Precise detection method for composite fault of power transmission line based on OPGW optical fiber sensing

Abstract

The present application relates to the technical field of power transmission line state monitoring and fault diagnosis, and in particular to a power transmission line composite fault accurate detection method based on OPGW optical fiber sensing, which takes the existing OPGW optical fiber of the power transmission line as a sensing carrier, builds a three-level distributed sensing network of master station-relay station-remote station, realizes high-precision time synchronization of the whole network by using OPGW link reciprocity; through wavelength division multiplexing and time division scheduling, the multi-parameter joint collection of the same fiber sound vibration, strain and temperature is completed, and independent strain field and temperature field are obtained by decoupling; the line is segmented according to the tower span, the propagation speed is updated based on real-time state parameters, the event space-time inversion positioning is realized; the directionality index is generated by calculating the directional index of the event neighborhood first arrival time, the propagation constraint is generated, the sub-event is converted into a characteristic node, the composite fault root cause identification is completed through the minimum cost causal inference, and support can be provided for the accurate detection and operation and maintenance decision of the power transmission line composite fault.

Description

Technical Field

[0001] This invention relates to the field of transmission line condition monitoring and fault diagnosis technology, and more specifically, to a method for accurate detection of complex faults in transmission lines based on OPGW fiber optic sensing. Background Technology

[0002] Transmission lines are the core transmission carriers of the power system, and their safe and stable operation directly determines the reliability of power grid supply. With the large-scale construction of my country's ultra-high voltage AC and DC power grid, the operation and maintenance of transmission lines currently face multiple sources of disturbance such as icing, lightning strikes, tree obstructions, conductor vibration / galloping, external damage, and abnormal temperature. Traditional manual inspection is inefficient, slow to respond, and incomplete in coverage. Moreover, the existing system has functional shortcomings in OPGW anomaly monitoring and fault location.

[0003] Existing OPGW fiber-based transmission line fault detection technologies mostly employ single-parameter acquisition or dual-end sensing architectures, which have several technical limitations: First, the time synchronization accuracy is insufficient, relying heavily on external satellite time synchronization, making it susceptible to electromagnetic interference and terrain obstruction, and unable to meet the requirements for high-precision time difference positioning; second, the problem of multi-parameter cross-sensitivity has not been effectively addressed, and the coupling interference of strain and temperature can lead to deviations in fault feature extraction; third, fixed propagation speeds are commonly used for positioning, without considering the impact of line tension and temperature changes on wave velocity, resulting in large positioning errors; fourth, it is difficult to distinguish between root causes and secondary events in complex faults, making it impossible to clarify the causal link of fault evolution, and easily leading to misjudgments and missed judgments.

[0004] Therefore, this invention proposes a method for accurate detection of complex faults in transmission lines based on OPGW optical fiber sensing. Summary of the Invention

[0005] To address the shortcomings of existing technologies, the present invention aims to provide a method for accurate detection of complex faults in transmission lines based on OPGW optical fiber sensing.

[0006] To achieve the above objectives, the present invention provides the following technical solution: The method for accurate detection of composite faults in transmission lines based on OPGW optical fiber sensing includes the following steps: Step 1: Construct a sensor network architecture. Using OPGW optical fibers laid along the entire transmission line as the sensing carrier, build a three-level distributed sensor network consisting of a master station, relay stations, and remote stations. Each level of station is equipped with reciprocal time-frequency transmission time synchronization, multi-parameter sensing acquisition, and data processing units. Adjacent stations transmit time synchronization signals and acquire sensor signals through OPGW optical fibers. The master station is responsible for the time reference management of the entire network, multi-terminal data fusion, and comprehensive fault analysis. Step 2: Achieve network-wide time synchronization. Utilize the reciprocity of the OPGW link for bidirectional time-frequency transmission to achieve high-precision time synchronization across the entire network. The output clock deviation is used for time alignment and time difference calculation of all sensor data. Step 3: Multi-parameter acquisition and decoupling. Achieve distributed joint acquisition of multiple parameters such as acoustic vibration, strain and temperature on the same OPGW fiber, and decouple cross-sensitive observations into independent strain and temperature fields. Step 4: Event spatiotemporal inversion and positioning. Divide the line into segments according to the tower span, use the decoupled strain temperature field to invert the segment state parameters, update the propagation speed of each segment online, and complete the event spatiotemporal inversion and positioning based on the speed. Step 5: Generate propagation direction constraints, calculate directional indices within the event neighborhood, determine the event propagation direction, and use it as a constraint condition for causal inference; Step Six: Causal Inference and Root Cause Identification. The detected sub-events are transformed into nodes with spatiotemporal and parametric features. Candidate causal edges are generated based on propagation constraints. The causal chain and root cause identification results are obtained and output through minimum cost selection.

[0007] Furthermore, in the three-level distributed sensor network architecture, the number and location of relay stations are determined by multiple constraints coupled together, including signal transmission, time synchronization accuracy, positioning accuracy, and engineering cost. First, the theoretical minimum number of relay stations is calculated and a redundancy coefficient is introduced to obtain the actual number. Then, in combination with engineering constraints, the relay station locations are optimized with the goal of minimizing the average geometric accuracy factor of the entire line. During the optimization process, towers that meet the conditions are selected as candidate points. The relay station locations are adjusted through an iterative algorithm, and the engineering feasibility is verified.

[0008] Furthermore, the high-precision time synchronization across the entire network specifically involves obtaining a unified timestamp for both ends directly through bidirectional time-frequency transmission for a dual-end architecture; for a multi-end architecture, firstly, the bidirectional reciprocal time synchronization of adjacent end stations is performed sequentially, then the clock deviation is transmitted forward based on the master station, while verifying and eliminating outliers through reverse transmission, and finally, a Kalman filter model is established to estimate the clock deviation of the entire network in real time, suppressing clock drift and random noise.

[0009] Furthermore, the multi-parameter distributed joint acquisition specifically involves carrying optical signals of different measurement systems on the same OPGW fiber through wavelength division multiplexing, separating sampling time slots through time division scheduling, and synchronously acquiring acoustic vibration, Brillouin frequency shift, and Rayleigh phase observations; the strain-temperature decoupling specifically involves using the change in relative steady-state baseline as the decoupling object, and using a linear sensitivity model to map the coupled observations into independent strain and temperature changes.

[0010] Furthermore, the event spatiotemporal inversion positioning specifically involves first dividing the line into segments based on tower coordinates and span, aggregating the effective tension and equivalent linear density state parameters of each segment, updating the propagation speed of each segment based on the tension wave velocity model, and calculating the travel time between any two points; then, extracting the event arrival time of each terminal station under a unified timestamp, and using the consistency of arrival time difference and segment travel time difference in the dual-terminal architecture to invert the one-dimensional location and occurrence time of the event.

[0011] Furthermore, for the multi-terminal architecture, the three-dimensional coordinates of the catenary of the OPGW conductor are first calculated in real time using the strain-temperature field obtained by decoupling. Then, the arrival time information of the multiple terminals is fused, and the three-dimensional coordinates and occurrence time of the fault source are solved by nonlinear least squares method. At the same time, the covariance matrix of the location result is calculated and the confidence level is evaluated.

[0012] Furthermore, the calculation of the directional index and the generation of propagation direction constraints specifically involve selecting a spatial neighborhood centered on the event source location, extracting the event arrival times of each discrete location within the neighborhood to form spatiotemporal sample pairs, performing linear robust fitting on the spatiotemporal sample pairs to obtain the slope, defining the directional index by the slope sign, determining the event propagation direction based on the directional index and using it as a constraint condition for causal inference.

[0013] Furthermore, the composite fault causal graph inference and root cause identification specifically involves transforming each detected event into a sub-event node containing the occurrence location, occurrence time, parametric feature vector, and directional index; for any two nodes that satisfy the temporal sequence relationship, candidate directed causal edges are generated by filtering through temporal consistency, directional consistency, and physical reachability constraints; and causal structures that satisfy acyclic and connectivity constraints are selected from the candidate edge set to determine the root cause candidate set.

[0014] Furthermore, for each root cause in the root cause candidate set, the overall time consistency residual cost of its corresponding causal structure is calculated. Based on the residual cost, the normalized confidence of each root cause is obtained through exponential mapping. The root cause with the highest confidence is selected as the final identification result, and the sub-event sequence, causal link structure and root cause confidence of the composite fault are output.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention constructs a three-level distributed sensor network consisting of a master station, a relay station, and a remote station. It utilizes the reciprocity of the OPGW link to achieve bidirectional time-frequency transmission and time synchronization. Combined with a Kalman filter model, it estimates the clock deviation of the entire network in real time, effectively suppressing clock drift and random noise, and achieving high-precision time synchronization across the entire network. Simultaneously, it employs wavelength division multiplexing to carry optical signals of different measurement systems. By separating sampling time slots through time division scheduling, it achieves distributed joint acquisition of multiple parameters such as acoustic vibration, strain, and temperature on the same OPGW fiber. Based on a linear sensitivity model, it decouples cross-sensitive observations to obtain independent strain and temperature fields, which can comprehensively and accurately reflect the operating status of the line and reduce the error in fault feature extraction caused by parameter coupling. 2. This invention segments the line according to the tower span, and uses the decoupled strain-temperature field to invert the effective tension and equivalent linear density of each segment. Based on the tension wave velocity model, the propagation speed of each segment is updated online. Combined with the arrival time information of multiple ends, the spatiotemporal inversion and positioning of events are realized. Under the multi-end architecture, the OPGW catenary mechanical model can also be integrated to complete the three-dimensional positioning of the fault source. At the same time, the directional index is calculated by the first arrival time in the event neighborhood to generate propagation direction constraints. The detected sub-events are transformed into nodes with spatiotemporal and parametric characteristics. The fault causal chain and root cause identification results are obtained through minimum cost causal inference, which can effectively clarify the evolution logic of complex faults and improve the accuracy of fault root cause identification. Attached Figure Description

[0016] Figure 1 The flowchart shows a method for accurate detection of complex faults in transmission lines based on OPGW fiber optic sensing. Figure 2 This is a flowchart illustrating the implementation of the event-level time synchronization steps based on OPGW reciprocal time-frequency transfer in this invention. Figure 3 This is a flowchart illustrating the implementation steps of the online updating of the segmented variable speed propagation model and the spatiotemporal inversion and localization of events in this invention. Detailed Implementation

[0017] Example, refer to Figure 1 The method for accurate detection of composite faults in transmission lines based on OPGW fiber optic sensing in this embodiment specifically includes the following steps: Step 1: Introduction to System Components.

[0018] This invention uses OPGW optical fibers laid along the entire transmission line as the sensing carrier to construct a three-level distributed sensor network architecture of master station-relay station-remote station (a two-end architecture is a special case when the number of relay stations is 0); the terminal stations along the entire line are numbered sequentially according to the line's direction. ,in Main site For remote stations, to As a relay station, each terminal station includes at least: a reciprocal time-frequency transmission time synchronization unit, a distributed acoustic and vibration acquisition unit, a distributed strain and temperature acquisition unit, and a data aggregation and processing unit. Adjacent terminal stations achieve time synchronization signal transmission and sensor signal acquisition through OPGW optical fiber. The master station performs unified time reference alignment, parameter decoupling, propagation modeling, event localization, direction discrimination, and causal inference on the acquisition results of all terminal stations.

[0019] S11. Functional definitions of each level of terminal station: Main site It is responsible for the functions of managing the network-wide time reference, integrating multi-terminal data, locating faults and inferring root causes, outputting results and interacting with maintenance personnel. relay station It is responsible for bidirectional time synchronization signal forwarding and timestamp recording between adjacent terminal stations, local sensor signal preprocessing and event screening, data caching and forwarding, and equipment self-diagnosis and calibration. remote station It is responsible for the acquisition of end-sensor signals and the transmission of time synchronization signals; S12. Quantitative design and layout optimization of multi-terminal sensor network relay stations: The number and location of relay stations are determined by a combination of constraints, including signal transmission, time synchronization accuracy, positioning accuracy, and engineering cost. The specific design method is as follows: S121. Calculation of the basic number of relay stations: Let the total length of the line be The optimal transmission length of a single fiber optic segment is In this embodiment, the length is taken as 60~70km. Based on the attenuation coefficient of G.652D fiber at 1550nm ≤ 0.2dB / km and the optical power budget of 15~20dB, the theoretical minimum number of repeater stations is determined. for: ; in It is a rounding function; Considering extreme cases such as equipment failure and communication interruption, a redundancy factor is introduced. For general AC lines, a value of 1.1 is used; for important UHV transmission lines, a value of 1.3 is used. Therefore, the actual number of relay stations... for: ; The rules for correcting special sections are as follows: For mountainous areas with significant signal attenuation, the length of a single segment is shortened to 40-50km, and the number of relay stations is increased accordingly; for areas prone to faults such as lightning strikes and external damage, 1-2 additional relay stations are added; for long-span sections with a span of >1000m, one relay station is set up on each side of the crossing tower. S122. Relay station location optimization based on GDOP minimization: The objective function for optimizing relay station locations is to minimize the average geometrical precision factor (GDOP) across the entire line, taking into account engineering constraints. ; in, The average GDOP value across the entire line; This represents the number of discrete sampling points along the line (in this embodiment, one sampling point is taken every 100m). Sampling points The GDOP value at that location is calculated using the following formula: , For three-dimensional positioning error, This refers to time synchronization error; The constraints are as follows: 1. Location constraints: The relay station must be set up on existing towers, with priority given to angle towers and tension towers; 2. Spacing constraints: , The distance between adjacent relay stations; 3. Accuracy constraint: The GDOP value at any sampling point ≤ 2.0; 4. Cost constraint: The total number of relay stations shall not exceed ; The optimization solution steps are summarized as follows: (1) Extract the coordinates of all towers along the line and select towers that avoid areas with strong electromagnetic interference and areas prone to geological disasters as initial candidate points; (2) The initial relay station layout is generated using the uniform distribution method; (3) Calculate the GDOP value of all sampling points along the entire line under the initial layout; (4) The relay station positions are iteratively adjusted using the particle swarm optimization algorithm until the objective function converges; (5) Verify the engineering feasibility of the optimal layout and make fine adjustments.

[0020] Step 2: Event-level time synchronization based on OPGW reciprocal time-frequency transfer.

[0021] like Figure 2 As shown, bidirectional time-frequency transmission and time synchronization are achieved using the reciprocity of the OPGW link. For a two-end architecture, a unified timestamp is directly obtained from both ends. For a multi-end architecture, nanosecond-level time synchronization of the entire network is achieved through chain reciprocal time synchronization and Kalman filtering. The clock deviation output is used for time alignment and arrival time difference calculation of all subsequent sensor data.

[0022] S21. Dual-end time synchronization signal interaction and timestamp acquisition: The first terminal station (master station) sends a time synchronization pulse sequence with a local transmission timestamp to the second terminal station (remote station); the second terminal station receives and records the reception timestamp, and then sends a return pulse sequence with a local transmission timestamp back to the first terminal station; the first terminal station receives and records the reception timestamp. The time synchronization pulses can be carried by an independent wavelength and staggered from the sensor acquisition time slot to suppress crosstalk; Let the clock of the first terminal station be... The second terminal station clock is Record four timestamps within a single time period: The time when the first terminal station sends the time synchronization pulse (in words) (Timer) The second terminal station receives the pulse at the time (in...) (Timer) The timing of the second terminal station sending the return pulse (in words) (Timer) The timing of the first terminal station receiving the transmitted pulse (in words) (Timer) S22. Solving for double-ended clock skew and time unification: By utilizing the reciprocity of round-trip propagation delay, the clock offset at both ends can be estimated. , defined as the deviation of the second terminal station clock relative to the first terminal station clock: ; in, The sign and size are used to map the data from the second terminal station to the time base of the first terminal station, or vice versa; To suppress jitter and occasional errors, adjustments can be made within multiple consecutive time synchronization cycles. Obtain by performing moving median filtering or robust regression. It is used to correct the timestamps of the sensor sampling; after correction, the timestamps of all sensor data at both ends are unified to the same time base, providing a high-precision time basis for subsequent arrival time difference inversion, event sequence determination and causal chain inference. S23, Multi-terminal chain reciprocal time synchronization protocol: For including For a multi-terminal architecture with individual terminals, perform network-wide time synchronization according to the following steps: S231, When adjacent end stations are paired: and By sequentially executing the above two-end reciprocal time synchronization procedure, the adjacent clock offsets are obtained. ; S232, Forward Clock Deviation Transmission: Based on the master station Using time as a reference, any terminal station The clock deviation relative to the master station is: ; S233, Reverse Validation and Anomaly Removal: From a remote station The clock skew is transmitted in reverse and compared with the forward result. When the skew exceeds 3 times the standard deviation, it is marked as an outlier and triggers a re-alignment of the segment. S24, Full network clock Kalman filter synchronization: To suppress clock drift and random noise, a Kalman filter model is established to estimate the clock skew of the entire network in real time. State vector: ,in For the first Clock drift rate of each terminal station; Observation vector: ; State transition equation: ,in Here is the state transition matrix. This is process noise; Observation equation: ,in For the observation matrix, To observe noise; Kalman filtering can improve the time synchronization accuracy of the entire network, meeting the requirements for three-dimensional meter-level positioning; at the same time, the time synchronization period can be adaptively adjusted.

[0023] Step 3: Distributed joint acquisition of multi-parameter data on the same fiber and decoupling of strain and temperature.

[0024] Joint acquisition of acoustic vibration and strain temperature is achieved on the same OPGW fiber, and the cross-sensitive observations are decoupled into independent strain and temperature fields. The output is used as the input for the propagation variable speed model and event feature extraction.

[0025] S31. Joint Acquisition Timing and Channel Organization: On the same optical fiber in OPGW, optical signals of different measurement systems are carried by wavelength division multiplexing, and sampling time slots are separated by time division scheduling, so that distributed acoustic vibration sampling and distributed strain and temperature sampling can be controlled to alternate or run in parallel in time, and the distance coordinates along the line can be collected. Multiparameter sequences include: Acoustic vibration observation Characterizes micro-vibrations or acoustic disturbances along the line; Brillouin frequency shift observations Characterizes the coupled changes in temperature and strain; Rayleigh phase observation : Characterizes phase changes that are strain-dominated or temperature-coupled; The specific physical implementation of the above observations can be accomplished by existing distributed sensing devices. This invention focuses on its unified time synchronization, decoupling and subsequent inference process. For a multi-terminal architecture, each relay station and remote station synchronously execute the above acquisition process and upload the preprocessed data to the main station. S32. Cross-sensitivity decoupling yields independent strain and temperature: For each distance position With time The change relative to the baseline is used as the object of decoupling: ; ; in The reference time selected for the fault-free or steady-state period can be determined by automatically selecting the time window with the minimum noise during the initial stage of operation; A linear sensitivity model is used to map the two types of observations to strain changes. With temperature change : ; in, The sensitivity coefficient is determined based on calibration tests or manufacturer parameters for the type of optical fiber and installation method used, and can be finely adjusted online after commissioning through the steady-state temperature drift section and the low-disturbance section. The sensitivity coefficient matrix must satisfy the invertibility condition to ensure decoupling is feasible; Decoupled output and They will be used for subsequent online updates of propagation speed and identification of slow variable fault evolution, respectively.

[0026] Step 4: Online update of the segmented variable-speed propagation model and spatiotemporal inversion and localization of events.

[0027] like Figure 3 As shown, the line is divided into segments according to the tower span, and the segment state parameters are inverted using the strain temperature field obtained by decoupling, thereby updating the propagation speed of each segment online; on this basis, the dual-end architecture uses the time difference of arrival to invert the one-dimensional position, and the multi-end architecture integrates the OPGW catenary mechanical model to achieve three-dimensional precise positioning.

[0028] S41. Line segmentation and parameter aggregation: Based on the tower coordinates and span, the line is divided into Each segment is denoted as _____. Corresponding length For each segment at time By pooling its state parameters and using conventional mechanical inversion methods in this field: Segmented effective tension It can be obtained from the average strain within the segment and the elastic parameters of the conductor through conventional mechanical relationships, or from the sag tension solution program combined with the strain-temperature field iteration; Piecewise equivalent linear density The mass per unit length is determined by the conductor's own weight and the mass of any additional loads that may be applied. It can be estimated by combining the temperature field, sag variation, and wind load model.

[0029] S42. Online update of segmented propagation speed and calculation of travel time: For events dominated by mechanical disturbances (rapid components of acoustic vibration and strain), the propagation velocity of each segment is updated using a tension wave velocity model. And calculate the travel time between any two points. : ; in, The coordinates are the location coordinates along the line, which can be any terminal station location or event candidate location; From arrive The number of segments traversed by the path; The determination is based on the output of step two. Convergence results and conductor structure parameters; The determination is based on the conductor type parameters and the estimated results of the additional load; For temperature-dominated slow-variable events, an equivalent velocity field based on thermal diffusion and convective heat transfer can be used as a substitute. Its update input also comes from This maintains a consistent framework for velocity to change online with the state; S43. Event Candidate Extraction and Multi-Terminal Arrival Time Determination: Under a unified timestamp, the acoustic-vibration channel With decoupled Event detection is performed separately, and the detection can employ adaptive threshold energy mutation discrimination and spatiotemporal connected component clustering: At each distance point Calculate the short-window energy or envelope amplitude and compare it with the steady-state noise statistics; aggregate the spatiotemporal points that exceed the threshold into candidate event regions to obtain the coarse localization interval and candidate occurrence time window of the event; For each candidate event, extract the arrival time of the event from the observation sequences of all terminal stations. The arrival time is defined as the moment when the candidate event region first crosses the threshold after matched filtering or correlation enhancement, in order to reduce the impact of noise on the first arrival time. S44. Inversion of one-dimensional event location and occurrence time in dual-end architecture: For dual-end architecture ( Let the coordinates of the two ends along the line be respectively. and The candidate event source location is The consistency between the arrival time difference at both ends and the time difference of the segmented journey is used for inversion. : ; in, The search interval is preferably selected as the coarse localization interval corresponding to the candidate event region, so as to reduce the amount of computation and improve stability; The residual threshold is calculated based on the online-updated segmented velocity from the previous sub-step. The value of the residual threshold in the above formula is determined by the time synchronization accuracy, sampling period and velocity inversion error, and can be obtained through statistics of fault-free disturbance sections after commissioning. Seek Then, the time of the event can be... Inversion or Furthermore, a consistency test is used to eliminate outlier results, thereby providing a reliable time of occurrence for subsequent event sequencing and causal inference. S45, Multi-terminal architecture based on OPGW catenary model for 3D positioning: For multi-terminal architecture ( The 3D positioning is achieved by integrating the OPGW catenary mechanical model with multi-end TDOA information. The specific steps are as follows: S451, OPGW Real-time Catenary Coordinate Calculation: OPGW conductors exhibit a catenary shape under gravity, and their coordinate equations are as follows: ; in, , The tension at the lowest point of the conductor. For conductor linear density, It is the acceleration due to gravity; The coordinates of the lowest point of the conductor are given; using the strain-temperature field obtained from decoupling in step three, the coordinates of each span are calculated in real time. The values ​​are used to obtain the real-time three-dimensional coordinate sequence of the OPGW conductor; S452, Multi-terminal TDOA 3D positioning solution: Let the three-dimensional coordinates of the fault source be... The time of occurrence is , No. The coordinates of each terminal station are The fault signal arrived at the first The time of each terminal station is Then the signal propagation time satisfies: ; in The length of the OPGW conductor element is given. The real-time propagation speed at the micro-element is calculated by the piecewise variable speed model in step S42. Using the arrival times of at least four terminal stations, the three-dimensional coordinates and occurrence time of the fault source are solved by nonlinear least squares method: ; in The total number of terminal stations, For reference station; S453. Confidence assessment of positioning results: The covariance matrix of the positioning results is calculated to obtain the 95% confidence interval of the 3D positioning error. When the confidence interval exceeds 10m, multi-terminal data resampling and secondary positioning are automatically triggered. The probability distribution cloud map of the fault location is output to provide an intuitive reference for operation and maintenance personnel.

[0030] Step 5: Calculation of directional index and generation of propagation direction constraints.

[0031] Within the event neighborhood, the directionality index is calculated by utilizing the trend of the first arrival time with distance to determine whether the propagation is from upstream to downstream or vice versa, and the direction information is used as a constraint for causal inference.

[0032] S51, Extraction of the first arrival time curve in the neighborhood: The location of the event source obtained in step four Select a spatial neighborhood centered on the target area. For each discrete location within this neighborhood Extract the first or peak time of the event. Forming spatiotemporal sample pairs ; S52, Calculation of directional index: right Perform linear robust fitting to obtain the slope And its symbol defines the directional index. : ; in, This represents the number of sampling points in the neighborhood, and its value is determined based on the spatial resolution of distributed sensing and the spatial influence range of the event space. and They are respectively and The mean; when When the absolute value is lower than the preset lower limit, it can be determined that the direction is not significant, and the weight of the direction constraint will be reduced or the direction constraint will not be applied in subsequent causal inference. pass The direction of event propagation can be obtained, and together with the segmented variable speed travel time, it forms a computable physical prior, avoiding the misjudgment of secondary fluctuations as source point triggers.

[0033] Step 6: Complex fault cause-effect graph inference, root cause identification, and confidence output.

[0034] The detected sub-events are transformed into nodes with time location and parametric features. Candidate causal edges are generated based on the consistency of travel time and directional constraints. The causal chain and root cause confidence are output by selecting the least cost.

[0035] S61. Sub-event nodeization and feature encoding: Record each event obtained in step four as a node. Its attributes include at least: Location of occurrence (Dual-terminal architecture uses one-dimensional coordinates, multi-terminal architecture uses three-dimensional coordinates) and the time of occurrence. (Obtained from inversion in step four); parametric eigenvectors ,Depend on The amplitude, rising edge, duration, and dominant frequency band within the event's spatiotemporal neighborhood are calculated; directional index. (Obtained from step five); S62. Candidate Causal Edge Generation and Constraint Filtering: For any two nodes ,satisfy The consistency between the time difference and the travel time predicted by the propagation model is calculated to generate candidate directed edges. Consistency checks include: Time consistency: utilizing Estimate from spread to The travel time, and with Compare; Directional consistency: requires that the edge direction be consistent with... The indicated propagation direction is compatible, or the constraint strength is reduced when the direction is not significant; Physical reachability: By combining the velocity domain and propagation range of event types, unreasonable edges are eliminated, thereby suppressing meaningless cross-domain connections; S63. Cause-and-effect diagram selection, root cause scoring, and confidence level calculation: On the candidate edge set, select causal structures that satisfy the constraints of acyclicity and connectivity to minimize the overall time consistency residual, using the root cause candidate set as an example. For the set of all nodes with an in-degree of zero, for each root cause candidate... Calculate the residual cost of its corresponding causal structure. Output the root cause confidence level. : ; in, For The set of causal edges selected for the root node can be obtained by solving the minimum-cost directed generation structure; The smaller the value, the stronger the spatiotemporal consistency of the propagation under the explanation of that root cause; The segmented speed change model calculation from step four ensures that the cost function is adaptively updated in response to the real-time status of the line. The normalized confidence level is determined by the sensitivity of the exponential mapping to residual differences, and is suitable for converting residual differences into comparable confidence outputs. The final output includes: a sequence of composite fault sub-events. The order of occurrence, the dominant direction of propagation, the causal link structure, and the root cause node. and its confidence level It can also simultaneously output the residuals of key causal edges for interpretation and verification by the operations and maintenance side.

[0036] Through the detailed description of the above embodiments, the accurate detection method for composite faults in transmission lines based on OPGW fiber optic sensing of the present invention fully utilizes the existing OPGW fiber optic resources of the transmission lines, eliminating the need for additional dedicated sensing lines and reducing the cost and difficulty of engineering implementation. This method solves the problems of low time synchronization accuracy and susceptibility to interference in traditional detection methods through a three-level distributed sensor network architecture and reciprocal time-frequency transfer synchronization technology; it achieves synchronous acquisition of multi-dimensional status information of the line through joint acquisition and decoupling technology of multiple parameters on the same fiber; and it improves the fault location accuracy and the ability to identify the root causes of composite faults through a segmented variable-speed propagation model and causal inference mechanism. This method can adapt to transmission lines with different terrains and voltage levels, providing reliable technical support for online status monitoring, fault early warning, and rapid response of transmission lines, and contributing to improving the safety and stability of power grid operation.

[0037] The preset parameters in the above formulas shall be set by those skilled in the art according to the actual situation.

[0038] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0039] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0040] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0041] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0042] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0043] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0044] The above description is merely a specific embodiment 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.

Claims

1. A method for accurate detection of composite faults in transmission lines based on OPGW fiber optic sensing, characterized in that, The method flow is as follows: Step 1: Construct a sensor network architecture. Using OPGW optical fibers laid along the entire transmission line as the sensing carrier, build a three-level distributed sensor network consisting of a master station, relay stations, and remote stations. Each level of station is equipped with reciprocal time-frequency transmission time synchronization, multi-parameter sensing acquisition, and data processing units. Adjacent stations transmit time synchronization signals and acquire sensor signals through OPGW optical fibers. The master station is responsible for the time reference management of the entire network, multi-terminal data fusion, and comprehensive fault analysis. Step 2: Achieve network-wide time synchronization. Utilize the reciprocity of the OPGW link for bidirectional time-frequency transmission to achieve high-precision time synchronization across the entire network. The output clock deviation is used for time alignment and time difference calculation of all sensor data. Step 3: Multi-parameter acquisition and decoupling. A distributed joint acquisition of multiple parameters such as acoustic vibration, strain and temperature is realized on the same OPGW fiber, and the cross-sensitive observations are decoupled into independent strain fields and temperature fields. Step 4: Event spatiotemporal inversion and positioning. Divide the line into segments according to the tower span, use the decoupled strain temperature field to invert the segment state parameters, update the propagation speed of each segment online, and complete the event spatiotemporal inversion and positioning based on the speed. Step 5: Generate propagation direction constraints, calculate directional indices within the event neighborhood, determine the event propagation direction, and use it as a constraint condition for causal inference; Step 6: Causal inference and root cause identification. The detected sub-events are transformed into nodes containing the location, time of occurrence, parametric feature vectors, and directional indicators. Candidate causal edges are generated based on propagation constraints. The causal chain and root cause identification results are obtained and output through minimum cost selection.

2. The method for accurate detection of composite faults in transmission lines based on OPGW optical fiber sensing according to claim 1, characterized in that, In the three-level distributed sensor network architecture, the number and location of relay stations are determined by multiple constraints coupled together, including signal transmission, time synchronization accuracy, positioning accuracy, and engineering cost. First, the theoretical minimum number of relay stations is calculated and a redundancy coefficient is introduced to obtain the actual number. Then, the relay station locations are optimized by combining engineering constraints with the goal of minimizing the average geometric accuracy factor of the entire line. During the optimization process, towers that meet the conditions are selected as candidate points. The relay station locations are adjusted through iterative algorithms and the engineering feasibility is verified.

3. The method for accurate detection of composite faults in transmission lines based on OPGW optical fiber sensing according to claim 1, characterized in that, Specifically, the high-precision time synchronization across the entire network involves obtaining a unified timestamp for both ends directly through bidirectional time-frequency transmission for a dual-end architecture; for a multi-end architecture, the two-end reciprocal time synchronization of adjacent stations is performed sequentially, and then the clock deviation is transmitted forward based on the master station. At the same time, outliers are verified and eliminated through reverse transmission. Finally, a Kalman filter model is established to estimate the clock deviation of the entire network in real time, suppressing clock drift and random noise.

4. The method for accurate detection of composite faults in transmission lines based on OPGW optical fiber sensing according to claim 1, characterized in that, The multi-parameter distributed joint acquisition specifically involves carrying optical signals of different measurement systems on the same OPGW fiber through wavelength division multiplexing, separating sampling time slots through time division scheduling, and synchronously acquiring acoustic vibration, Brillouin frequency shift, and Rayleigh phase observations; the strain-temperature decoupling specifically involves using the change in relative steady-state baseline as the decoupling object, and using a linear sensitivity model to map the coupled observations into independent strain and temperature changes.

5. The method for accurate detection of composite faults in transmission lines based on OPGW optical fiber sensing according to claim 1, characterized in that, The event spatiotemporal inversion positioning specifically involves first dividing the line into segments based on tower coordinates and span, then aggregating the effective tension and equivalent linear density state parameters of each segment, updating the propagation speed of each segment based on the tension wave velocity model, and calculating the travel time between any two points. Then, the arrival time of events at each terminal station is extracted under a unified timestamp. The dual-terminal architecture uses the consistency of arrival time difference and segmented travel time difference to invert the one-dimensional location and occurrence time of events.

6. The method for accurate detection of composite faults in transmission lines based on OPGW optical fiber sensing according to claim 5, characterized in that, For the multi-terminal architecture, the three-dimensional coordinates of the catenary of the OPGW conductor are first calculated in real time using the strain temperature field obtained by decoupling. Then, the arrival time information of the multi-terminals is fused, and the three-dimensional coordinates and occurrence time of the fault source are solved by nonlinear least squares method. At the same time, the covariance matrix of the location result is calculated and the confidence level is evaluated.

7. The method for accurate detection of composite faults in transmission lines based on OPGW optical fiber sensing according to claim 1, characterized in that, The calculation of the directional index and the generation of propagation direction constraints are specifically as follows: a spatial neighborhood is selected with the location of the event source as the center; the first arrival time of the event at each discrete location within the neighborhood is extracted to form a spatiotemporal sample pair; a linear robust fitting is performed on the spatiotemporal sample pair to obtain the slope; the directional index is defined by the slope sign; the propagation direction of the event is determined based on the directional index and used as a constraint condition for causal inference.

8. The method for accurate detection of composite faults in transmission lines based on OPGW optical fiber sensing according to claim 1, characterized in that, The composite fault causal graph inference and root cause identification specifically involves transforming each detected event into a sub-event node containing the occurrence location, occurrence time, parametric feature vector, and directional index; for any two nodes that satisfy the temporal sequence relationship, candidate directed causal edges are generated by filtering through temporal consistency, directional consistency, and physical reachability constraints; and causal structures that satisfy acyclic and connectivity constraints are selected from the candidate edge set to determine the root cause candidate set.

9. The method for accurate detection of composite faults in transmission lines based on OPGW optical fiber sensing according to claim 8, characterized in that, For each root cause in the root cause candidate set, calculate the overall time consistency residual cost of its corresponding causal structure. Based on the residual cost, obtain the normalized confidence of each root cause through exponential mapping. Select the root cause with the highest confidence as the final identification result, and output the sub-event sequence, causal link structure and root cause confidence of the composite fault.

Images