A fault prediction method and system for porcelain insulator cracks based on PIIF

Abstract

The present invention relates to the technical field of fault prediction, and discloses a fault prediction method and system for cracks in porcelain insulators based on PIIF, including: arranging a plurality of fiber grating sensors on the surface of a pin-type porcelain insulator and around the cracks, collecting and analyzing real-time data of the fiber grating sensors; based on the analyzed real-time data, combining a heat conduction model and a mechanical stress model, calculating the thermal stress distribution during the arc discharge process, and predicting the crack expansion path and speed; predicting whether a permanent fault occurs in the cracked pin-type porcelain insulator. Real-time dynamic monitoring of cracks in porcelain insulators and accurate calculation of thermal stress are achieved, effectively improving the prediction accuracy of crack expansion path and speed. In addition, by continuously monitoring the residual stress after arc discharge, the long-term structural stability of porcelain insulators can be accurately evaluated, and potential permanent faults can be warned in a timely manner, thereby improving the reliability and safety of the power system and reducing unexpected power outages and maintenance costs.

Description

Technical Field

[0001] The invention relates to the technical field of fault prediction, and in particular to a fault prediction method and system for porcelain insulator cracks based on PIIF. Background Art

[0002] With the continuous development of power systems, the reliability and safety of power transmission and distribution networks have become increasingly important. As a key insulating component in power equipment, porcelain insulators are widely used in high-voltage transmission lines and substations. Their main function is to provide electrical insulation and mechanical support. However, during long-term operation, porcelain insulators are often affected by complex external environments and power loads, such as frequent lightning strikes, accumulation of pollutants, repeated effects of mechanical stress and temperature stress, etc. These factors may cause structural degradation of insulators, especially cracks, and even cause electrical breakdown and mechanical failure of insulators in severe cases, leading to power system failures. For this reason, traditional porcelain insulator monitoring technology mainly relies on regular manual inspections and electrical tests. However, these methods usually have shortcomings such as poor real-time performance, inability to accurately locate cracks, and inability to effectively detect potential hidden damage. In recent years, with the development of fiber grating sensing technology, more and more studies have begun to explore the use of fiber optic sensors for health monitoring and fault prediction of porcelain insulators. These technologies make it possible to achieve high-precision, real-time, and online monitoring.

[0003] Despite this, the existing porcelain insulator monitoring scheme based on fiber optic sensing technology still has some shortcomings. First, the existing technology focuses on the monitoring of a single factor of stress or temperature, and fails to comprehensively consider the multi-physical field coupling effect, resulting in inaccurate prediction of crack expansion. Secondly, most traditional crack monitoring methods are based on static or quasi-static data analysis, which makes it difficult to accurately capture the rapid expansion of cracks caused by stress concentration during arc discharge. In addition, the existing methods lack in-depth research on residual stress analysis after cracks are generated, making it difficult to effectively predict the development trend of cracks and possible permanent failure risks. These shortcomings not only limit the accuracy of fault prediction, but also increase the uncertainty and maintenance cost of the power system. Therefore, how to combine real-time data, thermodynamics and mechanical models to accurately predict the dynamic expansion of cracks has become an important direction for current technological development. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problems solved by the present invention are: the dynamic monitoring of crack expansion in porcelain insulators in the prior art is not accurate enough, the expansion path and speed of cracks during arc discharge cannot be accurately predicted, and it is difficult to evaluate the impact of residual stress on the long-term structural stability and failure risk of porcelain insulators after cracks are generated.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a fault prediction method for porcelain insulator cracks based on PIIF, comprising:

[0007] PIIF is a discharge process with self-clearing characteristics that occurs in cracked porcelain insulators due to the influence of rain, which is called early failure of porcelain insulators;

[0008] Arrange multiple fiber Bragg grating sensors on the surface of the pin-type porcelain insulator and around the cracks to collect and analyze the real-time data of the fiber Bragg grating sensors;

[0009] Based on the analyzed real-time data, combined with the heat conduction model and mechanical stress model, the thermal stress distribution during arc discharge is calculated, and the crack expansion path and speed are predicted;

[0010] After the arc is extinguished, the residual stress change monitored by the fiber Bragg grating sensor is used to predict whether the cracked pin-type porcelain insulator will have permanent failure.

[0011] The calculation of the thermal stress distribution during the arc discharge process includes using the analyzed real-time data and combining the heat conduction and mechanical stress-strain relationship, and considering the overall effect of the porcelain insulator through spatial integration. The formula for the thermal stress distribution of the pin-type porcelain insulator is expressed as:

[0012]

[0013] Where S(r, t) represents the comprehensive thermal stress distribution at position r and time t, Ω represents the volume integration domain of the porcelain insulator, k(r) represents the thermal conductivity at position r, T(r, t) represents the temperature distribution at position r and time t, represents the gradient operator, ∈ kl represents the strain tensor at position r and time t, C ijkl represents the fourth-order elastic modulus tensor, E represents Young's modulus, α T Indicates the thermal expansion coefficient of the material, δ ij represents the diagonal angle in the tensor, V represents the volume, t represents the time, and r represents the spatial position vector; i and j represent the component directions of thermal stress; k and l represent the component directions of the strain tensor; represents the rate of change of temperature with space, represents the divergence of the calculated heat flux;

[0014] The value range of S(r, t) is from negative infinity to positive infinity, where positive values ​​indicate tensile stress regions, which usually appear in the possible directions of crack extension;

[0015] Negative values ​​indicate regions of compressive stress, which may inhibit crack propagation;

[0016] The value of S(r, t) reflects the degree of thermal stress concentration, and the maximum and minimum values ​​indicate the potential failure points of the structure.

[0017] The calculated thermal stress data is mapped to the three-dimensional surface of the three-dimensional geometric model of the pin-type porcelain insulator through finite element software to generate a thermal stress distribution map, where red represents high stress areas and blue represents low stress areas;

[0018] By performing frequency domain analysis on real-time stress-strain data and combining it with the thermal stress distribution diagram, the crack propagation path is dynamically calculated;

[0019] Combine Fourier transform analysis and real-time data processing to calculate the speed of crack expansion and determine whether the crack is expanding based on the strain change and critical frequency;

[0020] Residual stress, thermal stress distribution and crack growth rate are monitored in real time, and a fault judgment model is used to determine whether a permanent failure will occur in the porcelain insulator. When the residual stress exceeds the critical value or the crack growth rate reaches the critical rate, it is predicted that the structure of the porcelain insulator is about to fail and a fault warning is issued.

[0021] As a preferred solution of the fault prediction method of porcelain insulator cracks based on PIIF described in the present invention, wherein: the arrangement of multiple fiber grating sensors on the surface of the pin-type porcelain insulator and around the cracks includes, according to the structural characteristics of the pin-type porcelain insulator, analyzing the stress concentration area and thermal effect area that may appear in the PIIF process through historical fault data and using finite element analysis;

[0022] Arrange three fiber Bragg grating sensors at the starting point of the pin-type porcelain insulator crack, and arrange three fiber Bragg grating sensors on and around the extension path of the pin-type porcelain insulator crack to form a ring layout;

[0023] Four sensors are arranged in a square layout at the place where each pin-type porcelain insulator contacts the metal electrode;

[0024] Three fiber Bragg grating sensors are arranged in a triangular layout at the place where the geometric shape of each pin-type porcelain insulator changes suddenly;

[0025] From the center of the pin-type porcelain insulator crack outward, a fiber Bragg grating sensor is arranged every 10 mm to form a gradient monitoring;

[0026] Collecting and analyzing the real-time data of the fiber Bragg grating sensor includes performing noise filtering, signal smoothing and data correction on the temperature and stress strain data collected by the fiber Bragg grating sensor at different positions;

[0027] The real-time collected temperature and stress-strain data are mapped to the three-dimensional model of the porcelain insulator. According to the installation position and measurement data of the fiber grating sensor, the spatial distribution diagram of strain and temperature is generated. The temperature distribution diagram and stress-strain data distribution diagram are generated using finite element software.

[0028] Analyzing the temperature distribution map to identify the area with the fastest temperature increase and the highest peak value;

[0029] Analyzing the stress-strain data distribution diagram to identify areas of stress concentration;

[0030] Through time series analysis, the changes of temperature and strain over time are observed and the key time points in the arc discharge process are identified.

[0031] As a preferred solution of the fault prediction method of porcelain insulator cracks based on PIIF described in the present invention, wherein: the dynamic analysis of thermal stress distribution includes identifying the frequency components of drastic strain changes during crack propagation by Fourier transform analysis of temperature and stress-strain data;

[0032] The prediction of the crack expansion path and speed includes setting a critical frequency of stress change and a threshold value of strain change;

[0033] When the real-time frequency domain analysis results show that the strain has a significant peak near the critical frequency and the strain change exceeds the threshold, the crack is rapidly expanding;

[0034] In combination with the thermal stress distribution map, the expansion path of the crack is dynamically predicted by calculating the thermal stress concentration area at the current moment.

[0035] As a preferred solution of the fault prediction method of porcelain insulator cracks based on PIIF described in the present invention, wherein: predicting the crack expansion speed includes, through time series analysis, observing the changes of stress and strain over time, identifying the key time point of crack expansion and estimating its expansion speed;

[0036] The formula for calculating the crack propagation velocity through Fourier transform and real-time data analysis is expressed as:

[0037]

[0038] Where v(t) represents the crack propagation velocity calculated in real time, Δt represents the time interval, represents the frequency domain strain component obtained by Fourier transform, f i represents the i-th frequency component, H(x) represents the Heaviside function, and f 临界值 represents the critical rate of crack extension, Δ∈ i represents the strain change corresponding to the i-th frequency component, Δ∈临界值 represents the critical stress of the crack extension change, i represents the index of the current frequency component, and N represents the total number of frequency components;

[0039] The value range of v(t) is from 0 to positive infinity, which indicates the speed of crack expansion;

[0040] v(t) = 0 means the crack has not expanded;

[0041] v(t)>0 indicates that the crack is expanding.

[0042] As a preferred solution of the fault prediction method of porcelain insulator cracks based on PIIF described in the present invention, the prediction of whether a permanent fault occurs in a cracked pin-type porcelain insulator includes, after the arc is extinguished, continuing to use a fiber grating sensor to monitor the residual stress changes on the surface and inside of the pin-type porcelain insulator in real time, and collecting residual stress data;

[0043] The residual stress includes the stress that remains inside the material after the arc discharge ends;

[0044] Based on the residual stress data, combined with thermal stress distribution and historical crack extension data, a fault judgment model is established to predict whether a permanent fault occurs in a cracked pin-type porcelain insulator;

[0045] The formula of the fault judgment model is expressed as:

[0046]

[0047] Where F(r, t) represents the probability of permanent failure of a crack at position r at time t, V(r) represents the volume function, and σ r (r, t) represents residual stress, S(r, t) represents thermal stress distribution function, H(x) represents Heaviside function, f c (r, t) represents the crack growth rate, f 临界值 represents the critical rate of crack extension, σ 临界值 represents the critical stress for crack extension, Y represents the adjustment parameter used to control the stress effect in the formula, r represents the spatial position vector, and t represents time;

[0048] The value range of F(r, t) is 0 to 1.

[0049] As a preferred solution of the fault prediction method of porcelain insulator cracks based on PIIF described in the present invention, the prediction of whether a permanent fault occurs in a cracked pin-type porcelain insulator further includes:

[0050] When 0≤F(r, t)<0.25, the risk of crack extension is low and the structure of the pin-type porcelain insulator is stable;

[0051] When 0.25≤F(r, t)<0.5, the risk of crack extension increases and the structure of the pin-type porcelain insulator is stable;

[0052] When 0.5≤F(r, t)<0.75, the risk of crack expansion is high and the pin-type porcelain insulator is structurally unstable, but it can still be used and needs to be closely monitored;

[0053] When 0.75≤F(r, t)≤1, the risk of crack extension is very high and the pin-type porcelain insulator structure fails and needs to be replaced immediately.

[0054] A fault prediction system for porcelain insulator cracks based on PIIF, wherein:

[0055] The sensor placement module places multiple fiber grating sensors on the surface of the pin-type porcelain insulator and around the cracks to ensure that these sensors can accurately collect stress and temperature data;

[0056] Data acquisition and processing module, real-time acquisition of fiber Bragg grating sensor data, including temperature, stress and strain information;

[0057] The thermal stress calculation module calculates the thermal stress distribution during arc discharge based on real-time collected data combined with the heat conduction model and mechanical stress model;

[0058] Crack propagation prediction module, which predicts the crack propagation path and speed based on thermal stress distribution and historical crack propagation data;

[0059] The residual stress evaluation module monitors and analyzes the changes in residual stress after the arc is extinguished. By evaluating the residual stress, it is determined whether the cracks will lead to permanent failure of the porcelain insulator.

[0060] The fault prediction and early warning module predicts the failure risk of porcelain insulators based on the crack propagation rate and residual stress assessment results, and issues an early warning signal to indicate the need for maintenance or replacement.

[0061] Beneficial effects of the present invention: The PIIF-based porcelain insulator crack fault prediction method provided by the present invention realizes real-time dynamic monitoring of porcelain insulator cracks and accurate calculation of thermal stress by precisely arranging fiber grating sensors, effectively improving the prediction accuracy of crack expansion path and speed. In addition, by continuously monitoring the residual stress after arc discharge, the long-term structural stability of porcelain insulators can be accurately evaluated, and potential permanent faults can be warned in time, thereby improving the reliability and safety of the power system and reducing unexpected power outages and maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0063] Figure 1 An overall flow chart of a fault prediction method for porcelain insulator cracks based on PIIF provided in the first embodiment of the present invention;

[0064] Figure 2 A schematic diagram of a cracked porcelain insulator according to a fault prediction method of a porcelain insulator crack based on PIIF provided by the first embodiment of the present invention, wherein (a) is a pin type, (b) is a suspension type, and (c) is a column type;

[0065] Figure 3 A schematic diagram of the development process of an early fault of a porcelain insulator according to a fault prediction method of a porcelain insulator crack based on PIIF provided in the first embodiment of the present invention. DETAILED DESCRIPTION

[0066] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0067] Example 1, reference Figure 1 , as an embodiment of the present invention, provides a fault prediction method for porcelain insulator cracks based on PIIF, comprising:

[0068] S1: Multiple fiber Bragg grating sensors are arranged on the surface of the pin-type porcelain insulator and around the cracks to collect and analyze the real-time data of the fiber Bragg grating sensors.

[0069] PIIF is a discharge process with self-clearing characteristics that occurs when cracked porcelain insulators are affected by rain, which is called early failure of porcelain insulators.

[0070] like Figure 2 As shown in the figure, according to the structural characteristics of pin-type porcelain insulators, the stress concentration areas and thermal effect areas that may occur during the PIIF process are analyzed through historical failure data and finite element analysis.

[0071] Three fiber Bragg grating sensors are arranged at the starting point of the pin-type porcelain insulator crack, and three fiber Bragg grating sensors are arranged on and around the extension path of the pin-type porcelain insulator crack to form a ring layout.

[0072] Four sensors are arranged in a square layout at the place where each pin-type porcelain insulator contacts the metal electrode.

[0073] Three fiber grating sensors are arranged in a triangular layout at the place where the geometric shape of each pin-type porcelain insulator changes suddenly.

[0074] From the center of the pin-type porcelain insulator crack outward, a fiber grating sensor is arranged every 10 mm to form gradient monitoring.

[0075] Collecting and analyzing the real-time data of the fiber Bragg grating sensor includes performing noise filtering, signal smoothing and data correction on the temperature and stress strain data collected by the fiber Bragg grating sensor at different positions.

[0076] The temperature and stress-strain data collected in real time are mapped to the three-dimensional model of the porcelain insulator. The spatial distribution diagram of strain and temperature is generated according to the installation position and measurement data of the fiber grating sensor. The temperature distribution diagram and stress-strain data distribution diagram are generated using finite element software.

[0077] The temperature distribution map is analyzed to identify the areas with the fastest temperature increase and the highest peak value.

[0078] The stress-strain data distribution diagram is analyzed to identify areas of stress concentration.

[0079] Through time series analysis, the changes of temperature and strain over time are observed and the key time points in the arc discharge process are identified.

[0080] S2: Based on the analyzed real-time data, combined with the heat conduction model and mechanical stress model, the thermal stress distribution during the arc discharge process is calculated, and the crack expansion path and speed are predicted.

[0081] Using the real-time data after analysis and combining the relationship between heat conduction and mechanical stress-strain, and considering the overall effect of the porcelain insulator through spatial integration, the formula for the thermal stress distribution of the pin-type porcelain insulator is expressed as:

[0082]

[0083] Where S(r, t) represents the comprehensive thermal stress distribution at position r and time t, Ω represents the volume integration domain of the porcelain insulator, k(r) represents the thermal conductivity at position r, T(r, t) represents the temperature distribution at position r and time t, represents the gradient operator, ∈ kl represents the strain tensor at position r and time t, Cijkl represents the fourth-order elastic modulus tensor, E represents Young's modulus, α T Indicates the thermal expansion coefficient of the material, δ ij represents the diagonal angle in the tensor, V represents the volume, t represents the time, and r represents the spatial position vector; i and j represent the component directions of thermal stress; k and l represent the component directions of the strain tensor; represents the rate of change of temperature with space, Represents the divergence of the calculated heat flux.

[0084] The value range of S(r, t) is from negative infinity to positive infinity, where positive values ​​indicate tensile stress areas, which usually appear in the possible directions of crack extension.

[0085] Negative values ​​indicate regions of compressive stress, which may inhibit crack growth.

[0086] The size of the S(r, t) value reflects the degree of thermal stress concentration, and the maximum or minimum value indicates the potential failure point of the structure.

[0087] Through finite element software, the calculated thermal stress data is mapped to the three-dimensional surface of the three-dimensional geometric model of the pin-type porcelain insulator to generate a thermal stress distribution map, with red representing high stress areas and blue representing low stress areas.

[0088] Predicting the crack propagation path involves identifying the frequency components where strain changes dramatically during crack propagation by performing Fourier transform analysis on temperature and stress-strain data.

[0089] The method of predicting the crack expansion path includes setting a critical frequency of stress change and a threshold value of strain change.

[0090] When the real-time frequency domain analysis results show that the strain has a significant peak near the critical frequency and the strain change exceeds the threshold, the crack is rapidly expanding.

[0091] Combined with the thermal stress distribution map, the crack expansion path is dynamically predicted by calculating the thermal stress concentration area at the current moment.

[0092] Predicting the crack growth rate includes observing the changes of stress and strain over time through time series analysis, identifying the key time points of crack growth and estimating its growth rate;

[0093] The formula for calculating the crack propagation velocity through Fourier transform and real-time data analysis is expressed as:

[0094]

[0095] Where v(t) represents the crack propagation velocity calculated in real time, Δt represents the time interval, represents the frequency domain strain component obtained by Fourier transform, f i represents the i-th frequency component, H(x) represents the Heaviside function, and f 临界值 represents the critical rate of crack extension, Δ∈ i represents the strain change corresponding to the i-th frequency component, Δ∈ 临界值 represents the critical stress of the crack extension change, i represents the index of the current frequency component, and N represents the total number of frequency components.

[0096] The value range of v(t) is from 0 to positive infinity, which indicates the speed of crack expansion.

[0097] v(t)=0 means that the crack has not extended.

[0098] v(t)>0 indicates that the crack is expanding.

[0099] By combining the heat conduction model and the mechanical stress model, the real-time data is used to accurately calculate the thermal stress generated during the arc discharge process, and the overall effect of the porcelain insulator is considered through spatial integration to generate a detailed thermal stress distribution map, which clearly shows the high stress concentration area and helps to identify potential crack extension paths and possible failure points. The stress and strain data are analyzed by Fourier transform, the crack extension path is dynamically predicted, and the changes of stress and strain over time are observed by combining time series analysis, the key time points are identified and the crack extension speed is calculated, so as to achieve high-precision and real-time fault warning. In addition, the three-dimensional thermal stress distribution map generated by the finite element software visualizes the complex calculation results, provides an intuitive reference for engineering and technical personnel, and facilitates accurate maintenance decisions. This design not only improves the accuracy and real-time performance of crack extension prediction, but also has a powerful decision-making support function to adapt to changes in different working conditions and ensure the stability and safety of the power system.

[0100] S3: After the arc is extinguished, the residual stress changes monitored by the fiber Bragg grating sensor are used to predict whether the cracked pin-type porcelain insulator will have permanent failure.

[0101] like Figure 3 As shown, the changes from (a) to (e) clearly demonstrate how the arc induces crack propagation, the melting of the porcelain insulator material, and the final state of the crack formed after the water vapor is discharged.

[0102] Predicting whether a cracked pin-type porcelain insulator will have a permanent failure includes continuing to use a fiber grating sensor to monitor the residual stress changes on the surface and inside of the pin-type porcelain insulator in real time after the arc is extinguished, and collecting residual stress data.

[0103] The residual stress includes the stress that remains inside the material after the arc discharge ends.

[0104] Based on the residual stress data, combined with thermal stress distribution and historical crack extension data, a fault judgment model is established to predict whether a permanent fault occurs in a cracked pin-type porcelain insulator.

[0105] The formula of the fault judgment model is expressed as:

[0106]

[0107] Where F(r, t) represents the probability of permanent failure of a crack at position r at time t, V(r) represents the volume function, and σ r (r, t) represents residual stress, S(r, t) represents thermal stress distribution function, H(x) represents Heaviside function, f c (r, t) represents the crack growth rate, f 临界值 represents the critical rate of crack extension, σ 临界值 represents the critical stress for crack extension, Y represents the adjustment parameter used to control the stress influence in the formula, r represents the spatial position vector, and t represents time.

[0108] The value range of F(r, t) is 0 to 1.

[0109] When 0≤F(r, t)<0.25, the risk of crack extension is low and the structure of the pin-type porcelain insulator is stable.

[0110] When 0.25≤F(r, t)<0.5, the risk of crack extension increases and the structure of the pin-type porcelain insulator is stable.

[0111] When 0.5≤F(r, t)<0.75, the risk of crack extension is high and the pin-type porcelain insulator is structurally unstable, but it can still be used and closely monitored.

[0112] When 0.75≤F(r, t)≤1, the risk of crack extension is very high and the pin-type porcelain insulator structure fails and needs to be replaced immediately.

[0113] Through real-time monitoring and data collection, the continuous tracking of the status of pin-type porcelain insulators is ensured, and the dynamic information of crack expansion is captured in time. At the same time, the residual stress, thermal stress distribution and historical crack expansion data are comprehensively considered to build a multi-dimensional fault judgment model to improve the accuracy of prediction. The fault judgment model divides the fault risks into different levels through hierarchical management, and provides clear maintenance guidance, which not only improves the safety of the power system, but also optimizes the maintenance strategy and reduces operating costs. In addition, the design is suitable for applications in complex environments and can maintain stable prediction performance under harsh conditions such as high temperature and high pressure, thereby significantly improving the prediction accuracy of porcelain insulator crack expansion and fault warning capabilities.

[0114] On the other hand, this embodiment also provides a fault prediction system for porcelain insulator cracks based on PIIF, which includes:

[0115] The sensor placement module places multiple fiber grating sensors on the surface of the pin-type porcelain insulator and around the cracks to ensure that these sensors can accurately collect stress and temperature data.

[0116] The data acquisition and processing module collects the data of the fiber grating sensor in real time, including temperature, stress and strain information.

[0117] The thermal stress calculation module calculates the thermal stress distribution during arc discharge based on real-time collected data combined with the heat conduction model and mechanical stress model.

[0118] The crack extension prediction module predicts the crack extension path and speed based on the thermal stress distribution and historical crack extension data.

[0119] The residual stress evaluation module monitors and analyzes the changes in residual stress after the arc is extinguished. By evaluating the residual stress, it is determined whether the cracks will cause permanent failure of the porcelain insulator.

[0120] The fault prediction and early warning module predicts the failure risk of porcelain insulators based on the crack propagation rate and residual stress assessment results, and issues an early warning signal to indicate the need for maintenance or replacement.

[0121] If the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0122] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0123] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0124] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0125] In Example 2, several pin-type porcelain insulator samples with different crack states were prepared, and multiple fiber grating sensors were arranged on their surfaces and around the cracks. Specifically, three sensors were arranged at the starting point of the crack, and three sensors were arranged along the crack extension path, forming a ring layout. In addition, four sensors were arranged in a square layout in the area where the porcelain insulator contacted the metal electrode; three sensors were arranged at the location where the geometric shape suddenly changed, forming a triangular layout; and a sensor was arranged every 10 mm from the center of the crack to the outside, forming a gradient monitoring network.

[0126] During the experiment, these sensors were first used to collect temperature, stress and strain data on the surface and inside of the porcelain insulator in real time. After noise filtering, signal smoothing and data correction, the collected data was mapped to the three-dimensional geometric model of the porcelain insulator. The spatial distribution map of strain and temperature was generated using finite element analysis software, and analyzed to identify the areas with the fastest temperature rise and stress concentration. Next, the thermal stress distribution at each moment during the arc discharge process was calculated by combining the heat conduction model and the mechanical stress model, and the crack extension path and its speed were predicted by combining real-time data and Fourier transform. During the experiment, time series analysis was also used to observe the changes in stress and strain over time and identify the key time points of arc discharge.

[0127] After the arc was extinguished, the experiment continued to use fiber Bragg grating sensors to monitor the changes in residual stress. Based on these data, combined with thermal stress distribution and historical crack expansion data, a fault judgment model was established to predict whether the crack would lead to permanent failure of the porcelain insulator. Through repeated tests, the experiment collected multiple sets of data at different crack expansion levels to verify the reliability and accuracy of the model.

[0128] The experimental data are shown in Table 1.

[0129] Table 1 Experimental data table

[0130]

[0131] Through the analysis of the above experimental data, it can be clearly seen that the present invention has shown significant superiority in predicting crack expansion and fault risk assessment. First, from the corresponding relationship between the temperature rise rate and the maximum stress, it can be seen that when the temperature rise rate increases, the maximum stress value inside the porcelain insulator also increases, which shows that in high temperature and stress concentration areas, the crack expansion rate is significantly accelerated, which is consistent with the prediction model of the present invention. In the experimental data, as the temperature rise rate increases from 2.5℃ / s to 7.5℃ / s, the crack expansion rate increases from 0.12mm / s to 0.55mm / s, proving that in high stress and high temperature areas, the crack expansion is indeed faster.

[0132] Secondly, there is also a close correlation between the change in residual stress and the probability of failure. Experimental data show that the higher the residual stress, the greater the probability of failure of the porcelain insulator. The residual stress of sample F is 38MPa, and the corresponding probability of failure is as high as 98%, which is almost close to the failure state. This result shows that after the arc is extinguished, the residual stress inside the porcelain insulator is an important indicator for evaluating the stability of the structure. The present invention can effectively improve the accuracy of fault warning and avoid potential power system failures by monitoring the changes in residual stress in real time and combining historical data for fault prediction.

[0133] Through the real-time data collection, the stress and temperature data are analyzed in the frequency domain by applying Fourier transform, the key frequency components of crack expansion are identified, and combined with time series analysis, the crack expansion speed and path are dynamically predicted. During the analysis process, the algorithm accurately calculates the thermal stress distribution, identifies the high stress concentration area, and evaluates the structural stability of the porcelain insulator in combination with the residual stress data. The application of these algorithms enables early warning signals to be issued in time when the cracks expand rapidly, and through a reasonable sensor layout, comprehensive monitoring of each key area of ​​the porcelain insulator is achieved. Compared with the traditional static monitoring method, the algorithm of the present invention can perform real-time data processing and analysis, thereby achieving higher fault prediction accuracy and significantly improving the safety and reliability of the power system.

[0134] Compared with existing monitoring technologies, the present invention introduces a dynamic prediction model of thermal stress distribution and crack expansion, which not only takes into account real-time data, but also combines historical data and complex mechanical models. Traditional methods can often only detect after a fault occurs, and cannot provide advance warnings, and the real-time monitoring of crack expansion is not accurate enough. The present invention effectively solves these problems by introducing Fourier transform and time series analysis. It can issue warnings in time when cracks expand rapidly, and through reasonable sensor layout, it realizes comprehensive monitoring of key areas of porcelain insulators.

[0135] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A fault prediction method for porcelain insulator cracks based on PIIF, characterized in that: include: PIIF is a discharge process with self-clearing characteristics that occurs in cracked porcelain insulators due to the influence of rain, which is called early failure of porcelain insulators; Arrange multiple fiber Bragg grating sensors on the surface of the pin-type porcelain insulator and around the cracks to collect and analyze the real-time data of the fiber Bragg grating sensors; Based on the analyzed real-time data, combined with the heat conduction model and mechanical stress model, the thermal stress distribution during arc discharge is calculated, and the crack expansion path and speed are predicted; After the arc is extinguished, the residual stress change monitored by the fiber Bragg grating sensor is used to predict whether the cracked pin-type porcelain insulator will have permanent failure. The real-time data includes temperature and stress-strain data collected by the fiber grating sensor at different positions; The calculation of the thermal stress distribution during the arc discharge process includes using the analyzed real-time data and combining the heat conduction and mechanical stress-strain relationship, and considering the overall effect of the porcelain insulator through spatial integration. The formula for the thermal stress distribution of the pin-type porcelain insulator is expressed as: Where S(r,t) represents the comprehensive thermal stress distribution at position r and time t, Ω represents the volume integration domain of the porcelain insulator, k(r) represents the thermal conductivity at position r, T(r,t) represents the temperature distribution at position r and time t, represents the gradient operator, ∈ kl represents the strain tensor at position r and time t, C ijkl represents the fourth-order elastic modulus tensor, E represents Young's modulus, α T Indicates the thermal expansion coefficient of the material, δ ij represents the diagonal angle in the tensor, V represents the volume, t represents the time, and r represents the spatial position vector; i and j represent the component directions of thermal stress; k and l represent the component directions of the strain tensor; represents the rate of change of temperature with space, represents the divergence of the calculated heat flux; The value range of S(r,t) is from negative infinity to positive infinity, where positive values ​​indicate tensile stress regions, which usually appear in the possible directions of crack extension; Negative values ​​indicate regions of compressive stress, which may inhibit crack propagation; The value of S(r,t) reflects the degree of thermal stress concentration, and the maximum and minimum values ​​indicate the potential failure points of the structure; The calculated thermal stress data is mapped to the three-dimensional surface of the three-dimensional geometric model of the pin-type porcelain insulator through finite element software to generate a thermal stress distribution map, where red represents high stress areas and blue represents low stress areas; By performing frequency domain analysis on real-time stress-strain data and combining it with the thermal stress distribution diagram, the crack propagation path is dynamically calculated; Combine Fourier transform analysis and real-time data processing to calculate the crack expansion speed and determine whether the crack is expanding based on the strain change and critical frequency; Residual stress, thermal stress distribution and crack growth rate are monitored in real time, and a fault judgment model is used to determine whether a permanent failure will occur in the porcelain insulator. When the residual stress exceeds the critical value or the crack growth rate reaches the critical rate, it is predicted that the structure of the porcelain insulator is about to fail and a fault warning is issued.

2. The fault prediction method of porcelain insulator cracks based on PIIF according to claim 1, characterized in that: The arrangement of multiple fiber grating sensors on the surface of the pin-type porcelain insulator and around the cracks includes, based on the structural characteristics of the pin-type porcelain insulator, analyzing the stress concentration areas and thermal effect areas that may occur during the PIIF process through historical fault data and using finite element analysis; Arrange three fiber Bragg grating sensors at the starting point of the pin-type porcelain insulator crack, and arrange three fiber Bragg grating sensors on and around the extension path of the pin-type porcelain insulator crack to form a ring layout; Four sensors are arranged in a square layout at the place where each pin-type porcelain insulator contacts the metal electrode; Three fiber Bragg grating sensors are arranged in a triangular layout at the place where the geometric shape of each pin-type porcelain insulator changes suddenly; From the center of the pin-type porcelain insulator crack outward, a fiber Bragg grating sensor is arranged every 10 mm to form a gradient monitoring; Collecting and analyzing the real-time data of the fiber Bragg grating sensor includes performing noise filtering, signal smoothing and data correction on the temperature and stress strain data collected by the fiber Bragg grating sensor at different positions; The real-time collected temperature and stress-strain data are mapped to the three-dimensional model of the porcelain insulator. According to the installation position and measurement data of the fiber grating sensor, the spatial distribution diagram of strain and temperature is generated. The temperature distribution diagram and stress-strain data distribution diagram are generated using finite element software. Analyzing the temperature distribution map to identify the area with the fastest temperature increase and the highest peak value; Analyzing the stress-strain data distribution diagram to identify areas of stress concentration; Through time series analysis, the changes of temperature and strain over time are observed and the key time points in the arc discharge process are identified.

3. The fault prediction method of porcelain insulator cracks based on PIIF as claimed in claim 2, characterized in that: Predicting the crack propagation path includes identifying the frequency components of drastic strain changes during crack propagation by Fourier transform analysis of temperature and stress-strain data; The prediction of the crack expansion path includes setting a critical frequency of stress change and a threshold value of strain change; When the real-time frequency domain analysis results show that the strain has a significant peak near the critical frequency and the strain change exceeds the threshold, the crack is rapidly expanding; In combination with the thermal stress distribution map, the expansion path of the crack is dynamically predicted by calculating the thermal stress concentration area at the current moment.

4. The fault prediction method of porcelain insulator cracks based on PIIF as claimed in claim 3, characterized in that: Predicting the crack growth rate includes observing the changes of stress and strain over time through time series analysis, identifying the key time points of crack growth and estimating its growth rate; The formula for calculating the crack propagation velocity through Fourier transform and real-time data analysis is expressed as: Where v(t) represents the crack propagation velocity calculated in real time, Δt represents the time interval, represents the frequency domain strain component obtained by Fourier transform, f i represents the i-th frequency component, H(x) represents the Heaviside function, and f 临界值 represents the critical rate of crack extension, Δ∈ i represents the strain change corresponding to the i-th frequency component, Δ∈ 临界值 represents the critical stress of the crack extension change, i represents the index of the current frequency component, and N represents the total number of frequency components; The value range of v(t) is from 0 to positive infinity, which indicates the speed of crack expansion; v(t) = 0 means the crack has not expanded; v(t)>0 indicates that the crack is expanding.

5. The fault prediction method of porcelain insulator cracks based on PIIF as claimed in claim 4, characterized in that: The method of predicting whether a permanent fault occurs in a cracked pin-type porcelain insulator includes, after the arc is extinguished, continuing to use a fiber grating sensor to monitor the residual stress changes on the surface and inside of the pin-type porcelain insulator in real time and collecting residual stress data; The residual stress includes the stress that remains inside the material after the arc discharge ends; Based on the residual stress data, combined with thermal stress distribution and historical crack extension data, a fault judgment model is established to predict whether a permanent fault occurs in a cracked pin-type porcelain insulator; The formula of the fault judgment model is expressed as: Where F(r,t) represents the probability of permanent failure of a crack at position r at time t, V(r) represents the volume function, and σ r (r, t) represents residual stress, S(r, t) represents thermal stress distribution function, H(x) represents Heaviside function, f c (r,t) represents the crack growth rate, f 临界值 represents the critical rate of crack extension, σ 临界值 represents the critical stress for crack extension, γ represents the adjustment parameter used to control the stress effect in the formula, r represents the spatial position vector, and t represents time; The value range of F(r,t) is 0 to 1.

6. The fault prediction method of porcelain insulator cracks based on PIIF according to claim 5, characterized in that: The prediction of whether a permanent fault occurs in a cracked pin-type porcelain insulator also includes: When 0≤F(r,t)<0.25, the risk of crack extension is low and the structure of the pin-type porcelain insulator is stable; When 0.25≤F(r,t)<0.5, the risk of crack extension increases and the structure of the pin-type porcelain insulator is stable; When 0.5≤F(r,t)<0.75, the risk of crack extension is high and the pin-type porcelain insulator is structurally unstable, but it can still be used and needs to be closely monitored; When 0.75≤F(r,t)≤1, the risk of crack extension is very high and the pin-type porcelain insulator structure fails and needs to be replaced immediately.

7. A PIIF-based porcelain insulator crack fault prediction system, using the PIIF-based porcelain insulator crack fault prediction method as claimed in any one of claims 1 to 6, characterized in that: The sensor placement module places multiple fiber grating sensors on the surface of the pin-type porcelain insulator and around the cracks to ensure that these sensors can accurately collect stress and temperature data; Data acquisition and processing module, real-time acquisition of fiber Bragg grating sensor data, including temperature, stress and strain information; The thermal stress calculation module calculates the thermal stress distribution during arc discharge based on real-time collected data combined with the heat conduction model and mechanical stress model; Crack propagation prediction module, which predicts the crack propagation path and speed based on thermal stress distribution and historical crack propagation data; The residual stress evaluation module monitors and analyzes the changes in residual stress after the arc is extinguished. By evaluating the residual stress, it is determined whether the cracks will lead to permanent failure of the porcelain insulator. The fault prediction and early warning module predicts the failure risk of porcelain insulators based on the crack propagation rate and residual stress assessment results, and issues an early warning signal to indicate the need for maintenance or replacement.

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