Search

CN-121977644-A - Collaborative monitoring and early warning method for conductor sag, windage yaw and galloping

CN121977644ACN 121977644 ACN121977644 ACN 121977644ACN-121977644-A

Abstract

The invention discloses a collaborative monitoring and early warning method for wire sag, windage yaw and galloping, and belongs to the technical field of online monitoring of transmission lines. The method comprises the steps of firstly deploying a multi-mode sensing network, synchronously collecting wire strain, three-dimensional space point cloud and environmental wind field data, then carrying out time synchronization and space registration on multi-source data to construct a standardized fusion data unit, then reconstructing a wire center line based on the fusion data, extracting sag, wind deflection angle and galloping characteristic parameters, further establishing a nonlinear coupling dynamics model, simulating wire dynamic response under multi-physical field excitation, combining real-time working condition dynamic assessment risk, triggering grading early warning, and finally verifying early warning results through unmanned aerial vehicle inspection and optimizing model parameters by utilizing feedback information to form a self-learning closed loop. The invention realizes cooperative sensing and coupling analysis of three types of deformation, and improves the accuracy and the prospective of early warning.

Inventors

  • NIU ZHIHONG

Assignees

  • 山东鲁发科技有限公司

Dates

Publication Date
20260505
Application Date
20260123

Claims (10)

  1. 1. A method for cooperatively monitoring and pre-warning wire sag, windage yaw and galloping is characterized by comprising the following steps: s1, deploying a multi-mode sensing network, and synchronously collecting strain data, three-dimensional space point cloud data and environmental wind field data of a wire; S2, carrying out time synchronization and space registration on the multi-source data acquired in the step S1, and constructing a standardized fusion data unit; S3, reconstructing a wire central line based on the fusion data unit, and extracting sag values, wind deflection angles and galloping characteristic parameters; S4, based on the sag value, the wind deflection angle and the galloping characteristic parameters, a nonlinear coupling dynamics model is established so as to simulate the dynamic response of the lead under the excitation of multiple physical fields and predict the state track of the lead in the future period; S5, dynamically correcting a safety threshold value by combining with real-time working condition information, carrying out probability risk assessment based on the state track predicted in the step S4, generating instability probability in a future period, and triggering a grading early warning signal according to the instability probability; And S6, issuing early warning information and executing grading response, starting a closed loop verification mechanism, verifying an early warning result by using external inspection data, and optimizing the coupling dynamics model and the risk assessment parameters based on the verification result.
  2. 2. The method for collaborative monitoring and early warning of wire sag, windage and galloping according to claim 1, wherein in step S1, the multi-modal sensing network comprises: the distributed optical fiber sensor array is distributed along the trend of the wire and is used for collecting distributed strain data of the wire; the three-dimensional laser scanner is arranged at the end part of the tower cross arm and is used for collecting three-dimensional space point cloud data of the wire; The ultrasonic anemoclinograph is arranged at the end part of the tower cross arm and is used for collecting environmental wind field data.
  3. 3. The method for collaborative monitoring and early warning of wire sag, windage and galloping according to claim 1, wherein step S2 comprises: The second pulse signal provided by the global navigation satellite system is used as a unified time source to carry out time synchronization on the data of each sensor, so that the synchronization precision is better than 1 millisecond; a space reference control point network comprising tower coordinates, hanging point coordinates and an initial track of a lead is established in advance; the three-dimensional space point cloud data is subjected to preliminary coordinate transformation by utilizing scanner attitude data, and global optimization registration is performed based on the control point network; Mapping the distributed strain data to a uniform geographic space according to the optical path-distance mapping relation and the suspension point coordinates; integrating the data after time-space alignment to generate a standard data unit comprising a time stamp, a space position, a strain value, a displacement vector and wind field parameters.
  4. 4. The method of claim 1, wherein in step S3, the reconstructing the wire center line comprises: radial distance filtering is carried out on the registered point cloud so as to eliminate background interference; Dividing the filtered point cloud into local line segments, and determining the local main direction of each line segment as a tangential direction by adopting a principal component analysis method; And generating a continuous space curve of the central line of the lead by adopting a spline curve fitting algorithm by taking the tangential direction as constraint.
  5. 5. The method for collaborative monitoring and early warning of wire sag, windage and galloping according to claim 4, wherein in step S3: the sag value is calculated as the difference between the elevation of the center line of the wire at the center point of the span and the average elevation of the hanging points at the two ends; The wind deflection angle is an angle corresponding to the maximum transverse offset of a projection curve of the central line of the lead on a horizontal plane relative to a windless reference state; The extracting of the galloping characteristic parameters comprises the steps of identifying a low-frequency large-amplitude oscillation mode through a continuous interframe difference method, and identifying a galloping event through a time-frequency analysis method so as to extract dominant frequency, amplitude and rotation direction.
  6. 6. The method for collaborative monitoring and early warning of wire sag, windage and galloping according to claim 1, wherein in step S4, the establishing a nonlinear coupling dynamics model comprises: Dispersing the whole-gear lead into a plurality of discrete systems with concentrated mass nodes connected with springs; Establishing a system motion equation based on the Lagrangian equation; The method comprises the steps of introducing a term related to temperature gradient in the motion equation to characterize thermal expansion effect, introducing a wind load function constructed based on an actually measured wind field to characterize wind excitation, and introducing a mass eccentricity term reflecting ice coating non-uniformity.
  7. 7. The method of collaborative monitoring and early warning of wire sag, windage and galloping according to claim 6, wherein step S4 further comprises: And solving the coupling dynamics model by adopting a numerical integration method, taking the current wire state as an initial condition, taking the real-time wind load and the temperature field as excitation input, and predicting the displacement, the speed and the acceleration track of each node in a specified future period.
  8. 8. The method for collaborative monitoring and early warning of wire sag, windage and galloping according to claim 1, wherein step S5 comprises: based on a Bayesian inference framework, fusing real-time weather, line load and historical archive data, and dynamically correcting safety thresholds of sag, wind deflection angle and galloping frequency; based on the state track predicted in the step S4, introducing random disturbance factors to perform multiple Monte Carlo simulation, calculating the normalized risk ratio of each simulation path, and counting the path proportion of which the risk ratio exceeds 1 in the future period as the comprehensive instability probability; And comparing the comprehensive instability probability serving as a risk index with a preset multilevel early warning threshold value, and triggering an early warning signal of a corresponding level.
  9. 9. The method of claim 1, wherein in step S6, the initiating a closed loop verification mechanism comprises: after the early warning is triggered, automatically scheduling the unmanned aerial vehicle to carry out approaching inspection on the target gear within the appointed time, and obtaining a wire state image; analyzing the image data, comparing the result with early warning prediction content, and judging the effectiveness of early warning; If the early warning is effective, the complete data chain of the event is stored in a feature database to be used as a training sample, and if the event is false, the key parameters of the coupling dynamics model and the prior distribution in the risk assessment are adjusted by using a reinforcement learning algorithm.
  10. 10. The method for collaborative monitoring and early warning of wire sag, windage yaw and galloping according to claim 1, wherein the method further comprises the steps of initially compressing and extracting features of the original sensing data by an edge computing unit deployed at the tower side between the steps S1 and S2 or in the step S2, and uploading only key state indexes to a cloud server.

Description

Collaborative monitoring and early warning method for conductor sag, windage yaw and galloping Technical Field The invention belongs to the technical field of on-line monitoring and intelligent early warning of a power transmission line, and particularly relates to a collaborative monitoring and early warning method for conductor sag, windage yaw and galloping. Background With the promotion of smart power grid construction, higher requirements are put forward on the real-time sensing and safety early warning capabilities of the running state of the power transmission line. Under complex weather and load conditions, the overhead conductor mainly shows three types of dynamic deformation, namely sag, windage yaw and galloping. Sag is mainly caused by thermal expansion and contraction of a wire and dead weight, and affects the safety distance to the ground, windage is horizontal deviation of the wire under transverse windage load, which may cause insufficient inter-phase gap, and galloping is low-frequency and large-amplitude self-excited vibration, which is easy to cause mechanical fatigue and flashover accidents. Currently, independent technical routes are mostly adopted for monitoring the three types of deformation, for example, measurement and estimation of a single parameter are respectively performed through an inclination sensor, an image recognition device or an accelerometer array. However, there are significant limitations to such decentralized monitoring approaches. Firstly, the data collected by all subsystems are not uniform in space-time reference and format, effective fusion is difficult to carry out, collaborative characterization of the overall motion state of the lead cannot be constructed, and a data island is formed. Secondly, the existing early warning mechanism generally depends on a fixed experience safety threshold, and cannot fully consider the dynamic influence of real-time changing environmental factors and line operation conditions, so that the early warning accuracy is insufficient, and the risks of false alarm and missing alarm are high. Particularly under extreme composite meteorological conditions such as strong wind, icing and the like, complex coupling actions can be generated among sag, windage yaw and galloping, mutual aggravation is achieved, and the prior art system is difficult to early warn the risk of the interlocking faults due to lack of collaborative analysis and coupling modeling capability. Therefore, an integrated monitoring and early warning method capable of realizing collaborative sensing of sagging, windage yaw and galloping, data depth fusion and dynamic coupling modeling and intelligent risk assessment is needed in the art so as to improve the comprehensiveness of power transmission line state sensing, the accuracy of early warning decision and the defending capability against complex disasters. Disclosure of Invention In order to solve the problems, the invention discloses a method for cooperatively monitoring and pre-warning wire sag, windage yaw and galloping, which comprises the following steps: s1, deploying a multi-mode sensing network, and synchronously collecting strain data, three-dimensional space point cloud data and environmental wind field data of a wire; S2, carrying out time synchronization and space registration on the multi-source data acquired in the step S1, and constructing a standardized fusion data unit; S3, reconstructing a wire central line based on the fusion data unit, and extracting sag values, wind deflection angles and galloping characteristic parameters; S4, based on the sag value, the wind deflection angle and the galloping characteristic parameters, a nonlinear coupling dynamics model is established so as to simulate the dynamic response of the lead under the excitation of multiple physical fields and predict the state track of the lead in the future period; S5, dynamically correcting a safety threshold value by combining with real-time working condition information, carrying out probability risk assessment based on the state track predicted in the step S4, generating instability probability in a future period, and triggering a grading early warning signal according to the instability probability; And S6, issuing early warning information and executing grading response, starting a closed loop verification mechanism, verifying an early warning result by using external inspection data, and optimizing the coupling dynamics model and the risk assessment parameters based on the verification result. Preferably, in step S1, the multi-mode sensor network includes: the distributed optical fiber sensor array is distributed along the trend of the wire and is used for collecting distributed strain data of the wire; the three-dimensional laser scanner is arranged at the end part of the tower cross arm and is used for collecting three-dimensional space point cloud data of the wire; The ultrasonic anemoclinograph is arranged at the end part o