Fault diagnosis system and method for live installation of power transmission line independent of account information

Abstract

The application relates to the technical field of power transmission line monitoring, and discloses a live installation power transmission line fault diagnosis system and method which are independent of account information. The system comprises a plurality of intelligent diagnosis terminals capable of being live self-alignment installed on a power transmission line and a cloud platform. Self-alignment installation is realized through detection of magnetic gap change and correction of a closing angle. The method comprises the following steps: collecting electrical signals by using the terminals; obtaining a propagation time delay through cross-correlation analysis of terminal signals, and then performing spatial self-calibration to solve the relative spatial positions among the terminals; calculating a preliminary fault position based on the relative positions and the time when a fault wave arrives; and finally outputting a positioning result with high precision and high reliability through a weighted fusion based on confidence weights and an abnormal data self-suppression algorithm. The application solves the problem that the prior art is dependent on an account and has high installation risks, and realizes intelligentization, automation and high precision of fault diagnosis.

Description

Technical Field

[0001] This invention relates to the field of power transmission line monitoring technology, specifically to a fault diagnosis system and method for energized power transmission lines that does not rely on ledger information. Background Technology

[0002] As a crucial link in power transmission, the operating status of transmission lines directly affects the safety and stability of the power grid. When a line fault occurs, quickly and accurately locating the fault is essential for shortening power outage time and improving power supply reliability.

[0003] The existing fault diagnosis and location of transmission lines mainly rely on two types of technologies: centralized fault location systems and distributed online monitoring devices.

[0004] Centralized systems measure distance by analyzing voltage and current at substation ports, but their calculations are highly dependent on parameters such as line length, impedance, and capacitance. In real-world line environments with multiple branches and complex grounding, discrepancies between the recorded data and the actual physical conditions can lead to significant accumulation of location errors. Furthermore, the long signal transmission path makes transient characteristics susceptible to noise interference and channel delays after long-distance transmission, resulting in insufficient feature extraction accuracy.

[0005] While distributed monitoring devices can be deployed along the line, closer to the fault point, their installation method remains a major bottleneck in current applications. Existing devices mostly employ sensing structures that require installation under power outage conditions, or clamp-on transformers used during live-line work. For 110kV and above high-voltage lines, existing clamp-on structures generally suffer from insufficient insulation margin and poor casing withstand voltage. More importantly, the angle and tightness during installation are difficult to control precisely, which not only introduces safety risks but also measurement errors. Even with auxiliary means such as drones, manual approach to the conductor is usually still required to complete the final closure operation, resulting in low automation levels and failing to meet the safe, reliable, and consistent installation requirements of high-voltage lines.

[0006] At the algorithm level, traditional traveling wave positioning methods rely heavily on accurate line data, such as conductor wave velocity, phase sequence, and tower span. In actual engineering projects, the information in these records is often inaccurate due to untimely updates or discrepancies with the actual site conditions, directly causing fundamental errors in positioning calculations and hindering the flexibility of transferring and deploying the equipment across different lines.

[0007] Furthermore, when using multi-point monitoring to improve coverage, existing technologies still have shortcomings in effectively integrating data from various monitoring points. Simple information aggregation or averaging methods are insufficient to effectively assess and differentiate the reliability of data from different nodes. When the signal quality of a monitoring point deteriorates due to factors such as strong local electromagnetic interference, its abnormal data can interfere with the overall positioning results, thereby reducing the robustness and reliability of the diagnostic results. Summary of the Invention

[0008] The technical problem to be solved by the present invention is that existing power transmission line fault diagnosis systems suffer from problems such as reliance on line ledger information, complex and high-risk installation process, and insufficient positioning accuracy.

[0009] To address the aforementioned technical problems, this invention provides a fault diagnosis system and method for live-line transmission lines that does not rely on ledger information. This solution employs terminal self-alignment live-line installation, adaptive calibration of key parameters, and a ledger-free positioning algorithm to improve the safety, accuracy, and automation level of transmission line fault diagnosis.

[0010] The first aspect of this invention provides a fault diagnosis system for energized transmission lines that does not rely on ledger information, the system comprising:

[0011] At least two wire-coupled intelligent diagnostic terminals, each of which is configured to be automatically installed on the transmission line under power and used to couple and collect electrical signals from the transmission line;

[0012] A cloud-based data processing platform is connected to the at least two wire-coupled intelligent diagnostic terminals via a communication network. The cloud-based data processing platform is configured as follows:

[0013] Receives and aggregates electrical signals collected by the wire-coupled intelligent diagnostic terminal;

[0014] Based on the electrical signal, spatial self-calibration is performed to determine the relative spatial position between the at least two wire-coupled intelligent diagnostic terminals;

[0015] Based on the relative spatial position and the fault information in the electrical signal, the fault location is solved and output.

[0016] In one specific embodiment, the wire-coupled intelligent diagnostic terminal includes a split magnetic core, a Hall array arranged at the mating point of the split magnetic core, and a control module. During the energized automatic installation closing process, the control module uses the Hall array to detect changes in magnetic induction intensity between the air gaps of the magnetic core in real time, and inversely calculates the local air gap size at each measuring point based on a magnetic circuit model. A vectorization algorithm is used to determine the alignment degree of the magnetic core and calculate the eccentricity vector.

[0017] In one specific embodiment, the wire-coupled intelligent diagnostic terminal acquires electrical signals through electromagnetic and capacitive dual coupling, and utilizes the inherent consistency constraints of the two channels in steady-state and transient processes to construct and minimize a joint error function in order to adaptively estimate key parameters such as equivalent mutual inductance, coupling capacitance, and wire-to-ground capacitance.

[0018] By solving for the minimum value of the joint error function, parameter estimates are obtained, and the line voltage signal is reconstructed based on the estimated parameters.

[0019] Preferably, the system further includes a ground control and communication coordination unit. This unit is deployed when the uplink signal strength of any wire-coupled intelligent diagnostic terminal is detected to be lower than -75dBm or the link packet loss rate exceeds 10%, or the measured local electromagnetic interference intensity exceeds 120dBμV / m. This unit is used to undertake the functions of system clock reference maintenance, data aggregation, and channel coordination.

[0020] Preferably, during spatial self-calibration, the cloud-based data processing platform calculates the cross-correlation function between the current signals collected by any two adjacent wire-coupled intelligent diagnostic terminals, and determines the time delay at which the function reaches its maximum value as the propagation delay of the transient waveform between the two terminals. Subsequently, based on the propagation delay vectors between all adjacent terminals, the relative spatial position vector is solved by least squares estimation.

[0021] A second aspect of the present invention provides a method for diagnosing faults in energized transmission lines that does not rely on ledger information, the method comprising the following steps:

[0022] S1: Use at least two conductor-coupled intelligent diagnostic terminals distributed on the transmission line to collect the electrical signals of the transmission line;

[0023] S2: Perform spatial self-calibration, obtain the signal propagation delay by performing cross-correlation analysis on the electrical signal, and calculate the relative spatial position between the at least two wire-coupled intelligent diagnostic terminals based on the propagation delay;

[0024] S3: Identify the arrival time of the fault wave detected by each wire-coupled intelligent diagnostic terminal, and calculate the preliminary fault location based on the difference between the arrival times and the relative spatial position;

[0025] S4: Weighted fusion of the multiple preliminary fault locations calculated by multiple wire-coupled intelligent diagnostic terminals to obtain a global location estimate as the final fault location and output it.

[0026] In one specific embodiment, the uncertainty variance of the positioning result in the weighted fusion in step S4 is jointly determined by the signal-to-noise ratio, waveform correlation, and time delay estimation residual of each wire-coupled intelligent diagnostic terminal. The confidence weight of each wire-coupled intelligent diagnostic terminal is calculated based on the uncertainty variance, wherein the smaller the uncertainty variance, the larger the confidence weight.

[0027] The global location estimate is obtained by weighting multiple preliminary fault locations based on the confidence weights.

[0028] Preferably, a self-consistency check step is included after step S4. This step calculates the fusion residual between the global location estimate and each preliminary fault location. If any residual exceeds a preset confidence threshold, the corresponding terminal is determined to be an anomalous node. The confidence weight of the anomalous node is reduced by an exponential function, and the global location estimate is recalculated to achieve self-suppression of anomalous nodes and improve the robustness of the positioning results.

[0029] Preferably, the method further includes a step of automatically installing the wire-coupled intelligent diagnostic terminal under power before fault diagnosis. This step includes: transporting the terminal to the vicinity of the target wire and automatically aligning it; detecting the wire vibration amplitude and the ambient electric field strength, and automatically driving the split housing to close after confirming that the safety closure conditions are met; performing self-aligning angle correction based on the magnetic gap information fed back by the Hall array during the closure process, and performing coupling integrity detection and signal quality self-check after the housing is locked.

[0030] Preferably, the traveling wave velocity required to calculate the preliminary fault location in step S3 is obtained by performing a self-consistent solution on the electromagnetic and capacitive dual-coupling model of the conductor-coupled intelligent diagnostic terminal, rather than relying on pre-stored line ledger information, thereby ensuring the ledger independence of the entire diagnostic method.

[0031] This invention provides a fault diagnosis system and method for energized transmission lines that does not rely on ledger information. It has the following beneficial effects:

[0032] 1. This invention employs a split electromagnetic-capacitor composite coupling structure, combined with Hall array-based real-time magnetic gap detection and intelligent closure angle control technology, to achieve fully automated installation and disassembly of the device without power interruption. This self-aligning installation process eliminates the need for manual precision operations near high-voltage conductors, significantly reducing the inherent risks of live-line work, shortening installation and deployment time, and ensuring the consistency and reproducibility of the installation process.

[0033] 2. This invention solves the problem of unknown or inaccurate line parameters in field applications through algorithmic innovation. On the one hand, the system utilizes cross-correlation analysis of signals collected by each terminal to achieve self-calibration of the relative spatial positions between nodes; on the other hand, by constructing and minimizing a joint error function of electromagnetic and capacitive dual channels, it adaptively estimates key electrical parameters such as coupling capacitance and equivalent mutual inductance. This allows the device of this invention to be directly deployed on transmission lines of different voltage levels and types without the need for cumbersome pre-configuration of parameters.

[0034] 3. This invention proposes a multi-node result weighted fusion algorithm. This algorithm quantifies the uncertainty of the localization results based on factors such as the signal-to-noise ratio and waveform correlation of each diagnostic terminal signal, and assigns corresponding confidence weights, thus allowing nodes with higher signal quality to dominate the final result. Furthermore, the system automatically identifies and suppresses abnormal data caused by localized strong interference or single-point faults through a fusion residual and self-consistency verification mechanism, ensuring the robustness and accuracy of fault localization results in complex electromagnetic environments. Attached Figure Description

[0035] Figure 1 This is a schematic diagram of the system architecture of one embodiment of the present invention;

[0036] Figure 2 This is a structural block diagram of a wire-coupled intelligent diagnostic terminal according to an embodiment of the present invention;

[0037] Figure 3 This is a flowchart of a live installation and disassembly method according to an embodiment of the present invention;

[0038] Figure 4 This is a flowchart of a live-line fault diagnosis method that does not rely on ledger information, according to an embodiment of the present invention.

[0039] Among them, 10. Wire-coupled intelligent diagnostic terminal; 11. Upper shell module; 12. Lower shell module; 13. Power management module; 14. Control and communication module; 20. Protection and shielding layer; 31. Ground main control and communication coordination unit; 42. Cloud data processing platform; Detailed Implementation

[0040] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0041] See attached document Figure 1 The present invention provides a fault diagnosis system for live-line transmission lines that does not rely on ledger information. The system mainly consists of three parts: a number of conductor-coupled intelligent diagnostic terminals 10 deployed in a distributed manner, a ground main control and communication coordination unit 20, and a cloud data processing platform 30.

[0042] The conductor-coupled intelligent diagnostic terminal 10 is a device that can automatically install and disconnect while energized, and it is directly coupled to a high-voltage transmission line. This terminal has the function of measuring the power frequency and transient electrical parameters of the conductor, and also integrates control communication, self-sustaining power supply, and local preliminary analysis functions.

[0043] The ground control and communication coordination unit 20 is used to receive signals transmitted back from each conductor-coupled intelligent diagnostic terminal 10 and perform synchronization management. In application scenarios with poor signal transmission conditions or severe electromagnetic interference along the transmission line, this unit is configured to ensure the reliability of system communication and the accuracy of clock synchronization. Under good communication conditions, this unit may not be required.

[0044] The cloud-based data processing platform 30 is used to aggregate data from all distributed wire-coupled intelligent diagnostic terminals 10, perform time-difference matrix operations and fault location calculations, and finally output fault diagnosis results. This platform utilizes a self-learning algorithm to optimize the signal propagation model and characteristic parameters online.

[0045] The various components of the above system work together, and its overall workflow is as follows:

[0046] First, during the system deployment phase, the conductor-coupled intelligent diagnostic terminal 10 is transported to the vicinity of the predetermined installation location on the target power transmission line using conveyor tools such as live-line working robots, drone platforms, or insulated telescopic arms. Remote commands are sent via the ground control and communication coordination unit 20 or an independent remote control terminal, and the conductor-coupled intelligent diagnostic terminal 10 executes an automatic closing and locking procedure, reliably fixing itself to the energized conductor, thus completing the live-line installation process.

[0047] After installation, the wire-coupled intelligent diagnostic terminal 10 powers on and automatically starts up. It then automatically establishes communication links with other nearby terminals or the ground control and communication coordination unit 20 via its built-in wireless communication module, forming a distributed monitoring network. Each terminal uses its built-in BeiDou / GPS module to receive standard time signals, achieving nanosecond-level time synchronization across the entire network. Subsequently, the system enters a self-calibration phase. By performing correlation analysis on the background traveling wave signals generated along the entire line, it calculates the signal propagation delay between each terminal and then deduces their relative spatial positions on the line. This process requires no geometric or electrical ledger information about the line.

[0048] Under normal operating conditions, all conductor-coupled intelligent diagnostic terminals 10 continuously collect voltage and current signals of the line at a high sampling rate for local monitoring. The system continuously monitors the operating status of the line, and the data can be periodically uploaded to the cloud data processing platform 30 to form a panoramic view of the line's operating status.

[0049] When a short circuit or ground fault occurs at any point on the transmission line, the resulting traveling wave fault signal propagates along the conductor to both sides. Each conductor-coupled intelligent diagnostic terminal 10 deployed along the line captures this transient traveling wave signal. The terminal's local processing unit immediately performs multi-feature analysis on the acquired signal, identifying and confirming the occurrence of the fault transient through multiple criteria such as waveform derivative, phase change, and energy change, while accurately recording the arrival time of the fault traveling wave.

[0050] After confirming the fault, each wire-coupled intelligent diagnostic terminal 10 that captured the fault signal uploads transient waveform data with precise timestamps to the cloud data processing platform 30 via a wireless network. The cloud data processing platform 30 collects fault data from at least two terminals, and uses the arrival time difference of the fault wave recorded by each terminal and the relative position information obtained during the self-calibration phase to construct a set of positioning equations to calculate the location of the fault point. To improve positioning accuracy, the cloud data processing platform 30 adopts a weighted fusion algorithm, determining the confidence weight of each data source based on information such as the signal-to-noise ratio and feature clarity of the data uploaded by each terminal, ultimately obtaining a globally optimal fault location estimate.

[0051] Finally, the cloud-based data processing platform 30 publishes the calculated fault location information, along with its confidence interval, through the user terminal or power grid dispatching system interface, providing precise guidance for rapid line inspection and emergency repair. When maintenance or recycling of a conductor-coupled intelligent diagnostic terminal 10 is required, a separation command can be sent remotely. The conductor-coupled intelligent diagnostic terminal 10 will automatically execute the unlocking and disengagement procedures, safely detaching from the conductor for recycling.

[0052] See attached document Figure 2 This embodiment provides a modular structural design for a wire-coupled intelligent diagnostic terminal 10. The wire-coupled intelligent diagnostic terminal 10 adopts a split electromagnetic-capacitor composite coupling structure, which includes an upper shell module 11, a lower shell module 12, a control and communication module 13, and a protection and shielding layer 14.

[0053] The upper housing module 11 and the lower housing module 12 are connected by an electrically driven rotary closing mechanism, which drives the two housing modules to close and separate relative power transmission lines. After the two housing modules are closed, they are locked by a magnetic locking structure. The upper housing module 11 contains the electrically driven closing mechanism, the conductor positioning assembly, and part of a ring-shaped magnetic core. The lower housing module 12 integrates a signal conditioning unit, a capacitor voltage divider measurement plate, and a power management module. The control and communication module 13 is located inside the lower housing module 12.

[0054] The wideband current measurement unit of this terminal adopts a split-core zero-flux current transformer structure. The upper housing module 11 contains a half-ring core, and the lower housing module 12 contains the corresponding other half-ring core, compensation coil, and flux detection winding. When the device is closed, the upper and lower cores form a closed magnetic circuit for coupling conductor current.

[0055] During the closed-loop installation phase, a multi-point Hall magnetic field sensor array is used to achieve real-time magnetic gap detection and intelligent control of the closing angle in order to realize the self-alignment of the magnetic core. The principle is as follows:

[0056] Treating the magnetic core and air gap as a series magnetic circuit, the magnetic flux... With electric excitation satisfy: ;

[0057] The total magnetic circuit reluctance It is given by the following formula: ;

[0058] For commonly used high-permeability materials and short air gaps, the air gap term dominates, therefore it can be approximated as follows:

[0059] ;

[0060] in, For air gap size, The permeability of free space, The cross-sectional area of ​​the magnetic circuit. This is the length of the magnetic circuit in the magnetic core. denoted as ρ, where ρ is the relative permeability of the magnetic core.

[0061] Therefore, when a known excitation current is applied through the calibration coil Generate magnetomotive force At that time, the magnetic induction intensity generated at the air gap With air gap size Inversely proportional: ;

[0062] Several Hall sensors are arranged at equal angles around the mating surfaces of the magnetic core to measure the local normal magnetic induction intensity. The system is based on calibration coefficients. Through formula Calculate the local air gap size at each measuring point .in, By comparing and estimating the air gap air gap with target of (take Local error The lateral eccentricity vector of the magnetic core can be calculated. ;in, For the first The position normal unit vector of each sensor. This is the weighting coefficient for this measurement point.

[0063] Estimate the lateral eccentricity vector using each vector. ;

[0064] Among them, weight Desirable Alternatively, simply set it to 1 to reduce the impact of noise. Vector This indicates the offset of the magnetic core center relative to the conductor center (direction and amplitude are proportional). The closing process is controlled by the rotation angle θ of the upper shell around the hinge. For small angle approximations, fine-tuning of the angle is possible. Lateral displacement with the center of the magnetic core Linear relationship: ;

[0065] in This is the effective arm length from the end of the upper housing to the hinge. Therefore, the closing angle correction can be obtained from: ; where, vector magnitude Indicates the eccentricity (unit: mm). The direction needs to be... Decompose it into rotational direction (clockwise / counterclockwise) to move the center of the magnetic core along The direction is close to the center of the conductor. More precise control can be achieved using proportional-derivative control:

[0066] ;

[0067] in For proportional gain, For differential gain, The mapping factor (based on the geometric transformation matrix) for the current rotation direction of the upper shell to the eccentric vector is pre-calculated from the device's geometric parameters in engineering implementation or estimated linearly during runtime. A single proportional control can be used, and the maximum angle increment can be limited. (This invention takes) To ensure a smooth closure.

[0068] The criteria for successful device closure include the following conditions, which must be met simultaneously:

[0069] 1) Local air gap absolute tolerance, ;

[0070] in This is a preset air gap tolerance threshold, for example, 0.1 mm;

[0071] 2) eccentric vector magnitude, ,in The preset eccentricity threshold is, for example, 0.05mm;

[0072] 3) Magnetic flux stability, standard deviation of magnetic field strength within a short time window: ,in This is a preset magnetic flux stability threshold, for example, 0.5mT.

[0073] The wide-frequency voltage measurement unit of the wire-coupled intelligent diagnostic terminal 10 adopts a circumferential capacitor voltage divider structure. The arc-shaped conductive electrode plates set on the inner surfaces of the upper housing module 11 and the lower housing module 12 form a spatial electric field coupling with the wire.

[0074] To eliminate the capacitance between the conductor and ground Equivalent distributed capacitance The wires and the device form an equivalent mutual inductance through a magnetic circuit. Line distributed inductance Due to the dependence of parameters on the line ledger, this invention adopts a self-consistent solution approach with joint time-domain and frequency-domain constraints. It utilizes the inherent consistency constraints of electromagnetic and capacitive channels in steady-state and transient processes to inversely calculate the unknown parameters. From Kirchhoff constraints, we can obtain:

[0075] ,in, This is the potential of the conductor. For the current in the conductor, This is the equivalent capacitance of the conductor to ground. The current signal measured by the device. This is the coupling capacitance between the device and the wire.

[0076] By minimizing the following joint error function To achieve equivalent mutual inductance between the device and the conductor. Coupling capacitors and the equivalent capacitance of the conductor to ground The estimate is expressed as: ,in, The potential signal measured by the device. Distributed inductance for the line, These are the weighting coefficients.

[0077] After the parameters are calculated, the line voltage signal It can be reconstructed by the following formula: ;

[0078] The power management module 123 integrates a multi-source energy harvesting system, including an induced current energy harvesting unit, a photovoltaic auxiliary module, and a supercapacitor energy storage unit. This module employs an adaptive energy distribution control strategy. When the conductor current is high (e.g., within the range of 10A to 800A), the system is primarily powered by the induced current energy harvesting unit; when the conductor current is too low or absent, the system switches to power supply from the photovoltaic auxiliary module. The supercapacitor energy storage unit is used to maintain stable system operation during energy source switching or short-term interruptions.

[0079] The control and communication module 13 is the control core of the entire wire-coupled intelligent diagnostic terminal 10. Its hardware architecture adopts a collaborative working mode of microcontroller (MCU) and field-programmable gate array (FPGA). This module is responsible for the overall status perception, command response, data acquisition, algorithm execution, and self-diagnosis of the device. The communication function is implemented by the built-in Beidou / GPS dual-mode positioning module and the wireless self-organizing network communication unit supporting LoRa and 4G dual channels, which is used to obtain accurate time and location information and complete data interaction with the cloud platform and other terminals.

[0080] The terminal's overall casing consists of a protective and shielding layer 14. The outer layer uses an epoxy-silicone rubber composite insulation material to ensure insulation performance under high-voltage conditions. The inner surface is equipped with a metal shielding mesh to create a uniform electric field region, shield against external electromagnetic interference, and provide a stable reference ground for the wide-frequency voltage measurement unit. Simultaneously, the control system incorporates multiple detection and protection logics for overvoltage, overcurrent, arc discharge, and mechanical jamming to ensure the safety of the device during installation and operation.

[0081] See attached document Figure 3 This embodiment provides a live installation and dismantling method applicable to 110kV and above high voltage transmission lines. This method enables the conductor-coupled intelligent diagnostic terminal 10 to perform automatic closing and fixing, signal access and disconnection recovery under the condition that the transmission line is not powered off.

[0082] First, in the pre-positioning phase, a transport tool such as a live-line working robot, drone platform, or insulated telescopic arm is used to transport the wire-coupled intelligent diagnostic terminal 10 to the vicinity of the target wire, for example, at a distance of approximately 300 mm to 500 mm. Upon receiving a remote command, the control system of the wire-coupled intelligent diagnostic terminal 10 activates its internal attitude adjustment mechanism, using data from the gyroscope and vision module to align with the center of the wire. When the center deviation between the terminal and the wire is less than a preset threshold (e.g., ±3 mm), the system enters the pre-alignment completion state.

[0083] Simultaneously with pre-positioning, an array of electric field sensors deployed on the outer surface of the housing of the wire-coupled intelligent diagnostic terminal 10 detects the distribution of the ambient electric field intensity to determine the energized state of the wire. If the electric field intensity exceeds a safety threshold (e.g., 35 kV / cm), the control system will restrict its further approach. The control system also needs to comprehensively assess parameters such as wire temperature, potential gradient, and magnetic field stability to evaluate the environmental conditions of the installation point.

[0084] Subsequently, during the conductor proximity and safety assessment phase, the terminal's capacitive voltage divider sampling device samples the conductor's potential. Combined with the phase of the induced signal from the built-in current sensor, it determines the conductor's potential polarity, phase sequence, and corresponding phase. Its vision module employs edge detection and spectrum analysis algorithms to calculate the conductor's instantaneous vibration amplitude and angular displacement. When the conductor's vibration acceleration is less than a preset value (e.g., 0.5g) and the sway amplitude is less than a preset value (e.g., 10 mm), the conductor is deemed to be in a stable state.

[0085] This stage includes environmental safety signal interlocking logic: when an excessively high electric field strength is detected, the closing action is prohibited; when severe conductor vibration is detected, a predetermined delay (e.g., 5 seconds) is applied before reassessment; when potential difference fluctuations exceed the range, reference point calibration is performed. Only after all safety conditions are met can the control system generate a signal allowing closure.

[0086] During the automatic closing and locking phase, upon receiving the closing execution command, the control system drives the bidirectional electric lead screw mechanism located between the upper housing module 11 and the lower housing module 12, causing the upper housing module 11 to rotate and close at a set speed. During the closing process, a Hall sensor array monitors the closing angle and torque changes of the housing in real time. When the monitored data meets the criteria for successful closing, the control system reduces the closing speed. Finally, the magnetic latch engages and locks, simultaneously sending a closing completion signal. At this point, the mechanical position is locked, and the system switches to coupling verification mode.

[0087] After entering the coupling calibration and self-test phase, the control system of the wire-coupled intelligent diagnostic terminal 10 sequentially detects the magnetic flux density of the magnetic core, the reactance of the capacitive coupling network, and the status of the mechanical latch to assess the integrity of the coupling. If any parameter exceeds the preset range, the system executes an automatic correction procedure or sends a prompt message to the remote terminal. After completing the integrity test, the terminal completes a signal quality self-test within a specified time (e.g., 1 second) to ensure that the distortion rate of the acquired signal is less than a preset value (e.g., 3%) and the coupling coefficient is stable.

[0088] When maintenance or recycling of the terminal is required, an automatic separation and recycling phase is executed. The remote terminal sends a separation execution command to the target terminal. Upon receiving the command, the electric screw mechanism reverses its direction, causing the upper housing module 11 to open to its initial position of 120°. During this process, the magnetic locking latch releases, disengaging the mechanical fixation. After the housing is fully opened, the system enters a safety detection mode, confirming via an electric field sensor that the wire-coupled intelligent diagnostic terminal 10 has been safely separated from the wire. Subsequently, a drone or robot can retrieve the terminal.

[0089] See attached document Figure 4This embodiment provides a fault diagnosis method for energized transmission lines that does not rely on ledger information. This method is based on several conductor-coupled intelligent diagnostic terminals 10 distributed along the transmission line. Through the fusion analysis of time-domain, amplitude, and phase information of local electrical signals, it achieves adaptive identification and precise location of the fault start and end sections. This method does not rely on traditional ledger information (such as conductor span, wave velocity, phase sequence number, etc.) and can complete line self-calibration, feature self-alignment, and location inversion without manual configuration.

[0090] Step S1 of this method is the signal acquisition and preprocessing stage. Assume the system has N diagnostic nodes distributed along the line direction, with node numbers as follows: At any given time , No. The voltage and current signals measured at each node are denoted as follows: and The acquired signal is normalized and denoised to obtain the filtered signal. and : ; ;

[0091] in, and These are the average values ​​of the current and voltage signals, respectively. and These are the standard deviations of the current and voltage signals, respectively.

[0092] Step S2 of this method is the multi-feature extraction stage. Key parameters reflecting the characteristics of fault disturbances are extracted from the preprocessed transient signal to form a feature vector. : ;

[0093] in, Represents the derivative of the current waveform. This represents the phase abrupt change angle between voltage and current. This indicates the magnitude of the energy mutation.

[0094] Energy mutation amplitude The calculation is based on the following: First, the electromagnetic energy density per unit length of the line can be expressed as... ,in Capacitance per unit length Let be the inductance per unit length. The electromagnetic energy density per unit length of a circuit can be expressed as: ;

[0095] The rate of change of local energy measured by the device is defined as: ;

[0096] To identify energy abrupt change points, this invention employs a joint time-frequency domain abrupt change detection method. Its core is to calculate the instantaneous abrupt change amplitude of the energy change rate, and the mean energy value within a sliding window. It can be represented as:

[0097] ;

[0098] The absolute value of the difference between instantaneous energy and mean energy is defined as the amplitude of energy mutation: .

[0099] Step S3 of this method is the transient determination stage. To distinguish between fault transients and non-fault disturbances, a triple determination condition is set. When any node satisfies two or more criteria simultaneously, it is confirmed to enter the transient detection state. These criteria include:

[0100] 1) Waveform derivative criterion, if If it is, then it is determined to be a transient state, where This is a coefficient, with a value between 3 and 4. The standard deviation of the derivative noise;

[0101] 2) Phase abrupt change criterion, if If so, it is determined to be a transient state;

[0102] 3) Energy mutation criterion, if If it is, then it is determined to be a transient state, where This represents the standard deviation of energy fluctuations under normal operating conditions. This is an empirical coefficient, with a value ranging from 4 to 6.

[0103] Step S4 of this method is the self-calibration stage. This is due to the spatial coordinates of the nodes. The method is unknown; it reconstructs the equivalent distance relationship between nodes through signal cross-correlation self-calibration. First, it calculates the distance between any two adjacent nodes. and Current signal cross-correlation function:

[0104] ;

[0105] when When the maximum value is reached, This represents the propagation delay of the transient waveform detected by the two nodes. This is due to the signal propagation speed... Approximately consistent within the same conductor, a self-consistent equation can be established:

[0106] ,in This refers to the speed of signal propagation.

[0107] right This calculation is repeated at each node to construct N-1 constraint equations, and the relative spatial position vector is obtained through least squares estimation. Let be a column vector representing the time delays between nodes. This is the node difference matrix. This process is the self-calibration of the ledgerless space.

[0108] Step S5 of this method is the location and solution stage. Each node automatically identifies the arrival time of the fault traveling wave based on the current change criterion. The criterion is:

[0109] ,in This represents the standard deviation of current fluctuations during normal node operation. This is an empirical threshold (typically 3-5). This criterion can effectively distinguish between transient disturbances and steady-state noise. If the fault point is located at a node... and Between them, the arrival time difference between the two nodes is... Combined with the relative distance obtained during the self-calibration phase The distance from the fault point to the first fault can be calculated. Distance between nodes ,in Equivalent wave velocity calculated by the electromagnetic-capacitive dual coupling model.

[0110] To improve the robustness of localization, this method employs a multi-node result weighted fusion algorithm based on confidence weights. Assume each node... The independently calculated fault location estimate is , Indicates the first The distance of the estimated fault point relative to each node. The absolute position of the reference node from the starting point of the line. The relative distance increment is obtained from node measurements; The estimation error follows a mean of 0 and a variance of 0. The Gaussian distribution of is expressed as: ,in Indicates the first The uncertainty variance of the localization results of each node is determined by the signal-to-noise ratio of the node signal, the waveform correlation, and the time delay fitting residual.

[0111] To reflect the reliability of data from different nodes, node confidence weights are defined. , among which, among which And satisfy: .

[0112] variance Calculated from the correlation index of signal characteristics: ;

[0113] in, This represents the cross-correlation coefficient between the node's sampled signal and the network-wide average signal. To estimate the residual for time delay, and These are weighting coefficients. Representing the The variance of the delay estimation residuals for each node. By fusing the results from all nodes, the globally optimal fault location estimate is obtained as follows: The fusion formula satisfies the minimum variance unbiased estimation condition and can maintain optimal estimation performance even when the measurement accuracy of each node varies greatly.

[0114] Step S6 of this method involves fusing the residuals and performing a self-consistency test. The fusion residuals are defined as follows:

[0115] ;

[0116] If there are residuals at the nodes ( If the confidence threshold is set to 2 to 3, then the node is considered an anomalous node. For such anomalous nodes, their weights are... Will be reallocated to:

[0117] And recalculate the fusion position. This step enables self-suppression of abnormal nodes and optimizes the robustness of the localization results.

[0118] Step S7 of this method involves calculating the final output and confidence interval. The system ultimately outputs the fault location result. and its 95% confidence interval:

[0119] Among them, fusion variance Calculated by the following formula: The output provides both the most likely location of the fault and a quantitative boundary for the uncertainty of its estimation.

[0120] The final result provides both the most likely location of the fault and the uncertainty boundary of the location estimate, providing a quantitative basis for subsequent inspections.

Claims

1. A fault diagnosis system for live-line transmission lines that does not rely on ledger information, characterized in that, include: At least two wire-coupled intelligent diagnostic terminals, each of which is configured to be automatically installed on the transmission line under power and used to couple and collect electrical signals from the transmission line; The wire-coupled intelligent diagnostic terminal includes a split magnetic core, a Hall array arranged at the mating point of the split magnetic core, and a control module. The control module is configured as follows: During the closing process of the energized automatic installation, the Hall array is used to detect the change in magnetic induction intensity between the air gaps of the magnetic core in real time, and the local air gap size is inverted based on the preset magnetic circuit model. The eccentricity vector characterizing the alignment degree of the magnetic core is calculated based on the local air gap size. The closing angle is automatically corrected based on the eccentric vector to achieve self-aligning installation. A cloud-based data processing platform is connected to the at least two wire-coupled intelligent diagnostic terminals via a communication network. The cloud-based data processing platform is configured as follows: Receives and aggregates electrical signals collected by the wire-coupled intelligent diagnostic terminal; Based on the electrical signal, spatial self-calibration is performed to determine the relative spatial position between the at least two wire-coupled intelligent diagnostic terminals; Based on the relative spatial position and the fault information in the electrical signal, the fault location is solved and output.

2. The fault diagnosis system for live-line transmission lines that does not rely on ledger information as described in claim 1, characterized in that, The wire-coupled intelligent diagnostic terminal is also configured to: Electrical signals are acquired through a dual electromagnetic and capacitive coupling method. By utilizing the inherent consistency constraints between the electromagnetic and capacitive channels in steady-state and transient processes, a joint error function is constructed and minimized to adaptively estimate the parameters of equivalent mutual inductance, coupling capacitance, conductor-to-ground capacitance, and line distributed inductance. Based on these four parameters, the line voltage signal is reconstructed.

3. The fault diagnosis system for energized transmission lines that does not rely on ledger information as described in claim 1, characterized in that, The system also includes a ground control and communication coordination unit, which is configured as follows: When the uplink signal strength of any of the wire-coupled intelligent diagnostic terminals is detected to be lower than the first preset threshold, when the link packet loss rate exceeds the second preset threshold, or when the measured local electromagnetic interference intensity exceeds the third preset threshold, deployment is carried out to undertake the functions of system clock reference maintenance, data aggregation and channel coordination.

4. The fault diagnosis system for live-line transmission lines that does not rely on ledger information as described in claim 1, characterized in that, The cloud-based data processing platform is specifically configured as follows during the execution of space self-calibration: Calculate the cross-correlation function between the current signals collected by any two adjacent wire-coupled intelligent diagnostic terminals; The time delay at which the cross-correlation function reaches its maximum value is determined as the propagation delay of the transient waveform between the two wire-coupled intelligent diagnostic terminals. Based on the propagation delay between all adjacent wire-coupled intelligent diagnostic terminals, the relative spatial positions of the at least two wire-coupled intelligent diagnostic terminals are solved by least squares estimation.

5. A method for diagnosing faults in live-line transmission lines that does not rely on ledger information, applied to the live-line transmission line fault diagnosis system that does not rely on ledger information as described in any one of claims 1-4, characterized in that, Includes the following steps: S1: Use at least two conductor-coupled intelligent diagnostic terminals distributed on the transmission line to collect the electrical signals of the transmission line; S2: Perform spatial self-calibration, obtain the propagation delay by performing cross-correlation analysis on the electrical signals, and calculate the relative spatial position between the at least two wire-coupled intelligent diagnostic terminals based on the propagation delay; S3: Identify the arrival time of the fault wave detected by each wire-coupled intelligent diagnostic terminal, and calculate the preliminary fault location based on the difference between the arrival times and the relative spatial position; S4: Weighted fusion of the multiple preliminary fault locations calculated by multiple wire-coupled intelligent diagnostic terminals to obtain a global location estimate as the final fault location and output it.

6. The method for diagnosing faults in energized transmission lines that does not rely on ledger information, as described in claim 5, is characterized in that... In step S4, the weighted fusion specifically includes: The uncertainty variance of the positioning result is determined by comprehensively considering the signal-to-noise ratio, waveform correlation, and time delay fitting residual of each wire-coupled intelligent diagnostic terminal. The confidence weight of each wire-coupled intelligent diagnostic terminal is calculated based on the aforementioned uncertainty variance. The global location estimate is obtained by weighting multiple preliminary fault locations based on the confidence weights.

7. The method for diagnosing faults in energized transmission lines that does not rely on ledger information, as described in claim 6, is characterized in that... The process after step S4 also includes: Calculate the fusion residual between the global location estimate and each preliminary fault location; If any fusion residual exceeds a preset confidence threshold, the corresponding wire-coupled intelligent diagnostic terminal is determined to be an abnormal node. The confidence weight of the anomalous node is reduced, and the global position estimate is recalculated to achieve self-suppression of the anomalous node.

8. The method for diagnosing faults in energized transmission lines that does not rely on ledger information, as described in claim 5, is characterized in that... The method further includes a step of automatically installing the wire-coupled intelligent diagnostic terminal under power before fault diagnosis, specifically including: The wire-coupled intelligent diagnostic terminal is transported to the vicinity of the target wire and its attitude is adjusted to achieve automatic alignment; The vibration amplitude of the conductor and the intensity of the ambient electric field were tested to confirm that the safety closure conditions were met; The split-type housing is automatically driven to close, and self-aligned angle correction is performed based on the magnetic gap information fed back by the Hall array during the closing process; After the housing is locked, coupling integrity testing and signal quality self-test are performed.

9. The method for diagnosing faults in energized transmission lines that does not rely on ledger information, as described in claim 5, is characterized in that... In step S3, the traveling wave velocity required to calculate the preliminary fault location is obtained by performing a self-consistent solution on the electromagnetic and capacitive dual-coupling model of the conductor-coupled intelligent diagnostic terminal, rather than relying on line ledger information.

Images