An icing galloping test device and method for an electrified railway catenary under a natural wind field

Abstract

The application discloses an electrified railway catenary icing galloping test device and method under natural wind field, comprising: collecting multi-modal data; three-dimensional reconstruction of image data is carried out through a stereo vision algorithm to obtain three-dimensional space displacement time history of a marker point; filtering processing and time domain integration are carried out on acceleration signals of catenary icing galloping to obtain speed data and displacement data at the position of an acceleration sensor (A4); according to the three-dimensional space displacement time history of the marker point and the speed data and displacement data at the position of the acceleration sensor (A4), characteristic parameters of the catenary icing galloping are extracted through spectrum analysis, peak value calculation and phase difference analysis; according to the characteristic parameters, a critical wind speed interval and a critical wind direction angle range causing the catenary icing galloping are identified; the application reconstructs the three-dimensional space displacement time history of multiple positions of the catenary, and provides accurate full-field measurement data and quantitative analysis basis for catenary icing galloping mechanism research and disaster warning.

Description

Technical Field

[0001] This application relates to the field of high-speed railway catenary, and in particular to a test device and method for ice-covered galloping of electrified railway catenary under natural wind conditions. Background Technology

[0002] Ice accumulation and galloping of the overhead contact system on electrified railways is one of the core issues threatening the safe operation of high-speed railways. With my country's high-speed rail network exceeding 48,000 kilometers and covering complex climate and terrain regions such as high-altitude and mountainous areas, the probability of ice accumulation on the overhead contact system has significantly increased in low-temperature rain and snow. Ice accumulation leads to uneven mechanical loads and aerodynamic shape of the contact system, resulting in tension imbalance, wire deformation and sagging, and ultimately, conductor galloping. This can cause mechanical impact between the pantograph and the contact system, leading to arcing, wire breakage, power outages, and even train service disruptions. For example, in early 2024, ice accumulation and galloping caused delays or cancellations on multiple high-speed rail lines in North and Central China, severely impacting the Spring Festival travel rush and affecting 140 million passengers during the 2024 Spring Festival travel season.

[0003] However, the complex mechanism of catenary icing and galloping is not yet fully understood. Research on this topic needs to integrate multiple disciplines such as meteorology, materials mechanics, fluid dynamics, pantograph-catenary dynamics, and intelligent monitoring. By revealing the correlation between galloping and icing type, wind speed and direction, and conductor structural parameters, we can promote the construction of an active defense system for catenary icing and galloping. For example, we can develop icing early warning systems based on digital twins and efficient de-icing devices to reduce the risks of manual de-icing and improve the railway's disaster prevention capabilities.

[0004] The current research methods have the following limitations: (1) Distortion in indoor test simulation: It is difficult for laboratory wind tunnels to generate turbulent fields that match real terrain, and it is impossible to reproduce the turbulent characteristics of natural wind fields. In addition, the scaled-down model of the contact network ignores the dynamic coupling effect of conductor stiffness and tension. (2) Limited accuracy of numerical simulation: The calculation accuracy of simulation methods such as the finite element method depends entirely on the accuracy of the input parameters, especially the aerodynamic coefficients and wind field models of the icing contact network. At the same time, the simulation model itself needs to be verified by comparison with high-quality, high-fidelity real physical world test data to confirm its reliability, which is precisely what the existing technology cannot provide.

[0005] In summary, the existing technological system presents a cyclical dilemma: the data generated by wind tunnel tests lacks authenticity, and this distorted data is used to verify incomplete numerical models, failing to provide a basis for in-depth analysis of physical mechanisms. Therefore, a novel testing system and method are urgently needed to overcome these technological bottlenecks and provide reliable scientific evidence for the study of the icing mechanism of overhead contact lines and the development of disaster prevention technologies. Summary of the Invention

[0006] To address the difficulty in measuring the full-field spatial displacement of the overhead contact line under icing conditions in existing technologies, this application provides an experimental device and method for testing the icing and galloping of the overhead contact line of electrified railways under natural wind conditions. This method reconstructs the three-dimensional spatial displacement time history of multiple locations on the overhead contact line, providing accurate full-field measurement data and quantitative analysis basis for the study of the icing and galloping mechanism of the overhead contact line and for disaster early warning.

[0007] One aspect of this application provides a test device for ice-covered galloping of an electrified railway contact network under natural wind conditions, comprising: a contact network A1, including: a catenary, a contact wire, a dropper, an additional conductor, an insulator, and a support; with at least 5 spans; an ice-covered model A2, set on the catenary, contact wire, or additional conductor of the contact network; the ice-covered model is manufactured using additive manufacturing technology to reproduce the three-dimensional geometric shape and physical density of the ice; an anemometer A3, installed on the windward side of the contact network, for measuring the velocity component and wind direction angle of the incoming wind; an accelerometer A4, employing an accelerometer sensor, installed at the mid-span, quarter-span point, locator clamp, or dropper position of the catenary and contact wire, respectively, for measuring the acceleration signal of the ice-covered galloping of the contact network; and a CCD area array camera A5, for acquiring image data of marked points at different locations on the contact network and reconstructing the three-dimensional spatial displacement time history of the marked points using a stereo vision algorithm;

[0008] Furthermore, it also includes: a GPS timing module A13, used to provide a time reference based on the UTC standard and output a 1PPS synchronization pulse signal; a synchronization pulse generator A12, which receives and generates multiple synchronization trigger signals based on the 1PPS synchronization pulse signal; a multi-channel data acquisition unit A6, connected to the synchronization pulse generator, anemometer, and accelerometer, receiving the synchronization trigger signal, synchronously acquiring all sensor data, and synchronizing all acquired data according to the time reference; a signal and power transmission line A7, connecting the multi-channel data acquisition unit to each sensor for transmitting sensor signals; and a host computer A8, which receives and stores the sensor data acquired by the multi-channel data acquisition unit, processes the acquired data, and extracts the galloping characteristics including galloping frequency, amplitude, mode, and phase.

[0009] Another aspect of this application provides a test method for ice-covered galloping of the overhead contact line of an electrified railway under natural wind conditions, comprising: S1, acquiring multimodal data, including the velocity component and wind direction angle data of the incoming wind, the acceleration signal of the ice-covered galloping of the overhead contact line, and image data of the marked points of the overhead contact line; S2, performing three-dimensional reconstruction of the image data using a stereo vision algorithm to obtain the three-dimensional spatial displacement time history of the marked points; S3, filtering and integrating the acceleration signal of the ice-covered galloping of the overhead contact line to obtain the velocity data at the location where the accelerometer sensor A4 is installed. Displacement data; S4, based on the three-dimensional spatial displacement time history of the marked points, and the velocity and displacement data at the location where the accelerometer sensor A4 is installed, the characteristic parameters of the contact wire icing galloping are extracted through spectrum analysis, peak value calculation, and phase difference analysis; the characteristic parameters include galloping frequency, galloping amplitude, galloping mode, and phase information; S5, based on the characteristic parameters, and the velocity component and wind direction angle data of the incoming wind, the wind field-galloping response correlation analysis is performed through time alignment and cross-correlation analysis to identify the critical wind speed range and critical wind direction angle range that cause contact wire icing galloping.

[0010] Further, in S2, the three-dimensional spatial displacement time history of the marker points is obtained, including: distortion correction and coordinate system calibration of the multi-angle image sequence acquired by the CCD area array camera A5; identification and tracking of the two-dimensional pixel coordinates of each marker point in the image sequence using the digital image correlation method (DIC); triangulation of the two-dimensional coordinates of the same marker point acquired by different cameras according to the stereo vision algorithm to reconstruct the three-dimensional spatial coordinates of the marker point; and obtaining the three-dimensional spatial displacement time history of the marker points by performing differential calculation on the three-dimensional spatial coordinates of adjacent time points.

[0011] Further, in step S3, the velocity and displacement data at the location where the accelerometer sensor A4 is installed are obtained, including: performing low-pass filtering on the acquired acceleration signal to eliminate high-frequency noise; performing a first time-domain integration on the filtered acceleration signal to obtain a preliminary velocity time history at the location where the accelerometer sensor A4 is installed; performing high-pass filtering on the preliminary velocity time history to remove low-frequency drift components generated during the integration process to obtain corrected velocity data, which is used as the velocity data at the location where the accelerometer sensor A4 is installed; and performing a second time-domain integration on the corrected velocity data to obtain the displacement data at the location where the accelerometer sensor A4 is installed.

[0012] Further, in step S4, characteristic parameters of the contact wire icing galloping are extracted, including: performing spectral analysis on the three-dimensional spatial displacement time history and displacement data of the marked points using fast Fourier transform to identify the peak frequency in the power spectral density curve and obtain the galloping frequency; calculating the peak values ​​of the three-dimensional spatial displacement time history and displacement data, and statistically analyzing the displacement-peak distribution of each measuring point to obtain the galloping amplitude; calculating the vibration amplitude and phase information of each measuring point on the contact wire and catenary to obtain the galloping mode of the contact wire icing; and extracting the instantaneous phase using Hilbert transform based on the displacement and velocity data, and calculating the phase difference between different measuring points, between different directions, and between displacement and velocity to obtain the phase information.

[0013] Furthermore, the galloping frequency is obtained by performing Fast Fourier Transform (FFT) on the vertical, lateral, and longitudinal displacement components in the three-dimensional spatial displacement time history of the marked points, as well as the vertical, lateral, and longitudinal displacement components in the displacement data at the location where the accelerometer sensor A4 is installed, to obtain the power spectral density curves for each measurement point in each direction. In the power spectral density curves, frequency points with power spectral density values ​​greater than three times the background noise are searched and identified as peak frequencies. Among all identified peak frequencies, the frequency corresponding to the maximum power spectral density value is determined as the main frequency of the contact wire icing galloping. The peak frequencies corresponding to the next highest power spectral density values ​​are sorted from largest to smallest and determined as the harmonic frequencies of the contact wire icing galloping. The main frequency and the harmonic frequencies are used as the galloping frequency.

[0014] Furthermore, the gobble amplitude is obtained by: extracting the displacement response components of each measuring point within a preset range of the main frequency using a bandpass filter, based on the vertical, lateral, and longitudinal displacement components in the three-dimensional spatial displacement time history of the marked points, and the vertical, lateral, and longitudinal displacement components in the displacement data at the location where the accelerometer sensor A4 is installed; performing envelope analysis on the extracted displacement response components in each direction, calculating the analytic signal of each displacement response component using Hilbert transform, and extracting the magnitude of the analytic signal as the envelope; calculating the peak-to-peak displacement values ​​of each measuring point in the vertical, lateral, and longitudinal directions based on the envelope; taking the maximum peak-to-peak displacement value among all measuring points as the maximum gobble amplitude; calculating the arithmetic mean of the peak-to-peak displacement values ​​of all measuring points as the average gobble amplitude; and taking the maximum gobble amplitude and the average gobble amplitude as the gobble amplitude.

[0015] Furthermore, the galloping modes of the contact wire icing are obtained, including: extracting measuring points on the contact wire and the catenary; performing a fast Fourier transform on the vertical displacement components of the three-dimensional spatial displacement time history of each measuring point to obtain the phase angle; calculating the difference between the phase angles of the contact wire and the catenary as the phase difference of the corresponding measuring point pair; calculating the ratio of the lateral direction to the vertical direction of all measuring points based on the peak-to-peak displacement to obtain the lateral-vertical amplitude ratio; when the absolute value of the phase difference is less than 30°, the galloping mode is determined to be a vertical galloping mode; when the absolute value of the phase difference is greater than 150°, the galloping mode is determined to be a torsional galloping mode; when the lateral-vertical amplitude ratio is greater than 1.5, the galloping mode is determined to be a lateral swinging mode.

[0016] Compared to existing technologies, the advantages of this application are:

[0017] To address the challenges in measuring the full-field spatial displacement of overhead contact line icing, the time asynchrony of data from multiple sensors, inaccurate extraction of galloping characteristic parameters, and the lack of quantification of the time delay between wind field and galloping response in existing technologies, this application provides a test device and method for galloping of electrified railway overhead contact line icing under natural wind fields. It achieves high-precision time synchronization of multi-sensor data through GPS timing, reconstructs the three-dimensional spatial displacement time history of multiple locations on the overhead contact line using a CCD area array camera combined with stereo vision algorithms, integrates accelerometer data, and accurately extracts the frequency, amplitude, modal, and phase characteristic parameters of the galloping through spectrum analysis, Hilbert transform, and cross-correlation analysis. It also quantifies the time delay from wind field excitation to galloping response, establishes a time-delay-corrected wind field-galloping synchronization correspondence, and accurately identifies the critical wind speed range and critical wind direction angle range under different galloping modes. This provides accurate full-field measurement data and quantitative analysis basis for the study of overhead contact line icing galloping mechanisms and disaster early warning. Attached Figure Description

[0018] This application will be further described by way of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting; in these embodiments, the same reference numerals denote the same structures, wherein:

[0019] Figure 1 This is a schematic diagram of the architecture of an electrified railway catenary ice-covered galloping test device under natural wind field, according to some embodiments of this application;

[0020] Figure 2 This is an exemplary flowchart of a catenary icing and galloping test method according to some embodiments of this application;

[0021] Figure 3 These are images of ice-covered overhead contact lines dancing according to some embodiments of this application;

[0022] Figure 4The time-domain curves of the contact wire icing and dancing shown in some embodiments of this application are as follows.

[0023] Explanation of the labels in the diagram:

[0024] Contact wire, A1; Contact wire icing model, A2; Anemometer, A3; Accelerometer, A4; CCD area array camera, A5; Multi-channel data acquisition instrument, A6; Signal transmission line, A7; Host computer, A8; Power supply unit, A9; Remote monitoring device, A10; Wireless network card and related devices, A11; Synchronous pulse generator, A12; GPS timing module, A13; Data processing software, A14. Detailed Implementation

[0025] The methods and systems provided in the embodiments of this application will now be described in detail with reference to the accompanying drawings.

[0026] Example 1

[0027] This application aims to address the fundamental deficiencies in the reproduction of catenary icing and galloping behavior in existing technologies by providing a full-scale test system and method for catenary icing and galloping under natural wind conditions in electrified railways. This system constructs a test platform with physical properties highly consistent with real catenary lines and integrates a precise measurement system, thereby enabling the study of catenary icing and galloping phenomena under natural wind excitation within a controllable and repeatable framework.

[0028] A test device for icing and galloping of electrified railway overhead contact lines under natural wind conditions comprises the following modules: Overhead contact line A1; Overhead contact line icing model A2; Anemometer A3; Accelerometer A4; CCD area array camera A5; Multi-channel data acquisition unit A6; Signal transmission line A7; Host computer A8; Power supply device A9 for powering the sensors, data acquisition unit, and host computer, including photovoltaic panels and batteries; Remote monitoring device A10; Wireless network card and related devices for remote signal transmission A11; Synchronization pulse generator A12; GPS timing module A13; Data processing software for signal acquisition and analysis A14. Figure 1 As shown.

[0029] The experimental apparatus and method provided in this application comprise six tightly coupled subsystems:

[0030] (1) Physical Reproduction Subsystem: A full-size electrified railway catenary entity with multiple spans (5 spans or more). The catenary is erected using components (caten wire, contact wire, droppers, additional conductors, insulators, etc.) that meet operational standards and construction parameters (including supports, foundations, cantilever arms, conductor tension, span, structural height, etc.) to accurately reproduce the dynamic characteristics of the real catenary, such as mass, stiffness, damping distribution, and boundary conditions.

[0031] (2) Icing Simulation Subsystem: One or more artificial icing models. The icing model is made by additive manufacturing process (such as 3D printing) to ensure a highly accurate replication of the complex aerodynamic shape (including irregular protrusions and ice ridges) and surface roughness of real icing. At the same time, by adjusting the formulation of the manufacturing material, its density can be matched with that of a specific type of ice (such as rime and frost).

[0032] (3) Spatiotemporal Synchronous Multimodal Sensing Subsystem: A sensor network distributed along the catenary. This network includes, but is not limited to, three-dimensional ultrasonic anemometers, non-contact three-dimensional displacement measurement systems (CCD camera arrays based on digital image correlation), high-sensitivity accelerometers, tension sensors, and distributed optical fibers. This subsystem aims to capture key physical quantities during the gyratory process in a comprehensive and multi-dimensional manner, including but not limited to wind speed, wind direction, catenary amplitude, catenary acceleration, and tension changes.

[0033] (4) Data Acquisition Subsystem: A central data acquisition unit. This subsystem provides a unified time reference for all measurement channels in the sensor network, realizes synchronous acquisition of all sensors through hardware triggering, and marks all data streams with a unified UTC timestamp.

[0034] (5) Photovoltaic functional subsystem: The system uses a combination of photovoltaic panels and batteries to power the host computer, sensors, data acquisition instrument and host computer, ensuring the effective operation of the system in areas with scarce power.

[0035] (6) Remote data transmission subsystem: A cellular network is used to construct a remote monitoring device and a wireless data transmission device to meet the needs of remote monitoring and data transmission.

[0036] A test device and method for ice-covered galloping of electrified railway overhead contact lines under natural wind conditions, characterized by comprising the following steps, such as... Figure 2 As shown:

[0037] Step 1: Trial Planning and System Deployment

[0038] Site selection: Based on the research objective (reproduction of catenary icing galloping behavior), a geographical location with representative meteorological conditions was selected. The site should be open to minimize the interference of topography on the natural dominant wind field; at the same time, the number of days with an annual cumulative wind speed of not less than 8 m / s should be greater than 90 days to ensure sufficient natural wind excitation conditions.

[0039] Contact line design and construction: Based on the principles described in the patent, design and construct a full-size physical replica of the contact line with no fewer than five spans. Precisely set key parameters such as span, tension, structural height, and anchoring method to ensure consistency with the dynamic characteristics of the target operating line.

[0040] Sensor network layout: Sensor installation locations are designed based on analysis requirements. A 3D ultrasonic anemometer is installed on the windward side of the middle measurement span, at a height no lower than the contact wire, to capture undisturbed incoming wind information. Along the measurement span, at key locations such as the mid-span, quarter-span points, locator points, and dropper clamps, accelerometers and CCD camera target points are deployed in pairs on the catenary and contact wire to capture the multi-mode and traveling wave characteristics of galloping. Optical fibers are laid along the contact wire and additional conductors to capture the multi-point dynamic response of the contact network system when icing galloping occurs.

[0041] System Integration and Debugging: Install and integrate all subsystems, including power supply, data acquisition, time synchronization, and remote communication modules. Perform end-to-end debugging of the entire system, focusing on verifying the accuracy of the time synchronization module to ensure that the time deviation between channels meets the design requirement of less than 0.1ms.

[0042] Step 2: Preparation and Installation of the Icing Model

[0043] Acquisition of real icing parameters: After a natural icing event occurs, a high-precision non-contact 3D scanning device is used to scan the icing on the actual contact wire to capture the complete 3D geometric information of the icing, including its uneven distribution along the axial and circumferential directions of the wire, cross-sectional shape (such as crescent, fan, ice ridge, etc.), ice wing angle, ice density, etc.

[0044] Digital Model Processing: Using the aforementioned digital model, an artificial ice-covered model was created using additive manufacturing technology (i.e., 3D printing). The selected printing material was a tunable-density polymer composite. By incorporating lightweight or heavy microspheres of different proportions and sizes into the base polymer, the average density of the model was precisely adjusted to match the target ice type. Specifically, the density of rime ice was adjusted to approximately 0.1–0.3 g / cm³, and the density of rain-frosting ice was adjusted to approximately 0.9 g / cm³.

[0045] Installation and Fixing: The printed icing model is designed as a hollow tubular or semi-tubular structure, with its inner diameter precisely matching the outer diameter of the contact wire, catenary, and additional conductors to be installed. During installation, the model is tightly fitted onto the wire and secured with lightweight fasteners such as nylon cable ties.

[0046] Step 3: Synchronous monitoring and data collection of dancing events

[0047] Wind field measurement (a3): Near the middle of the test line (such as the middle of the third span), at least one three-dimensional ultrasonic anemometer is set up at a height not lower than the contact line height to measure the three velocity components (longitudinal, transverse, and vertical) and wind direction angle of the incoming wind.

[0048] Displacement measurement (a5): A non-contact measurement method is adopted to avoid the influence of the added mass on the dynamic characteristics of the conductor. Single or multiple synchronously triggered CCD area array cameras are used to photograph the marked points on the conductor from different angles, and the three-dimensional spatial displacement time history of the marked points is reconstructed through stereo vision algorithms.

[0049] Velocity / acceleration measurement (a4): Acceleration sensors are installed in pairs at key locations (such as mid-span, quarter-span, locator clamp, dropper clamp). Velocity information can be obtained by integrating the acceleration signal or differentiating the displacement signal.

[0050] Fiber optic measurement: Tension sensors are deployed at the anchoring positions to monitor tension fluctuations in the contact network system when galloping occurs; distributed fiber optic sensors are laid along the line to obtain continuously distributed strain or temperature information along the line.

[0051] Step 4: Data Acquisition and Time Synchronization: The system is equipped with a high-precision GPS timing module (a13). The 1PPS signal output by this module is sent to the synchronization pulse generator (a12) and distributed to all channels of the multi-channel data acquisition unit (a6) and all sensors (CCD cameras) that require external triggering. The acquired data is collected by the multi-channel data acquisition unit (a6) and written to the host computer (a8) in real time.

[0052] Step 5: Energy and Long-Distance Transmission

[0053] Energy supply (a9): A combination of photovoltaic panels and batteries is used to provide continuous and stable power to the entire test system (including host computer, sensors, data acquisition instrument, etc.).

[0054] Remote monitoring and data transmission (a10, a11): The system is equipped with a cellular network-based wireless data transmission module and a remote monitoring camera. Operators can view on-site video in real time, monitor the operating status of each device, issue data acquisition commands, adjust system parameters, and remotely download and back up key experimental data through a remote terminal.

[0055] Step Six: Depending on the research requirements, change to different icing models, adjust the contact wire structure parameters, and repeat steps two through four.

[0056] The beneficial effects of this application are reflected in:

[0057] This invention overcomes the deficiencies in the realism of wind tunnel tests: by directly utilizing natural wind fields as the excitation source and employing full-size, multi-span contact wire entities, this application fundamentally solves the problems of wind field simulation distortion and the inability to satisfy the aeroelastic similarity law, thus ensuring the physical authenticity of the test conditions.

[0058] The problem of icing model distortion has been solved: Based on the technical approach of "3D scanning + additive manufacturing", this application has achieved high-precision reproduction of the real icing geometry and physical density, which greatly improves the accuracy of aerodynamic load simulation, which is a prerequisite for realizing reliable gazing analysis.

[0059] The bottleneck of data asynchronicity has been eliminated: By introducing GPS-based nanosecond-level high-precision time synchronization technology, this application has achieved, for the first time, synchronous measurement of the dynamic response of large-scale, distributed structures. This makes it possible to analyze the propagation dynamics of galloping waves, the phase relationship of motion at various points, and the transient details of wind-structure interaction, providing unprecedented data support for revealing the deep physical mechanisms of galloping.

[0060] This has enabled a shift from "passive observation" to "active experimentation": by changing high-fidelity icing models of different types and distributions, researchers can conduct systematic and repeatable scientific experiments in real natural environments, thereby establishing a quantitative database of the relationship between "meteorological conditions, icing morphology, and dancing response".

[0061] Providing fundamental support for next-generation operation and maintenance technologies: The high-fidelity, multi-physics, spatiotemporally synchronized dataset generated by this application is an ideal data source for training and validating AI models for predictive maintenance of icing galloping, as well as for building and calibrating digital twin systems for electrified railway infrastructure. It has significant engineering application value and forward-looking significance.

[0062] Example 2

[0063] (1) Experimental Planning and Design: Based on the research objective of the catenary icing and galloping behavior, a test site with typical icing and strong wind meteorological conditions was first determined. Subsequently, the test apparatus was designed:

[0064] 1) Contact line system: A simple chain-type suspension contact line with no less than five spans is adopted. Key parameters are as follows: the cross-sectional area of ​​the contact wire is 150 mm², and the tension can be adjusted within the range of 10 kN to 30 kN; the cross-sectional area of ​​the catenary wire is 120 mm², and the tension can also be adjusted within the range of 10 kN to 30 kN; the span is set at 50 m; the spacing between the droppers is 10 m; and the span of the additional conductor is 50 m.

[0065] 2) Measurement point layout: Determine the model, quantity, and specific installation location of various sensing equipment such as anemometers, accelerometers, and CCD area array cameras on the contact wire.

[0066] (2) Equipment preparation: Based on the above design, purchase or customize all the components required for the test, mainly including: contact wire parts and foundation, 3D printed contact wire ice model, wind speed and direction instrument, accelerometer, CCD area array camera, multi-channel data acquisition instrument, signal transmission line, host computer, power supply device composed of photovoltaic panel and battery, remote monitoring device, wireless network card and related equipment, synchronous pulse generator, GPS timing module and dedicated data processing software.

[0067] (3) Equipment construction and integration: Construct a full-size catenary test section at the selected site according to the design parameters. Complete the installation and system integration of all sensors, data acquisition instruments, power supply and communication equipment to ensure reliable connection of each unit.

[0068] (4) Installation of icing model: According to the research needs, the icing model with a specific shape and density, which is prepared in advance by 3D printing, is firmly installed on the contact wire (including contact wire, catenary wire and additional conductor) to be analyzed.

[0069] (5) Synchronous Triggering and Data Acquisition: The experiment enters the observation stage. When the catenary gallops, the synchronous pulse generator, under the high-precision unified time base provided by the GPS timing module, synchronously triggers the anemometer, accelerometer, and CCD area array camera to accurately measure the dynamic parameters such as the displacement and velocity of the catenary.

[0070] (6) Data transmission and storage: All collected data is transmitted in real time to the on-site host computer for local storage via a multi-channel data acquisition instrument. When necessary, the data is transmitted synchronously or asynchronously to a remote data center via a wireless transmission device.

[0071] (7) Data processing and analysis: After a single dance event measurement is completed, the collected data is analyzed using the data processing software in the host computer, or it can be remotely transmitted to the computing center for processing to extract the dance feature parameters.

[0072] (8) Comparative experiment: In order to study the effects of different icing conditions, icing models with different shapes or densities can be replaced and the above steps (4) to (7) can be repeated to obtain comparative data.

[0073] (9) Conclusion: A comprehensive analysis of the data from all test rounds led to the conclusion that there is an inherent relationship between icing morphology, wind field parameters and catenary galloping response.

[0074] Specifically,

[0075] S2, using a stereo vision algorithm to reconstruct the image data into three dimensions, including: performing distortion correction and coordinate system calibration on the multi-angle image sequence acquired by the CCD area array camera (A5); using the digital image correlation method (DIC) to identify and track the two-dimensional pixel coordinates of each marker point in the image sequence; based on the stereo vision algorithm, performing triangulation on the two-dimensional coordinates of the same marker point acquired by different cameras to reconstruct the three-dimensional spatial coordinates of the marker point; and obtaining the three-dimensional spatial displacement time history of the marker point by performing differential calculation on the three-dimensional spatial coordinates of adjacent time points.

[0076] S3, the acceleration signal of the contact wire icing and dancing is filtered and integrated in the time domain, including: low-pass filtering the acquired acceleration signal to eliminate high-frequency noise; performing a first time-domain integration on the filtered acceleration signal to obtain the preliminary velocity time history at the location where the accelerometer (A4) is installed; high-pass filtering the preliminary velocity time history to remove the low-frequency drift components generated during the integration process to obtain corrected velocity data, which is used as the velocity data at the location where the accelerometer (A4) is installed; and a second time-domain integration is performed on the corrected velocity data to obtain the displacement data at the location where the accelerometer (A4) is installed.

[0077] S4, extract the characteristic parameters of the catenary icing dance, including:

[0078] Spectral analysis of the three-dimensional spatial displacement time history and displacement data of the marked points was performed using Fast Fourier Transform (FFT) to identify peak frequencies in the power spectral density curves, thus obtaining the galloping frequencies. This included performing FFT on the vertical, lateral, and longitudinal displacement components in the three-dimensional spatial displacement time history of the marked points, as well as the vertical, lateral, and longitudinal displacement components in the displacement data at the location where the accelerometer (A4) was installed, to obtain power spectral density curves for each measurement point in each direction. In the power spectral density curves, frequency points with power spectral density values ​​greater than three times the background noise were searched and identified as peak frequencies. Among all identified peak frequencies, the frequency corresponding to the maximum power spectral density value was determined as the main frequency of the catenary icing galloping. The peak frequencies with power spectral density values ​​less than the main frequency were sorted from largest to smallest to determine the harmonic frequencies of the catenary icing galloping. The main frequency and the harmonic frequencies were used as the galloping frequencies.

[0079] By calculating the three-dimensional spatial displacement time history and the peak values ​​of displacement data, and statistically analyzing the displacement-peak distribution at each measuring point, the galloping amplitude is obtained. This includes: extracting the displacement response components within a preset range of the main frequency for each measuring point using a bandpass filter based on the vertical, lateral, and longitudinal displacement components in the three-dimensional spatial displacement time history of the marked points, as well as the vertical, lateral, and longitudinal displacement components in the displacement data at the location where the accelerometer (A4) is installed; performing envelope analysis on the extracted displacement response components in each direction, using Hilbert transform to calculate the analytic signal of each displacement response component, and extracting the magnitude of the analytic signal as the envelope; calculating the peak-to-peak displacement values ​​at each measuring point in the vertical, lateral, and longitudinal directions based on the envelope; taking the maximum peak-to-peak displacement value among all measuring points as the maximum galloping amplitude; calculating the arithmetic mean of the peak-to-peak displacement values ​​at all measuring points as the average galloping amplitude; and taking the maximum galloping amplitude and the average galloping amplitude as the galloping amplitude.

[0080] By calculating the vibration amplitude and phase information of each measuring point on the contact wire and the catenary, the galloping modes of the contact wire covered with ice are obtained. This includes: extracting the measuring points on the contact wire and the catenary; performing a fast Fourier transform on the vertical displacement component of the three-dimensional spatial displacement time history of each measuring point to obtain the phase angle; calculating the difference between the phase angles of the contact wire and the catenary as the phase difference of the corresponding measuring point pair; calculating the ratio of the lateral direction to the vertical direction of all measuring points based on the peak-to-peak displacement to obtain the lateral-vertical amplitude ratio; when the absolute value of the phase difference is less than 30°, the galloping mode is determined to be a vertical galloping mode; when the absolute value of the phase difference is greater than 150°, the galloping mode is determined to be a torsional galloping mode; when the lateral-vertical amplitude ratio is greater than 1.5, the galloping mode is determined to be a lateral oscillation mode.

[0081] Based on displacement and velocity data, instantaneous phase is extracted using Hilbert transform, and phase differences between different measuring points, different directions, and between displacement and velocity are calculated to obtain phase information.

[0082] S5, identify the critical wind speed range and critical wind direction angle range that cause catenary icing and galloping, including: time-aligning the incoming wind velocity component data, wind direction angle data, and the obtained galloping frequency, galloping amplitude, galloping mode, and phase information, so that the wind field data and galloping response data correspond to the same time series; based on the time-aligned data, calculate the cross-correlation function between the incoming wind velocity component and the galloping amplitude within a preset time window, and identify the time delay between wind speed changes and the galloping response by searching for the time lag corresponding to the maximum value of the cross-correlation function; perform time-shift correction on the wind field data using the time delay to establish a synchronous correspondence between wind field parameters and galloping response parameters; and determine the incoming wind velocity... The degree component is decomposed into wind speed components along the transverse, vertical, and longitudinal directions of the contact network, and the wind speed magnitude and composite wind speed in the three directions are calculated. Based on the wind direction angle data, the distribution of galloping amplitude in different wind direction angle intervals is statistically analyzed, and the wind direction angle range corresponding to the galloping amplitude exceeding the preset threshold is identified as the critical wind direction angle range. Based on the composite wind speed data, the distribution of galloping amplitude in different wind speed intervals is statistically analyzed, and the wind speed range corresponding to the galloping amplitude jumping from below the preset threshold to above the preset threshold is identified as the critical wind speed interval for galloping initiation. Based on the galloping mode information, the critical wind speed intervals and critical wind direction angle ranges corresponding to the vertical galloping mode, torsional galloping mode, transverse oscillation mode, and coupled galloping mode are statistically analyzed respectively.

[0083] The foregoing illustrative description of the present application and its embodiments is not restrictive and can be implemented in other specific forms without departing from the spirit or essential characteristics of the present application. The accompanying drawings are only one embodiment of the present application, and the actual structure is not limited thereto. Therefore, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the present application, such designs should fall within the scope of protection of this application. Furthermore, the word "comprising" does not exclude other elements or steps, and the word "a" preceding an element does not exclude the inclusion of "a plurality" of that element. Terms such as "first," "second," etc., are used to indicate names and do not indicate any specific order.

Claims

1. A test device for ice-covered galloping of electrified railway overhead contact lines under natural wind conditions, characterized in that, include: The overhead contact system (A1) includes: catenary wire, contact wire, droppers, auxiliary conductors, insulators, and supports; the number of spans shall not be less than 5. An icing model (A2) is placed on the catenary, contact wire, or additional conductor of the overhead contact system. The icing model is made using additive manufacturing technology to reproduce the three-dimensional geometry and physical density of the icing. Anemometer (A3), installed on the windward side of the overhead contact line, is used to measure the velocity component and wind direction angle of the incoming wind. Accelerometer (A4): An accelerometer is installed at the mid-span, quarter-span, positioner clamp, or dropper wire position of the catenary and contact wire to measure the acceleration signal of the contact wire icing. The CCD area array camera (A5) is used to acquire image data of marker points at different locations on the overhead contact line and reconstruct the three-dimensional spatial displacement time history of the marker points through stereo vision algorithms.

2. The electrified railway contact network icing and galloping test device under natural wind field as described in claim 1, characterized in that: Also includes: The GPS timing module (A13) is used to provide a time reference based on the UTC standard and output a 1PPS synchronization pulse signal; Synchronization pulse generator (A12) receives and generates multiple synchronization trigger signals based on 1PPS synchronization pulse signal; The multi-channel data acquisition unit (A6) connects to a synchronous pulse generator, an anemometer, and an accelerometer. It receives synchronous trigger signals, synchronously acquires data from all sensors, and performs time synchronization on all acquired data according to a time base. Signal and power transmission line (A7) connects the multi-channel data acquisition instrument to each sensor and is used to transmit sensor signals; The host computer (A8) receives and stores sensor data acquired by the multi-channel data acquisition instrument, and processes the acquired data to extract the dancing features, including dancing frequency, amplitude, mode and phase.

3. A test method for ice-covered galloping of the overhead contact line of an electrified railway under natural wind conditions, characterized in that, include: S1, collect multimodal data, which includes the velocity component and wind direction angle data of the incoming wind, the acceleration signal of the catenary icing and dancing, and the image data of the catenary marker points; S2, the image data is reconstructed in three dimensions using a stereo vision algorithm to obtain the three-dimensional spatial displacement time history of the marked points; S3, the acceleration signal of the ice-covered catenary is filtered and integrated in the time domain to obtain the velocity and displacement data at the location where the accelerometer sensor (A4) is installed; S4. Based on the three-dimensional spatial displacement time history of the marked points, as well as the velocity and displacement data at the location where the accelerometer (A4) is installed, characteristic parameters of the contact wire icing galloping are extracted through spectrum analysis, peak value calculation, and phase difference analysis. The characteristic parameters include galloping frequency, galloping amplitude, galloping mode, and phase information. S5. Based on the characteristic parameters, as well as the velocity component and wind direction angle data of the incoming wind, wind field-galloping response correlation analysis is performed through time alignment and cross-correlation analysis to identify the critical wind speed range and critical wind direction angle range that cause catenary icing and galloping.

4. The processing method according to claim 1, characterized in that: S2 yields the three-dimensional spatial displacement time history of the marked point, including: Distortion correction and coordinate system calibration were performed on the multi-angle image sequences acquired by the CCD area array camera (A5); The Digital Image Correlation (DIC) method was used to identify and track the two-dimensional pixel coordinates of each marker point in an image sequence. Based on stereo vision algorithms, triangulation is performed on the two-dimensional coordinates of the same marker point obtained by different cameras to reconstruct the three-dimensional spatial coordinates of the marker point; The three-dimensional spatial displacement time history of the marked point is obtained by performing differential calculations on the three-dimensional spatial coordinates of adjacent time points.

5. The processing method according to claim 3, characterized in that: S3, obtains the velocity and displacement data at the location where the accelerometer sensor (A4) is installed, including: The acquired acceleration signal is low-pass filtered to eliminate high-frequency noise; The first time-domain integration of the filtered acceleration signal yields the preliminary velocity-time history at the location where the accelerometer (A4) is installed. The initial velocity time history is high-pass filtered to remove the low-frequency drift components generated during integration, and the corrected velocity data is obtained as the velocity data at the location where the accelerometer (A4) is installed. The corrected velocity data is integrated a second time in the time domain to obtain the displacement data at the location where the accelerometer sensor (A4) is installed.

6. The processing method according to claim 3, characterized in that: S4, extract the characteristic parameters of the catenary icing dance, including: By performing spectral analysis on the three-dimensional spatial displacement time history and displacement data of the marked points using fast Fourier transform, the peak frequency in the power spectral density curve is identified, and the dancing frequency is obtained. By calculating the three-dimensional spatial displacement time history and the peak value of displacement data, the displacement-peak value distribution of each measuring point is statistically analyzed to obtain the galloping amplitude. By calculating the vibration amplitude and phase information at each measuring point on the contact wire and catenary, the galloping mode of the contact wire covered with ice is obtained; Based on displacement and velocity data, the instantaneous phase is extracted using Hilbert transform, and the phase difference between different measuring points, between different directions, and between displacement and velocity is calculated to obtain phase information.

7. The processing method according to claim 6, characterized in that: The dance frequency was obtained, including: Fast Fourier Transform (FFT) is performed on the vertical, lateral, and longitudinal displacement components in the three-dimensional spatial displacement time history of the marked points, as well as the vertical, lateral, and longitudinal displacement components in the displacement data at the location where the accelerometer (A4) is installed, to obtain the power spectral density curves of each measurement point in each direction. In the power spectral density curve, search for frequency points whose power spectral density value is more than 3 times the background noise, and identify the corresponding frequency points as peak frequencies. Among all the identified peak frequencies, the frequency corresponding to the maximum power spectral density value is determined as the main frequency of the catenary ice-covered dancing. The peak frequencies corresponding to the power spectral density values ​​below the main frequency are sorted from largest to smallest to determine the harmonic frequencies of the contact wire icing. The main frequency and each harmonic frequency are used as the dancing frequency.

8. The processing method according to claim 7, characterized in that: The amplitude of the dance is obtained, including: Based on the vertical, lateral, and longitudinal displacement components in the three-dimensional spatial displacement time history of the marked points, as well as the vertical, lateral, and longitudinal displacement components in the displacement data at the location where the accelerometer (A4) is installed, the displacement response components of each measuring point within the preset range of the main frequency are extracted using a bandpass filter. Envelope analysis is performed on the extracted displacement response components in each direction. The analytic signal of each displacement response component is calculated using Hilbert transform, and the magnitude of the analytic signal is extracted as the envelope. Based on the envelope, calculate the peak-to-peak displacement of each measuring point in the vertical, horizontal, and longitudinal directions; The maximum displacement peak-to-peak value among all measuring points is taken as the maximum gallop amplitude; Calculate the arithmetic mean of the peak-to-peak displacement values ​​at all measuring points as the average gallop amplitude; The maximum and average dance amplitudes are used as the dance amplitude values.

9. The processing method according to claim 8, characterized in that: The dancing modes of the ice-covered overhead contact line were obtained, including: Extract measuring points on the contact wire and catenary wire; The phase angle is obtained by performing a fast Fourier transform on the vertical displacement components of the three-dimensional spatial displacement time history of each measuring point. Calculate the difference in phase angle between the contact wire and the catenary wire, and use it as the phase difference between the corresponding measuring point pairs; Based on the peak-to-peak displacement, calculate the ratio of the lateral to the vertical direction of all measuring points to obtain the lateral-vertical amplitude ratio. When the absolute value of the phase difference is less than 30°, the dancing mode is determined to be a vertical dancing mode; When the absolute value of the phase difference is greater than 150°, the galloping mode is determined to be a torsional galloping mode; When the ratio of horizontal to vertical amplitude is greater than 1.5, the dancing mode is determined to be a horizontal oscillating mode.

10. A system for processing test data of contact wire icing and dancing, used to implement the method described in any one of claims 3 to 9.

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