's state prediction value, the measured temperature at the discrete points is obtained. 𝑜𝑏𝑠 As observed values, the Kalman filter algorithm dynamically adjusts the weights based on the covariance of the prediction and observation errors, outputting the assimilated optimal temperature estimate for each sampling point. 𝑜𝑝𝑡.

[0059] The temperature shift and strain shift are determined using the Brillouin frequency shift equation; specifically, it is known that the Brillouin frequency shift is affected by both temperature and strain.

[0060]

[0061] Among them, △𝜈 B For the total frequency shift, 𝐶 𝜖 For strain coefficient, 𝐶 𝑇 Δt is the temperature coefficient, Δt is the strain offset, and ΔT is the temperature offset.

[0062] Using the optimal temperature estimate obtained in step 6 𝑜𝑝𝑡 Calculate the frequency shift caused by temperature:

[0063]

[0064] Where T ref This is the reference temperature for the sampling point.

[0065] From the total frequency shift Δf B Subtract the frequency shift Δt caused by temperature from the middle. B,temp The strain frequency shift was obtained.

[0066] △𝜈 B,strain =△𝜈 B -△𝜈 B,temp

[0067] Converting strain frequency shift into mechanical strain offset:

[0068] △𝜖=△𝜈 B,strain / 𝐶 𝜖

[0069] The steps for identifying optical cable interruption risk based on strain offset and strain time-series curves of m sampling points include: determining whether the strain offset Δu of each sampling point reaches the deviation threshold; identifying sampling points among the n sampling points that do not include the m sampling points where the strain offset Δu is greater than the deviation threshold as optical cable interruption risk points; comparing the sampling points among the n sampling points where the strain offset Δu is greater than the deviation threshold with the corresponding strain time-series curve Vbm(t); identifying sampling points that match the change characteristics of the strain time-series curve Vbm(t) as having no optical cable interruption risk, and identifying sampling points that do not match the change characteristics of the strain time-series curve Vbm(t) as optical cable interruption risk points.

[0070] This invention is implemented by computer means and also includes a computer-readable storage medium storing a computer program for executing the power communication optical cable interruption identification method of this invention.

[0071] It also includes an electronic device, said electronic device comprising:

[0072] Processor; memory for storing instructions executable by the processor;

[0073] The processor is configured to read the executable instructions from the memory and execute the power communication optical cable interruption identification method of the present invention.

[0074] The above description is merely a preferred embodiment of the present invention; however, the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and its improved concepts, should be covered within the scope of protection of the present invention.

Claims

1. A method for identifying power communication optical cable interruptions, characterized in that: Includes the following steps: n sampling points are set along the OPGW line, and the Brillouin frequency shift curve of each sampling point is obtained. m of the n sampling points are arranged on both sides of the connection point between the OPGW and the tower mounting plate. There is at least one sampling point on each side of each connection point, where m≤n. Based on the Brillouin frequency shift curves at the initial stage of OPGW construction and the Brillouin frequency shift curves at preset time intervals, the abnormal change characteristics of m sampling points are extracted to obtain the strain time series curves of m sampling points. Real-time acquisition of numerical weather grid data and current values ​​covering the OPGW; The numerical weather grid data is interpolated using inverse distance weighting and assigned to each sampling point to obtain the grid-predicted temperature for that sampling point. The heat balance equation of OPGW was established and solved to obtain the calculated temperature of each sampling point of OPGW; Sensors were deployed along the micro-meteorological sections of the OPGW to obtain the measured temperature at discrete points; Based on the calculated temperature at each sampling point and the measured temperature at discrete points, the optimal temperature estimate for each sampling point is obtained by Kalman filtering assimilation. The temperature frequency shift and strain frequency shift are obtained using the Brillouin frequency shift equation; The risk of optical cable interruption is identified based on strain frequency shift and strain time-series curves at m sampling points.

2. The identification method according to claim 1, characterized in that: Of the n sampling points, m sampling points are arranged on both sides of the connection point between the OPGW and the tower mounting plate, including: setting one sampling point at a preset position on both sides of the pre-twisted wire stripping point closest to the connection point, and one of the sampling points is located on the jumper wire.

3. The identification method according to claim 1, characterized in that: The process involves extracting abnormal change features from m sampling points based on the Brillouin frequency shift curve during the initial construction of the OPGW and the Brillouin frequency shift curve at preset time intervals, to obtain strain time-series curves for m sampling points, including: Based on the Brillouin frequency shift curve Vb(0) of the OPGW after its installation without external load and only affected by the tension after initial installation, and the Brillouin frequency shift curve Vb(t) at preset time intervals t, abnormal change characteristics of m sampling points are extracted to obtain the strain time series curve Vbm(t) of m sampling points. The strain time series curve Vbm(t) of each of the m sampling points reflects the strain change trend of each sampling point. The preset time t satisfies that the internal operating environment and the external environment are the same at the selected time node, so that the time series curve reflects the stress change trend of the m sampling points under the influence of time and external forces.

4. The identification method according to claim 3, characterized in that: The process of establishing and solving the OPGW thermal balance equation to obtain the calculated temperature at each sampling point of the OPGW includes: The heat balance equation of OPGW: Among them, 𝑞 𝑠 To absorb heat from sunlight, 𝐼 2 T c ) represents the Joule heating of the electric current as a function of temperature, 𝑞 𝑐 For convection cooling, 𝑞 𝑟 For radiative heat dissipation, The rate of change of heat stored in a conductor, i.e., the thermal inertia term or transient term, is given by ρ, the density of the conductor material, and c. p The specific heat capacity under constant pressure of a conductor material. The rate of change of conductor temperature over time is calculated by substituting the values ​​of weather forecast grid data and real-time conductor current into the OPGW heat balance equation to obtain the calculated temperature T at each sampling point. c .

5. The identification method according to claim 4, characterized in that: The process of obtaining the optimal temperature estimate for each sampling point by assimilating the calculated temperature at each sampling point and the measured temperature at discrete points using Kalman filtering includes: Using the temperature at each sampling point as a state variable, the temperature is calculated using... 𝑐 As the state prediction value, the measured temperature of the obtained discrete points is used as the actual temperature. 𝑜𝑏𝑠 As observed values, the Kalman filter algorithm dynamically adjusts the weights based on the covariance of the prediction and observation errors, outputting the assimilated optimal temperature estimate for each sampling point. 𝑜𝑝𝑡 .

6. The identification method according to claim 5, characterized in that: The determination of temperature frequency shift and strain frequency shift based on the Brillouin frequency shift equation includes: The Brillouin frequency shift equation is: Among them, △𝜈 B For the total frequency shift, 𝐶 𝜖 For strain coefficient, 𝐶 𝑇 Here, Δt is the temperature coefficient, Δt is the strain offset, and ΔT is the temperature offset. Using the optimal temperature estimate 𝑇 𝑜𝑝𝑡 Calculate the frequency shift caused by temperature: Where T ref The reference temperature for the sampling point; From the total frequency shift Δf B Subtract the frequency shift Δt caused by temperature from the middle. B,temp The strain frequency shift was obtained. △𝜈 B,strain =△𝜈 B -△𝜈 B,temp Converting strain frequency shift into mechanical strain offset: △𝜖=△𝜈 B,strain / 𝐶 𝜖 。 7. The identification method according to claim 6, characterized in that: The method of identifying optical cable interruption risk based on strain frequency shift and strain time-series curves at m sampling points includes: Converting strain frequency shift into mechanical strain offset: △𝜖=△𝜈 B,strain / 𝐶 𝜖 Determine whether the strain offset Δu at each sampling point reaches the deviation threshold. Among the n sampling points, those sampling points whose strain offset Δu is greater than the deviation threshold (excluding the m sampling points) are identified as optical cable interruption risk points. Compare the sampling points among the m sampling points with strain offset Δu greater than the deviation threshold with the corresponding strain time series curve Vbm(t). Sampling points that match the change characteristics of the strain time series curve Vbm(t) are identified as having no optical cable interruption risk, while sampling points that do not match the change characteristics of the strain time series curve Vbm(t) are identified as optical cable interruption risk points.

8. A computer-readable storage medium, characterized in that, The storage medium stores a computer program for performing the method described in any one of claims 1-7.

9. An electronic device, characterized in that, The electronic device includes: processor; Memory used to store the processor's executable instructions; The processor is configured to read the executable instructions from the memory and execute the instructions to implement the method described in any one of claims 1-7.

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