Intelligent icing monitoring method and device for high-voltage transmission line

Abstract

The invention discloses an intelligent icing monitoring method and device for a high-voltage power transmission line, and relates to the technical field of power system monitoring, and the method comprises the steps: positioning a temperature abnormal section when a distributed optical fiber temperature sensing signal is abnormal, obtaining the meteorological humidity information of the temperature abnormal section, and carrying out the icing monitoring when the meteorological humidity information is greater than the icing critical humidity. The method comprises the following steps: generating icing formation threatening early warning information, then starting intelligent diagnosis of a dielectric property sensing signal and a vibration spectrum signal corresponding to a temperature abnormal section to obtain a dielectric constant variation and a vibration spectrum, and generating thin ice risk early warning information based on the dielectric constant variation and the vibration spectrum. And then the axial tension signal of the temperature abnormal section is fused, the icing thickness is determined based on the axial tension signal, thick ice risk early warning information is generated, various kinds of generated early warning information are output to an operation and maintenance decision center, and intelligent decision support is provided for accurate deicing of the high-voltage transmission line. And the direct monitoring accuracy of the icing thickness is improved.

Description

Technical Field

[0001] This application relates to the field of power system monitoring technology, and in particular to an intelligent icing monitoring method and device for high-voltage transmission lines. Background Technology

[0002] The operational demands of high-voltage transmission lines under complex weather conditions are constantly increasing. When ice forms on high-voltage transmission lines, whether it is a solid ice layer or ice cones, it will at least affect power transmission, and in severe cases, it can lead to transmission line breakage or even the collapse of high-voltage towers. Currently, monitoring of icing on high-voltage transmission lines mainly relies on inspection workers along the line, which suffers from low monitoring timeliness and insufficient accuracy.

[0003] The above content is only used to help understand the technical solution of the present invention and does not represent an admission that the above content is prior art. Summary of the Invention

[0004] The main purpose of this application is to provide an intelligent icing monitoring method and device for high-voltage transmission lines, aiming to solve the technical problem of low accuracy in direct monitoring of icing thickness in the prior art.

[0005] To achieve the above objectives, this application provides an intelligent icing monitoring method for high-voltage transmission lines, the method comprising: The system acquires real-time monitoring data from multi-source heterogeneous sensors deployed on high-voltage towers and conductors. The monitoring data includes dielectric property sensing signals, vibration spectrum signals, axial tension signals, and distributed optical fiber temperature sensing signals. When the duration of the distributed optical fiber temperature sensing signal in the phase transition temperature threshold range reaches the target duration, the temperature anomaly section is located, and the meteorological humidity information of the temperature anomaly section is obtained. When the meteorological humidity information is greater than the critical humidity for icing, an early warning information for icing formation is generated. After generating the early warning information for icing formation, intelligent diagnosis of the dielectric property sensing signal and vibration spectrum signal corresponding to the temperature anomaly section is initiated to obtain the change in dielectric constant and the vibration spectrum. When the change in dielectric constant is greater than the static baseline and the vibration spectrum is an icing characteristic mode, thin ice risk warning information is generated. Based on the thin ice risk warning information, the axial tension signal of the temperature anomaly section is fused, the ice thickness is determined based on the axial tension signal, and when the ice thickness is greater than a preset safety threshold, thick ice risk warning information is generated. The early warning information on icing formation, the early warning information on thin ice risk, and the early warning information on thick ice risk are output to the operation and maintenance decision center to provide intelligent decision support for precise de-icing of high-voltage transmission lines.

[0006] In one embodiment, when the duration of the distributed optical fiber temperature sensing signal within the phase transition temperature threshold range reaches a target duration, the step of locating the temperature anomaly segment includes: The distributed optical fiber temperature sensing signal is divided into multiple sliding windows at multiple scales to obtain multiple sliding windows, and the trend features of temperature time series data in each sliding window are extracted. Identify continuous spatial sequences that conform to the precursor features of icing formation in the trend features, and extract the start and end positions and temperature gradient distribution of the continuous spatial sequences; The duration of the temperature gradient distribution within the phase transition temperature threshold range is determined, and the duration is matched with the temperature patterns of historical icing events in a multi-dimensional similarity to obtain the matching degree. When the matching degree exceeds a preset confidence level threshold, the start and end positions are determined to be high-risk areas for icing. By combining the coordinates of the line towers and the fiber optic mileage information, the starting and ending tower numbers corresponding to the high-risk icing area are output, and the temperature anomaly section is located based on the starting and ending tower numbers.

[0007] In one embodiment, the step of generating an early warning message for icing formation when the meteorological humidity information is greater than the critical humidity for icing includes: When the meteorological humidity information is greater than the critical humidity for icing, meteorological forecast information for the temperature anomaly section is obtained; A comprehensive icing meteorological index is generated based on the meteorological humidity information and the meteorological forecast information. When the comprehensive icing meteorological index is greater than the icing index threshold and the duration is greater than the preset time threshold, an early warning information for icing formation is generated based on the comprehensive icing meteorological index.

[0008] In one embodiment, after generating the early warning information for icing formation, the step of initiating intelligent diagnosis of the dielectric property sensing signal and vibration spectrum signal corresponding to the temperature anomaly section to obtain the dielectric constant change and vibration spectrum includes: After generating the early warning information for icing formation, adaptive filtering and baseline calibration are performed on the dielectric property sensing signal corresponding to the temperature anomaly section to extract the dynamic change of dielectric constant related to the icing medium. Simultaneously perform wavelet packet transform and mode decomposition on the vibration spectrum signal corresponding to the temperature anomaly section to separate the characteristic frequency band components; Feature extraction is performed on the characteristic frequency band components to determine the natural frequency shift characteristics, harmonic component enhancement characteristics, and modal damping change characteristics. A high-resolution vibration spectrum is generated based on the natural frequency shift characteristics, harmonic component enhancement characteristics, and modal damping change characteristics. Extract the vibration spectrum from the vibration spectrum diagram.

[0009] In one embodiment, the step of generating thin ice risk warning information when the change in dielectric constant is greater than the static baseline and the vibration spectrum is an icing characteristic mode includes: When the change in dielectric constant is greater than the static baseline, it is determined that a thin ice layer has formed, and the ice type is identified. When the vibration spectrum exhibits high-frequency mode decay and low-frequency energy accumulation characteristics, the icing type is determined to be rime. If the dielectric constant is greater than the static baseline and the vibration spectrum shows multi-order harmonic resonance, then the icing type is determined to be rime ice. The risk level is determined based on the change in dielectric constant and the vibration spectrum, and thin ice risk warning information is generated based on the icing type and the risk level.

[0010] In one embodiment, the step of determining the icing thickness based on the thin ice risk warning information, by fusing the axial tension signal of the temperature anomaly section, includes: Based on the thin ice risk warning information, the axial tension signal of the temperature anomaly section is fused with the thin ice risk warning information to determine the load change of the conductor; Determine the real-time wind speed, wind direction, and ambient temperature, and couple the wind speed, wind direction, and ambient temperature with the load change to obtain a compensation input value. Based on the icing thickness mechanical equation and the compensation input value, the icing thickness is obtained.

[0011] In one embodiment, after the step of obtaining the icing thickness based on the icing thickness mechanical equation and the compensation input value, the method further includes: The dielectric verification thickness and the spectral verification thickness are obtained based on the dielectric property sensing signal and the vibration spectrum signal. The dielectric verification thickness, the spectral verification thickness, and the icing thickness value are cross-verified respectively to obtain the dielectric thickness verification result and the spectral thickness verification result. When the dielectric thickness verification result or the spectral thickness verification result is not within the preset error range, confidence fusion is performed based on the dielectric thickness verification result, the spectral thickness verification result and the icing thickness value to obtain the target icing thickness.

[0012] In one embodiment, the step of generating a thick ice risk warning when the ice thickness exceeds a preset safety threshold includes: When the ice thickness exceeds a preset safety threshold, the current sag, tension, and tower stress state are calculated based on the ice thickness and conductor mechanical properties. The degree of deviation from the preset safety threshold is determined based on the current sag, tension, and tower stress state. The overload risk level and response time are determined based on the degree of deviation and the critical load state. Thick ice risk warning information is generated based on the overload risk level and the processing response time.

[0013] In one embodiment, the step of outputting the early warning information for icing formation, the early warning information for thin ice risk, and the early warning information for thick ice risk to the operation and maintenance decision center includes: The risk levels of the ice formation precursor warning information, the thin ice risk warning information, and the thick ice risk warning information are determined by the graded early warning model. Match the risk level to the case library, and determine the corresponding disposal suggestions and impact range assessment reports for the ice formation precursor warning information, the thin ice risk warning information and the thick ice risk warning information from the case library; The proposed handling measures and the impact assessment report will be output to the Operation and Maintenance Decision Center.

[0014] Furthermore, to achieve the above objectives, this application also proposes an intelligent icing monitoring device for high-voltage transmission lines, which includes: The data acquisition module is used to acquire real-time monitoring data from multi-source heterogeneous sensors deployed on high-voltage towers and conductors. The monitoring data includes dielectric property sensing signals, vibration spectrum signals, axial tension signals, and distributed optical fiber temperature sensing signals. The icing early warning module is used to locate the temperature anomaly section when the duration of the distributed optical fiber temperature sensing signal in the phase transition temperature threshold range reaches the target duration, and to obtain the meteorological humidity information of the temperature anomaly section. When the meteorological humidity information is greater than the critical humidity for icing, it generates an icing formation precursor warning message. The thin ice early warning module is used to initiate intelligent diagnosis of the dielectric property sensing signal and vibration spectrum signal corresponding to the temperature anomaly section after generating the early warning information of ice formation, to obtain the change in dielectric constant and vibration spectrum. When the change in dielectric constant is greater than the static baseline and the vibration spectrum is an ice formation characteristic mode, thin ice risk early warning information is generated. The thick ice warning module is used to integrate the axial tension signal of the temperature anomaly section based on the thin ice risk warning information, determine the ice thickness based on the axial tension signal, and generate thick ice risk warning information when the ice thickness is greater than a preset safety threshold. The early warning output module is used to output the early warning information of the ice formation precursor, the early warning information of the thin ice risk, and the early warning information of the thick ice risk to the operation and maintenance decision center, so as to provide intelligent decision support for the precise de-icing of high-voltage transmission lines.

[0015] Furthermore, to achieve the above objectives, this application also proposes an intelligent icing monitoring device for high-voltage transmission lines. The intelligent icing monitoring device for high-voltage transmission lines includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. The computer program is configured to implement the steps of the intelligent icing monitoring method for high-voltage transmission lines as described above.

[0016] In addition, to achieve the above objectives, the present invention also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the steps of the intelligent icing monitoring method for high-voltage transmission lines as described above.

[0017] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the intelligent icing monitoring method for high-voltage transmission lines as described above.

[0018] This application provides an intelligent icing monitoring method for high-voltage transmission lines. When a distributed optical fiber temperature sensor signal shows an anomaly, it locates the temperature anomaly section and acquires the meteorological humidity information of that section. When the meteorological humidity exceeds the critical humidity for icing, it generates an early warning message for icing formation. Then, it initiates intelligent diagnosis of the dielectric characteristic sensing signal and vibration spectrum signal corresponding to the temperature anomaly section, obtaining the change in dielectric constant and the vibration spectrum. Based on the change in dielectric constant and the vibration spectrum, it generates a thin ice risk warning message. Next, it fuses the axial tension signal of the temperature anomaly section, determines the icing thickness based on the axial tension signal, and generates a thick ice risk warning message. All generated warning messages are output to the operation and maintenance decision center, providing intelligent decision support for precise de-icing of high-voltage transmission lines. This improves the accuracy of direct monitoring of icing thickness. Attached Figure Description

[0019] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0020] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart illustrating an embodiment of the intelligent icing monitoring method for high-voltage transmission lines according to this application. Figure 2 This is a schematic diagram of a high-voltage transmission line according to an embodiment of the intelligent icing monitoring method for high-voltage transmission lines in this application; Figure 3 This is a flowchart illustrating the icing type determination process of an embodiment of the intelligent icing monitoring method for high-voltage transmission lines according to this application. Figure 4 This is a frost spectrum diagram of an embodiment of the intelligent icing monitoring method for high-voltage transmission lines according to this application; Figure 5 This is a spectrum diagram of rime ice from one embodiment of the intelligent icing monitoring method for high-voltage transmission lines according to this application. Figure 6 This is a schematic diagram of the module structure of an intelligent icing monitoring device for high-voltage transmission lines according to an embodiment of this application; Figure 7 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the intelligent icing monitoring method for high-voltage transmission lines in the embodiments of this application.

[0022] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0023] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0024] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0025] The main solution of this application embodiment is to acquire real-time monitoring data from multi-source heterogeneous sensors deployed on high-voltage towers and conductors. The monitoring data includes dielectric property sensing signals, vibration spectrum signals, axial tension signals, and distributed optical fiber temperature sensing signals. When the duration of the distributed optical fiber temperature sensing signal in the phase transition temperature threshold range reaches the target duration, the temperature anomaly section is located, and the meteorological humidity information of the temperature anomaly section is obtained. When the meteorological humidity information is greater than the critical humidity for icing, an early warning information for icing formation is generated. After generating the early warning information for icing formation, intelligent diagnosis of the dielectric property sensing signal and vibration spectrum signal corresponding to the temperature anomaly section is initiated to obtain the change in dielectric constant and the vibration spectrum. When the change in dielectric constant is greater than the static baseline and the vibration spectrum is an icing characteristic mode, thin ice risk warning information is generated. Based on the thin ice risk warning information, the axial tension signal of the temperature anomaly section is fused, the ice thickness is determined based on the axial tension signal, and when the ice thickness is greater than a preset safety threshold, thick ice risk warning information is generated. The early warning information on icing formation, the early warning information on thin ice risk, and the early warning information on thick ice risk are output to the operation and maintenance decision center to provide intelligent decision support for precise de-icing of high-voltage transmission lines.

[0026] Currently, the demand for high-voltage transmission lines operating under complex weather conditions is constantly increasing. When ice forms on high-voltage transmission lines, whether it is a solid ice layer or ice cones, it will at least affect power transmission, and in severe cases, it can lead to transmission line breakage or even the collapse of high-voltage towers. Currently, monitoring of icing on high-voltage transmission lines mainly relies on inspection workers along the lines, which suffers from low monitoring timeliness and insufficient accuracy.

[0027] This application provides a solution that, when a distributed optical fiber temperature sensing signal shows anomalies, locates the temperature anomaly section and acquires the meteorological humidity information of the anomaly section. When the meteorological humidity exceeds the critical humidity for icing, an early warning of icing formation is generated. Then, intelligent diagnosis of the dielectric characteristic sensing signal and vibration spectrum signal corresponding to the temperature anomaly section is initiated to obtain the change in dielectric constant and vibration spectrum. Based on the change in dielectric constant and vibration spectrum, a thin ice risk warning is generated. Next, the axial tension signal of the temperature anomaly section is fused to determine the icing thickness and generate a thick ice risk warning. All generated warning information is output to the operation and maintenance decision center, providing intelligent decision support for precise de-icing of high-voltage transmission lines. This improves the accuracy of direct monitoring of icing thickness.

[0028] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device capable of performing the above functions, such as an intelligent icing monitoring device for high-voltage transmission lines. This embodiment does not specifically limit this. The following uses an intelligent icing monitoring device for high-voltage transmission lines as an example to describe this embodiment and the following embodiments.

[0029] All actions involving the acquisition of signals, information, or data in this application are carried out in accordance with the relevant data protection laws and policies of the country where the application is located, and with the authorization of the owner of the relevant device.

[0030] This application provides an intelligent icing monitoring method for high-voltage transmission lines, referring to... Figure 1 , Figure 1This is a flowchart illustrating the first embodiment of the intelligent icing monitoring method for high-voltage transmission lines according to this application.

[0031] In this embodiment, the intelligent icing monitoring method for high-voltage transmission lines includes steps S10 to S40: Step S10: Acquire real-time monitoring data from multi-source heterogeneous sensors deployed on high-voltage towers and conductors. The monitoring data includes dielectric property sensing signals, vibration spectrum signals, axial tension signals, and distributed optical fiber temperature sensing signals. It should be noted that multi-source heterogeneous sensors refer to a collection of various types of sensors deployed at different locations along high-voltage transmission lines, operating based on different physical principles. Monitoring data refers to raw physical signals or pre-converted digital quantities collected in real time by various sensors, reflecting the line's operating status and environmental conditions. Dielectric property sensing signals refer to electrical signals measured by capacitive icing sensors, reflecting changes in the dielectric constant of insulating media (such as air, ice, and water), used to identify the icing or water accumulation on the conductor surface. Vibration spectrum signals refer to the frequency domain characteristic signals obtained after Fourier transform or wavelet analysis of raw conductor vibration data collected by high-frequency accelerometers. Axial tension signals refer to real-time mechanical tension data along the conductor's axial direction, measured by tension sensors installed at tower suspension points or on the conductor. Distributed fiber optic temperature sensing signals refer to temperature distribution data over the entire continuous space of the line obtained by demodulating backscattered light signals using communication optical fibers laid parallel to the conductor as distributed temperature sensors.

[0032] In the specific implementation, refer to Figure 2 , Figure 2 This is a schematic diagram of a high-voltage transmission line. Multiple sensors of various types can be deployed on the high-voltage towers, and distributed optical fibers are simultaneously laid along the transmission line. Data related to the line, high-voltage towers, and environment are then acquired based on the sensors and distributed optical fibers, including but not limited to dielectric property sensing signals, vibration spectrum signals, axial tension signals, and distributed optical fiber temperature sensing signals. Specifically, capacitive icing sensors output dielectric property sensing signals to capture changes in the dielectric constant caused by icing; high-frequency accelerometers output vibration spectrum signals to analyze changes in modal parameters caused by icing; tension sensors output axial tension signals to monitor changes in the mechanical load on the conductors; and the distributed optical fiber temperature measurement system generates continuous spatial temperature distribution data by analyzing the backscattered light signal from the optical fiber.

[0033] Step S20: When the duration of the distributed optical fiber temperature sensing signal in the phase transition temperature threshold range reaches the target duration, locate the temperature abnormal section and obtain the meteorological humidity information of the temperature abnormal section. When the meteorological humidity information is greater than the critical humidity for icing, generate an early warning information for icing formation. It should be noted that the phase transition temperature threshold range generally refers to the critical temperature range corresponding to the phase transition of a substance. In this embodiment, the phase transition temperature range specifically refers to the critical temperature range of the phase transition process from water to ice, which is usually close to but slightly below 0°C, for example, the temperature range from -2°C to 0.5°C. Within this temperature range, the surface of the conductor has the thermodynamic conditions for icing. The critical humidity for icing refers to the lowest relative humidity threshold in the environment that can cause icing on the surface of the conductor. It is a historical empirical value or statistical value, such as 85% or 90%. When the ambient humidity exceeds this critical humidity threshold for icing, supercooled water droplets or water vapor in the air have the conditions to condense and freeze on the surface of the conductor.

[0034] In its implementation, the distributed fiber optic temperature sensing signals are first monitored and spatiotemporally analyzed in real time. When a section's temperature is consistently within the phase transition temperature threshold range (e.g., -2°C to 0.5°C) for a duration exceeding a preset threshold (e.g., 30 minutes), that section is marked as a temperature anomaly. Subsequently, meteorological sensors deployed near that section or the regional meteorological monitoring network are automatically invoked to obtain its current humidity data. By comparing the real-time humidity value with the critical humidity for icing, if the humidity consistently exceeds the critical value, it is comprehensively determined that the meteorological conditions for icing are mature, thereby generating an early warning message for icing formation with a clear location, time, and probability assessment.

[0035] In one feasible implementation, when the duration of the distributed optical fiber temperature sensing signal within the phase transition temperature threshold range reaches a target duration, the step of locating the temperature anomaly segment includes: The distributed optical fiber temperature sensing signal is divided into multiple sliding windows at multiple scales to obtain multiple sliding windows, and the trend features of temperature time series data in each sliding window are extracted. Identify continuous spatial sequences that conform to the precursor features of icing formation in the trend features, and extract the start and end positions and temperature gradient distribution of the continuous spatial sequences; The duration of the temperature gradient distribution within the phase transition temperature threshold range is determined, and the duration is matched with the temperature patterns of historical icing events in a multi-dimensional similarity to obtain the matching degree. When the matching degree exceeds a preset confidence level threshold, the start and end positions are determined to be high-risk areas for icing. By combining the coordinates of the line towers and the fiber optic mileage information, the starting and ending tower numbers corresponding to the high-risk icing area are output, and the temperature anomaly section is located based on the starting and ending tower numbers.

[0036] In the specific implementation, the distributed fiber optic temperature sensing signal is segmented using a multi-scale sliding window. During implementation, windows of different time lengths can be used to segment the temperature time-series data. The window can be set according to the time span, such as a short window of 15 minutes, a medium window of 1 hour, and a long window of 4 hours, to capture the temperature change trend from transient to long-term. Trend features, including the linear fitting slope, are extracted from the data within each window. .

[0037]

[0038] in, The number of data points within the window. For timestamps, This is the temperature value.

[0039] Then, based on the extracted trend features, continuous spatial sequences that conform to the precursor features of icing formation are identified. These features are usually manifested as a sustained and stable temperature within the phase transition temperature threshold range, and spatial coherence; therefore, in this embodiment, they are represented by a temperature gradient distribution.

[0040]

[0041] in, For position Temperature gradient at that location, For position The temperature at that location This refers to the spatial spacing between adjacent sensing units.

[0042] By identifying continuous segments with small gradients and temperatures consistently below a threshold, the duration of temperature gradient distribution within the phase transition temperature threshold range is determined. This duration is then matched with historical icing time and temperature patterns using multi-dimensional similarity analysis to obtain the matching degree. Specifically:

[0043] Where A is the current temperature sequence and B is the historical icing pattern sequence. For time-ordered paths.

[0044] If the final calculated matching degree Exceeding the preset credit level threshold If the starting and ending positions are determined, the section between them is identified as a high-risk area for icing. Finally, by combining the preset line tower coordinates and fiber optic mileage information, the high-risk area is mapped to its actual physical location, and its corresponding starting and ending tower numbers are output, thereby completing the precise location of the temperature anomaly section.

[0045] In one feasible implementation, the step of generating an early warning message for icing formation when the meteorological humidity information is greater than the critical humidity for icing includes: When the meteorological humidity information is greater than the critical humidity for icing, meteorological forecast information for the temperature anomaly section is obtained; A comprehensive icing meteorological index is generated based on the meteorological humidity information and the meteorological forecast information. When the comprehensive icing meteorological index is greater than the icing index threshold and the duration is greater than the preset time threshold, an early warning information for icing formation is generated based on the comprehensive icing meteorological index.

[0046] In its implementation, the process of generating early warning information for icing formation can be described as follows: This process is automatically triggered when the real-time meteorological humidity information of a certain temperature anomaly zone exceeds the critical humidity for icing. First, short-term meteorological forecast information for the area where the temperature anomaly zone is located is obtained through a data interface. This information typically comes from professional meteorological services or built-in micro-meteorological forecasting models, including key data such as the probability of precipitation type, temperature change trends, and wind speed and direction for the area in the next few hours. Then, the system integrates the real-time monitored humidity data with the acquired meteorological forecast information to generate a comprehensive Icing Meteorological Index (IMI) to quantify the overall risk of icing caused by current and recent meteorological conditions.

[0047]

[0048] in, This is the current real-time measured relative humidity value. This is the critical humidity threshold for icing. The probability of freezing rain events occurring in the near future, used for weather forecasting. The conductor temperature measured in the current distributed optical fiber. The current dew point temperature is obtained from meteorological information. This is the phase transition temperature threshold. These are weighting coefficients used to adjust the contribution of each component, and they satisfy... , It is a very small constant used to prevent the denominator from being zero. The slope coefficient of the S-shaped function controls the sensitivity when the temperature approaches the threshold.

[0049] The IMI index is continuously calculated. When the IMI value exceeds the preset icing index threshold and the duration of this state exceeds the preset time threshold, it is determined that the icing precursor conditions are fully met.

[0050] Step S30: After generating the early warning information for icing formation, intelligent diagnosis of the dielectric property sensing signal and vibration spectrum signal corresponding to the temperature anomaly section is initiated to obtain the change in dielectric constant and the vibration spectrum. When the change in dielectric constant is greater than the static baseline and the vibration spectrum is an icing characteristic mode, thin ice risk warning information is generated. It should be noted that thin ice risk warning information refers to structured alarm information generated when a thin ice layer has formed on the surface of a conductor, based on multi-sensor data fusion analysis. This information typically includes the location of the ice layer, the current estimated thickness, the type of ice layer, the risk level, the formation time, and its evolution trend.

[0051] Understandably, generating an early warning message for icing formation indicates a potential for icing. Therefore, an intelligent diagnostic process can be automatically initiated, involving the sensing of dielectric properties and vibration spectrum signals for the corresponding temperature anomaly zone. First, the dielectric signal is filtered and calibrated, extracting its change relative to a baseline value under dry conditions to characterize the accumulation of icing. Simultaneously, the vibration signal undergoes time-frequency transformation and mode decomposition to identify typical modes such as frequency shifts, increased harmonics, or altered damping characteristics caused by icing. When the diagnostic results simultaneously satisfy the condition that the change in dielectric constant exceeds the static baseline threshold and the vibration spectrum has a high degree of matching with the icing characteristic mode library, the system determines that a thin ice layer has formed. It then automatically generates and sends a thin ice risk warning message containing spatial location, preliminary thickness inversion, ice type identification, and risk level assessment results.

[0052] In one feasible implementation, after generating the early warning information for icing formation, the step of initiating intelligent diagnosis of the dielectric property sensing signal and vibration spectrum signal corresponding to the temperature anomaly section to obtain the dielectric constant change and vibration spectrum includes: After generating the early warning information for icing formation, adaptive filtering and baseline calibration are performed on the dielectric property sensing signal corresponding to the temperature anomaly section to extract the dynamic change of dielectric constant related to the icing medium. Simultaneously perform wavelet packet transform and mode decomposition on the vibration spectrum signal corresponding to the temperature anomaly section to separate the characteristic frequency band components; Feature extraction is performed on the characteristic frequency band components to determine the natural frequency shift characteristics, harmonic component enhancement characteristics, and modal damping change characteristics. A high-resolution vibration spectrum is generated based on the natural frequency shift characteristics, harmonic component enhancement characteristics, and modal damping change characteristics. Extract the vibration spectrum from the vibration spectrum diagram.

[0053] In the specific implementation, upon receiving the early warning information of icing formation, the processing of the dielectric property sensing signal corresponding to the specified temperature anomaly section is immediately initiated. This signal is susceptible to interference from the complex electromagnetic environment on site; therefore, an adaptive filtering algorithm is used to suppress noise in real time. Its weight update formula can be expressed as:

[0054] in Here is the filter weight vector. Step size factor For error signals, The input signal vector is given. After filtering, the signal needs to undergo dynamic baseline calibration to eliminate the effects of sensor drift and slow environmental changes, and to extract the dynamic change in dielectric constant purely caused by the icing medium.

[0055]

[0056] here This is a real-time capacitance measurement value. The current temperature The reference capacitance value of the lower drying conductor.

[0057] Simultaneously, the system performs time-frequency analysis on the vibration acceleration signal of this section, and decomposes the signal using wavelet packet transform, with the general formula as follows:

[0058] in It is a wavelet packet function. and These are the scaling and translation parameters, respectively. Wavelet packet transform can decompose a signal into different frequency bands without redundancy or omission, thereby precisely separating the characteristic frequency band components caused by icing.

[0059] Subsequently, deep feature extraction is performed on the separated characteristic frequency band components, and the inherent frequency shift characteristics are determined by tracking the relative change of the peak frequency of the spectrum relative to the historical baseline. The ratio of the energy of specific harmonics (such as the second and third harmonics) to the fundamental frequency energy is calculated to determine the harmonic component enhancement characteristics, and the damping ratio is estimated using Hilbert transform or logarithmic attenuation method to determine the modal damping variation characteristics. Based on these characteristics, the system generates a high-resolution time-frequency vibration spectrum map, which comprehensively reflects the changes in frequency, energy, and damping with time and space. Finally, key vibration spectrum feature vectors for icing detection are extracted from this spectrum.

[0060] In one feasible implementation, the step of generating thin ice risk warning information when the change in dielectric constant is greater than the static baseline and the vibration spectrum is an icing characteristic mode includes: When the change in dielectric constant is greater than the static baseline, it is determined that a thin ice layer has formed, and the ice type is identified. When the vibration spectrum exhibits high-frequency mode decay and low-frequency energy accumulation characteristics, the icing type is determined to be rime. If the dielectric constant is greater than the static baseline and the vibration spectrum shows multi-order harmonic resonance, then the icing type is determined to be rime ice. The risk level is determined based on the change in dielectric constant and the vibration spectrum, and thin ice risk warning information is generated based on the icing type and the risk level.

[0061] In the specific implementation, refer to Figure 3 , Figure 3 The flowchart illustrates the icing type determination process. First, the presence of icing is assessed. When the detected change in dielectric constant exceeds the static baseline threshold determined based on historical dry conditions, a thin layer of ice is considered to have formed on the conductor surface, and icing type identification is initiated. Icing type identification primarily distinguishes between rime and frost. Rime is formed by the impact and freezing of tiny supercooled water droplets, resulting in a loose and porous structure. Its vibration spectrum characteristics show significant energy attenuation in high-frequency modes (typically more than three times the fundamental frequency), while energy accumulates significantly in low-frequency bands (especially within the 1-3 times fundamental frequency range). The energy ratio between the high-frequency and low-frequency bands is calculated. If this ratio is below an empirical threshold and the low-frequency energy continues to increase, the icing type is determined to be rime. Frost is formed by the impact and spreading freezing of supercooled raindrops, resulting in a hard and dense structure that significantly alters the conductor's stiffness and mass distribution. Its vibration spectrum characteristics show obvious multi-harmonic resonance peaks. The determination is made by detecting the presence of clear harmonics with energy significantly higher than the background noise in the spectrum. Additionally, hard frost and ice layers also cause a more significant change in dielectric constant. (Refer to...) Figure 4 Figure 5 . Figure 4 This is a spectrum diagram of rime ice. Figure 5 This is a spectrum diagram of rime ice. After type identification is completed, the risk level assessment stage will begin. Risk Level Determined by a fusion function, which expresses the change in dielectric constant. Total energy of harmonic waves As the core input parameter.

[0062]

[0063] in, These are weighting coefficients used to balance the contributions of dielectric and vibrational characteristics. This is a reference energy value. Based on the calculated... The value falls into a preset risk range. Combined with the identified icing types, a structured thin ice risk warning is finally generated.

[0064] Step S40: Based on the thin ice risk warning information, the axial tension signal of the temperature abnormal section is fused, the ice thickness is determined based on the axial tension signal, and when the ice thickness is greater than a preset safety threshold, thick ice risk warning information is generated.

[0065] It should be noted that the axial tension signal refers to the physical signal collected by a tension sensor installed at the suspension point of the high-voltage conductor or ground wire, reflecting the tensile force on the conductor along its axial direction. This signal directly characterizes the comprehensive mechanical load borne by the conductor.

[0066] In its implementation, the system is activated after a thin ice risk warning is triggered. First, it reads the real-time axial tension signal data corresponding to the temperature anomaly section indicated by the warning. After preprocessing the tension signal to remove transient fluctuations caused by wind vibration and temperature changes, the system inputs it into an icing load inversion model built based on mechanical principles. This model, combining conductor parameters, span, real-time environmental wind speed, and temperature data, calculates the additional load generated by icing, thereby accurately estimating the current average icing thickness. This thickness value is then continuously compared to a preset safety threshold. Once the icing thickness is detected to continuously exceed the safety threshold, a thick ice risk warning is immediately generated, including the location of the exceedance, the current thickness, the degree of exceedance, and anticipated risks such as excessive sag, wire breakage, and tower collapse.

[0067] In one feasible implementation, the step of determining the icing thickness based on the thin ice risk warning information, by fusing the axial tension signal of the temperature anomaly section, includes: Based on the thin ice risk warning information, the axial tension signal of the temperature anomaly section is fused with the thin ice risk warning information to determine the load change of the conductor; Determine the real-time wind speed, wind direction, and ambient temperature, and couple the wind speed, wind direction, and ambient temperature with the load change to obtain a compensation input value. Based on the icing thickness mechanical equation and the compensation input value, the icing thickness is obtained.

[0068] In practice, based on the generated thin ice risk warning information, the temperature anomaly sections that require key monitoring are first identified, and the real-time measurement values ​​of the conductor axial tension sensor within those sections are read. Subsequently, the system performs precise calculations of load changes. The total tension of the conductor is contributed by multiple factors, the core of which is isolating the additional load caused by icing. This is achieved by querying the baseline tension value of the line under icy, standard weather conditions. Taking into account the thermal expansion and contraction effect of the conductor caused by changes in ambient temperature, the tension change was initially calculated.

[0069]

[0070] in, is the coefficient of linear expansion of the conductor. Baseline tension The corresponding reference temperature.

[0071] The data also includes the impact of wind load. To achieve accurate compensation, data from miniature weather stations deployed on poles can be accessed to obtain real-time wind speeds. and wind direction The load generated by wind on the conductor Calculated according to aerodynamic formulas:

[0072] in, air density, The resistance coefficient of the conductor. The diameter of the conductor (including the estimated thickness of thin ice). This is the span length. This is a coupling term because the conductor diameter... It depends on the thickness of the ice cover.

[0073] To more accurately determine the icing thickness, the net tension change, after removing the effects of wind load and temperature, can be used. Substituting into the mechanical equation for icing thickness, its basic form is:

[0074] in, The density of ice, It is the acceleration due to gravity. Let be the equivalent icing thickness to be solved. This represents the original diameter of the conductor. Based on this, the precise ice thickness value can be determined.

[0075] In one feasible implementation, after the step of obtaining the icing thickness based on the icing thickness mechanical equation and the compensation input value, the method further includes: The dielectric verification thickness and the spectral verification thickness are obtained based on the dielectric property sensing signal and the vibration spectrum signal. The dielectric verification thickness, the spectral verification thickness, and the icing thickness value are cross-verified respectively to obtain the dielectric thickness verification result and the spectral thickness verification result. When the dielectric thickness verification result or the spectral thickness verification result is not within the preset error range, confidence fusion is performed based on the dielectric thickness verification result, the spectral thickness verification result and the icing thickness value to obtain the target icing thickness.

[0076] In the specific implementation, the dielectric property sensing signal and vibration spectrum signal, which have been processed in the previous intelligent diagnostic process, are first used to derive independent thickness estimates, namely dielectric verification thickness and spectrum verification thickness, through their respective established calibration models. The dielectric verification thickness is mainly derived based on the mapping relationship between the change in dielectric constant and the ice layer thickness; while the spectrum verification thickness is calculated based on the physical correlation model between vibration modal parameters and ice mass / thickness.

[0077] Subsequently, a cross-validation mechanism is initiated, comparing the primary ice thickness value calculated based on the axial tension signal with the two independent verification thicknesses mentioned above. This process calculates the relative deviation between them and determines whether the deviation is within a preset error range determined based on sensor accuracy and historical data statistics, thereby generating dielectric thickness verification results and spectral thickness verification results respectively.

[0078] When any verification result exceeds the preset tolerance range, the source model of each thickness value is comprehensively considered under the current meteorological conditions, historical accuracy and signal quality, and the main ice thickness value and the verification thickness value are weighted and fused to calculate the final target ice thickness.

[0079] In one feasible implementation, the step of generating a thick ice risk warning when the ice thickness exceeds a preset safety threshold includes: When the ice thickness exceeds a preset safety threshold, the current sag, tension, and tower stress state are calculated based on the ice thickness and conductor mechanical properties. The degree of deviation from the preset safety threshold is determined based on the current sag, tension, and tower stress state. The overload risk level and response time are determined based on the degree of deviation and the critical load state. Thick ice risk warning information is generated based on the overload risk level and the processing response time.

[0080] In practical implementation, after determining that the icing thickness exceeds the preset safety threshold, the advanced mechanical state analysis module is first activated. This module uses the accurately inverted icing thickness as its core input, combined with the conductor's inherent mechanical properties, span, and real-time ambient temperature. Through a built-in catenary equation or finite element simulation model, it calculates in real-time key mechanical state quantities of the conductor under the current ice load, such as the current sag, overall tension, and unbalanced tension on both sides of the tower. Subsequently, these calculated real-time state quantities are compared with the preset safety threshold, quantitatively calculating the degree of deviation for each indicator. This degree of deviation comprehensively reflects the current danger level of the line.

[0081] Based on this deviation, further risk assessment is performed. The core of this process is to compare the current state with the critical load state, and comprehensively consider the predicted trend of continuous icing increase. Through an evaluation engine based on a rule base or machine learning model, the system automatically determines a quantified overload risk level and simultaneously estimates the expected response time for reaching the next more dangerous state or for a failure, providing a clear time window for operational decisions.

[0082] Finally, by integrating all the analysis results, a structured thick ice risk warning message is generated. This message not only includes over-limit alarms, but also provides detailed information such as the location of the hazardous section, current mechanical state data, risk level, suggested response time, and recommended handling strategies, such as immediate ice melting, load transfer, or emergency inspection.

[0083] Step S50: Output the early warning information of icing formation, the early warning information of thin ice risk, and the early warning information of thick ice risk to the operation and maintenance decision center to provide intelligent decision support for precise de-icing of high-voltage transmission lines.

[0084] It should be noted that the operation and maintenance decision center refers to the centralized intelligent command platform in the power system operation and maintenance system, which is the central hub for intelligent control and emergency command of line status.

[0085] Understandably, through integrated communication modules, the early warning information on icing formation precursors, thin ice risk, and thick ice risk generated in the early stages is transmitted in real time to the operation and maintenance decision-making center according to standardized data protocols and formats. The decision-making center's data platform receives and parses this information, then deeply integrates it with the power grid topology, real-time operating mode, meteorological information, and emergency plan database. Subsequently, through a visual human-machine interface, the spatial distribution, severity level, and evolution trend of icing risks are intuitively displayed to dispatching and operation and maintenance personnel. For each warning message, intelligent correlation or automatic generation of specific handling suggestions is possible. For example, for a precursor warning, the suggestion is "increase the frequency of video patrols"; for a thin ice warning, the prompt is "prepare DC de-icing devices"; and for a thick ice warning, a direct alarm may be issued stating "Immediate de-icing needs to be initiated in section XX, with an estimated operation time window of 30 minutes." This provides strong intelligent support for operation and maintenance personnel to formulate precise and efficient de-icing strategies, achieving closed-loop management from "monitoring" to "decision-making" to "execution."

[0086] In one feasible implementation, the step of outputting the early warning information for icing formation, the early warning information for thin ice risk, and the early warning information for thick ice risk to the operation and maintenance decision center includes: The risk levels of the ice formation precursor warning information, the thin ice risk warning information, and the thick ice risk warning information are determined by the graded early warning model. Match the risk level to the case library, and determine the corresponding disposal suggestions and impact range assessment reports for the ice formation precursor warning information, the thin ice risk warning information and the thick ice risk warning information from the case library; The proposed handling measures and the impact assessment report will be output to the Operation and Maintenance Decision Center.

[0087] In its implementation, the three types of early warning information are first input into the hierarchical early warning model. Based on multiple dimensions such as early warning type, monitoring data confidence level, meteorological condition deterioration trend, and icing thickness, a quantitative risk level (e.g., "Attention," "Early Warning," "Danger," or levels 1-5) is dynamically calculated and assigned to each early warning using a built-in rule engine or lightweight machine learning classifier. After determining the risk level, the system uses this as the core keyword to intelligently match it against a historical case library. This case library is a continuously updated knowledge base that stores environmental parameters, response measures, and response effects of icing events over the years. The matching process uses a similarity calculation algorithm to find similar records between the current early warning and historical cases in terms of risk level, spatial location, meteorological conditions, and icing characteristics. It then automatically extracts the most appropriate response suggestions that have been verified in practice (e.g., "activate DC de-icing device," "adjust operating mode," or "strengthen inspection of specific sections"). Simultaneously, it generates a potential impact range assessment report based on the current power grid topology and load conditions, predicting potentially affected lines or areas. Ultimately, the structured early warning information is bound to the disposal suggestions and impact assessment reports generated by its intelligent matching. Through standardized data interfaces and communication protocols, these are pushed to the visualization platform of the operation and maintenance decision-making center, providing dispatch and operation personnel with full-chain information support from "risk perception" to "decision suggestions," significantly improving the accuracy and efficiency of responding to ice-covered disasters.

[0088] This embodiment provides an intelligent icing monitoring method for high-voltage transmission lines. When a distributed optical fiber temperature sensor signal shows an anomaly, it locates the temperature anomaly section and acquires the meteorological humidity information of that section. When the meteorological humidity exceeds the critical humidity for icing, an early warning of icing formation is generated. Then, intelligent diagnosis of the dielectric characteristic sensing signal and vibration spectrum signal corresponding to the temperature anomaly section is initiated to obtain the change in dielectric constant and the vibration spectrum. Based on the change in dielectric constant and the vibration spectrum, a thin ice risk warning is generated. Next, the axial tension signal of the temperature anomaly section is fused to determine the icing thickness, generating a thick ice risk warning. All generated warnings are output to the operation and maintenance decision center, providing intelligent decision support for precise de-icing of high-voltage transmission lines. This improves the accuracy of direct monitoring of icing thickness.

[0089] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the intelligent icing monitoring method for high-voltage transmission lines. Any simple modifications based on this technical concept are within the protection scope of this application.

[0090] This application also provides an intelligent icing monitoring device for high-voltage transmission lines, please refer to... Figure 6 The intelligent icing monitoring device for high-voltage transmission lines includes: Data acquisition module 10 is used to acquire real-time monitoring data from multi-source heterogeneous sensors deployed on high-voltage towers and conductors. The monitoring data includes dielectric property sensing signals, vibration spectrum signals, axial tension signals, and distributed optical fiber temperature sensing signals. The icing warning module 20 is used to locate the temperature anomaly section when the duration of the distributed optical fiber temperature sensing signal in the phase transition temperature threshold range reaches the target duration, and to obtain the meteorological humidity information of the temperature anomaly section. When the meteorological humidity information is greater than the critical humidity for icing, it generates an icing formation precursor warning message. Thin ice warning module 30 is used to initiate intelligent diagnosis of dielectric property sensing signal and vibration spectrum signal corresponding to the temperature abnormality section after generating the early warning information of ice formation, to obtain the change in dielectric constant and vibration spectrum, and to generate thin ice risk warning information when the change in dielectric constant is greater than the static baseline and the vibration spectrum is the characteristic mode of ice formation. Thick ice warning module 40 is used to integrate the axial tension signal of the temperature abnormal section based on the thin ice risk warning information, determine the ice thickness based on the axial tension signal, and generate thick ice risk warning information when the ice thickness is greater than a preset safety threshold. The early warning output module 50 is used to output the early warning information of the icing formation precursor, the early warning information of the thin ice risk, and the early warning information of the thick ice risk to the operation and maintenance decision center, so as to provide intelligent decision support for the precise de-icing of high-voltage transmission lines.

[0091] In one feasible implementation, the icing warning module 20 is further used to perform multi-scale sliding window segmentation on the distributed optical fiber temperature sensing signal to obtain multiple sliding windows, and extract the trend features of temperature time series data within each sliding window. Identify continuous spatial sequences that conform to the precursor features of icing formation in the trend features, and extract the start and end positions and temperature gradient distribution of the continuous spatial sequences; The duration of the temperature gradient distribution within the phase transition temperature threshold range is determined, and the duration is matched with the temperature patterns of historical icing events in a multi-dimensional similarity to obtain the matching degree. When the matching degree exceeds a preset confidence level threshold, the start and end positions are determined to be high-risk areas for icing. By combining the coordinates of the line towers and the fiber optic mileage information, the starting and ending tower numbers corresponding to the high-risk icing area are output, and the temperature anomaly section is located based on the starting and ending tower numbers.

[0092] In one feasible implementation, the icing warning module 20 is further configured to acquire meteorological forecast information for the temperature anomaly zone when the meteorological humidity information is greater than the critical humidity for icing. A comprehensive icing meteorological index is generated based on the meteorological humidity information and the meteorological forecast information. When the comprehensive icing meteorological index is greater than the icing index threshold and the duration is greater than the preset time threshold, an early warning information for icing formation is generated based on the comprehensive icing meteorological index.

[0093] In one feasible implementation, the thin ice warning module 30 is further configured to, after generating the ice formation precursor warning information, perform adaptive filtering and baseline calibration on the dielectric property sensing signal corresponding to the temperature anomaly section, and extract the dynamic change of dielectric constant related to the ice-covering medium. Simultaneously perform wavelet packet transform and mode decomposition on the vibration spectrum signal corresponding to the temperature anomaly section to separate the characteristic frequency band components; Feature extraction is performed on the characteristic frequency band components to determine the natural frequency shift characteristics, harmonic component enhancement characteristics, and modal damping change characteristics. A high-resolution vibration spectrum is generated based on the natural frequency shift characteristics, harmonic component enhancement characteristics, and modal damping change characteristics. Extract the vibration spectrum from the vibration spectrum diagram.

[0094] In one feasible implementation, the thin ice warning module 30 is further configured to determine that a thin ice layer has formed when the change in dielectric constant is greater than the static baseline, and to identify the type of icing. When the vibration spectrum exhibits high-frequency mode decay and low-frequency energy accumulation characteristics, the icing type is determined to be rime. If the dielectric constant is greater than the static baseline and the vibration spectrum shows multi-order harmonic resonance, then the icing type is determined to be rime ice. The risk level is determined based on the change in dielectric constant and the vibration spectrum, and thin ice risk warning information is generated based on the icing type and the risk level.

[0095] In one feasible implementation, the thick ice warning module 40 is further configured to fuse the axial tension signal of the temperature anomaly section with the thin ice risk warning information based on the thin ice risk warning information to determine the load change of the conductor. Determine the real-time wind speed, wind direction, and ambient temperature, and couple the wind speed, wind direction, and ambient temperature with the load change to obtain a compensation input value. Based on the icing thickness mechanical equation and the compensation input value, the icing thickness is obtained.

[0096] In one feasible implementation, the thick ice warning module 40 is further configured to obtain the dielectric verification thickness and the spectrum verification thickness based on the dielectric property sensing signal and the vibration spectrum signal. The dielectric verification thickness, the spectral verification thickness, and the icing thickness value are cross-verified respectively to obtain the dielectric thickness verification result and the spectral thickness verification result. When the dielectric thickness verification result or the spectral thickness verification result is not within the preset error range, confidence fusion is performed based on the dielectric thickness verification result, the spectral thickness verification result and the icing thickness value to obtain the target icing thickness.

[0097] In one feasible implementation, the thick ice warning module 40 is further configured to calculate the current sag, tension and tower stress state based on the ice thickness and conductor mechanical properties when the ice thickness is greater than a preset safety threshold, and determine the degree of deviation from the preset safety threshold based on the current sag, the tension and the tower stress state. The overload risk level and response time are determined based on the degree of deviation and the critical load state. Thick ice risk warning information is generated based on the overload risk level and the processing response time.

[0098] In one feasible implementation, the early warning output module 50 is further configured to determine the corresponding risk levels of the ice formation precursor early warning information, the thin ice risk early warning information, and the thick ice risk early warning information through a graded early warning model. Match the risk level to the case library, and determine the corresponding disposal suggestions and impact range assessment reports for the ice formation precursor warning information, the thin ice risk warning information and the thick ice risk warning information from the case library; The proposed handling measures and the impact assessment report will be output to the Operation and Maintenance Decision Center.

[0099] The intelligent icing monitoring device for high-voltage transmission lines provided in this application adopts the intelligent icing monitoring method for high-voltage transmission lines in the above embodiments, which can solve the technical problem of low accuracy in direct monitoring of icing thickness. Compared with the prior art, the beneficial effects of the intelligent icing monitoring device for high-voltage transmission lines provided in this application are the same as those of the intelligent icing monitoring method for high-voltage transmission lines provided in the above embodiments, and other technical features in the intelligent icing monitoring device for high-voltage transmission lines are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0100] This application provides an intelligent icing monitoring device for high-voltage transmission lines. The intelligent icing monitoring device for high-voltage transmission lines includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the intelligent icing monitoring method for high-voltage transmission lines in the above embodiment 1.

[0101] The following is for reference. Figure 7 This document illustrates a structural schematic diagram of an intelligent icing monitoring device for high-voltage transmission lines suitable for implementing embodiments of this application. The intelligent icing monitoring device for high-voltage transmission lines in this application embodiment may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), vehicle terminals (e.g., vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 7 The intelligent icing monitoring device for high-voltage transmission lines shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0102] like Figure 7 As shown, the intelligent icing monitoring device for high-voltage transmission lines may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in ROM (Read Only Memory) 1002 or a program loaded from storage device 1003 into RAM (Random Access Memory) 1004. RAM 1004 also stores various programs and data required for the operation of the intelligent icing monitoring device for high-voltage transmission lines. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via bus 1005. Input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, LCDs (Liquid Crystal Displays), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the intelligent icing monitoring equipment for high-voltage transmission lines to exchange data wirelessly or via wired communication with other devices. Although the figure shows intelligent icing monitoring equipment for high-voltage transmission lines with various systems, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems can be implemented alternatively.

[0103] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0104] The intelligent icing monitoring device for high-voltage transmission lines provided in this application employs the intelligent icing monitoring method for high-voltage transmission lines described in the above embodiments, and can solve the technical problem of intelligent icing monitoring for high-voltage transmission lines. Compared with the prior art, the beneficial effects of the intelligent icing monitoring device for high-voltage transmission lines provided in this application are the same as those of the intelligent icing monitoring method for high-voltage transmission lines provided in the above embodiments, and other technical features of the intelligent icing monitoring device for high-voltage transmission lines are the same as those disclosed in the method of the previous embodiment, and will not be repeated here.

[0105] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0106] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0107] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the intelligent icing monitoring method for high-voltage transmission lines in the above embodiments.

[0108] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, RAM (Random Access Memory), ROM (Read Only Memory), EPROM (Erasable Programmable Read Only Memory or Flash Memory), optical fibers, CD-ROM (CD-Read Only Memory), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0109] The aforementioned computer-readable storage medium may be included in an intelligent icing monitoring device for high-voltage transmission lines; or it may exist independently and not assembled into an intelligent icing monitoring device for high-voltage transmission lines.

[0110] The aforementioned computer-readable storage medium carries one or more programs that, when executed by an intelligent icing monitoring device for high-voltage transmission lines, cause the intelligent icing monitoring device for high-voltage transmission lines to: acquire real-time monitoring data collected by multi-source heterogeneous sensors deployed on high-voltage towers and conductors, the monitoring data including dielectric property sensing signals, vibration spectrum signals, axial tension signals, and distributed optical fiber temperature sensing signals; when the duration of the distributed optical fiber temperature sensing signal being within the phase transition temperature threshold range reaches a target duration, locate the temperature anomaly section and acquire the meteorological humidity information of the temperature anomaly section; when the meteorological humidity information is greater than the critical humidity for icing, generate an early warning information for icing formation; After generating the early warning information for icing formation, intelligent diagnosis of the dielectric property sensing signal and vibration spectrum signal corresponding to the temperature anomaly section is initiated to obtain the change in dielectric constant and the vibration spectrum. When the change in dielectric constant is greater than the static baseline and the vibration spectrum is an icing characteristic mode, a thin ice risk warning information is generated. Based on the thin ice risk warning information, the axial tension signal of the temperature anomaly section is fused, and the icing thickness is determined based on the axial tension signal. When the icing thickness is greater than a preset safety threshold, a thick ice risk warning information is generated. The early warning information for icing formation, the thin ice risk warning information, and the thick ice risk warning information are output to the operation and maintenance decision center to provide intelligent decision support for precise de-icing of high-voltage transmission lines.

[0111] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including LAN (Local Area Network) or WAN (Wide Area Network)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0112] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0113] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0114] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described intelligent icing monitoring method for high-voltage transmission lines, thereby solving the technical problem of intelligent icing monitoring for high-voltage transmission lines. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the intelligent icing monitoring method for high-voltage transmission lines provided in the above embodiments, and will not be repeated here.

[0115] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the intelligent icing monitoring method for high-voltage transmission lines as described above.

[0116] The computer program product provided in this application can solve the technical problem of intelligent icing monitoring for high-voltage transmission lines. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the intelligent icing monitoring method for high-voltage transmission lines provided in the above embodiments, and will not be repeated here.

[0117] The above are only some embodiments of this application and do not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A method for intelligent ice monitoring of high voltage transmission lines, characterized in that, The intelligent icing monitoring method for high-voltage transmission lines comprises: obtaining real-time collected monitoring data of multi-source heterogeneous sensors deployed on high-voltage line towers and conductors, the monitoring data comprising dielectric property perception signals, vibration spectrum signals, axial tension signals and distributed optical fiber temperature sensing signals; when the duration of the distributed optical fiber temperature sensing signals in the phase transition temperature threshold interval reaches a target duration, locating a temperature abnormal section and obtaining meteorological humidity information of the temperature abnormal section, and when the meteorological humidity information is greater than an icing critical humidity, generating icing formation precursor warning information; after generating the icing formation precursor warning information, starting intelligent diagnosis of the dielectric property perception signals and vibration spectrum signals corresponding to the temperature abnormal section to obtain a dielectric constant change and a vibration spectrum, and when the dielectric constant change is greater than a static baseline and the vibration spectrum is an icing characteristic mode, generating thin ice risk warning information; based on the thin ice risk warning information, fusing the axial tension signals of the temperature abnormal section, determining an icing thickness based on the axial tension signals, and when the icing thickness is greater than a preset safety threshold, generating thick ice risk warning information; outputting the icing formation precursor warning information, the thin ice risk warning information and the thick ice risk warning information to an operation and maintenance decision center to provide intelligent decision support for accurate deicing of high-voltage transmission lines.

2. The method of claim 1, wherein, The step of locating the temperature abnormal section when the duration of the distributed optical fiber temperature sensing signals in the phase transition temperature threshold interval reaches a target duration comprises: performing multi-scale sliding window segmentation on the distributed optical fiber temperature sensing signals to obtain a plurality of sliding windows, and extracting trend features of temperature time series data in each sliding window; identifying continuous spatial sequences in the trend features that conform to icing formation precursor characteristics, and extracting the start and end positions and temperature gradient distribution of the continuous spatial sequences; determining the duration of the temperature gradient distribution in the phase transition temperature threshold interval, and performing multi-dimensional similarity matching of the duration with historical icing event temperature patterns to obtain a matching degree; when the matching degree exceeds a preset signal level threshold, determining that the start and end positions are icing high-risk areas; combining line tower coordinates and optical fiber mileage information to output the start and end tower numbers corresponding to the icing high-risk areas, and locating the temperature abnormal section based on the start and end tower numbers.

3. The method of claim 1, wherein, The step of generating icing formation precursor warning information when the meteorological humidity information is greater than an icing critical humidity comprises: when the meteorological humidity information is greater than an icing critical humidity, obtaining meteorological prediction information of the temperature abnormal section; generating a comprehensive icing meteorological index according to the meteorological humidity information and the meteorological prediction information; when the comprehensive icing meteorological index is greater than an icing index threshold and the duration is greater than a preset time threshold, generating icing formation precursor warning information according to the comprehensive icing meteorological index.

4. The method of claim 1, wherein, The step of generating icing formation precursor warning information after generating the icing formation precursor warning information, starting intelligent diagnosis of the dielectric property perception signals and vibration spectrum signals corresponding to the temperature abnormal section to obtain a dielectric constant change and a vibration spectrum comprises: After the icing formation precursor warning information is generated, the dielectric property sensing signal corresponding to the temperature anomaly section is adaptively filtered and baseline calibrated to extract a dynamic change amount of dielectric constant related to icing medium; The vibration frequency spectrum signal corresponding to the temperature anomaly section is synchronously subjected to wavelet packet transform and modal decomposition to separate a characteristic frequency band component; The characteristic frequency band component is subjected to feature extraction to determine inherent frequency offset features, harmonic component enhancement features and modal damping change features, and a high-resolution vibration frequency spectrum is generated according to the inherent frequency offset features, harmonic component enhancement features and modal damping change features; A vibration frequency spectrum is extracted from the vibration frequency spectrum.

5. The method of claim 1, wherein, When the dielectric constant change amount is greater than the static baseline and the vibration frequency spectrum is an icing characteristic modal, generating thin-ice risk warning information includes: When the dielectric constant change amount is greater than the static baseline, it is determined that a thin-ice layer has been formed, and an icing type is identified; When the dielectric constant is greater than the static baseline and the vibration frequency spectrum presents high-frequency modal attenuation and low-frequency energy aggregation features, it is determined that the icing type is rime; When the dielectric constant is greater than the static baseline and the vibration frequency spectrum presents multi-order harmonic resonance, it is determined that the icing type is glaze; A risk level is determined according to the dielectric constant change amount and the vibration frequency spectrum, and thin-ice risk warning information is generated according to the icing type and the risk level.

6. The method of claim 1, wherein, Based on the thin-ice risk warning information, the axial tension signal of the temperature anomaly section is fused, and the icing thickness is determined based on the axial tension signal, which includes: Based on the thin-ice risk warning information, the axial tension signal of the temperature anomaly section is fused with the thin-ice risk warning information to determine the load change of the conductor; Real-time wind speed, wind direction and environmental temperature are determined, and the wind speed, wind direction and environmental temperature are coupled and compensated with the load change to obtain a compensation input value, and the icing thickness is obtained based on an icing thickness mechanical equation and the compensation input value.

7. The method of claim 6, wherein, After the icing thickness is obtained based on the icing thickness mechanical equation and the compensation input value, the method further includes: A dielectric verification thickness and a frequency spectrum verification thickness are obtained from the dielectric property sensing signal and the vibration frequency spectrum signal; The dielectric verification thickness, the frequency spectrum verification thickness and the icing thickness value are cross-verified respectively to obtain a dielectric thickness verification result and a frequency spectrum thickness verification result; When the dielectric thickness verification result or the frequency spectrum thickness verification result is not within a preset error range, a confidence fusion is performed based on the dielectric thickness verification result, the frequency spectrum thickness verification result and the icing thickness value to obtain a target icing thickness.

8. The method of claim 1, wherein, When the icing thickness is greater than a preset safety threshold, generating thick-ice risk warning information includes: When the icing thickness is greater than the preset safety threshold, the current sag, tension and tower stress state are calculated according to the icing thickness and conductor mechanical properties, and the deviation degree from the preset safety threshold is determined according to the current sag, the tension and the tower stress state; The overload risk level and the processing response time are determined according to the deviation degree and the critical load state; Generate thick ice risk early warning information according to the overload risk level and the processing response time.

9. The method of claim 1, wherein, The step of outputting the ice formation precursor early warning information, the thin ice risk early warning information and the thick ice risk early warning information to the operation and maintenance decision center comprises: Determine the corresponding risk levels of the ice formation precursor early warning information, the thin ice risk early warning information and the thick ice risk early warning information through a hierarchical early warning model. Match the risk levels with a case library to determine the corresponding treatment suggestions and impact range assessment reports of the ice formation precursor early warning information, the thin ice risk early warning information and the thick ice risk early warning information from the case library. Output the treatment suggestions and the impact range assessment reports to the operation and maintenance decision center.

10. An intelligent icing monitoring device for high voltage power lines, characterized in that, The intelligent ice monitoring device for high-voltage transmission lines comprises: A data acquisition module for acquiring real-time monitoring data collected by multi-source heterogeneous sensors deployed on high-voltage line towers and conductors, the monitoring data including dielectric property sensing signals, vibration spectrum signals, axial tension signals and distributed optical fiber temperature sensing signals; An ice early warning module for locating a temperature abnormal section when the duration of the distributed optical fiber temperature sensing signals in a phase transition temperature threshold interval reaches a target duration, and acquiring meteorological humidity information of the temperature abnormal section, and generating ice formation precursor early warning information when the meteorological humidity information is greater than an ice formation critical humidity; A thin ice early warning module for starting intelligent diagnosis of dielectric property sensing signals and vibration spectrum signals corresponding to the temperature abnormal section after the ice formation precursor early warning information is generated, obtaining a dielectric constant change and a vibration spectrum, and generating thin ice risk early warning information when the dielectric constant change is greater than a static baseline and the vibration spectrum is an ice characteristic mode; A thick ice early warning module for fusing axial tension signals of the temperature abnormal section based on the thin ice risk early warning information, determining ice thickness based on the axial tension signals, and generating thick ice risk early warning information when the ice thickness is greater than a preset safety threshold; An early warning output module for outputting the ice formation precursor early warning information, the thin ice risk early warning information and the thick ice risk early warning information to an operation and maintenance decision center to provide intelligent decision support for precise deicing of high-voltage transmission lines.

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