A multi-parameter perception-based distribution line fuse intelligent monitoring method and device

Abstract

The application relates to the technical field of power distribution network monitoring, and particularly discloses a power distribution network line fuse intelligent monitoring method and device based on multi-parameter sensing, which comprises the following steps: collecting fuse lattice strain, contact micro-displacement, harmonic spectrum and medium decomposition products, calculating a harmonic thermal equivalent coefficient, decoupling to generate a lattice intrinsic strain field, identifying a micro-wear phase point and a medium effective heat absorption rate; inputting the above parameters into a multi-physical field degradation deduction model to output whisker critical bridging risk, contact resistance nonlinear jump probability distribution and limit breaking capacity attenuation coefficient; calculating an actual protection capability boundary according to the risk value and generating a protection characteristic drift trajectory, deducing a selective protection mismatch area and an evolution trend after time-space alignment of multi-stage fuse drift trajectories, and generating and issuing a reconstruction instruction containing an adjusted rated current, a corrected upper-level delay or a started bypass protection. The application realizes quantitative identification of a fuse microscopic degradation process and dynamic reconstruction of protection capability.

Description

Technical Field

[0001] This invention relates to the field of power distribution network monitoring technology, specifically to a method and device for intelligent monitoring of power distribution line fuses based on multi-parameter sensing. Background Technology

[0002] As the final link in the power system, the distribution network directly serves a large number of electricity users, and its operational reliability is crucial to the normal order of social production and residents' lives. As the most widely used primary equipment in distribution network lines, fuses play a critical role in overload protection and short-circuit fault isolation, and their health status directly determines the continuity and security of power supply from the distribution network.

[0003] Currently, the main development trends in fuse condition monitoring technology are as follows: First, threshold detection methods based on electrical parameters monitor parameters such as the effective value of the current flowing through the fuse and the surface temperature of the contacts, triggering an alarm when these parameters exceed preset thresholds. Second, visual inspection methods based on image recognition capture images of the fuse using a camera to determine if the fuse tube has fallen or if there is obvious burning. Third, fault reporting methods based on communication technology transmit fault signals to the maintenance master station via auxiliary contacts after the fuse trips. These technologies, to a certain extent, enable remote sensing of fuse status, providing data support for distribution network operation and maintenance.

[0004] However, existing technologies have the following systemic limitations:

[0005] First, monitoring methods are limited to macroscopic appearances and cannot detect microscopic degradation processes. Fuse failure is not an instantaneous event, but a gradual evolutionary process from the microscopic to the macroscopic. In actual operation, fuses are subjected to long-term coupled stress from multiple physical fields: metal whiskers grow on the fuse surface due to electrochemical migration and stress migration; the contact surface deteriorates due to long-term fretting wear; and the arc-extinguishing medium's insulation performance declines due to repeated arc decomposition and metal vapor contamination. These microscopic degradation processes are characterized by their high degree of concealment and long evolution cycle. Existing monitoring methods based on macroscopic electrical quantities cannot achieve early identification, and the cause can often only be traced after the fault occurs.

[0006] Secondly, the lack of understanding of multi-physics coupling effects prevents the revelation of latent failure mechanisms. More critically, the aforementioned microscopic degradation does not occur in isolation but involves complex nonlinear coupling relationships: whisker growth alters the local electric field distribution, affecting the Joule heating effect; the lifting effect of whiskers on the contact surface disturbs the contact pressure, accelerating contact resistance jumps; metal particles detached during whisker growth contaminate the arc-extinguishing medium, lowering the breakdown threshold; and corrosive gases generated by medium decomposition accelerate contact surface oxidation. This degradation coupling effect causes the actual state of the fuse to deviate from the design expectations, and current technologies analyze each physical field separately, failing to reveal the latent failure mechanisms under coupling effects.

[0007] Third, there is a mismatch between the static setting of protection settings and the dynamic degradation of equipment status. The protection characteristics of fuses, namely rated current, breaking capacity, and operating time, are fixed at the factory. However, in actual operation, with the accumulation of micro-degradation, their actual protection capability will dynamically drift. In a distribution network topology with multi-stage fuse cascade protection, the attenuation of the protection capability of a certain stage fuse may lead to the disruption of the selective coordination relationship between upstream and downstream stages, causing cascading tripping or protection failure to operate, and expanding the scope of power outages. In existing technologies, protection settings are all statically set and cannot be dynamically adjusted according to the actual degradation state of the equipment. This contradiction between static settings and dynamic degradation has existed for a long time without effective resolution.

[0008] In summary, the key technical bottlenecks that urgently need to be overcome in the field of fuse monitoring are: how to break through the limitations of macroscopic appearance monitoring and achieve quantitative perception of the microscopic degradation process of fuses; how to reveal the nonlinear coupling mechanism of multi-physics field degradation and achieve early identification of hidden failure risks; and how to achieve dynamic reconstruction of protection capabilities based on microscopic degradation deduction. Summary of the Invention

[0009] The purpose of this invention is to provide a method and device for intelligent monitoring of distribution network line fuses based on multi-parameter sensing, so as to solve the technical problems of existing technologies that cannot sense the degradation process of the micro-physical field of fuses, cannot reveal the dynamic drift of protection capability caused by multi-physical field coupling, and the mismatch between static protection settings and the actual degradation state of equipment.

[0010] To solve the above-mentioned technical problems, the present invention specifically provides the following technical solution:

[0011] A method for intelligent monitoring of distribution network line fuses based on multi-parameter sensing includes the following steps:

[0012] S1. Obtain the lattice strain of the fuse, the fretting displacement of the contact, the harmonic spectrum and the decomposition products of the medium, calculate the harmonic thermal equivalence coefficient, decouple the generated lattice intrinsic strain field, and identify the fretting wear phase point and the effective heat absorption rate of the medium.

[0013] S2. Input the intrinsic strain field of the lattice, the phase point of fretting wear, the effective heat absorption rate and the harmonic thermal equivalence coefficient into the multiphysics field degradation model, and output the whisker critical bridging risk, the nonlinear jump probability distribution of the contact resistance of the contact and the attenuation coefficient of the ultimate breaking capacity.

[0014] S3. Calculate the actual protection capability boundary of the fuse based on the output of the multiphysics degradation model, deduce its selective protection mismatch region and evolution trend in the cascaded topology, and generate and issue reconfiguration instructions that include adjusting the rated current, correcting the upper-level delay, or starting the bypass protection.

[0015] As a preferred embodiment of the present invention, S1 specifically includes:

[0016] S11. Lattice strain distribution data are acquired by an optical fiber Bragg grating array embedded on the surface of the fuse, the amplitude sequence of contact micro-displacement is acquired by a high-frequency capacitive micro-displacement sensor, the harmonic current spectrum characteristics are acquired by a broadband current transformer, and the concentration of chemical decomposition products is acquired by an electrochemical gas sensor array.

[0017] S12. Based on the amplitude and phase of each harmonic in the harmonic current spectrum characteristics, calculate its equivalent heating weight with the fundamental current, generate the harmonic thermal equivalence coefficient, and based on the harmonic thermal equivalence coefficient, remove the thermal strain component caused by the Joule heating of the harmonics from the lattice strain distribution data to generate the lattice intrinsic strain field that only reflects the electro-stress effect.

[0018] S13. Perform sliding window peak detection on the micro-displacement amplitude sequence, and mark the moment when the displacement amplitude changes abruptly as the contact micro-displacement wear phase point; calculate the arc energy value that the remaining medium can absorb based on the ratio of the concentration of the chemical decomposition products to the initial total amount of the arc extinguishing medium, combined with the preset medium heat absorption efficiency calibration curve, as the effective heat absorption rate of the arc extinguishing medium.

[0019] As a preferred embodiment of the present invention, S12 specifically includes:

[0020] S121. The edge sensing node performs spectral analysis on the collected harmonic current spectral characteristics and extracts the amplitude ratio and phase information of each harmonic current relative to the fundamental current.

[0021] S122. Based on the equivalent heating effect of each harmonic current relative to the fundamental frequency, calculate the weighted heating contribution value of each harmonic current, and sum the weighted heating contribution values ​​of all harmonics to generate the harmonic thermal equivalence coefficient used to characterize the additional Joule heat intensity of the harmonics.

[0022] S123. Obtain the pre-calibrated thermal expansion characteristic parameters of the fuse material and the transient temperature field distribution on the fuse surface measured in real time by distributed temperature sensors;

[0023] S124. Based on the harmonic thermal equivalence coefficient and the transient temperature field distribution on the fuse surface, calculate the thermal strain component caused by the Joule heating added by the harmonic current, subtract the thermal strain component from the original acquired lattice strain distribution data, and generate the intrinsic lattice strain field that only reflects the lattice distortion caused by the electro-stress effect.

[0024] As a preferred embodiment of the present invention, S2 specifically includes:

[0025] S21. Input the lattice intrinsic strain field, contact fretting wear phase point, arc extinguishing medium effective heat absorption rate and harmonic thermal equivalence coefficient into the pre-trained fuse multiphysics field degradation derivation model. The model includes a whisker growth dynamics submodule, a contact resistance evolution submodule and a dielectric breakdown characteristic submodule.

[0026] S22. The whisker growth kinetics submodule is based on the coupling of electrochemical migration kinetics and lattice strain energy to simulate the electromigration and stress migration process of metal atoms on the surface of the filament, calculate the whisker nucleation density and growth rate, and output the probability density integral value of the whisker length reaching the filament gap distance as the whisker critical bridging risk assessment value.

[0027] S23. The contact resistance evolution submodule calculates the Joule heat accumulation effect of the contact surface based on the coupling relationship between the phase point of the contact fretting wear and the harmonic thermal equivalence coefficient, and introduces the whisker critical bridging risk assessment value as a negative disturbance factor of the contact pressure, and outputs the nonlinear jump probability distribution of the contact resistance of the contact through Monte Carlo simulation.

[0028] S24. The dielectric breakdown characteristic submodule calculates the dielectric heat capacity decay rate based on the effective heat absorption rate of the arc-extinguishing dielectric, and calculates the dielectric breakdown field strength reduction coefficient based on the concentration of metal particles generated by whisker growth. The two are coupled together to dynamically correct the ultimate breaking capacity decay coefficient of the fuse.

[0029] As a preferred embodiment of the present invention, S22 specifically includes:

[0030] S221. The whisker growth dynamics submodule obtains the intrinsic strain field of the lattice as input and extracts the lattice distortion gradient and strain energy density distribution at each position on the surface of the filament.

[0031] S222. Based on the electrochemical migration kinetics model, the electromigration flux of metal atoms on the fuse surface is calculated according to the harmonic thermal equivalence coefficient, and the stress migration flux driven by the strain gradient is calculated according to the intrinsic strain field of the lattice.

[0032] S223. The electromigration flux and stress migration flux are coupled and superimposed to generate the total mass transport rate of metal atoms on the surface of the filament, and the nucleation position and nucleation density of the whisker at the grain boundary are simulated accordingly; based on the whisker growth kinetic equation after nucleation, combined with the local strain energy release rate and atomic diffusion coefficient, the growth rate and length evolution trajectory of the whisker in the time and space dimensions are calculated.

[0033] S224. Compare the current whisker length with the minimum gap distance between adjacent conductors of the fuse, calculate the probability density that the whisker length reaches or exceeds the gap distance, and generate the whisker critical bridging risk assessment value after integration.

[0034] As a preferred embodiment of the present invention, S23 specifically includes:

[0035] S231. The contact resistance evolution submodule obtains the phase point of contact fretting wear and identifies the time node and rupture intensity of the oxide film periodically broken due to fretting wear on the contact surface.

[0036] S232. Calculate the additional Joule heat accumulation generated by the harmonic current flowing through the contact point of the contact according to the harmonic thermal equivalence coefficient, and determine the temperature rise rate and peak temperature of the contact point after each oxide film rupture by combining the fretting wear phase point.

[0037] S233. Introduce the whisker critical bridging risk assessment value as a negative disturbance factor of contact pressure, and calculate the dynamic attenuation of contact pressure based on the lifting effect of whisker growth on the contact surface; substitute the temperature rise rate, peak temperature and dynamic attenuation of contact pressure into the physical model of contact resistance, and calculate the transient jump amplitude of resistance caused by thermal softening and pressure reduction in the micro-region of the contact point.

[0038] S234. Considering the randomness of the fretting wear cycle and harmonic thermal effect, Monte Carlo simulation is performed on the resistance jump events in multiple wear cycles, the probability density of the jump amplitude exceeding the preset threshold is statistically analyzed, and the nonlinear jump probability distribution of the contact resistance of the contact is output.

[0039] As a preferred embodiment of the present invention, S24 specifically includes:

[0040] S241. The medium breakdown characteristic submodule obtains the effective heat absorption rate of the arc-extinguishing medium, compares it with the initially calibrated rated heat absorption rate of the medium, and calculates the percentage of heat capacity decay caused by chemical decomposition of the medium.

[0041] S242. Based on the stated heat capacity attenuation percentage, combined with the thermal conductivity and specific heat capacity parameters of the dielectric material, the reduction in the limiting arc time that the dielectric can withstand under the current arc energy impact is deduced, and a dielectric heat capacity attenuation coefficient is generated.

[0042] S243. Obtain the concentration of metal particles generated synchronously in step S22 by the whisker growth kinetics submodule. The metal particles are metal deposits that fall off from the surface of the fused wire or fail to form complete whiskers during whisker growth. Calculate the suspension density of the metal particles in the arc-extinguishing medium based on the concentration of the metal particles. Combine this with the correlation model between the dielectric breakdown field strength and the impurity concentration to deduce the degree of degradation of the dielectric insulation strength by the metal particles and generate the dielectric breakdown field strength reduction coefficient.

[0043] S244. The dielectric thermal capacity attenuation coefficient and the dielectric breakdown field strength reduction coefficient are coupled and multiplied to obtain the dielectric comprehensive degradation factor. This factor is then multiplied by the initial ultimate breaking capacity calibrated at the factory of the fuse, and the ultimate breaking capacity attenuation coefficient is output after dynamic correction.

[0044] As a preferred embodiment of the present invention, S3 specifically includes:

[0045] S31. The edge sensing node calculates the actual protection capability boundary of the fuse in the current degraded coupling state based on the whisker critical bridging risk assessment value, the nonlinear jump probability distribution of the contact resistance and the attenuation coefficient of the ultimate breaking capacity, and generates the joint evolution curve of the three in the time dimension as the protection characteristic drift trajectory.

[0046] S32. Based on the cascaded coordination relationship of multi-level fuses in the topology of the target distribution network line, the protection characteristic drift trajectory of each fuse is spatiotemporally aligned with a unified fault travel time axis as a reference, the temporal overlap area of ​​the protection capability decline of each fuse is identified, and the selective protection mismatch area and its boundary movement direction and rate under the actual protection capability boundary are deduced.

[0047] S33. Generate a dynamic reconfiguration instruction for protection settings. The instruction includes adjusting the rated current setting of the fuse according to the drift trajectory of the protection characteristics to compensate for the nonlinear superposition effect, correcting the delayed action time of the upper-level fuse to avoid the expected concentrated period of the jump probability distribution in advance, or activating the bypass solid-state protection device to replace the fuse whose attenuation coefficient is about to fall below the safety threshold. The instruction is then sent to the corresponding execution terminal.

[0048] As a preferred embodiment of the present invention, S32 specifically includes:

[0049] S321. Obtain the protection characteristic drift trajectory of each fuse in the target distribution network line topology generated in step S31. The drift trajectory includes the actual protection capability boundary change curve of each fuse on the time axis. Based on the time sequence of the standard fault current crossing from the fault point to the power supply side, establish a unified fault crossing time axis. Map the protection characteristic drift trajectory of each fuse according to its position in the topology to the corresponding time period on the time axis to achieve the spatiotemporal alignment of the protection characteristics of multi-level fuses.

[0050] S322. In the coordinate system after spatiotemporal alignment, compare the actual protection capacity boundary curves of two adjacent fuses to identify the spatiotemporal region where the protection capacity boundary of the upper fuse is lower than that of the lower fuse, and mark it as the selective protection mismatch region.

[0051] S323. Perform time-series tracking on the boundary points of the mismatched region, calculate the moving rate of the boundary points on the fault crossing time axis over time, and the trend of expansion or contraction of the fault current range covered by the mismatched region over time.

[0052] S324. Based on the moving rate and the changing trend, fit and generate the time-varying evolution trajectory of the mismatch region, including the expected location of the boundary of the mismatch region and the cumulative effect of the degree of mismatch within a future preset time window.

[0053] A smart monitoring device for distribution network line fuses based on multi-parameter sensing is used to implement a smart monitoring method for distribution network line fuses based on multi-parameter sensing, comprising:

[0054] Edge sensing nodes, deployed at fuses in the target distribution network, are used to acquire lattice strain distribution data on the fuse surface, micro-displacement amplitude sequence of the contact surface, harmonic current spectrum characteristics flowing through the fuse, and concentration of chemical decomposition products of the arc-extinguishing medium inside the fuse tube. Based on the harmonic current spectrum characteristics, a harmonic thermal equivalence coefficient is calculated. Based on this coefficient, the lattice strain distribution data is thermally-electrically decoupled to generate an intrinsic lattice strain field that eliminates non-fundamental thermal effects. Simultaneously, the micro-displacement amplitude sequence is used to identify the micro-wear phase point of the contact, and the effective heat absorption rate of the arc-extinguishing medium is calculated based on the concentration of chemical decomposition products.

[0055] The multi-physics degradation deduction module is used to input the intrinsic strain field of the lattice, the phase point of the contact fretting wear, the effective heat absorption rate of the arc extinguishing medium and the harmonic thermal equivalence coefficient into the pre-trained fuse multi-physics degradation deduction model, and output the whisker critical bridging risk assessment value, the nonlinear jump probability distribution of the contact resistance and the attenuation coefficient of the ultimate breaking capacity.

[0056] The protection capability dynamic reconfiguration module is used to calculate the actual protection capability boundary of the fuse under the current degraded coupling state based on the whisker critical bridging risk assessment value, the nonlinear jump probability distribution of the contact resistance, and the ultimate breaking capacity attenuation coefficient, and simultaneously generate the protection characteristic drift trajectory; according to the cascade coordination relationship of multi-level fuses in the target distribution network line topology, the protection characteristic drift trajectories of each fuse are spatiotemporally aligned, and the selective protection mismatch region and the time-varying evolution trend of the mismatch region under the actual protection capability boundary are deduced; and a protection setting dynamic reconfiguration instruction is generated and sent to the corresponding execution terminal.

[0057] Compared with the prior art, the present invention has the following advantages:

[0058] 1. By calculating the harmonic thermal equivalence coefficient and stripping the thermal strain, the electro-induced stress effect and harmonic thermal effect in the lattice strain data were accurately decoupled, generating a lattice intrinsic strain field that eliminates non-fundamental thermal interference, providing a clean data foundation for subsequent degradation deduction.

[0059] 2. By constructing a three-dimensional coupled failure inference model, the perturbation effect of whisker growth on contact pressure and its accelerating effect on resistance jump are revealed. Whisker particles are introduced into dielectric breakdown analysis to correct the breaking capacity attenuation coefficient, thereby realizing the quantitative identification and early warning of the hidden degradation process of fuses.

[0060] 3. This invention elevates the microscopic degradation deduction to a protection characteristic drift trajectory, realizing the spatiotemporal alignment and evolution prediction of the mismatch region of multi-level fuse protection capabilities, and generating dynamic reconfiguration instructions to shift the protection capability adjustment from post-event remediation to pre-event avoidance, thus solving the selective protection mismatch problem caused by equipment degradation. Attached Figure Description

[0061] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings in the following description are merely exemplary, and those skilled in the art can derive other embodiments based on the provided drawings without creative effort.

[0062] Figure 1 This is a flowchart illustrating the method described in Embodiment 1 of the present invention.

[0063] Figure 2 This is a frame diagram of the device described in Embodiment 2 of the present invention. Detailed Implementation

[0064] The technical solutions of 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.

[0065] The concepts involved in this application will first be described with reference to the accompanying drawings. It should be noted that the following descriptions of various concepts are only for the purpose of making the content of this application easier to understand and do not constitute a limitation on the scope of protection of this application; furthermore, the embodiments and features in the embodiments of this application can be combined with each other unless otherwise specified. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0066] Example 1

[0067] like Figure 1 As shown, this invention provides an intelligent monitoring method for distribution network line fuses based on multi-parameter sensing, comprising the following steps:

[0068] S1. Obtain the fuse lattice strain, contact fretting displacement, harmonic spectrum, and dielectric decomposition products; calculate the harmonic thermal equivalence coefficient; decouple the generated lattice intrinsic strain field; and identify the fretting wear phase point and the effective heat absorption rate of the dielectric. Specifically, this includes:

[0069] S11. High-fidelity acquisition of multi-source heterogeneous state data, specifically:

[0070] S111. An optical fiber Bragg grating array is embedded at equal intervals along the axial direction on the fuse surface of the target distribution network line fuse. The optical fiber Bragg grating array consists of three single-mode optical fibers, each single-mode optical fiber is engraved with five Bragg grating sensing points, and the spacing between adjacent sensing points is five millimeters. The center wavelength drift of each sensing point is collected by a demodulator, and the center wavelength drift is converted into a strain distribution matrix along the fuse axis according to the pre-calibrated strain-wavelength sensitivity coefficient to generate lattice strain distribution data.

[0071] S112. High-frequency capacitive micro-displacement sensors are arranged symmetrically at the contact surfaces of the upper and lower contacts of the fuse. The probe of the high-frequency capacitive micro-displacement sensor maintains a fixed air gap of 0.5 mm with the contact surface. The normal relative displacement of the contact surface is monitored in real time at a sampling frequency of 50 kHz. The capacitance change is converted into a voltage signal by a charge amplifier. After filtering out power frequency and its harmonic interference by a bandpass filter, a micro-displacement amplitude sequence of the contact is generated.

[0072] S113. A broadband current transformer is connected in series in the conductive circuit of the fuse. The frequency response range of the broadband current transformer is from the power frequency to 2,500 Hz. The instantaneous waveform of the current is acquired by a high-speed analog-to-digital converter at a sampling rate of 256 points per cycle. The amplitude and phase angle of each harmonic are extracted by the fast Fourier transform algorithm to generate the harmonic current spectrum characteristics.

[0073] S114. An electrochemical gas sensor array is arranged in the arc-extinguishing medium filling area inside the fused tube. The electrochemical gas sensor array includes a metal oxide semiconductor sensor sensitive to gaseous products of silicon dioxide and an electrochemical cell sensor sensitive to zinc vapor. The working electrode of the sensor is maintained at a set potential by a potentiostat. The current response value caused by the change in the concentration of gaseous products generated by the arc thermal decomposition of the arc-extinguishing medium is collected. The current response value is converted into a mass concentration value according to the preset gas type calibration curve to generate the concentration of chemical decomposition products.

[0074] S12. Decoupling of harmonic thermal effects and generation of lattice intrinsic strain field, specifically:

[0075] S121. Harmonic current spectral characteristic analysis and phase information extraction, specifically:

[0076] The edge sensing node receives the digital current waveform sequence after analog-to-digital conversion from the broadband current transformer via a high-speed data bus. It then applies a Hanning window function to the digital current waveform sequence to truncate it and suppress spectral leakage. Finally, it calls the Fast Fourier Transform algorithm to convert the time-domain current signal into a frequency-domain spectrum sequence.

[0077] In the frequency domain spectrum sequence, the position of the spectral line corresponding to the fundamental frequency is identified as the reference point. The amplitude of the spectral line corresponding to each harmonic frequency from the second harmonic to the twenty-fifth harmonic is extracted in sequence. The ratio of each harmonic amplitude to the fundamental amplitude is calculated to obtain the amplitude ratio of each harmonic current relative to the fundamental current.

[0078] Simultaneously, the phase angle of each harmonic spectral line relative to the zero-crossing point of the fundamental voltage is extracted. By comparing the difference between the phase of the harmonic current and the phase of the fundamental voltage, the phase information of each harmonic current relative to the fundamental current is determined. The phase information is used to characterize the time lead or lag relationship between the harmonic current and the fundamental current.

[0079] S122. Accumulation of harmonic-weighted heating contributions and generation of thermal equivalence coefficients, specifically:

[0080] The edge sensing node retrieves the pre-written table of resistivity and temperature coefficient of the fuse material and the frequency characteristic curve of the skin effect penetration depth from the local memory. Based on the frequency characteristic curve of the skin effect penetration depth, it determines the effective conductive layer depth of the current on the cross section of the fuse conductor at each harmonic frequency, and calculates the equivalent AC resistance value corresponding to each harmonic by combining the fuse geometry.

[0081] The amplitude percentage of each harmonic extracted from S121 is squared to obtain the squared amplitude percentage value. The squared amplitude percentage value is then multiplied by the equivalent AC resistance value of the corresponding harmonic and the phase correction factor of the harmonic relative to the fundamental wave to obtain the weighted heating contribution value of the harmonic current to generate Joule heat in the fuse.

[0082] The weighted heating contribution values ​​of all subharmonics are arithmetically summed, and the summation result is divided by the baseline value of the heat generated by the fundamental current on a unit resistance. The resulting quotient is the harmonic thermal equivalence coefficient. The harmonic thermal equivalence coefficient is a dimensionless value used to quantify the multiple relationship between the additional heating caused by the harmonic current and the heating of the fundamental current.

[0083] S123. Material thermal expansion characteristic parameter retrieval and transient temperature field acquisition, specifically:

[0084] The edge sensing node reads the thermal expansion characteristics of the fuse material, which have been pre-calibrated through material thermodynamic experiments, from the non-volatile memory. The thermal expansion characteristics include the linear expansion coefficient of the lattice constant as a function of temperature within a specific temperature range, as well as a correction term for the coefficient as a function of temperature.

[0085] Meanwhile, the edge sensing nodes receive monitoring data uploaded by the distributed temperature sensor through the fiber optic communication interface. The distributed temperature sensor uses fiber Raman spectroscopy multiplexing fiber multiplexed with the fiber Bragg grating array. It uses optical time-domain reflectometry to measure the ratio of backscattered Raman light intensity at each location of the fiber. Based on the calibration relationship between the Raman scattered light intensity ratio and temperature, the temperature values ​​of each measuring point distributed along the fuse axis are calculated, generating the transient temperature field distribution on the fuse surface. The transient temperature field distribution data and the lattice strain distribution data are synchronized and aligned in the sampling timestamp.

[0086] S124. Calculation of thermal strain components and decoupling generation of lattice intrinsic strain field, specifically:

[0087] The edge sensing node performs a point-to-point multiplication operation between the transient temperature field distribution on the fuse surface obtained in S123 and the thermal expansion characteristic parameters of the fuse material to obtain the material free expansion strain caused by temperature change. The free expansion strain is then multiplied by the harmonic thermal equivalence coefficient calculated in S122 to calculate the thermal strain component caused by the Joule heat added by the harmonic current at each location of the fuse.

[0088] The edge sensing node takes the raw lattice strain distribution data acquired by the fiber Bragg grating array in S11 as the input of the total strain field. While maintaining the spatiotemporal coordinate alignment, it subtracts the calculated thermal strain component from the strain value at each location point of the total strain field to achieve decoupling and separation of thermal strain and electro-induced strain. The residual strain field obtained after the subtraction operation is the intrinsic lattice strain field that only reflects the lattice distortion caused by the movement of metal ions between the lattices during electrochemical migration. The intrinsic lattice strain field eliminates the interference of harmonic thermal effects and is used to accurately characterize the degree of microstructural damage caused by electrochemical migration inside the fuse.

[0089] S13. Fretting wear phase identification and arc extinguishing medium state quantification, specifically:

[0090] S131. The edge sensing node applies a sliding window with a length of 500 sampling points to the micro-displacement amplitude sequence uploaded by the high-frequency capacitive micro-displacement sensor, calculates the root mean square value of the displacement amplitude in each sliding window as the reference amplitude, and when it detects that the displacement amplitude of ten consecutive sampling points in the window exceeds 1.5 times the reference amplitude and the duration is less than five milliseconds, it is determined to be a micro-impact event, and the start time of the impact event is recorded as the step change moment point.

[0091] The frequency and phase distribution of step change moments within a unit of time are statistically analyzed. When the frequency exceeds a preset wear threshold and the phase distribution is concentrated near the current zero-crossing point, the step change moment is marked as the contact fretting wear phase point. The contact fretting wear phase point is used to characterize the high-risk moment when the oxide layer on the contact surface breaks and regenerates.

[0092] S132. The edge sensing node receives the concentration of chemical decomposition products uploaded by the electrochemical gas sensor array, extracts the concentration of silica gaseous products and zinc vapor, calculates the ratio of the current concentration value to the background concentration when the arc extinguishing medium is initially filled, and obtains the medium decomposition degree coefficient.

[0093] Based on the pre-established calibration curve of the heat absorption efficiency of the arc extinguishing medium, which was obtained by conducting arc heat absorption tests on quartz sand samples with different decomposition degrees in a laboratory environment, the decomposition degree coefficient of the medium was mapped to the percentage of heat absorption efficiency. The percentage of heat absorption efficiency was multiplied by the initial total heat absorption capacity rating of the arc extinguishing medium to calculate the arc energy value that the remaining medium can absorb, which is taken as the effective heat absorption rate of the arc extinguishing medium. The effective heat absorption rate of the arc extinguishing medium is used to characterize the remaining arc extinguishing capacity of the arc extinguishing medium in the current chemical state.

[0094] S2. Input the lattice intrinsic strain field, fretting wear phase point, effective heat absorption rate, and harmonic thermal equivalence coefficient into the multiphysics degradation model, and output the whisker critical bridging risk, the contact resistance nonlinear jump probability distribution, and the ultimate breaking capacity attenuation coefficient; specifically including:

[0095] S21. Construction and data input of multiphysics degradation extrapolation model, specifically:

[0096] The edge sensing node locally deploys a pre-trained fuse multiphysics degradation model, which consists of three coupled sub-modules: whisker growth dynamics, contact resistance evolution, and dielectric breakdown characteristics. The edge sensing node simultaneously inputs the lattice intrinsic strain field generated by decoupling in S124, the contact fretting wear phase point marked in S13, the effective heat absorption rate of the arc extinguishing medium calculated in S13, and the harmonic thermal equivalence coefficient generated in S122 into the fuse multiphysics degradation model as the initial and boundary conditions for the coupled calculation of the three sub-modules.

[0097] S22. Whisker growth kinetics simulation and bridging risk assessment, specifically:

[0098] S221. Analysis of the intrinsic strain field of the lattice and extraction of strain energy density, specifically:

[0099] The whisker growth dynamics submodule receives the intrinsic lattice strain field transmitted by the edge sensing node through the data interface. The intrinsic lattice strain field is three-dimensional tensor data, which characterizes the degree of lattice distortion caused by electrochemical migration at various locations on the fuse surface. The submodule performs spatial numerical differentiation operations on the intrinsic lattice strain field along the fuse axis and radial direction, calculates the strain change rate between adjacent sensing points, and generates a lattice distortion gradient distribution field.

[0100] Meanwhile, based on the strain energy density calculation formula in elasticity theory, the components of the intrinsic strain field of the crystal lattice are substituted into the strain energy density calculation formula for dot product operation to obtain the strain energy density distribution at each position on the filament surface. The strain energy density distribution is used to characterize the mechanical potential energy stored inside the crystal lattice, which is the physical driving force for stress migration.

[0101] S222. Coupled calculation of electrochemical migration and stress migration flux, specifically:

[0102] The whisker growth kinetics submodule constructs an atomic migration model based on the electrochemical migration kinetics theory. According to the current density enhancement effect and Joule heating effect reflected by the harmonic thermal equivalence coefficient, the driving effect of electron wind on metal atoms is calculated. Combined with the temperature dependence described by the Arrhenius equation, the electromigration diffusion coefficient of metal atoms on the filament surface is determined. The electromigration diffusion coefficient and the electric field intensity gradient are multiplied by a scalar to generate the electromigration flux density vector.

[0103] Simultaneously, based on the lattice distortion gradient distribution field calculated by S221 and combined with the North-Einstein relation in stress migration theory, the submodule calculates the chemical potential gradient caused by the strain gradient, multiplies the chemical potential gradient with the atomic mobility to generate the stress migration flux density vector; in the three-dimensional coordinate system, the electromigration flux density vector and the stress migration flux density vector are vector superimposed to obtain the total mass transport flux field characterizing the metal atoms under the combined drive of electro-induced stress and mechanical stress.

[0104] S223. Whisker nucleation simulation and growth trajectory deduction, specifically:

[0105] The whisker growth kinetics submodule identifies the atomic flux convergence region at the grain boundary intersection on the filament surface based on the total mass transport flux field. In the atomic flux convergence region, the critical nucleation work is calculated using classical nucleation theory. The critical nucleation work is compared with the local thermal fluctuation energy to determine the whisker nucleation location and the whisker nucleation density per unit area.

[0106] Based on the governing equations of whisker growth kinetics after nucleation, which comprehensively consider both surface diffusion control mechanisms and lattice diffusion control mechanisms, and combined with the enhancement effect of the local strain energy release rate extracted from S221 on the atomic surface diffusion coefficient, a differential equation for the axial growth rate of whiskers is established. By solving the differential equations through time iteration, the cumulative growth of whisker length in the time dimension is calculated, generating a trajectory curve of whisker length evolution over time. The trajectory curve is used to predict the geometric dimensions of whiskers at different times.

[0107] S224. Critical bridging probability integral and risk assessment value generation, specifically:

[0108] The whisker growth dynamics submodule obtains the minimum gap distance between adjacent conductors and the insulation gap distance between the fuse and ground in the fuse structure design as the critical bridging threshold; the whisker length evolution trajectory generated by S223 is compared with the critical bridging threshold in real time. When the whisker length is close to the critical bridging threshold, the probability density function of the whisker growth length is established based on the stochastic process theory. The probability density function takes into account the random fluctuation of the growth rate and the statistical distribution characteristics of the nucleation position.

[0109] The probability density function is integrally calculated over the interval from the critical bridging threshold to infinity to determine the cumulative probability that the whisker length reaches or exceeds the minimum gap distance. The cumulative probability is output as the whisker critical bridging risk assessment value, which is a dimensionless value between zero and one, used to quantify the current risk level of short-circuit faults caused by the growth of metal whiskers on the fuse surface.

[0110] S23. Nonlinear evolution derivation of contact resistance and calculation of jump probability, specifically:

[0111] S231. Analysis of fretting wear phase points and identification of oxide film rupture events, specifically:

[0112] The contact resistance evolution submodule receives the contact fretting wear phase points marked by the edge sensing node in step S13 through the data bus. The contact fretting wear phase points are the timestamp sequence of the displacement amplitude step change when fretting wear occurs on the contact surface. The submodule performs time window interception on the contact fretting wear phase points, extracts the fretting displacement amplitude change curve within a preset time before and after each phase point, and calculates the slope change rate and amplitude jump height of the change curve.

[0113] Based on the slope change rate and amplitude jump height, combined with the pre-calibrated oxide film rupture characteristic threshold, the specific time points at which the surface oxide film of the contact surface undergoes periodic mechanical rupture due to fretting wear are identified; based on the ratio of amplitude jump height to the reference amplitude of the fretting displacement amplitude sequence, the rupture intensity coefficient of each oxide film rupture event is calculated, and the rupture intensity coefficient is used to quantify the proportion of fresh metal contact area exposed after the oxide film breaks.

[0114] S232. Harmonic Joule heat accumulation calculation and transient temperature field derivation, specifically:

[0115] The contact resistance evolution submodule receives the harmonic thermal equivalent coefficient, multiplies the harmonic thermal equivalent coefficient with the effective value of the fundamental current flowing through the contact, and obtains the equivalent thermal current value. Based on the equivalent thermal current value and the resistivity parameters of the contact material, it calculates the additional Joule heat power density generated by the harmonic current flowing through the contact point.

[0116] By combining the time nodes of oxide film rupture events identified by S231, the initial moment when the contact point changes from an oxide insulation state to a metal contact state after each oxide film rupture is determined. Based on the additional Joule heat power density and the thermal diffusivity and specific heat capacity parameters of the contact material, a one-dimensional unsteady-state heat conduction differential equation is established. The heat conduction differential equation is solved to calculate the transient temperature rise rate and the peak temperature when the contact micro-region reaches thermal equilibrium after each oxide film rupture. The transient temperature rise rate is used to characterize the speed of temperature rise at the contact point, and the peak temperature is used to characterize the highest temperature level of the contact micro-region.

[0117] S233. Introduction of whisker perturbation factor and calculation of contact resistance transient jump, specifically:

[0118] The contact resistance evolution submodule receives the whisker critical bridging risk assessment value and converts it into a negative disturbance factor of the contact pressure based on a pre-established mapping model of the influence of whisker growth on contact pressure. Based on the negative disturbance factor, it calculates the dynamic attenuation of contact pressure caused by the physical lifting effect of whisker growth on the contact surface of the contact, and at the same time calculates the effective contact area change rate caused by the whiskers occupying the contact gap.

[0119] The transient temperature rise rate, peak temperature, dynamic decay of contact pressure, and effective contact area change rate calculated by S232 are substituted into the resistance physics model based on Holm contact theory. In the resistance physics model, the resistivity of the material after thermal softening is calculated according to the temperature coefficient correction relationship of temperature on the material resistivity, and the change of contact shrinkage resistance is calculated according to the dynamic decay of contact pressure. The transient jump amplitude of the resistance at the contact point under the dual effects of thermal softening and mechanical pressure reduction is obtained by comprehensive calculation. The transient jump amplitude of the resistance is the instantaneous increment of the contact resistance after the oxide film breaks down relative to before the breakage.

[0120] S234. Monte Carlo random sampling simulation and generation of jump probability distribution, specifically:

[0121] The contact resistance evolution submodule considers the random fluctuation characteristics of the fretting wear cycle and introduces a periodic random disturbance that follows a normal distribution to the phase point of the fretting wear of the contact. At the same time, it considers the random distribution characteristics of the harmonic current phase and introduces a phase random offset that follows a uniform distribution to the harmonic thermal equivalence coefficient.

[0122] Based on periodic random disturbances and phase random offsets, the Monte Carlo method is used to randomly sample and simulate resistance jump events within multiple wear cycles. In each sampling, the calculation process of steps S231 to S233 is repeated according to the randomized input parameters to obtain the resistance transient jump amplitude under the corresponding scenario. The ratio of the number of samples with resistance transient jump amplitudes exceeding the preset safety threshold to the total number of samples in all sampling simulation results is counted to generate a probability density distribution curve of resistance jump amplitudes exceeding the preset safety threshold. The probability density distribution curve is used as the output of the nonlinear jump probability distribution of the contact resistance. The nonlinear jump probability distribution is a probability density function that varies with time and is used to characterize the risk level of sudden abnormal increase in contact resistance at different times in the future.

[0123] S24. Coupled analysis of arc-extinguishing medium degradation and correction of ultimate breaking capacity, specifically:

[0124] S241. Calculation of heat capacity decay and determination of retention rate of arc extinguishing medium, specifically:

[0125] The dielectric breakdown characteristic submodule receives the effective heat absorption rate of the arc-extinguishing medium calculated by the edge sensing node in step S13 through the data interface, and reads the rated initial heat absorption rate calibrated at the time of manufacture of the fuse from the local non-volatile memory.

[0126] The effective heat absorption rate of the arc-extinguishing medium is calculated by comparing it with the rated initial heat absorption rate to obtain the heat absorption capacity retention rate of the medium. Based on the calibration relationship curve between the heat absorption capacity retention rate and the heat capacity decay rate established in advance through laboratory arc tests, the calculated heat absorption capacity retention rate of the medium is mapped to the heat capacity decay percentage. The heat capacity decay percentage is used to quantify the degree of thermal performance degradation of the arc-extinguishing medium due to chemical decomposition.

[0127] S242. Derivation of the limiting arc time and generation of the heat capacity decay coefficient, specifically:

[0128] The dielectric breakdown characteristic submodule establishes a dielectric thermal response model based on the percentage of thermal capacity decay and the thermal conductivity, specific heat capacity, and density thermodynamic parameters of the arc-extinguishing dielectric material. A standard arc energy impact waveform is input into the dielectric thermal response model to deduce the reduction in time required for the dielectric temperature to rise to the critical failure temperature under the current thermal capacity decay state relative to the initial rated state. Based on the ratio of the reduction in the limiting arc time to the rated limiting arc time, a dielectric thermal capacity decay coefficient is generated. This coefficient characterizes the remaining effective performance of the arc-extinguishing dielectric in the thermal dimension.

[0129] S243. Calculation of suspended density of metal particles and deduction of breakdown field strength degradation, specifically:

[0130] The dielectric breakdown characteristics submodule obtains the metal particle concentration from the intermediate data synchronously calculated by the whisker growth kinetics submodule during step S22. The metal particles are discrete metal deposits that have detached from the fused wire surface or have not formed a complete whisker structure during the whisker growth process. Based on the metal particle concentration and the arc extinguishing medium filling volume, the suspension distribution density of the metal particles in the arc extinguishing medium is calculated.

[0131] A pre-established correlation model between dielectric breakdown field strength and impurity concentration is invoked. The correlation model is obtained by fitting test data of power frequency breakdown voltage of quartz sand medium under different metal particle doping concentrations. The suspension distribution density is substituted into the correlation model to calculate the percentage decrease of dielectric power frequency breakdown field strength relative to pure quartz sand medium, and a dielectric breakdown field strength reduction coefficient is generated. The dielectric breakdown field strength reduction coefficient is used to characterize the degree of performance degradation of the arc extinguishing medium in the insulation dimension.

[0132] S244. Dynamic correction of multi-dimensional deterioration coupling and limit breaking capability, specifically:

[0133] The dielectric breakdown characteristics submodule performs a scalar multiplication operation on the dielectric thermal capacity attenuation coefficient and the dielectric breakdown field strength reduction coefficient to obtain the dielectric comprehensive degradation factor that takes into account the synergistic degradation of thermal performance and insulation performance; and reads the initial ultimate breaking capacity rating of the fuse from the equipment parameter database.

[0134] The dielectric degradation factor is multiplied by the initial rated ultimate breaking capacity, and the ultimate breaking capacity attenuation coefficient is output after dynamic correction. The ultimate breaking capacity attenuation coefficient is a physical quantity with the dimension of current. It is used to characterize the degree of attenuation of the actual value of the maximum short-circuit current that the fuse can safely break under the current chemical state of the arc-extinguishing medium relative to the factory rated value. It serves as a key input parameter for the dynamic reconfiguration of the protection setting in the subsequent step S3.

[0135] S3. Calculate the actual protection capability boundary of the fuse based on the output of the multiphysics degradation model, deduce its selective protection mismatch region and evolution trend in the cascaded topology, and generate and issue reconfiguration commands including adjusting rated current, correcting upstream delay, or activating bypass protection; specifically including:

[0136] S31. Degradation coupling state assessment and protection characteristic drift trajectory generation, specifically:

[0137] S311. The edge sensing node receives the whisker critical bridging risk assessment value output in step S22, the contact resistance nonlinear jump probability distribution output in step S234, and the ultimate breaking capacity attenuation coefficient output in step S244.

[0138] S312. The minimum operating current threshold of the fuse is adjusted downward based on the whisker critical bridging risk assessment value. The adjustment amount is positively correlated with the whisker critical bridging risk assessment value. The adjusted minimum fusing current boundary is calculated. The maximum safe breaking current upper limit of the fuse is determined based on the ultimate breaking capacity attenuation coefficient, which serves as the upper boundary of the actual protection capability boundary. The actual protection capability boundary of the fuse under the current degraded coupling state is determined by combining the adjusted minimum fusing current boundary and the maximum safe breaking current upper limit. The actual protection capability boundary is a two-dimensional closed interval with the current value as the vertical axis and time as the horizontal axis.

[0139] S313. The time-axis alignment and normalization of the whisker critical bridging risk assessment value over time, the cumulative risk curve of the nonlinear jump probability distribution of the contact resistance, and the time-varying curve of the ultimate breaking capacity attenuation coefficient are performed to generate the protection characteristic drift trajectory. The protection characteristic drift trajectory is used to characterize the comprehensive degradation trend of the fuse protection performance over time.

[0140] S32. Cascaded protection mismatch region deduction and time-varying evolution analysis, specifically:

[0141] S321. The drift trajectory acquisition of multi-level protection characteristics is aligned with the fault travel time axis. Specifically:

[0142] The edge sensing node obtains the protection characteristic drift trajectory of each level of fuse in the target distribution network line topology generated in step S31 through the distribution network communication network. The protection characteristic drift trajectory is a structured data packet containing time series data, where each timestamp corresponds to a set of actual protection capability boundary parameters, including the minimum fusing current boundary value and the maximum breaking current upper limit value.

[0143] Edge sensing nodes analyze the electrical connection relationships in the topology to determine the physical order of fuses on the outgoing side of the substation, branch line fuses, and user-end fuses in the fault current propagation path. Based on the propagation sequence of the standard fault current from the farthest fault point on the line to the power supply side substation, a unified fault crossing time axis is established. The time axis takes the fault occurrence time as the zero point, the horizontal axis is the fault current propagation time, and the vertical axis is the fault current amplitude.

[0144] Based on the installation location and conductor length parameters of each fuse in the topology, the expected time delay of the fault current propagating from the fault point to the location of each fuse is calculated. The drift trajectory of the protection characteristics of each fuse is then horizontally shifted and mapped on the fault crossing time axis according to the expected time delay. This ensures that the protection characteristic curve of the lower-level fuse is located to the left of the curve of the upper-level fuse on the time axis and maintains a fixed time interval corresponding to the propagation sequence, thereby achieving the alignment of the protection characteristics of multiple fuses in a unified spatiotemporal coordinate system.

[0145] S322. Comparison of adjacent protection capacity boundaries and identification of mismatch areas, specifically:

[0146] In the coordinate system after spatiotemporal alignment, the edge sensing node extracts the actual protection capability boundary curves of the two adjacent fuses along the time axis at each moment. Specifically, for each moment, the minimum fusing current boundary value of the upper-level fuse and the minimum fusing current boundary value of the lower-level fuse are obtained.

[0147] Calculate the difference between the minimum breaking current boundary value of the upstream fuse and the minimum breaking current boundary value of the downstream fuse. When the difference is less than zero, it indicates that the minimum operating current of the upstream fuse is lower than that of the downstream fuse, and there is a risk that the upstream fuse will operate prematurely without the downstream fuse operating. Similarly, compare the maximum breaking current upper limit value of the upstream fuse with that of the downstream fuse. When the maximum breaking current upper limit value of the upstream fuse is less than that of the downstream fuse, it indicates that the upstream fuse cannot provide backup protection in the high breaking current range. Perform a spatial intersection operation on the fault current range that meets any of the above conditions and the time window corresponding to that moment. Mark it as a selective protection mismatch region in the spatiotemporal coordinate system. The selective protection mismatch region is an irregular closed region in the spatiotemporal coordinate system, which represents the failure of the selective coordination relationship of cascaded protection in this spatiotemporal region.

[0148] S323. Mismatch region boundary tracking and movement rate calculation, specifically:

[0149] The edge sensing node discretizes the boundary contour of the selective protection mismatch region marked in step S322 and extracts feature points on the boundary contour as boundary tracking points. For each boundary tracking point, the change in its position coordinates on the fault passage time axis is calculated between two consecutive adjacent time sampling times. The ratio of the change in position coordinates to the time sampling interval is used as the moving rate of the boundary tracking point. The moving rate includes the time drift rate along the time axis and the amplitude change rate along the current axis.

[0150] Simultaneously, the coverage width of the selective protection mismatch region in the current dimension is statistically analyzed, which is the difference between the upper and lower boundary current values ​​of the region. The rate of change of the coverage width between continuous time sampling moments is calculated. When the rate of change is positive, the mismatch region is determined to be in an expanding trend. When the rate of change is negative, the mismatch region is determined to be in a contracting trend. The moving rate and the expansion or contraction trend are vector synthesized to generate the dynamic evolution feature vector of the mismatch region.

[0151] S324. Time-varying evolution trajectory fitting and cumulative effect prediction, specifically:

[0152] The edge sensing node extrapolates the future position of the boundary of the mismatched region based on the moving rate and expansion / contraction trend of the boundary points of the mismatched region calculated in step S323 using a time series prediction algorithm. The time series prediction algorithm is an autoregressive integral moving average model or a long short-term memory neural network. Its inputs are the boundary point position coordinates, moving rate and mismatched region area at historical moments, and its output is the expected position coordinates of the boundary points at each prediction moment within a preset future time window.

[0153] Based on the expected location coordinates, the geometry of the mismatched area at future times is reconstructed. The degree to which the protection capability of the upper-level fuse in the mismatched area is lower than that of the lower-level fuse is calculated. The degree quantification value is integrated and accumulated over time to generate the cumulative effect quantification value of the mismatch degree. Combining the expected location coordinates, the geometry of the mismatched area, and the cumulative effect quantification value, the time-varying evolution trajectory of the mismatched area is fitted and generated. The time-varying evolution trajectory is used to characterize the development trend of the spatial location movement, coverage change, and mismatch severity of the selective protection mismatched area within the future time window.

[0154] S33. The generation and execution of dynamic reconfiguration instructions for protection settings are issued to the terminal, specifically as follows:

[0155] S331. The edge sensing node calls the protection setting calculation engine, reads the whisker critical bridging risk assessment value time series curve in the protection characteristic drift trajectory generated in step S31 and the harmonic thermal equivalent coefficient generated in step S122, and performs nonlinear function mapping on the whisker critical bridging risk assessment value and the harmonic thermal equivalent coefficient. Specifically, the product term and the cross-coupling term of the two are added to generate a nonlinear superposition effect quantity.

[0156] The rated current setting value of the fuse is corrected downward based on the nonlinear superposition effect. The correction algorithm is to multiply the rated current setting value by a compensation coefficient that is greater than zero and less than one. The compensation coefficient is determined by looking up the preset attenuation characteristic table based on the magnitude of the nonlinear superposition effect. This makes the corrected rated current value lower than the superposition value of the actual minimum fusing current caused by whisker growth and the harmonic heat accumulation effect, and generates a rated current setting value adjustment command.

[0157] S332. The edge sensing node reads the time-varying evolution trajectory of the mismatched region generated in step S32, extracts the expected position coordinates of the boundary of the mismatched region on the fault crossing time axis, and reads the nonlinear jump probability distribution of the contact resistance generated in step S234, and identifies the time point corresponding to the probability density peak in the nonlinear jump probability distribution as the expected concentrated period.

[0158] Within a preset lead time window before the expected concentrated period arrives, a delay action time correction instruction is generated. The specific content of the correction instruction is to extend the delay action time setting value of the upper-level fuse or reduce its instantaneous trip setting value. The amount of extension is dynamically calculated based on the magnitude of the mismatch degree quantification value in the time-varying evolution trajectory of the mismatch region, ensuring that the actual protection capability boundary of the lower-level fuse is always higher than that of the upper-level fuse during the expected concentrated period, so that the lower-level fuse has priority action rights.

[0159] S333. The edge sensing node monitors the ultimate breaking capacity attenuation coefficient output in step S244 in real time. The ultimate breaking capacity attenuation coefficient is compared with a preset safety threshold. The safety threshold is the product of the initial ultimate breaking capacity rating of the fuse and a preset percentage safety margin. The difference between the ultimate breaking capacity attenuation coefficient and the safety threshold is calculated. When the difference is less than the preset warning margin, it is determined that the fuse is about to lose its safe breaking capacity. A switching instruction to start the bypass solid-state protection device is generated. The switching instruction includes disconnecting the main circuit where the fuse is located and closing the drive signal sequence of the solid-state switch protection device connected in parallel with the fuse.

[0160] S334. The edge sensing node encapsulates the rated current setting adjustment command, delay action time correction command, or switching command according to a preset communication protocol format to generate a protection setting dynamic reconfiguration command data frame. The data frame includes a command type identifier, target device address, setting value parameters, and check code. The protection setting dynamic reconfiguration command data frame is sent to the corresponding digital fuse trip controller, protection measurement and control terminal, or solid-state switch drive circuit through an RS485 wired communication link or a 4G wireless communication network to drive the execution terminal to complete the online adjustment of the protection setting or the switching operation of the protection device.

[0161] Example 2

[0162] like Figure 2 As shown, a multi-parameter sensing-based intelligent monitoring device for distribution network line fuses is used to implement a multi-parameter sensing-based intelligent monitoring method for distribution network line fuses, including:

[0163] A. Edge-aware nodes, specifically including:

[0164] A1. Fiber Bragg grating array, embedded on the surface of a fused wire, used to acquire lattice strain distribution data;

[0165] A2. A high-frequency capacitive micro-displacement sensor, placed on the contact surface of the contact head, is used to collect the amplitude sequence of micro-displacement.

[0166] A3. Wideband current transformer, connected to the outgoing side of the fuse, is used to collect harmonic current spectrum characteristics;

[0167] A4. Electrochemical gas sensor array, located inside the fused tube, is used to collect the concentration of chemical decomposition products;

[0168] A5. Harmonic thermal decoupling unit, specifically:

[0169] The spectrum analysis subunit is used to perform spectrum analysis on the spectral characteristics of harmonic currents and extract the amplitude ratio and phase information of each harmonic current relative to the fundamental current.

[0170] The equivalent coefficient calculation subunit is used to calculate the weighted heating contribution value of each harmonic based on the equivalent heating effect of each harmonic current relative to the fundamental frequency, and to accumulate and generate the harmonic thermal equivalent coefficient.

[0171] The thermal strain calculation subunit is used to obtain the thermal expansion characteristic parameters of the fuse material and the transient temperature field distribution on the fuse surface. Based on the harmonic thermal equivalence coefficient and the transient temperature field distribution, the thermal strain component caused by the Joule heating added by the harmonic current is calculated.

[0172] The intrinsic field generation sub-unit is used to subtract the thermal strain component from the original acquired lattice strain distribution data to generate the lattice intrinsic strain field.

[0173] A6. Micro-motion phase recognition unit, used to perform sliding window peak detection on the micro-motion displacement amplitude sequence, and mark the moment when the displacement amplitude changes abruptly as the contact micro-motion wear phase point;

[0174] A7. Medium heat absorption calculation unit, used to calculate the effective heat absorption rate of the arc extinguishing medium based on the ratio of the concentration of chemical decomposition products to the initial total amount of the arc extinguishing medium, combined with the preset medium heat absorption efficiency calibration curve.

[0175] B. Multiphysics Degradation Inference Module, specifically including:

[0176] B1. Whisker growth kinetics submodule, specifically:

[0177] The strain energy extraction unit is used to extract the lattice distortion gradient and strain energy density distribution at various locations on the surface of the filament from the intrinsic strain field of the lattice.

[0178] The migration flux calculation unit is used to calculate the electric migration flux based on the harmonic thermal equivalence coefficient and the stress migration flux based on the intrinsic strain field of the lattice, and couple the two to generate the total mass transport rate of metal atoms.

[0179] The nucleation simulation unit is used to simulate the nucleation location and nucleation density of whiskers at grain boundary junctions based on the total mass transport rate.

[0180] The growth tracking unit is used to calculate the growth rate and length evolution trajectory of the whiskers in the time and space dimensions based on the whisker growth kinetic equation after nucleation, combined with the local strain energy release rate and atomic diffusion coefficient.

[0181] The risk quantification unit is used to compare the whisker length with the minimum gap distance between adjacent conductors of the fuse, and calculate the probability density integral value of the whisker length reaching or exceeding the gap distance, which is used as the whisker critical bridging risk assessment value.

[0182] B2. Contact Resistance Evolution Submodule, specifically:

[0183] The wear cycle identification unit is used to identify the time point and intensity of periodic rupture of the oxide film on the contact surface based on the phase point of the contact fretting wear.

[0184] The Joule heat accumulation unit is used to calculate the additional Joule heat accumulation generated by the harmonic current based on the harmonic thermal equivalence coefficient, and to determine the temperature rise rate and peak temperature of the contact point after the oxide film rupture by combining the fretting wear phase point.

[0185] The pressure disturbance unit is used to introduce the critical bridging risk assessment value of whiskers as a negative disturbance factor of contact pressure, and calculate the dynamic attenuation of contact pressure based on the lifting effect of whisker growth on the contact surface.

[0186] The transient amplitude calculation unit is used to substitute the temperature rise rate, peak temperature and dynamic decay of contact pressure into the physical model of contact resistance to calculate the transient amplitude of resistance jump caused by thermal softening and pressure reduction in the micro-region of the contact point.

[0187] The probability distribution generation unit is used to perform Monte Carlo simulation of resistance jump events in multiple wear cycles, statistically analyze the probability density of jump amplitude exceeding a preset threshold, and output the nonlinear jump probability distribution of contact resistance.

[0188] B3. Dielectric breakdown characteristics submodule, specifically:

[0189] The heat capacity decay calculation unit is used to compare the effective heat absorption rate of the arc extinguishing medium with the rated heat absorption rate initially calibrated by the medium, calculate the percentage of heat capacity decay caused by chemical decomposition of the medium, and deduce the amount of reduction in the ultimate arc burning time that the medium can withstand, and generate the heat capacity decay coefficient of the medium.

[0190] The particle concentration acquisition unit is used to synchronously acquire the concentration of metal particles generated during whisker growth from the whisker growth kinetics submodule.

[0191] The field strength reduction calculation unit is used to calculate the suspension density of metal particles in the arc-extinguishing medium based on the concentration of metal particles. Combined with the correlation model between the dielectric breakdown field strength and the impurity concentration, it deduces the degree of degradation of the dielectric insulation strength by metal particles and generates the dielectric breakdown field strength reduction coefficient.

[0192] The integrated correction unit is used to couple and multiply the dielectric thermal capacity attenuation coefficient and the dielectric breakdown field strength reduction coefficient to obtain the dielectric comprehensive degradation factor. This factor is then multiplied by the initial ultimate breaking capacity calibrated at the factory of the fuse, and the output ultimate breaking capacity attenuation coefficient is dynamically corrected.

[0193] C. The protection capability dynamic reconfiguration module, specifically including:

[0194] C1. Drift trajectory generation unit, used to generate the drift trajectory of the protection characteristic by the joint evolution curve of the whisker critical bridging risk assessment value, the nonlinear jump probability distribution of the contact resistance and the attenuation coefficient of the ultimate breaking capacity in the time dimension.

[0195] C2. Spatiotemporal alignment deduction unit, specifically:

[0196] The trajectory acquisition subunit is used to acquire the protection characteristic drift trajectory of fuses at all levels in the target distribution network line topology;

[0197] The spatiotemporal mapping subunit is used to establish a unified fault travel time axis based on the time sequence of the standard fault current traveling from the fault point to the power supply side. The drift trajectory of the protection characteristics of each level of fuse is mapped to the corresponding time period of this time axis according to the topological position, so as to realize the spatiotemporal alignment of the protection characteristics of multi-level fuses.

[0198] The mismatch identification subunit is used to compare the actual protection capability boundary curves of two adjacent fuses in the spatiotemporally aligned coordinate system, identify the spatiotemporal region where the protection capability boundary of the upper level is lower than that of the lower level, and mark it as a selective protection mismatch region.

[0199] The evolution tracking subunit is used to perform time-series tracking of the boundary points of the mismatch region, calculate the moving rate of the boundary points on the fault crossing time axis over time, and the trend of expansion or contraction of the fault current range covered by the mismatch region over time.

[0200] The trajectory fitting subunit is used to fit and generate the time-varying evolution trajectory of the mismatch region based on the moving rate and changing trend, including the cumulative effect of the expected location of the mismatch region boundary and the degree of mismatch within a future preset time window.

[0201] C3. Reconstruction instruction generation unit, used to generate dynamic reconstruction instructions for protection settings. The instructions include adjusting the rated current setting value of the fuse according to the drift trajectory of the protection characteristics, correcting the delay action time of the upper-level fuse to avoid the expected concentrated period of the jump probability distribution in advance, or activating the bypass solid-state protection device to replace the fuse whose ultimate breaking capacity attenuation coefficient is about to fall below the safety threshold.

[0202] C4. Communication unit, used to send the dynamic reconfiguration command of protection settings to the corresponding execution terminal.

[0203] D. Execution terminal, installed at each level of fuse or in the substation, is used to receive and execute dynamic reconfiguration commands for protection settings, including an execution mechanism for adjusting the rated current setting of the fuse, a protection device for correcting the delay action time of the upper-level fuse, and a switchable bypass solid-state protection device.

[0204] As can be seen from the above description, the embodiments of the present invention achieve the following technical effects:

[0205] This invention achieves precise decoupling and pure perception of the microscopic physical field characteristics of fused wires, fundamentally improving the data quality of condition monitoring. Through the calculation of the harmonic thermal equivalence coefficient and the stripping of thermal strain components in step S12, this invention achieves precise decoupling of the electro-stress effect and the harmonic-added Joule heating effect in the fused wire lattice strain distribution data. In existing technologies, lattice strain data is often mixed with artifacts of thermal expansion and contraction caused by harmonic currents, making it impossible for subsequent analysis to distinguish between true material fatigue and transient thermal disturbances. The lattice intrinsic strain field generated by this invention eliminates interference from non-fundamental thermal effects, retaining only lattice distortions reflecting electro-stress, providing pure input data for subsequent whisker growth deduction; it fundamentally solves the industry problem of signal mixing and feature submersion in multi-physics coupled monitoring, raising the identification accuracy of microscopic degradation features to an unprecedented level.

[0206] This invention establishes a multiphysics-based three-dimensional coupled failure prediction mechanism to quantitatively reveal and provide early warning of the latent degradation process of fuses. Through steps S22, S23, and S24, which construct submodules for whisker growth kinetics, contact resistance evolution, and dielectric breakdown characteristics, a nonlinear coupled prediction chain is established: lattice strain → whisker growth → contact pressure disturbance → resistance jump, and whisker growth → metal particles → dielectric breakdown threshold decrease. In existing technologies, whisker growth, a microscopic phenomenon, has long been considered an unmonitorable precursor to latent failure, and contact resistance degradation and dielectric decomposition have been analyzed in isolation. This invention reveals the negative disturbance effect of whisker growth on contact pressure and its accelerating effect on contact resistance jump. Simultaneously, it introduces the metal particles generated by whisker growth into the dielectric breakdown characteristic analysis, correcting the ultimate breaking capacity attenuation coefficient. This three-dimensional coupled prediction mechanism transforms the fuse degradation process from a black box to a transparent one, enabling the identification of latent risks months or even earlier before a failure occurs, achieving truly predictive maintenance.

[0207] This invention pioneers a dynamic reconfiguration paradigm for grid-level protection capabilities based on the coupling effect of micro-degradation, achieving spatiotemporal adaptive selective protection for distribution networks. Through steps S32 and S33, the invention elevates the micro-degradation deduction results of a single fuse to a protection characteristic drift trajectory, and realizes spatiotemporal alignment and evolution trend prediction of mismatched protection capabilities across multiple fuse levels. In existing technologies, protection settings are static once set, failing to adapt to dynamic drift in protection capabilities caused by equipment aging and environmental stress changes. This invention maps the drift trajectories of fuses at each level onto a unified fault travel time axis, accurately identifying the boundary movement direction and rate of the mismatched region, and generating dynamic reconfiguration commands including rated current adjustment, upper-level delay correction, and bypass device activation. It shifts the adjustment of protection capabilities from post-event remediation to pre-event avoidance, and expands from single-point compensation to cascaded coordination, fundamentally solving the problem of loss of protection selectivity caused by the accumulation of equipment micro-degradation, and significantly improving the power supply reliability and operational safety of distribution networks under complex operating conditions.

[0208] The embodiments and / or implementation methods described above are merely preferred embodiments and / or implementation methods for implementing the technology of the present invention, and are not intended to limit the implementation methods of the technology of the present invention in any way. Any person skilled in the art may make some modifications or alterations to other equivalent embodiments without departing from the scope of the technical means disclosed in the present invention, but these should still be regarded as the technology or embodiments that are substantially the same as the present invention.

[0209] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. The above descriptions are only preferred embodiments of this application. It should be noted that due to the limitations of written expression, while there are objectively infinite specific structures, those skilled in the art can make several improvements, modifications, or changes without departing from the principles of this application, and can also combine the above technical features in an appropriate manner. These improvements, modifications, changes, or combinations, or the direct application of the inventive concept and technical solution to other situations without modification, should all be considered within the scope of protection of this application.

Claims

1. A method for intelligent monitoring of distribution network line fuses based on multi-parameter sensing, characterized in that, include: The process involves acquiring fuse lattice strain, contact fretting displacement, harmonic spectrum, and dielectric decomposition products; calculating harmonic thermal equivalence coefficients; decoupling the generated intrinsic lattice strain field; and identifying the fretting wear phase point and the effective heat absorption rate of the dielectric. Specifically, this includes: Lattice strain distribution data are acquired by an optical fiber Bragg grating array embedded on the surface of the fuse, the amplitude sequence of micro-displacement of the contact is acquired by a high-frequency capacitive micro-displacement sensor, the harmonic current spectrum characteristics are acquired by a broadband current transformer, and the concentration of chemical decomposition products is acquired by an electrochemical gas sensor array. Harmonic thermal equivalence coefficients are generated based on the harmonic current spectrum characteristics, and a lattice intrinsic strain field reflecting only the electro-induced stress effect is generated based on these harmonic thermal equivalence coefficients; specifically: Edge sensing nodes perform spectral analysis on the acquired harmonic current spectral characteristics, extracting the amplitude ratio and phase information of each harmonic current relative to the fundamental current; based on the equivalent heating effect of each harmonic current relative to the fundamental frequency, they calculate the weighted heating contribution value of each harmonic current, and accumulate the weighted heating contribution values ​​of all harmonics to generate the harmonic thermal equivalence coefficient used to characterize the intensity of Joule heating added by the harmonics; they acquire the pre-calibrated thermal expansion characteristic parameters of the fuse material and the transient temperature field distribution of the fuse surface measured in real time by distributed temperature sensors; based on the harmonic thermal equivalence coefficient and the transient temperature field distribution of the fuse surface, they calculate the thermal strain component caused by the Joule heating added by the harmonic current, subtract the thermal strain component from the original acquired lattice strain distribution data, and generate the intrinsic lattice strain field that only reflects the lattice distortion caused by the electro-stress effect; The sliding window peak detection is performed on the micro-displacement amplitude sequence, and the moment when the displacement amplitude changes abruptly is marked as the contact micro-displacement wear phase point; based on the ratio of the concentration of chemical decomposition products to the initial total amount of arc extinguishing medium, combined with the preset medium heat absorption efficiency calibration curve, the arc energy value that the remaining medium can absorb is calculated as the effective heat absorption rate of the arc extinguishing medium. The intrinsic strain field of the lattice, the fretting wear phase point, the effective heat absorption rate, and the harmonic thermal equivalence coefficient are input into the multiphysics degradation model, which outputs the whisker critical bridging risk, the nonlinear jump probability distribution of the contact resistance, and the attenuation coefficient of the ultimate breaking capacity; specifically including: The intrinsic strain field of the lattice, the phase point of the contact fretting wear, the effective heat absorption rate of the arc extinguishing medium, and the harmonic thermal equivalence coefficient are input into the pre-trained fuse multiphysics field degradation derivation model. The model includes a whisker growth dynamics submodule, a contact resistance evolution submodule, and a medium breakdown characteristic submodule. The whisker growth kinetics submodule calculates the whisker nucleation density and growth rate, and outputs the probability density integral value of the whisker length reaching the fuse gap distance, which serves as the whisker critical bridging risk assessment value. The contact resistance evolution submodule calculates the Joule heat accumulation effect on the contact surface based on the coupling relationship between the contact fretting wear phase point and the harmonic thermal equivalence coefficient, and outputs the nonlinear jump probability distribution of the contact resistance through Monte Carlo simulation. The dielectric breakdown characteristic submodule calculates the dielectric heat capacity decay rate based on the effective heat absorption rate of the arc-extinguishing dielectric, and calculates the dielectric breakdown field strength reduction coefficient based on the concentration of metal particles generated by whisker growth. The two are coupled together to dynamically correct the ultimate breaking capacity decay coefficient of the fuse. Based on the output of the multiphysics degradation model, the actual protection capability boundary of the fuse is calculated, its selective protection mismatch region and evolution trend in the cascaded topology are deduced, and reconfiguration instructions including adjusting the rated current, correcting the upper-level delay, or starting the bypass protection are generated and issued.

2. The intelligent monitoring method for distribution network line fuses based on multi-parameter sensing according to claim 1, characterized in that, The whisker growth kinetics submodule calculates the whisker nucleation density and growth rate, and outputs the probability density integral value of the whisker length reaching the filament gap distance, which serves as the whisker critical bridging risk assessment value. Specifically, it includes: The whisker growth dynamics submodule obtains the intrinsic strain field of the lattice as input and extracts the lattice distortion gradient and strain energy density distribution at each position on the surface of the filament. Based on the electrochemical migration kinetics model, the electromigration flux of metal atoms on the fuse surface is calculated according to the harmonic thermal equivalence coefficient, and the stress migration flux driven by the strain gradient is calculated according to the intrinsic strain field of the lattice. The electromigration flux and stress migration flux are coupled and superimposed to generate the total mass transport rate of metal atoms on the surface of the filament, and the nucleation position and nucleation density of whiskers at the grain boundary are simulated accordingly. Based on the whisker growth kinetic equation after nucleation, combined with the local strain energy release rate and atomic diffusion coefficient, the growth rate and length evolution trajectory of whiskers in the time and space dimensions are calculated. The current whisker length is compared with the minimum gap distance between adjacent conductors of the fuse, and the probability density of the whisker length reaching or exceeding the gap distance is calculated. After integration, the critical bridging risk assessment value of the whisker is generated.

3. The intelligent monitoring method for distribution network line fuses based on multi-parameter sensing according to claim 2, characterized in that, The contact resistance evolution submodule calculates the Joule heat accumulation effect on the contact surface based on the coupling relationship between the phase point of the contact fretting wear and the harmonic thermal equivalence coefficient, and outputs the nonlinear jump probability distribution of the contact resistance through Monte Carlo simulation, specifically including: The contact resistance evolution submodule obtains the phase point of contact fretting wear and identifies the time node and rupture intensity of the oxide film periodically broken due to fretting wear on the contact surface. The additional Joule heat accumulation generated by the harmonic current flowing through the contact point is calculated based on the harmonic thermal equivalence coefficient, and the temperature rise rate and peak temperature of the contact point after each oxide film rupture are determined by combining the fretting wear phase point. The critical bridging risk assessment value of the whiskers is introduced as a negative disturbance factor of the contact pressure. The dynamic attenuation of the contact pressure is calculated based on the lifting effect of whisker growth on the contact surface. The temperature rise rate, peak temperature and dynamic attenuation of the contact pressure are substituted into the physical model of the contact resistance to calculate the transient jump amplitude of the resistance caused by thermal softening and pressure reduction in the micro-region of the contact point. Considering the randomness of the fretting wear cycle and harmonic thermal effect, Monte Carlo simulation is performed on the resistance jump events in multiple wear cycles. The probability density of the jump amplitude exceeding the preset threshold is statistically analyzed, and the nonlinear jump probability distribution of the contact resistance of the contact is output.

4. The intelligent monitoring method for distribution network line fuses based on multi-parameter sensing according to claim 3, characterized in that, The dielectric breakdown characteristic submodule calculates the dielectric heat capacity decay rate based on the effective heat absorption rate of the arc-extinguishing dielectric, and simultaneously calculates the dielectric breakdown field strength reduction coefficient based on the concentration of metal particles generated by whisker growth. The coupling of these two submodules dynamically corrects the fuse's ultimate breaking capacity decay coefficient, specifically including: The medium breakdown characteristic submodule obtains the effective heat absorption rate of the arc-extinguishing medium, compares it with the initially calibrated rated heat absorption rate of the medium, and calculates the percentage of heat capacity decay caused by chemical decomposition of the medium. Based on the stated heat capacity attenuation percentage, combined with the thermal conductivity and specific heat capacity parameters of the dielectric material, the reduction in the limiting arc time that the dielectric can withstand under the current arc energy impact is deduced, and the dielectric heat capacity attenuation coefficient is generated. The concentration of metal particles calculated by the whisker growth kinetics submodule is obtained, and the suspension density of the metal particles in the arc extinguishing medium is calculated based on the concentration of the metal particles. Combined with the correlation model between the dielectric breakdown field strength and the impurity concentration, the degree of degradation of the dielectric insulation strength by the metal particles is deduced, and the dielectric breakdown field strength reduction coefficient is generated. The dielectric thermal capacity attenuation coefficient and the dielectric breakdown field strength reduction coefficient are coupled and multiplied to obtain the dielectric comprehensive degradation factor. This factor is then multiplied by the initial ultimate breaking capacity calibrated at the factory of the fuse, and the ultimate breaking capacity attenuation coefficient is output after dynamic correction.

5. The intelligent monitoring method for distribution network line fuses based on multi-parameter sensing according to claim 4, characterized in that, Based on the output of the multiphysics degradation model, the actual protection capability boundary of the fuse is calculated, and its selective protection mismatch region and evolution trend in the cascaded topology are deduced. Reconfiguration commands are generated and issued, including adjusting the rated current, correcting the upstream delay, or activating bypass protection. Specifically, these commands include: The edge sensing node calculates the actual protection capability boundary of the fuse in the current degraded coupling state based on the whisker critical bridging risk assessment value, the nonlinear jump probability distribution of the contact resistance, and the attenuation coefficient of the ultimate breaking capacity, and generates the joint evolution curve of the three in the time dimension as the protection characteristic drift trajectory. The drift trajectories of the protection characteristics of each fuse are spatiotemporally aligned to identify the temporal overlap region of the decrease in the protection capability of each fuse, and to deduce the selective protection mismatch region and the moving direction and rate of its boundary under the actual protection capability boundary. Generate a dynamic reconfiguration instruction for protection settings. The instruction includes adjusting the rated current setting of the fuse according to the drift trajectory of the protection characteristics to compensate for the nonlinear superposition effect, correcting the delayed action time of the upper-level fuse to avoid the expected concentrated period of the jump probability distribution in advance, or activating the bypass solid-state protection device to replace the fuse whose attenuation coefficient is about to fall below the safety threshold. The instruction is then sent to the corresponding execution terminal.

6. The intelligent monitoring method for distribution network line fuses based on multi-parameter sensing according to claim 5, characterized in that, The protection characteristic drift trajectories of each fuse are spatiotemporally aligned to identify the temporal overlap region of the decrease in protection capability of each fuse. The selective protection mismatch region and its boundary movement direction and rate under the actual protection capability boundary are deduced, specifically including: The drift trajectory of the protection characteristics of each level of fuse in the target distribution network line topology is obtained. Based on the time sequence of the standard fault current traveling from the fault point to the power supply side, a unified fault travel time axis is established. The drift trajectory of the protection characteristics of each level of fuse is mapped to the corresponding time period of the time axis according to its position in the topology, so as to realize the spatiotemporal alignment of the protection characteristics of multi-level fuses. In the spatiotemporally aligned coordinate system, by comparing the actual protection capacity boundary curves of two adjacent fuses, the spatiotemporal region where the protection capacity boundary of the upper fuse is lower than that of the lower fuse is identified and marked as the selective protection mismatch region. The boundary points of the mismatched region are time-tracked to calculate the moving rate of the boundary points on the fault crossing time axis over time, as well as the expansion or contraction trend of the fault current range covered by the mismatched region over time. Based on the moving rate and changing trend, a time-varying evolution trajectory of the mismatch region is fitted and generated, including the expected location of the mismatch region boundary and the cumulative effect of the degree of mismatch within a future preset time window.

7. A smart monitoring device for distribution network line fuses based on multi-parameter sensing, characterized in that, A method for intelligent monitoring of distribution network line fuses based on multi-parameter sensing, as described in any one of claims 1-6, includes: Edge sensing nodes, deployed at fuses in the target distribution network, are used to acquire lattice strain distribution data on the fuse surface, micro-displacement amplitude sequence of the contact surface, harmonic current spectrum characteristics flowing through the fuse, and concentration of chemical decomposition products of the arc-extinguishing medium inside the fuse tube. Based on the harmonic current spectrum characteristics, a harmonic thermal equivalence coefficient is calculated. Based on this coefficient, the lattice strain distribution data is thermally-electrically decoupled to generate an intrinsic lattice strain field that eliminates non-fundamental thermal effects. Simultaneously, the micro-displacement amplitude sequence is used to identify the micro-wear phase point of the contact, and the effective heat absorption rate of the arc-extinguishing medium is calculated based on the concentration of chemical decomposition products. The multi-physics degradation deduction module is used to input the intrinsic strain field of the lattice, the phase point of the contact fretting wear, the effective heat absorption rate of the arc extinguishing medium and the harmonic thermal equivalence coefficient into the pre-trained fuse multi-physics degradation deduction model, and output the whisker critical bridging risk assessment value, the nonlinear jump probability distribution of the contact resistance and the attenuation coefficient of the ultimate breaking capacity. The protection capability dynamic reconfiguration module is used to calculate the actual protection capability boundary of the fuse under the current degraded coupling state based on the whisker critical bridging risk assessment value, the nonlinear jump probability distribution of the contact resistance, and the ultimate breaking capacity attenuation coefficient, and simultaneously generate the protection characteristic drift trajectory; according to the cascade coordination relationship of multi-level fuses in the target distribution network line topology, the protection characteristic drift trajectories of each fuse are spatiotemporally aligned, and the selective protection mismatch region and the time-varying evolution trend of the mismatch region under the actual protection capability boundary are deduced; and a protection setting dynamic reconfiguration instruction is generated and sent to the corresponding execution terminal.

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