Conductor galloping and sag monitoring method and system based on Beidou differential analysis

Abstract

The invention discloses a conductor galloping and sag monitoring method and system based on Beidou differential analysis. The method comprises the following steps: monitoring a lead position, a conductivity change rate, a surface temperature and electric field intensity data of different heights, and calculating an electric field intensity gradient sequence between adjacent heights; and in the incremental time interval, determining a signal drift coefficient according to the incremental wire position data and the incremental electric field intensity data. When the conductivity change rate is greater than a preset threshold value and the surface temperature of the wire is lower than the dew point temperature, multiplying the signal drift coefficient by the ratio of the conductivity change rate to a preset reference rate to obtain a conductive film correction coefficient, and taking the product of the correction coefficient and the real-time electric field intensity as a correction amount; and deducting from the real-time wire position data to obtain correction data. By implementing the technical scheme provided by the invention, the accuracy of the lead position monitoring data in the salt water mist environment is improved.

Description

Technical Field

[0001] The present application relates to the field of measurement that is not dedicated to a specific variable, and in particular to a method and system for monitoring conductor galloping and sag based on Beidou differential analysis. Background Art

[0002] As the scale of my country's power grid construction continues to expand, the importance of safe and stable operation of transmission lines has become increasingly prominent. Especially in coastal areas, real-time status monitoring of transmission lines is of great significance for ensuring power supply reliability and preventing safety accidents. Therefore, developing high-precision transmission line monitoring technology to achieve accurate monitoring of conductor positions has become a key requirement for power system operation and maintenance.

[0003] At present, in the relevant technology, the satellite positioning system is usually used to monitor the position of the conductor. This method installs a monitoring terminal on the conductor, uses the satellite differential positioning principle to obtain the spatial position information of the conductor, and transmits the monitoring data to the monitoring center in real time for analysis and processing, thereby realizing real-time monitoring of the conductor position.

[0004] However, when saltwater fog occurs in coastal areas, the conductivity of saltwater fog or the formation of micro-scale conductive films on the surface of the conductor by saltwater fog particles changes the charge distribution characteristics on the surface of the conductor, thereby affecting the propagation characteristics of the signal, causing the monitoring data to drift and reducing the accuracy of conductor position monitoring. This data drift phenomenon is difficult to be effectively corrected by conventional signal processing methods, making the monitoring results unable to meet the needs of actual applications. Summary of the invention

[0005] The present application provides a method and system for monitoring conductor galloping and sag based on Beidou differential analysis, which are used to improve the accuracy of conductor position monitoring data in a saltwater fog environment.

[0006] In a first aspect of the present application, a method for monitoring conductor galloping and sag based on Beidou differential analysis is provided, the method comprising: The conductor position, the conductivity change rate of the conductor surface, the conductor surface temperature and the electric field strength data at different heights in the vertical direction of the monitoring point obtained by Beidou differential monitoring are collected in real time; the difference of the electric field strength data between adjacent heights is calculated to obtain the electric field strength gradient sequence; in the increasing time interval corresponding to the increasing electric field strength gradient sequence, the signal drift coefficient is determined according to the corresponding increasing conductor position data and the increasing electric field strength data in the increasing time interval; when the conductivity change rate is greater than the preset conductivity change rate threshold and the conductor surface temperature is lower than the dew point temperature, the signal drift coefficient is multiplied by the ratio of the conductivity change rate to the preset reference rate to obtain the conductive film correction coefficient; the product of the conductive film correction coefficient and the real-time electric field strength data collected in real time is used as the conductor position data correction amount; the conductor position data correction amount is deducted from the real-time conductor position data collected in real time to obtain the corrected conductor position data.

[0007] In the above embodiment, the correlation characteristics of signal drift and electric field distribution are determined by analyzing the changing trend of the electric field intensity gradient at different heights; based on the change of the conductor position within the period of increasing electric field gradient, the signal drift coefficient is used for quantitative evaluation; when the conditions for the formation of a conductive film on the conductor surface are detected, the conductivity change rate is introduced as a correction factor, thereby realizing dynamic compensation of signal drift, identifying and reducing the position data drift caused by environmental factors such as salt water mist and the formation of a conductive film on the conductor surface by salt water mist particles, and improving the accuracy and reliability of conductor position monitoring.

[0008] In combination with some embodiments of the first aspect, in some embodiments, after the product of the conductive film correction coefficient and the real-time electric field strength data collected in real time is used as the wire position data correction amount, the method further includes: The water film thickness and salt concentration on the conductor surface are obtained; when the salt concentration is greater than a preset concentration threshold and the water film thickness is less than a preset thickness threshold, a second conductor position data correction amount is obtained based on a ratio of the salt concentration to a preset reference concentration; the conductor position data correction amount and the second conductor position data correction amount are deducted from the real-time conductor position data collected in real time to obtain corrected conductor position data.

[0009] In the above embodiment, by real-time monitoring of the water film thickness and salt concentration, the special situation of salt water mist analysis is identified. When the water film becomes thinner and the salt is enriched, a supplementary correction amount is established based on the salt concentration ratio to compensate for the signal distortion caused by the change of the surface microscopic conductive properties, and a correction mechanism based on the surface environmental characteristics is established. In view of the situation that salt water mist salt analysis may appear on the surface of the conductor, a layered correction strategy is adopted to reduce the monitoring error caused by the change of the surface state, improve the robustness of the conductor position monitoring, especially in complex environments such as coasts and industrial areas, and show adaptability, thereby improving the monitoring accuracy.

[0010] In conjunction with some embodiments of the first aspect, in some embodiments, in an increasing time interval corresponding to an increasing electric field intensity gradient sequence, determining a signal drift coefficient according to increasing wire position data and increasing electric field intensity data corresponding to the increasing time interval specifically includes: Acquire the reference conductor position under standard weather conditions; record the maximum amplitude point and the actual vibration period in the conductor position within a single preset standard vibration period; calculate the position deviation between the maximum amplitude and the reference conductor position, and the time deviation between the actual vibration period and the standard vibration period to form a periodic drift coefficient sequence; perform Fourier transform on the periodic drift coefficient sequence to obtain a frequency component spectrum; extract the frequency component corresponding to the inverse of the actual vibration period from the frequency component spectrum as the drift component; subtract the drift component from the incremental conductor position data to obtain the conductor position data with periodic drift eliminated; and use the ratio of the change in the conductor position data with periodic drift eliminated to the change in the incremental electric field strength data as the signal drift coefficient.

[0011] In the above embodiment, by comparing the reference conductor position with the actual vibration characteristics, a periodic drift coefficient sequence is established to capture the time domain characteristics of the conductor vibration. By performing Fourier transform on the sequence and extracting the frequency component corresponding to the actual vibration period, the signal drift caused by the periodic vibration of the conductor is identified and reduced, the interference effect of mechanical vibration on position monitoring is reduced, and the stability of conductor position monitoring is improved, especially in severe weather conditions such as strong winds, maintaining a high monitoring accuracy.

[0012] In combination with some embodiments of the first aspect, in some embodiments, calculating the position deviation between the maximum amplitude and the reference wire position, and the time deviation between the actual vibration period and the standard vibration period to form a period drift coefficient sequence specifically includes: Analyze the amplitude distribution of the conductor position data and identify the vibration standing wave node position; calculate the deviation between the conductor position data at the standing wave node position and the reference conductor position to obtain the node drift coefficient; calculate the deviation rate of the conductor position data at the maximum amplitude point relative to the node drift coefficient as the drift correction coefficient of the non-node area; use a preset piecewise continuous function to calculate the drift coefficient of the transition area between the standing wave node position and the non-node area; combine the node drift coefficient, drift correction coefficient and transition area drift coefficient to form a periodic drift coefficient sequence.

[0013] In the above embodiment, by identifying the characteristics of the conductor vibration standing wave node, a segmented drift correction mechanism based on spatial distribution is established. Based on the positional relationship between the node and the maximum amplitude point, the vibration characteristics of different areas of the conductor are differentiated, and a continuous transition function is used to achieve smooth correction between the vibration node and the non-node area, and a segmented continuous function is introduced to process the transition area to achieve a smooth transition of the drift coefficient of the entire line segment. The integrity of the conductor position monitoring is improved, the vibration state of the entire line segment of the conductor is captured, and the accuracy loss of the traditional monitoring method near the vibration node is reduced.

[0014] In combination with some embodiments of the first aspect, in some embodiments, after the product of the conductive film correction coefficient and the real-time electric field strength data collected in real time is used as the wire position data correction amount, the method further includes: Obtain weather information and visibility data corresponding to the conductor position; when the weather information is rainy and foggy and the visibility data is less than a preset visibility threshold, calculate the visibility attenuation rate per unit time; deduct the conductor position data correction amount and the third conductor position data correction amount from the real-time conductor position data collected in real time to obtain corrected conductor position data.

[0015] In the above embodiment, by introducing the weather visibility monitoring mechanism, a method for correcting position data under rainy and foggy weather conditions is established. When the visibility is lower than the threshold, the signal attenuation compensation amount is calculated based on the visibility attenuation rate, and the signal distortion caused by the change of the atmospheric environment is corrected in a targeted manner, thereby improving the reliability of wire position monitoring under severe weather conditions, reducing the accuracy reduction caused by signal attenuation in rainy and foggy weather in traditional monitoring methods, and ensuring the stable operation of the system under various weather conditions.

[0016] In combination with some embodiments of the first aspect, in some embodiments, after obtaining weather information and visibility data corresponding to the wire position, the method further includes: Acquire atmospheric ion concentration data corresponding to the conductor position; when the atmospheric ion concentration data exceeds a preset atmospheric ion concentration threshold, determine whether a corona discharge signal is detected; if a corona discharge signal is detected, acquire historical electric field strength data per unit time; eliminate abnormal electric field strength data corresponding to the corona discharge signal in the historical electric field strength data to obtain corrected electric field strength data; and use the product of the conductive film correction coefficient and the corrected electric field strength data as the conductor position data correction amount.

[0017] In the above embodiment, an abnormal correction mechanism for electric field strength data is established by monitoring the atmospheric ion concentration and corona discharge phenomenon. When the corona discharge phenomenon is detected, the abnormal values ​​caused by the discharge disturbance are identified and eliminated by analyzing the historical electric field strength data, and a more realistic electric field distribution feature is obtained. The data is corrected in combination with the conductive film characteristics, eliminating the interference of ionospheric disturbance on the wire position monitoring, improving the anti-interference ability of the wire position monitoring, and maintaining a high monitoring accuracy, especially in a high-voltage ionization environment, providing more reliable data support for the evaluation of the safe operation status of the transmission line.

[0018] In conjunction with some embodiments of the first aspect, in some embodiments, in the increasing time interval corresponding to the increasing electric field intensity gradient sequence, after determining the signal drift coefficient according to the increasing wire position data and the increasing electric field intensity data corresponding to the increasing time interval, the method further includes: Obtaining lightning activity warning information; lightning activity warning information includes lightning warning period and lightning warning area; when the conductor position is in the lightning warning area and the change rate of the electric field strength data is greater than the preset mutation threshold, the real-time period is marked as the pre-disturbance period of lightning activity; the deviation rate between the pre-lightning electric field strength data in the pre-disturbance period of lightning activity and the historical pre-lightning electric field strength data is calculated as the lightning data credibility; when the lightning data credibility is lower than the preset credibility threshold, suspending the calculation of the signal drift coefficient; after detecting that the lightning activity has ended, starting the preset stable waiting time timing, and resuming the calculation of the signal drift coefficient after the stable waiting time ends.

[0019] In the above embodiment, by introducing a lightning activity warning mechanism, a credibility assessment system for monitoring data during lightning disturbances is established. By comparing and analyzing the current electric field data before lightning with historical data, the credibility of the monitoring data is quantitatively assessed, and the signal processing is intelligently interrupted when the credibility is lower than the threshold, thereby reducing the distortion of monitoring data caused by lightning activity and improving the reliability of the system under extreme weather conditions. By setting a stable waiting time, normal monitoring can be smoothly restored after the lightning activity ends, which improves the adaptability and data accuracy of the conductor galloping and sag monitoring system in severe weather.

[0020] In a second aspect, an embodiment of the present application provides a conductor gallop and sag monitoring system, which comprises: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code comprises computer instructions, and the one or more processors call the computer instructions to enable the conductor gallop and sag monitoring system to perform the method described in the first aspect and any possible implementation manner of the first aspect.

[0021] In a third aspect, an embodiment of the present application provides a computer program product comprising instructions. When the above-mentioned computer program product runs on a conductor galloping and sag monitoring system, the above-mentioned conductor galloping and sag monitoring system executes the method described in the first aspect and any possible implementation method of the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, comprising instructions. When the above instructions are executed on a conductor galloping and sag monitoring system, the above conductor galloping and sag monitoring system executes the method described in the first aspect and any possible implementation method of the first aspect.

[0023] It can be understood that the conductor galloping and sag monitoring system provided in the second aspect, the computer program product provided in the third aspect, and the computer storage medium provided in the fourth aspect are all used to execute the conductor galloping and sag monitoring method based on Beidou differential analysis provided in the embodiment of the present application. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method, which will not be repeated here.

[0024] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. This application determines the correlation characteristics of signal drift and electric field distribution by analyzing the changing trend of electric field intensity gradient at different heights; based on the change of wire position within the period of increasing electric field gradient, the signal drift coefficient is used for quantitative evaluation. When the conditions for the formation of a conductive film on the wire surface are detected, the conductivity change rate is introduced as a correction factor to achieve dynamic compensation for signal drift, identify and reduce the position data drift caused by environmental factors such as salt water fog and the formation of a conductive film on the wire surface by salt water fog particles, and improve the accuracy and reliability of wire position monitoring.

[0025] 2. This application identifies special situations of salt water mist salt analysis by real-time monitoring of water film thickness and salt concentration. When the water film becomes thinner and the salt is enriched, a supplementary correction value is established based on the salt concentration ratio to specifically compensate for the signal distortion caused by changes in the microscopic conductive properties of the surface, and a correction mechanism based on surface environmental characteristics is established. In view of the situation where salt water mist salt analysis may appear on the surface of the conductor, a layered correction strategy is used to reduce the monitoring error caused by changes in the surface state, thereby improving the robustness of conductor position monitoring, especially in complex environments such as coasts and industrial areas, and improving monitoring accuracy.

[0026] 3. This application establishes a periodic drift coefficient sequence by comparing the reference conductor position with the actual vibration characteristics, capturing the time domain characteristics of the conductor vibration. By performing Fourier transform on the sequence and extracting the frequency components corresponding to the actual vibration period, the signal drift caused by the periodic vibration of the conductor is identified and reduced, the interference effect of mechanical vibration on position monitoring is reduced, and the stability of conductor position monitoring is improved, especially in severe weather conditions such as strong winds, maintaining a high monitoring accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a flow chart of a method for monitoring conductor galloping and sag based on Beidou differential analysis in an embodiment of the present application; Figure 2 is another flow chart of a method for monitoring conductor galloping and sag based on Beidou differential analysis in an embodiment of the present application; Figure 3 It is a schematic diagram of an exemplary hardware structure of a conductor galloping and sag monitoring system in an embodiment of the present application. DETAILED DESCRIPTION

[0028] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification and appended claims of the present application, the singular expressions "one", "a kind of", "said", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to and includes any or all possible combinations of one or more listed items.

[0029] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0030] In related technologies, conductor position monitoring usually adopts a method based on a combination of electric field strength measurement and satellite positioning. This method installs a monitoring terminal on the conductor, uses the satellite differential positioning principle to obtain the spatial position information of the conductor, and transmits the monitoring data to the monitoring center in real time for analysis and processing, thereby realizing real-time monitoring of the conductor position. However, in actual applications, due to the complex changes in the atmospheric environment, especially when salt water fog occurs in coastal areas, a conductive film is easily formed on the surface of the conductor, or there is conductive salt water fog around the conductor, which changes the electric field distribution characteristics around the conductor, not only affecting the accuracy of the electric field measurement, but also causing drift during signal transmission, reducing the reliability of conductor position monitoring.

[0031] In the embodiment of the present application, a method for monitoring conductor sway and sag based on Beidou differential analysis is proposed. By combining the electric field strength gradient analysis at different heights, a quantitative evaluation method for the influence of the conductive film effect on the signal is established. In particular, when the conditions for the formation of the conductive film are identified, the conductivity change rate is used as a correction factor to dynamically adjust the signal drift coefficient to compensate for the measurement error caused by the conductive film effect. This multi-dimensional data processing method significantly improves the accuracy and reliability of conductor position monitoring under severe weather conditions.

[0032] Figure 1 It is a flow chart of a method for monitoring conductor galloping and sag based on Beidou differential analysis in an embodiment of the present application, comprising the following steps: S101, real-time collection of the wire position, the conductivity change rate of the wire surface, the wire surface temperature, and the electric field strength data at different heights in the vertical direction of the monitoring point obtained by Beidou differential monitoring; Specifically, a monitoring network is constructed by deploying Beidou differential base stations and mobile stations on the transmission lines. The base station is fixed on stable ground, and the mobile station is fixed on the conductor. By comparing the carrier phase difference between the base station and the mobile station in real time, combined with RTK (Real Time Kinematic) technology, the three-dimensional position coordinates of the conductor can be calculated. At the same time, conductivity sensors and temperature sensors are deployed on the surface of the conductor, and the conductivity changes on the surface of the conductor are measured using the four-electrode method. Field strength meters are installed at different heights perpendicular to the conductor to construct an electric field strength monitoring array to achieve dynamic monitoring of the spatial electric field distribution.

[0033] S102, calculating the difference of electric field intensity data between adjacent heights to obtain an electric field intensity gradient sequence; Specifically, by processing the electric field strength data of measurement points at different heights, the difference in electric field strength between adjacent measurement points is calculated to obtain the spatial distribution characteristics of the electric field strength. Using the differential algorithm, for adjacent measurement points at heights h1 and h2, the electric field strength difference ΔE = E(h2) - E(h1). By sorting the differences in the continuous time series to form an electric field strength gradient sequence, common mode interference can be eliminated and the spatial variation characteristics of the electric field strength can be highlighted.

[0034] S103, determining a signal drift coefficient in an increasing time interval corresponding to the increasing electric field intensity gradient sequence according to the corresponding increasing wire position data and increasing electric field intensity data in the increasing time interval; Specifically, the increasing electric field intensity gradient sequence is the increasing segment in the electric field intensity gradient sequence; the signal drift coefficient is the ratio of the change amount of the increasing wire position data to the change amount of the increasing electric field intensity data.

[0035] First, the increasing segment is identified from the electric field intensity gradient sequence, that is, the time interval in which the electric field intensity gradient continues to increase over time. In this interval, the changing relationship between the wire position data and the electric field intensity data is analyzed. The ratio of the change in wire position to the change in electric field intensity is calculated using a numerical analysis method, that is, the signal drift coefficient = change in wire position / change in electric field intensity. This processing method establishes a quantitative relationship between the change in wire position and the change in electric field intensity, providing a basis for subsequent data correction.

[0036] In some embodiments, large oscillations can cause a nonlinear relationship between the electric field strength and the conductor position. First, a high-pass filter is used to remove low-frequency drift, and a wavelet transform is used to reduce noise on the collected electric field strength and conductor position data. Then, a fast Fourier transform is used to analyze the spectral characteristics of the conductor position data, identify the main oscillation frequency components, and calculate the oscillation amplitude. When the amplitude exceeds the set threshold, the large oscillation processing flow is triggered. The oscillation period is evenly divided into multiple sub-intervals according to the phase angle, and the sub-interval length is adaptively adjusted according to the oscillation amplitude. A shorter sub-interval is used in the larger amplitude area to ensure the accuracy of the linear approximation. The local drift coefficient is calculated in each sub-interval by the least squares method.

[0037] In order to improve the calculation accuracy, a nonlinear correction term is established based on the physical model. The geometric nonlinearity and aerodynamic effects caused by the wire oscillation are considered, and the nonlinear term is decomposed into correction quantities of various orders through Taylor expansion. Cubic spline interpolation is used to achieve a smooth transition between adjacent sub-intervals, and a tension coefficient is introduced to control the curvature change of the transition area. At the same time, a time-varying state equation is established to describe the dynamic change of the drift coefficient with the oscillation process, and the Kalman filter algorithm is used to update the state estimation in real time. Finally, the drift coefficients of multiple measuring points are weighted fused, the reliability of the calculation results is evaluated through residual analysis, and the calculated drift coefficients are statistically analyzed and the final results are output.

[0038] In other embodiments, the following steps may be used to replace step S103 above, by identifying the standing wave nodes of the conductor vibration and calculating the drift coefficients of different regions, and combining Fourier transform to eliminate the influence of periodic drift, thereby obtaining a more accurate signal drift coefficient and improving the calculation accuracy of the correlation between the electric field strength and the conductor position.

[0039] Get the baseline traverse position under standard weather conditions.

[0040] Specifically, the direct measurement method uses laser ranging or video image processing technology to obtain the spatial coordinates of the conductor. Data is collected multiple times when the temperature, humidity, and wind speed are within the standard weather range and there is no precipitation. By establishing a mathematical model of weather parameters and conductor position, and using long-term historical monitoring data for regression analysis, the reference position under standard conditions can be predicted.

[0041] The maximum amplitude point and actual vibration period in the conductor position within a single preset standard vibration period are recorded.

[0042] Specifically, the conductor position data is collected in real time, and continuous sampling is maintained through the data cache queue. When the intersection of the conductor position and the reference position is detected, the cycle timer is started, and the time difference between the two adjacent intersections is recorded as the actual vibration cycle. During this period, the local maximum of the position data is identified by the peak detection algorithm, and the three-point comparison method is used to ensure that the true maximum amplitude point is captured, while smoothing filtering is used to eliminate the influence of high-frequency noise. For complex vibration modes, the wavelet analysis method is used to decompose the vibration components and extract the periodic characteristics of the main vibration modes.

[0043] Analyze the amplitude distribution of the conductor position data and identify the locations of vibration standing wave nodes.

[0044] Specifically, the collected position data is analyzed in the time and frequency domains, and the spectrum characteristics are obtained through fast Fourier transform to identify the main vibration frequency components. Then, in the spatial domain, a discrete sampling point array is established along the extension direction of the wire, the vibration envelope of each sampling point is calculated, and the amplitude distribution curve is obtained through least squares fitting. On this basis, the minimum search algorithm is used to locate the local minimum point of the amplitude, and the position of the standing wave node is determined in combination with the threshold judgment.

[0045] The deviation between the conductor position data at the standing wave node position and the reference conductor position is calculated to obtain the node drift coefficient.

[0046] Specifically, a local coordinate system is established at the identified standing wave node position, and the precise position data at the node is obtained through data interpolation. The sliding time window technology is used to calculate the average deviation value of each node position during the observation period. At the same time, a weighted average algorithm is introduced to assign different weights to the deviations at different times according to the data reliability. For the case of multiple nodes, a spatial correlation analysis model is established, considering the mutual influence between nodes, and the overall drift trend is obtained by least squares fitting, and finally the drift coefficient of each node is calculated.

[0047] The deviation rate of the wire position data at the maximum amplitude point relative to the node drift coefficient is calculated as the drift correction coefficient of the non-node area.

[0048] Specifically, an observation window is established at the position of the maximum amplitude point, and an accurate position data sequence is obtained through numerical interpolation. A reference benchmark is established based on the drift coefficients of adjacent nodes, and the relative deviation between the maximum amplitude point position data and the reference benchmark is calculated using normalization processing. In the calculation process, a time-weighted function is introduced, focusing on the data characteristics of the amplitude stability stage, and the steady-state deviation rate is obtained through least squares fitting. For the case of multiple maximum amplitude points, a spatial distribution model is established, and the correction coefficient of each point is calculated through an interpolation algorithm to ensure the continuity of the drift characteristics of the non-node area.

[0049] The preset piecewise continuous function is used to calculate the drift coefficient of the transition area between the standing wave node position and the non-node area.

[0050] Specifically, the spatial range of the transition area is determined, and 15% to 25% of the distance between adjacent nodes is usually taken as the transition interval. The cubic Hermite interpolation function is used as the basic form, and boundary conditions are set at the node position and the boundary of the non-node area to ensure the continuity of the function value and the first-order derivative. Different weight functions can be selected for different types of transition characteristics: linear transition uses piecewise linear function, smooth transition uses cubic spline function, and exponential function is introduced to the drastic change area for correction. The function parameters are determined by iterative optimization so that the drift coefficient distribution in the transition area meets the physical continuity requirements.

[0051] The node drift coefficient, drift correction coefficient and transition region drift coefficient are combined to form a periodic drift coefficient sequence.

[0052] Specifically, a unified spatial coordinate system is established, the node drift coefficient is used as the reference point, and a piecewise function is used to describe the drift characteristics of each region. In the process of data splicing, the smooth transition of the regional boundaries is ensured by weight allocation, the node area is given a higher weight, the transition area uses a gradual weight, and the non-node area is assigned weight according to the distance attenuation law. A combination of sliding average and spline smoothing is used to eliminate numerical jumps in the splicing process, and the physical continuity of the combined sequence is ensured by numerical verification.

[0053] Perform Fourier transform on the periodic drift coefficient sequence to obtain the frequency component spectrum.

[0054] Specifically, the sequence is preprocessed, the length is standardized by zero padding, and the Hanning window function is applied to reduce spectrum leakage. The fast Fourier transform algorithm is used for frequency domain conversion, and the basic spectrum distribution is calculated. In order to improve the frequency resolution, the wavelet transform is used for refinement analysis in the key frequency band, and the local frequency features are extracted by multi-scale decomposition. For non-stationary characteristics, the short-time Fourier transform is introduced, and the appropriate time window is set to obtain the time-frequency joint distribution characteristics, so as to fully describe the frequency composition of the drift coefficient.

[0055] The frequency component corresponding to the inverse of the actual vibration period is extracted from the frequency component spectrum as the drift component.

[0056] Specifically, the inverse of the actual vibration period is calculated to determine the target frequency value. A search window is established in the spectrum, and the window width is dynamically adjusted according to the frequency resolution. The interpolation peak detection algorithm is used to accurately locate the target frequency position, while considering the influence of spectrum leakage, and the frequency deviation is corrected by the energy centroid method. For complex spectra, a time-frequency analysis model based on Gabor transform is established to achieve accurate extraction of frequency components, and the time domain signal is reconstructed through inverse transform.

[0057] The drift component is subtracted from the incremental conductor position data to obtain conductor position data with periodic drift eliminated.

[0058] Specifically, the extracted drift component is reconstructed into a time domain signal through inverse Fourier transform, and the same time reference as the original wire position data is established. Data alignment technology is used to ensure that the two sets of data correspond accurately on the time axis, and the sampling points are filled in through the interpolation algorithm. In the signal subtraction process, an adaptive weight coefficient is introduced to dynamically adjust the subtraction weight according to the data reliability, and the high-frequency noise generated by the subtraction process is eliminated through digital filtering. For nonlinear drift, a phase compensation model is established to correct the phase deviation in the subtraction process.

[0059] The ratio of the change in the wire position data for eliminating periodic drift to the change in the incremental electric field strength data is taken as the signal drift coefficient.

[0060] Specifically, the conductor position data and electric field strength data after eliminating periodic drift are synchronously sampled to ensure that the two sets of data have the same time reference. The central difference method is used to calculate the data change, and the sliding window technology is used to extract the local change characteristics. When calculating the ratio, data smoothing is introduced to eliminate the influence of mutation points on the calculation results. For small change areas, a threshold limit strategy is adopted to avoid calculation errors caused by the divisor close to zero, and the weighted average method is used to improve the stability of the calculation results.

[0061] In the above steps, by comparing the reference conductor position with the actual vibration characteristics, a periodic drift coefficient sequence was established to capture the time domain characteristics of the conductor vibration. By performing Fourier transform on the sequence and extracting the frequency components corresponding to the actual vibration period, the signal drift caused by the periodic vibration of the conductor was identified and reduced, and the interference of mechanical vibration on position monitoring was reduced; by identifying the characteristics of the conductor vibration standing wave nodes, a segmented drift correction mechanism based on spatial distribution was established. Based on the positional relationship between the node and the maximum amplitude point, the vibration characteristics of different areas of the conductor were differentiated, and a continuous transition function was used to achieve smooth correction between the vibration node and the non-node area. A piecewise continuous function was introduced to process the transition area, and a smooth transition of the drift coefficient of the entire line segment was achieved. The accuracy of conductor position monitoring under complex vibration conditions was improved, and the adaptability to spatially uneven vibration was improved.

[0062] In some other embodiments, after executing step S103, the following steps may be further executed, specifically including the following steps: Get warning information about lightning activity.

[0063] Specifically, the lightning activity warning information includes the lightning warning period and the lightning warning area. Macro warning data is obtained through the warning information interface of the meteorological department, and real-time data of the local area is collected by lightning monitoring equipment deployed on site. In the data processing process, the spatial interpolation algorithm is used to expand the information of discrete warning points into continuous warning areas, and the start and end time of the warning period is determined through time series analysis.

[0064] When the conductor is located in the lightning warning area and the rate of change of the electric field strength data is greater than the preset mutation threshold, the real-time period is marked as the pre-disturbance period of lightning activity.

[0065] Specifically, the spatial positioning algorithm is used to determine whether the conductor is located in the warning area, and a spatial mapping relationship based on GPS coordinates is established, and the real-time position judgment is performed in combination with the boundary of the warning area. At the same time, the electric field strength data is monitored in real time, and the short-term change rate is calculated using the sliding window technology, and the mutation characteristics are determined by adaptive threshold judgment. The fuzzy logic method is used to comprehensively judge the spatial conditions and electric field conditions. When the two conditions are met at the same time, the time period marking mechanism is triggered, the start timestamp is automatically recorded, and the data cache is established.

[0066] The deviation rate between the electric field strength data before lightning in the disturbance period before lightning activity and the electric field strength data before historical lightning is calculated as the reliability of lightning data.

[0067] Specifically, the electric field strength data of the previous period of historical lightning activity is extracted from the database to establish a standard feature template. The data of the current disturbance period is normalized, and the sliding window technology is used to calculate the correlation with the historical data. In the calculation of the deviation rate, a time weighting function is introduced to give a higher weight to recent historical data. The deviation trend is fitted by the least squares method, and the credibility score is determined by combining the confidence interval analysis, while the influence of seasonality and weather conditions is considered for correction.

[0068] When the lightning data credibility is lower than the preset credibility threshold, the calculation of the signal drift coefficient is suspended.

[0069] Specifically, a credibility threshold judgment model is established, and a multi-level warning threshold is set through fuzzy logic method. When the credibility is lower than the preset threshold, the progressive pause mechanism is started, and the data cache technology is used to save the calculation state before the pause. The soft switching of the calculation process is realized through the smooth transition algorithm to avoid data jumps caused by sudden interruptions. At the same time, a data marking mechanism is established to record the characteristic parameters of the pause time period to provide a basis for subsequent resumption of calculation.

[0070] After detecting that the lightning activity has ended, the preset stabilization waiting time is started, and the calculation of the signal drift coefficient is resumed after the stabilization waiting time has ended.

[0071] Specifically, the end of lightning activity is confirmed through multi-source data fusion, including comprehensive judgment of meteorological warning cancellation information and on-site monitoring data. The preset stable waiting timer is started, and the stability of key parameters such as electric field strength and conductor position is continuously monitored during the waiting period. The sliding variance analysis is used to evaluate the degree of data fluctuation, and stable thresholds are set for the electric field strength change rate, conductor position deviation rate and signal-to-noise ratio respectively. Only when these three parameters meet the stability requirements at the same time for a preset time, the system is judged to be fully restored to stability. When the preset waiting time is reached and the parameters are stable, the calculation process is gradually resumed.

[0072] In the above steps, by identifying lightning activity warning information and monitoring the sudden change characteristics of electric field strength, the signal drift coefficient calculation is suspended and resumed in time to avoid the influence of abnormal data caused by lightning activity on the calculation results, thereby improving the reliability of the drift coefficient under severe weather conditions.

[0073] S104, when the conductivity change rate is greater than a preset conductivity change rate threshold and the conductor surface temperature is lower than the dew point temperature, multiplying the signal drift coefficient by the ratio of the conductivity change rate to the preset reference rate to obtain a conductive film correction coefficient; Specifically, the conductivity sensor is used to monitor the change of the conductivity of the conductor surface in real time, and the difference method is used to calculate the change rate. At the same time, the temperature and humidity sensor is used to obtain the conductor surface temperature and environmental parameters, and the current dew point temperature is calculated by the dew point formula. Set two criteria that must be met at the same time: one is that the rate of change of the conductor surface conductivity exceeds the preset threshold, indicating that the surface conductive properties have changed significantly; the other is that the conductor surface temperature is lower than the current ambient dew point temperature, indicating that a condensation film has formed on the surface. When these two conditions are met at the same time, start the correction calculation process. The adaptive weight method is used to calculate the ratio of the conductivity change rate to the preset reference rate. The influence of instantaneous fluctuations is eliminated through numerical smoothing, and finally the conductive film correction coefficient is obtained.

[0074] In some embodiments, when complex weather conditions occur, a segmented correction strategy needs to be adopted: a multi-level correction model that considers temperature gradients is established. During rainfall, temperature stratification occurs on the surface of the conductor. By establishing a four-point temperature measurement model (upper, lower, inner, and outer), a comprehensive evaluation method is used to determine the effective surface temperature.

[0075] In other embodiments, for areas with severe pollution deposition, a conductivity evolution model considering salt density deposition is established to capture the conductivity mutation characteristics during the pollution accumulation process. A conductivity evolution model considering salt density deposition is established: First, a salt density accumulation function based on a time series is established, and the salt density deposition rate is calculated in combination with meteorological parameters such as wind speed and precipitation. The model uses a piecewise linearization method to describe the conductivity change characteristics under different humidity conditions, and introduces a mutation factor at the critical humidity point. By real-time monitoring of the salt density value on the surface of the conductor, a salt density-conductivity mapping relationship is established in combination with historical data, and the model parameters are dynamically updated using the recursive least squares method. When precipitation scouring occurs, the model introduces an attenuation function to describe the conductivity reduction process, thereby realizing the quantification of the conductivity evolution characteristics of the entire process.

[0076] S105, taking the product of the conductive film correction coefficient and the real-time electric field strength data collected in real time as the wire position data correction amount; Specifically, the electric field strength sensor is used to collect field strength data in real time, and digital filtering technology is used to eliminate high-frequency noise. At the same time, the conductive film correction coefficient is smoothed in time to ensure data continuity. During the calculation process, the sliding window technology is used to achieve data synchronization, the adaptive weight method is used to perform numerical correction, a data quality assessment mechanism is established, and abnormal values ​​are marked and processed in real time to ensure the reliability of the correction calculation.

[0077] S106, deducting the conductor position data correction amount from the real-time conductor position data collected in real time to obtain corrected conductor position data.

[0078] Specifically, the real-time wire position data is preprocessed, and the measurement noise is eliminated through digital filtering. At the same time, the position correction value is calibrated for time consistency to ensure the synchronization of the two sets of data. In the deduction process, an adaptive step size algorithm is used to achieve progressive correction, and a buffer area is set to avoid data jumps. A data quality assessment mechanism is established to monitor the rationality of the deduction process in real time, and automatically trigger the correction parameter optimization process when an abnormality occurs.

[0079] In the above embodiment, the correction of the conductor position data is achieved by constructing a coupling analysis mechanism of the conductor surface conductivity and the electric field distribution. In the data processing process, a dynamic calculation model of the signal drift coefficient and the conductive film correction coefficient is established to identify and reduce the position data drift caused by environmental factors such as salt water mist and salt water mist particles forming a conductive film on the conductor surface. This correction method based on multi-parameter coupling improves the accuracy and reliability of conductor position monitoring.

[0080] In other embodiments of the present application, under special climatic conditions in coastal areas, the surface of the conductor may be thinned by water film accompanied by salt precipitation. In this case, it is difficult to accurately evaluate the influence of the conductive film on the signal by relying on conductivity changes and temperature monitoring. By adopting the wire galloping and sag monitoring method based on Beidou differential analysis provided by this application, a supplementary correction method based on salt concentration ratio can be established to identify and compensate for changes in the microscopic conductive properties of the wire surface under salt fog weather, thereby improving the adaptability of wire position monitoring under extreme meteorological conditions and improving the accuracy and reliability of monitoring data.

[0081] like Figure 2 As shown, another flow chart of the wire galloping and sag monitoring method based on Beidou differential analysis provided in an embodiment of the present application includes the following steps: S201, real-time collection of the wire position, the conductivity change rate of the wire surface, the wire surface temperature, and the electric field strength data at different heights in the vertical direction of the monitoring point obtained by Beidou differential monitoring; S202, calculating the difference of electric field intensity data between adjacent heights to obtain an electric field intensity gradient sequence; S203, determining a signal drift coefficient in an increasing time interval corresponding to the increasing electric field intensity gradient sequence according to the corresponding increasing wire position data and increasing electric field intensity data in the increasing time interval; S204, when the conductivity change rate is greater than a preset conductivity change rate threshold and the conductor surface temperature is lower than the dew point temperature, multiplying the signal drift coefficient by the ratio of the conductivity change rate to the preset reference rate to obtain a conductive film correction coefficient; S205, obtaining the water film thickness and salt concentration on the surface of the conductor; Specifically, the water film thickness is measured by a capacitive sensor, and the salt concentration is monitored in real time by a conductivity probe. During the measurement process, the temperature and humidity sensor data are used to compensate for environmental parameters, and the thickness is dynamically estimated by establishing a water film evaporation model. The salt concentration measurement uses ion selective electrode technology, combined with a digital signal processing algorithm to eliminate environmental interference, and the long-term stability of the measurement accuracy is ensured by a self-calibration mechanism.

[0082] S206, when the salt concentration is greater than a preset concentration threshold and the water film thickness is less than a preset thickness threshold, obtaining a second wire position data correction value based on a ratio of the salt concentration to a preset reference concentration; Specifically, threshold judgments are made for the measured salt concentration and water film thickness, and measurement fluctuations are eliminated through digital filtering. When the salt concentration exceeds the preset threshold and the water film thickness is lower than the preset threshold, it indicates that a high-concentration thin layer of electrolyte membrane has formed on the surface of the conductor. An adaptive algorithm is used to calculate the ratio of the measured concentration to the reference concentration, and a mapping relationship between the correction amount and the concentration ratio is established. The final position data correction amount is determined through a mathematical model.

[0083] In some embodiments, after executing step S206, the following steps may be further executed, specifically including the following steps: Obtain weather information, visibility data and atmospheric ion concentration data corresponding to the wire location.

[0084] Specifically, basic weather information such as temperature, humidity, and wind speed is obtained through the meteorological station, the visibility meter is used to monitor the atmospheric transparency in real time, and the atmospheric ion concentration is measured by the ion counter. In the data collection process, a distributed sensor network is used to achieve multi-point collaborative monitoring, and the environmental parameter field around the conductor is established through the spatial interpolation algorithm. In the case of salt water fog weather, it is impossible to directly detect the concentration of salt water fog in the surrounding environment. Detecting the visibility in the surrounding environment can indirectly obtain the concentration of salt water fog.

[0085] When the atmospheric ion concentration data exceeds a preset atmospheric ion concentration threshold, it is determined whether a corona discharge signal is detected.

[0086] Specifically, the corona signal detection mechanism is activated when the monitoring value exceeds the preset threshold. The detection process uses ultrasonic sensors and ultraviolet imaging equipment to build a dual-modal detection system, and identifies corona discharge through acoustic and optical feature analysis. The digital signal processing algorithm is used to extract the characteristic parameters of corona discharge, and a corona discharge pattern recognition model based on machine learning is established to achieve accurate discrimination of corona discharge. The corona discharge pattern recognition model is trained by collecting a large amount of labeled corona discharge data. The data contains acoustic and optical characteristic parameters under different weather conditions, pollution levels and voltage levels. The model adopts a deep learning network structure. The input features include ultrasonic signal spectrum, ultraviolet light intensity distribution and time series features, and the output is the type and intensity level of corona discharge. By learning from historical data, the model can accurately identify different forms of corona discharge patterns and quantitatively evaluate the discharge intensity. When encountering severe weather conditions, an adaptive detection strategy needs to be adopted. In rainy and foggy weather, the air ionization degree of UHV lines is significantly increased, so a dynamic threshold adjustment mechanism considering temperature, humidity and air pressure is established to effectively avoid false alarms and erroneous alarms. In a strong electromagnetic interference environment, weak corona discharge signals are identified by setting up multi-stage filtering algorithms and anti-interference technology.

[0087] If a corona discharge signal is detected, the historical electric field intensity data per unit time is obtained.

[0088] Specifically, the electric field intensity data is collected in real time through a high sampling rate electric field sensing device, and a segmented storage strategy is used to establish a time series database. When a corona discharge signal is detected, the data retrieval mechanism is triggered to extract historical data within a specified time window before the discharge moment. The data acquisition process uses a cache mechanism and parallel processing technology to ensure the rapid reading and processing efficiency of large-scale historical data.

[0089] Abnormal electric field strength data corresponding to the corona discharge signal in the historical electric field strength data are eliminated to obtain corrected electric field strength data.

[0090] Specifically, the historical electric field strength data is analyzed in time and frequency by wavelet transform to identify the high-frequency abnormal components caused by corona discharge. The abnormal detection threshold is determined by statistical feature analysis, and the data screening criteria are established in combination with the time characteristics of corona discharge. The abnormal data is smoothed by adaptive filtering algorithm, and the excluded data segments are reconstructed by interpolation algorithm, and finally the corrected electric field strength data sequence is obtained.

[0091] The product of the conductive film correction coefficient and the corrected electric field strength data is used as the wire position data correction amount.

[0092] Specifically, the conductive film correction coefficient reflects the change in the surface state of the conductor, and the electric field strength data is corrected to characterize the influence of the external environment. The two are multiplied to establish a comprehensive correction value. Numerical stability analysis is used in the calculation process to ensure the reliability of the correction value, and the dimensional matching between different physical quantities is ensured through data normalization. A correction value effectiveness evaluation mechanism is established to ensure the rationality of the correction result.

[0093] When the weather information is rainy and foggy and the visibility data is less than the preset visibility threshold, the visibility attenuation rate per unit time is calculated.

[0094] Specifically, meteorological sensors are used to identify rainy and foggy weather conditions, and visibility meters are used to monitor atmospheric transparency in real time. When visibility is lower than the preset threshold, the attenuation rate calculation mechanism is started, and the sliding time window method is used to calculate the rate of change of visibility per unit time. During the calculation process, digital filtering is used to eliminate short-term fluctuations, and the trend change characteristics are determined by statistical analysis methods, and a dynamic calculation model that takes into account weather conditions is established. This dynamic calculation model is trained on visibility data under historical rainy and foggy weather conditions, and includes visibility change characteristics under rainfall of different intensities, fog of different concentrations, and a variety of complex weather conditions. Based on the time series analysis method, the model comprehensively considers the impact of meteorological factors such as precipitation intensity, relative humidity, and temperature on visibility. It can adaptively adjust the calculation parameters according to the real-time weather conditions to achieve accurate prediction of the visibility attenuation rate.

[0095] The wire position data correction amount and the third wire position data correction amount are deducted from the real-time wire position data collected in real time to obtain the corrected wire position data; the third wire position data correction amount is the ratio of the visibility attenuation rate to the preset benchmark attenuation rate.

[0096] In the above steps, an abnormal correction mechanism for electric field strength data is established by monitoring the atmospheric ion concentration and corona discharge phenomenon. When the corona discharge phenomenon is detected, the abnormal values ​​caused by the discharge disturbance are identified and eliminated by analyzing the historical electric field strength data, and a more realistic electric field distribution feature is obtained. The data is corrected in combination with the conductive film characteristics to eliminate the interference of ionospheric disturbances on the wire position monitoring; by introducing the weather visibility monitoring mechanism, a position data correction method based on rainy and foggy weather conditions is established. When the visibility is lower than the threshold, the signal attenuation compensation amount is calculated based on the visibility attenuation rate, and the signal distortion caused by the atmospheric environment change is corrected in a targeted manner, which improves the anti-interference ability of the wire position monitoring, especially in high-voltage ionization environments and rainy and foggy weather. High monitoring accuracy is maintained.

[0097] S207, subtracting the conductor position data correction amount and the second conductor position data correction amount from the real-time conductor position data collected in real time to obtain corrected conductor position data.

[0098] Steps S201-S204, S207 and Figure 1 In the illustrated embodiment, steps S101 - S104 and S106 are similar, and the descriptions of steps S101 - S104 and S106 may be referred to, and will not be repeated here.

[0099] In the above embodiment, a comprehensive data correction method is constructed by combining electric field intensity gradient analysis, conductivity change monitoring and water film salinity characteristic evaluation. In particular, under the special condition of identifying the thinning of water film accompanied by salt precipitation, a dual correction mechanism is used for data compensation, which overcomes the accuracy loss problem of traditional monitoring methods in salty fog weather in coastal areas, improves the adaptability of conductor sway and sag monitoring systems in complex meteorological environments, and provides more reliable monitoring guarantee for the safe operation of transmission lines.

[0100] An exemplary conductor galloping and sag monitoring system 300 provided in an embodiment of the present application is introduced below. Figure 3 Schematic diagram of an exemplary hardware structure of a conductor galloping and sag monitoring system 300 provided in an embodiment of the present application.

[0101] In some embodiments, the conductor sway and sag monitoring system 300 is a computer device or the conductor sway and sag monitoring system 300 includes a computer device. The computer device includes a processor, a memory and a network interface connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data. The network interface of the computer device is used to communicate with other external terminals or servers through a network connection. In some embodiments, the network interface can be a wired network interface, and in some embodiments, the network interface can also be a wireless network interface. When the computer program is executed by the processor, the method in the embodiment of the present application is implemented.

[0102] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0103] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0104] As used in the above embodiments, the term "when..." may be interpreted as "if..." or "after..." or "in response to determining..." or "in response to detecting...", depending on the context. Similarly, the phrases "upon determining..." or "if (the stated condition or event) is detected" may be interpreted as "if determining..." or "in response to determining..." or "upon detecting (the stated condition or event)" or "in response to detecting (the stated condition or event)", depending on the context.

[0105] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented by software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state hard disk), etc.

[0106] Those skilled in the art can understand that to implement all or part of the processes in the above-mentioned embodiments, the processes can be completed by computer programs to instruct related hardware, and the programs can be stored in computer-readable storage media. When the programs are executed, they can include the processes of the above-mentioned method embodiments. The aforementioned storage media include: ROM or random access memory RAM, magnetic disk or optical disk and other media that can store program codes.

Claims

1. A method for monitoring conductor galloping and sag based on Beidou differential analysis, characterized in that: include: The wire position, the rate of change of the wire surface conductivity, the wire surface temperature and the electric field strength data at different heights in the vertical direction of the monitoring point obtained by Beidou differential monitoring are collected in real time; Calculating the difference of the electric field intensity data between adjacent heights to obtain an electric field intensity gradient sequence; In an increasing time interval corresponding to an increasing electric field intensity gradient sequence, a signal drift coefficient is determined according to the increasing wire position data and the increasing electric field intensity data corresponding to the increasing time interval; the increasing electric field intensity gradient sequence is an increasing segment in the electric field intensity gradient sequence; the signal drift coefficient is a ratio of a change in the increasing wire position data to a change in the increasing electric field intensity data; When the conductivity change rate is greater than a preset conductivity change rate threshold and the conductor surface temperature is lower than the dew point temperature, the signal drift coefficient is multiplied by the ratio of the conductivity change rate to the preset reference rate to obtain a conductive film correction coefficient; The product of the conductive film correction coefficient and the real-time electric field strength data collected in real time is used as the correction amount of the wire position data; The conductor position data correction amount is deducted from the real-time conductor position data collected in real time to obtain corrected conductor position data.

2. The method according to claim 1, characterized in that After the product of the conductive film correction coefficient and the real-time electric field strength data collected in real time is used as the wire position data correction amount, the method further includes: Obtain the water film thickness and salt concentration on the conductor surface; When the salt concentration is greater than a preset concentration threshold and the water film thickness is less than a preset thickness threshold, a second wire position data correction amount is obtained based on a ratio of the salt concentration to a preset reference concentration; The wire position data correction amount and the second wire position data correction amount are deducted from the real-time wire position data collected in real time to obtain corrected wire position data.

3. The method according to claim 1, characterized in that The step of determining the signal drift coefficient in the increasing time interval corresponding to the increasing electric field intensity gradient sequence according to the increasing wire position data and the increasing electric field intensity data corresponding to the increasing time interval specifically includes: Obtain the reference traverse position under standard weather conditions; Recording the maximum amplitude point and the actual vibration period in the conductor position within a single preset standard vibration period; the actual vibration period is the time difference between two consecutive arrivals of the conductor at the reference conductor position; Calculating the position deviation between the maximum amplitude and the reference wire position, and the time deviation between the actual vibration period and the standard vibration period, to form a period drift coefficient sequence; Performing Fourier transform on the periodic drift coefficient sequence to obtain a frequency component spectrum; Extracting a frequency component corresponding to the inverse of the actual vibration period from the frequency component spectrum as a drift component; Subtract the drift component from the incremental wire position data to obtain wire position data with periodic drift eliminated; The ratio of the change in the periodic drift elimination wire position data to the change in the incremental electric field strength data is used as the signal drift coefficient.

4. The method according to claim 3, characterized in that The calculating of the position deviation between the maximum amplitude and the reference wire position, and the time deviation between the actual vibration period and the standard vibration period to form a period drift coefficient sequence specifically includes: Analyzing the amplitude distribution of the wire position data to identify the vibration standing wave node positions; Calculating the deviation between the conductor position data at the standing wave node position and the reference conductor position to obtain a node drift coefficient; Calculating the deviation rate of the wire position data at the maximum amplitude point relative to the node drift coefficient as the drift correction coefficient of the non-node area; A preset piecewise continuous function is used to calculate the drift coefficient of the transition area between the standing wave node position and the non-node area; The node drift coefficient, the drift correction coefficient and the transition region drift coefficient are combined to form the periodic drift coefficient sequence.

5. The method according to claim 1, characterized in that After the product of the conductive film correction coefficient and the real-time electric field strength data collected in real time is used as the wire position data correction amount, the method further includes: Obtaining weather information and visibility data corresponding to the wire position; When the weather information is rainy and foggy weather and the visibility data is less than a preset visibility threshold, calculating the visibility attenuation rate per unit time; The wire position data correction amount and the third wire position data correction amount are deducted from the real-time wire position data collected in real time to obtain corrected wire position data; the third wire position data correction amount is the ratio of the visibility attenuation rate to a preset benchmark attenuation rate.

6. The method according to claim 5, characterized in that After obtaining the weather information and visibility data corresponding to the wire position, the method further includes: Acquiring atmospheric ion concentration data corresponding to the conductor position; When the atmospheric ion concentration data exceeds a preset atmospheric ion concentration threshold, determining whether a corona discharge signal is detected; If a corona discharge signal is detected, obtaining historical electric field intensity data within the unit time; Eliminating abnormal electric field strength data corresponding to the corona discharge signal in the historical electric field strength data to obtain corrected electric field strength data; The product of the conductive film correction coefficient and the corrected electric field strength data is used as the wire position data correction amount.

7. The method according to claim 1, characterized in that After determining the signal drift coefficient in the increasing time interval corresponding to the increasing electric field intensity gradient sequence according to the increasing wire position data and the increasing electric field intensity data corresponding to the increasing time interval, the method further includes: Obtaining lightning activity warning information; the lightning activity warning information includes a lightning warning period and a lightning warning area; When the conductor is located in the lightning warning area and the rate of change of the electric field strength data is greater than a preset mutation threshold, marking the real-time period as a pre-disturbance period of lightning activity; Calculate the deviation rate between the electric field strength data before lightning in the early disturbance period of the lightning activity and the electric field strength data before historical lightning, which is the lightning data credibility; When the credibility of the lightning data is lower than a preset credibility threshold, suspending the calculation of the signal drift coefficient; After detecting that the lightning activity has ended, the preset stable waiting time is started, and after the stable waiting time has ended, the calculation of the signal drift coefficient is resumed.

8. A conductor galloping and sag monitoring system, characterized in that: The conductor gallop and sag monitoring system comprises: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code comprises computer instructions, and the one or more processors call the computer instructions to enable the conductor gallop and sag monitoring system to execute the method as described in any one of claims 1-7.

9. A computer program product comprising instructions, characterized in that When the computer program product runs on a conductor galloping and sag monitoring system, the conductor galloping and sag monitoring system is enabled to perform the method according to any one of claims 1 to 7.

10. A computer-readable storage medium comprising instructions, characterized in that: When the instruction is executed on a conductor galloping and sag monitoring system, the conductor galloping and sag monitoring system is enabled to perform the method according to any one of claims 1 to 7.

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