Low-voltage fault arc recognition and diagnosis method and device fusing multi-factor equivalent model

Abstract

The application discloses a low-voltage fault arc recognition and diagnosis method and device fusing a multi-factor equivalent model, and the method steps comprise the following steps: extracting a characteristic parameter set of a fault arc generated by different source and load equipment under different space factors; establishing characteristic curves of different types of fault arcs; determining arc simulation parameters according to the characteristic curves of the different types of fault arcs, inputting the arc simulation parameters into a fault arc true type reproduction device, simulating fault arc equivalent models under multiple space factors, and generating fault arc signals of different fault types; extracting fault symptom features from the fault arc signals of different fault types, and statistically distributing interval probabilities to form a fault feature database; acquiring a signal to be diagnosed and extracting characteristic parameters, inputting the characteristic parameters into the fault feature database for matching, and diagnosing whether it is an early fault arc and a corresponding fault type. The application has the advantages of simple implementation method, high recognition and diagnosis precision and reliability, and low missing report and false report rates.

Description

Technical Field

[0001] This invention relates to the field of low-voltage fault arc diagnosis technology, and in particular to a method and apparatus for low-voltage fault arc identification and diagnosis that integrates a multi-factor equivalent model. Background Technology

[0002] With the increasing complexity of power systems and the expansion of electricity consumption, the safe operation of power supply and consumption faces new challenges, making fault arc identification and diagnosis crucial. For the measurement and diagnosis of low-voltage fault arcs, traditional analytical methods are typically employed, such as visual inspection, thermal imaging technology, current and voltage waveform analysis, sound detection, gas detection, and instantaneous power outage methods. However, these methods have the following problems:

[0003] 1. Traditional analysis methods typically diagnose faults by extracting single fault arc features from power grid data or historical fault records for a specific region. However, on the one hand, due to limited data sources, it is impossible to cover the characteristics of fault arcs in different regions and under varying environments; on the other hand, fault arcs are highly random, their inherent characteristics are difficult to fully extract, and single features cannot accurately characterize fault arcs, nor can they fully consider multi-dimensional influencing factors. Consequently, they cannot comprehensively represent the dynamic changes in fault arc characteristics. Therefore, the actual identification accuracy and reliability of traditional analysis methods are not high, and the false alarm and missed alarm rates are relatively high. Furthermore, with the widespread application of renewable energy and power electronic equipment, the nonlinear loads and spatiotemporal influencing factors are complex, and factors such as line current conduction interference and background noise will further increase the difficulty of identification.

[0004] 2. Due to the differences in insulation and wiring materials of different electrical circuits, the disaster-causing mechanism of electric arc faults is not entirely the same, and the early signs are diverse. The arc generation process involves multiple physical field characteristics such as electric, magnetic and thermal fields, and is accompanied by phenomena such as high-frequency harmonics, non-periodic current / voltage fluctuations and temperature anomalies. The causes and signs of electric arc fault hazards are still unclear, and it is difficult to detect the early signs of electric arc faults through traditional analysis methods, making it impossible to predict the occurrence of electric arc faults in advance. Summary of the Invention

[0005] The technical problem to be solved by the present invention is as follows: In view of the technical problems existing in the prior art, the present invention provides a low-voltage fault arc identification and diagnosis method and device that integrates a multi-factor equivalent model, which is simple to implement, has high identification and diagnosis accuracy and reliability, and low false alarm rate. It can efficiently and accurately identify and diagnose early fault arcs and fully explore the early fault symptoms and regular characteristics of fault arcs.

[0006] To solve the above-mentioned technical problems, the technical solution proposed by this invention is as follows:

[0007] A method for identifying and diagnosing low-voltage fault arcs by integrating a multi-factor equivalent model, comprising the following steps:

[0008] Extract the set of characteristic parameters of fault arcs generated by different source load devices under different spatial factors;

[0009] Based on the set of characteristic parameters, characteristic curves for different types of fault arcs are established;

[0010] Based on the characteristic curves of the different types of fault arcs, the arc simulation parameters are determined and input into a pre-constructed fault arc simulation device. The fault arc simulation device simulates equivalent models of fault arcs of different fault types under various spatial factors to generate fault arc signals of different fault types according to the arc simulation parameters. The equivalent model of the fault arc is a simplified model for simulating fault arcs with real fault arc characteristics. The arc simulation parameters include discharge duration, discharge intensity, and fault type.

[0011] Fault symptom features that characterize the early signs of fault arcs are extracted from the fault arc signals of different fault types, and the distribution interval probabilities of the early signs of fault arcs are statistically analyzed to form a fault symptom feature database.

[0012] The system acquires the real-time monitored signals to be diagnosed and extracts the corresponding feature parameters. The extracted feature parameters are then input into the fault symptom feature database for matching to diagnose whether it is an early fault arc and, if it is diagnosed as an early fault arc, to determine the corresponding fault type.

[0013] Furthermore, the source and load devices include any combination of photovoltaic arrays, energy storage devices, converters, and electric vehicles; the spatial factors include any combination of insulation aging and damage, loose electrical connections, humid air, and high temperature of conductors; the characteristic parameters of the fault arc include any one or more of the fault arc's current, impedance, energy characteristics, and volt-ampere characteristics; the fault arc characteristic curves include energy characteristic curves and / or volt-ampere characteristic curves; and the fault types include series faults, parallel faults, and grounding faults. In the process of simulating and generating fault arc signals of different fault types using the fault arc simulation device, the process also includes multi-dimensional parameter observation of the fault arc signals generated by the fault arc simulation device to generate fault arc signals with multiple different parameters, including any combination of the number of arcs, energy intensity, arc current, arc voltage, temperature, arc light, and arc sound.

[0014] Furthermore, the source and load devices include any combination of photovoltaic arrays, energy storage devices, converters, and electric vehicles; the spatial factors include any combination of insulation aging and damage, loose electrical connections, humid air, and high temperature of conductors; the characteristic parameters of the fault arc include any one or more of the fault arc's current, impedance, energy characteristics, and volt-ampere characteristics; the fault arc characteristic curves include energy characteristic curves and / or volt-ampere characteristic curves; the fault types include series faults, parallel faults, and grounding faults; the process of generating fault arc signals of different fault types by the fault arc simulation device simulating the characteristics of real fault arcs according to the arc simulation parameters also includes multi-dimensional parameter observation of the fault arc signals generated by the fault arc simulation device to generate fault arc signals with multiple different parameters, including any combination of arc number, energy intensity, arc current, arc voltage, temperature, arc light, and arc sound.

[0015] Furthermore, the fault arc simulation device is equipped with a fixed electrode and a movable electrode. The fixed electrode is mounted on a fixed slide, and the movable electrode is mounted on a movable slide. The fixed slide and the movable slide are connected by a lead screw. The movable electrode is connected to the load side. By adjusting the distance and time between the fixed electrode and the movable electrode, the discharge intensity and discharge duration can be controlled. By controlling the load input on the load side, the fault arc simulation can be reproduced for different fault types.

[0016] Furthermore, the early signs of the fault arc include any one or more of the following: line high-frequency harmonics, line power aperiodic fluctuations, and line temperature rise anomalies. The line power aperiodic fluctuations include line voltage aperiodic fluctuations and / or line current aperiodic fluctuations. The fault sign characteristics corresponding to the line high-frequency harmonics include any one or more of the following: the number of harmonic components, the harmonic distribution frequency band, and the harmonic amplitude. The fault sign characteristics corresponding to the line power aperiodic fluctuations include any one or more of the following: fluctuation duration, fluctuation speed, and amplitude fluctuation variance. The fault sign characteristics corresponding to the line temperature rise anomalies include the temperature rise duration and / or the temperature rise rate.

[0017] Furthermore, when an early-stage fault arc is diagnosed, a fault-causing disaster risk assessment step is also included, including:

[0018] The coupling degree parameter between each fault symptom feature in the fault feature database is calculated in advance, and a corresponding weight value is set for each fault symptom feature according to the calculation result of the coupling degree parameter. The coupling degree parameter includes any one or a combination of two or more of coupling frequency, coupling probability and coupling degree.

[0019] When an early-stage fault arc is diagnosed, the feature parameters extracted in real time are weighted using the corresponding weight values ​​to obtain a risk metric value to assess the current disaster risk level.

[0020] Furthermore, the coupling degree model is used to calculate the coupling degree parameters between single factors of each fault symptom feature, the nonlinear dynamics model is used to calculate the coupling degree parameters between two factors of each fault symptom feature, the NK model is used to calculate the coupling degree parameters between multiple factors of each fault symptom feature, and the entropy weight method is used to assign weights to determine the weight values ​​of each fault symptom feature.

[0021] Furthermore, when an early fault arc is diagnosed, the system also includes an electrical fire early warning judgment step, including:

[0022] Fire dynamics modeling was carried out, and the variation laws of flame characteristic distribution and smoke motion characteristic distribution of fault arc generated by the fault arc simulation device under different parameter input conditions were statistically analyzed. The first relationship model between fault arc parameters and flame characteristic distribution and the second relationship model between fault arc parameters and smoke motion characteristic distribution were established respectively.

[0023] The first relational model and the second relational model are used to construct a correspondence between fault arc parameters and critical conditions for electrical fires.

[0024] When an early fault arc is diagnosed, the feature parameters extracted in real time are compared with the electrical fire critical conditions to determine whether the electrical fire critical conditions have been met.

[0025] Furthermore, the flame characteristic distribution includes any one or more of flame morphology distribution, thermal radiation characteristics, temperature distribution in the flame, and flame combustion rate, and the flue gas motion characteristic distribution includes any one or more of flue gas motion stage, flue gas temperature distribution, and flue gas motion rate.

[0026] A low-voltage fault arc identification and diagnosis method integrating a multi-factor equivalence model includes a processor and a memory, wherein the memory is used to store a computer program and the processor is used to execute the computer program to perform the method described above.

[0027] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described above.

[0028] Compared with the prior art, the advantages of the present invention are as follows:

[0029] 1. This invention considers the multi-dimensional and multi-type source-load factors and spatial factors of the power system. By extracting the characteristic parameter set of fault arcs generated by different source-load devices under different spatial factors, it establishes characteristic curves of different types of fault arcs using this parameter set. This allows for the control of a fault arc simulation device to generate fault arcs of different fault types, achieving equivalent simulation of fault arcs from multiple source-load and multi-spatial factors. This enables the full simulation of a large number of fault arc signals of different fault types, solving problems such as limited data sources and difficulty in covering fault arc characteristics in different regions and variable environments in traditional analysis methods. Simultaneously, by extracting fault symptom features and statistically analyzing the corresponding distribution interval probabilities from the fault arcs generated by the fault arc simulation device, a fault symptom feature database is established. This database can fully explore the early electrical symptom patterns of fault arcs by considering multiple source-load and spatiotemporal factors, and uncover the correlation between early fault symptom characteristics and features. Therefore, this database can accurately and reliably identify actual early fault arcs and diagnose specific fault types when identifying early fault arcs.

[0030] 2. This invention further quantitatively calculates the multi-factor coupling measure of the risk of fire caused by fault arc, obtaining the coupling frequency, coupling probability, and coupling degree between factors respectively. The coupling degree parameter is weighted and configured to calculate the fire risk measure value. This can fully explore the early electrical signs of fault arc, such as high-frequency harmonics, non-periodic fluctuations of current / voltage, and temperature anomalies. It can effectively analyze the risk coupling complexity of the early electrical signs of fault arc, further explore the correlation between each early electrical sign factor and the arc fault, and thus assess the risk level of fire caused by fault arc in real time.

[0031] 3. This invention further analyzes the fault arc data generated by the fault arc simulation device based on fire dynamics modeling and analysis methods, so as to fully explore the influence law of the energy intensity of the fault arc on electrical fires, and realize the establishment of a relationship model between fault arcs and electrical fires. When an early fault arc is diagnosed, the feature parameters extracted in real time are compared with the critical conditions of electrical fires, so as to determine whether the critical conditions of electrical fires have been reached, thereby providing timely early warning of electrical fires. Attached Figure Description

[0032] Figure 1 This is a schematic diagram illustrating the implementation process of the low-voltage fault arc identification and diagnosis method that integrates a multi-factor equivalent model in this embodiment.

[0033] Figure 2 This is a schematic diagram illustrating the implementation principle of simulating and reproducing fault arcs in this embodiment.

[0034] Figure 3This is a schematic diagram of the structural principle of the fault arc simulation device used in a specific application embodiment of the present invention.

[0035] Figure 4 This is a schematic diagram illustrating the classification of early signs of electric arc faults and the principle of risk assessment for electric arc faults in this embodiment.

[0036] Figure 5 This is a schematic diagram illustrating the implementation principle of identifying the critical conditions for fault arc disasters in this embodiment. Detailed Implementation

[0037] The present invention will be further described below with reference to the accompanying drawings and specific preferred embodiments, but this does not limit the scope of protection of the present invention.

[0038] To facilitate understanding, the relevant technical background of the present invention will first be introduced by way of example.

[0039] Electric arcs typically arise from circuit interruptions or electrical equipment malfunctions. When a circuit is interrupted, current continues to flow between the points of interruption, forming an arc. The key to this process is that, under high voltage conditions, the current can penetrate insulating materials and be sustained within the arc, leading to the formation of an arc with high temperature and high current density. The generation of an arc is usually accompanied by intense electron and ion movement, producing intense heat and light radiation. An arc requires specific current and voltage conditions to sustain itself, depending on the arc's length, circuit parameters (such as resistance and inductance), and material properties (such as the melting point and thermal conductivity of insulating materials). Under high current and voltage conditions, an arc can exist stably, releasing a large amount of energy, which can potentially pose a serious threat to the power system.

[0040] Electric arcs exhibit diversity and randomness. The diversity is primarily manifested in the varying degrees of arc combustion due to factors such as voltage and current levels, load power, electrode materials, and external environmental conditions. The current in the branch containing the arc is also highly variable, influenced by the impedance characteristics of the downstream load, resulting in complex and diverse time-domain and frequency-domain characteristics of the arc's series current. The randomness of the arc is macroscopically reflected in the fluctuations and inconsistent duration of arc combustion, the unstable flickering of the electric spark, and the lack of a fixed, repetitive pattern in the waveform distortion characteristics of the arc current, such as spikes, peaks, and flat shoulders. The diversity and randomness of arc currents pose significant challenges to arc fault detection.

[0041] The energy release of an electric arc exhibits complex nonlinear characteristics, dependent on multiple factors including current and voltage. While higher temperatures and current densities are generally associated with greater energy release, the nonlinear nature of the arc further complicates this. The energy release of an arc also changes dynamically during its development, with different stages potentially exhibiting different energy release patterns, and a nonlinear relationship exists between arc voltage and current. Although the arc's volt-ampere characteristics may show a certain pattern under steady-state conditions, these characteristics can change significantly under transient conditions, such as the initial formation stage of the arc. For example, an increase in arc length typically leads to an increase in arc voltage, indicating that arc length has a significant impact on its volt-ampere characteristics. Furthermore, the arc current varies under different power and current conditions, and the sampled waveform of the arc current also differs considerably under different loads.

[0042] In summary, fault arcs are highly random, their inherent characteristics are difficult to fully extract, and the disaster-causing mechanism of fault arcs is complex with diverse early signs.

[0043] Traditional fault arc analysis methods rely on sufficient data sources and extracted arc features for accuracy. However, existing technologies have limited data sources, typically concentrated on power grid data and historical fault records from specific regions. This results in insufficient coverage of geographical and conditional diversity, making it impossible for the final analysis results to comprehensively represent the characteristics of fault arcs in different regions and under varying environments. Consequently, they may fail to accurately reflect the actual situation and characteristics of fault arcs, lacking a global perspective and the support of multi-dimensional data. Furthermore, traditional fault arc analysis methods are based on identification using single or a few fixed features, failing to fully consider multi-dimensional influencing factors. This leads to incomplete analysis of energy and voltage-current characteristics, an inability to fully explore the dynamic changes of fault arcs, and difficulty in accurately capturing and analyzing complex influencing factors. Therefore, they cannot accurately and reliably achieve early fault arc identification and diagnosis.

[0044] This invention considers the multi-dimensional and multi-type source-load factors and spatial factors of power systems. By extracting the characteristic parameter set of fault arcs generated by different source-load devices under different spatial factors, it establishes characteristic curves for different types of fault arcs using this parameter set. This allows for the description of fault arc characteristics for different fault types. A fault arc simulation device enables equivalent simulation of fault arcs from multiple source-load and multi-spatial factors, allowing for the full simulation of a large number of fault arc signals of different fault types. This solves the problems of limited data sources and difficulty in covering fault arc characteristics in different regions and variable environments in traditional analysis methods. Simultaneously, by extracting fault symptom features from the fault arcs generated by the device and statistically analyzing the corresponding distribution interval probabilities, a fault symptom feature database is established. This database can fully explore the early electrical symptom patterns of fault arcs considering multiple source-load and spatiotemporal factors, and uncover the correlation between early fault symptom characteristics and features. Therefore, this database can accurately and reliably identify actual early fault arcs and diagnose specific fault types when identifying early fault arcs.

[0045] The present invention will be further described below with reference to specific embodiments.

[0046] like Figure 1 As shown, the steps of the low-voltage fault arc identification and diagnosis method integrating a multi-factor equivalent model in this embodiment include:

[0047] Step 1: Extraction of characteristic parameters of multi-factor fault arc: Extract the set of characteristic parameters of fault arc generated by different source load devices under different spatial factors.

[0048] In this embodiment, as Figure 2 As shown, the source and load devices include photovoltaic arrays, energy storage devices, converters, electric vehicles, etc. Spatial factors include insulation aging and damage, loose electrical connections, air humidity, high temperature of conductors, etc. The characteristic parameters of the fault arc include the fault arc's current, impedance, energy characteristics, and volt-ampere characteristics, etc., which can be selected and configured according to actual needs. Furthermore, characteristic parameters can be extracted according to multiple time scales (50 milliseconds, 100 milliseconds, 200 milliseconds, 500 milliseconds, etc.) to obtain a more comprehensive set of fault arc characteristic parameters.

[0049] After extracting the characteristic parameters of the fault arc in this embodiment, a multidimensional dataset can be formed, which can be represented as a matrix X. By comprehensively analyzing the fault arcs generated by various types of source load devices under different spatial factors, more comprehensive and accurate information can be obtained, thereby enabling a more comprehensive understanding of the behavior of the fault arc and accurate characterization of the characteristics of different types of fault arcs.

[0050] The parameters of fault arcs exhibit specific patterns. Exploring these patterns can help predict changes in the fault arc. For example, there is a correlation between the current and arc length in a fault arc; therefore, the arc length can be predicted by analyzing the current in the fault arc. In this embodiment, the process of generating fault arc signals of different fault types by simulating the characteristics of real fault arcs using a fault arc simulation device also includes multi-dimensional parameter observation of the fault arcs generated by the device. This generates fault arc signals with various parameters, enabling multi-dimensional parameter observation of the fault arc. This allows for a thorough understanding of the characteristics of different types of fault arcs and the evolution trajectory of the arc state, leading to a better understanding of the arc's dynamic characteristics.

[0051] The aforementioned parameters can specifically include the number of arcs, energy intensity, arc current, arc voltage, temperature, arc light, and arc sound. Specifically, we can first collect data on various fault arcs generated by power source devices such as photovoltaic arrays, energy storage devices, converters, and electric vehicles under spatial factors such as insulation aging and damage, loose electrical connections, humid air, and high wire temperatures. Then, we can observe the parameters such as the number of arcs, energy intensity, arc current, arc voltage, temperature, arc light, and arc sound in each fault arc to obtain various types of fault arcs under different parameters. The specific parameter configuration can be selected and configured according to actual needs.

[0052] Step 2. Characteristic curve establishment: Establish characteristic curves for different types of fault arcs based on the set of characteristic parameters.

[0053] In this embodiment, the fault arc characteristic curves include energy characteristic curves and current-voltage characteristic curves. The energy characteristic curve describes the behavior of the fault arc at different energy levels, and this curve helps to understand the behavior of the fault arc under different energy states. The current-voltage characteristic curve describes the voltage (U) and current (I) variation of the fault arc, i.e., the IV curve, and this curve describes the relationship between the voltage and current of the fault arc. This embodiment extracts characteristic parameter sets such as current, impedance, energy characteristics, and current-voltage characteristics of the fault arc based on different source-load types, time scales, and spatial factors. By analyzing the data in the characteristic parameter sets, corresponding characteristic curves for different types of fault arcs are established, forming a simplified characterization method for fault arc energy that considers multiple factors.

[0054] In specific application embodiments, principal component analysis, factor analysis, cluster analysis, or multiple regression analysis can be used to analyze the multidimensional data of the characteristic parameter set, so as to facilitate multidimensional parameter analysis and observation, fully explore the unique patterns and laws of arc faults, and obtain more accurate fault arc energy characteristic curves and volt-ampere characteristic curves.

[0055] In this embodiment, the fault types include series faults, parallel faults, and ground faults, simplifying the faults into three types: series, parallel, and ground faults. It is understood that more detailed fault type classifications can be made according to actual needs to further improve the accuracy of identification.

[0056] Step 3. Fault Arc Simulation: Based on the characteristic curves of different types of fault arcs, the arc simulation parameters are determined and input into a pre-constructed fault arc simulation device. The fault arc simulation device simulates equivalent fault arc models of different fault types under various spatial factors to generate fault arc signals of different fault types according to the arc simulation parameters. The equivalent fault arc model is a simplified model that simulates and generates fault arcs with real fault arc characteristics. The arc simulation parameters include discharge duration, discharge intensity, and fault type.

[0057] Traditional data sources, such as power grid data or historical fault records for specific regions, are limited and cannot cover the characteristics of fault arcs in different regions and under varying environments. This embodiment establishes characteristic curves for different types of fault arcs. Using these curves, arc simulation parameters for different types of fault arcs can be obtained, such as discharge duration, discharge intensity, and fault type. These parameters are then input into a pre-constructed fault arc simulation device. This device simulates a fault arc equivalent model with realistic fault arc characteristics under various spatial factors, generating fault arc signals for different fault types. The fault arc equivalent model includes three types: series faults, parallel faults, and ground faults. Simplified models of fault arc generation are established to simulate these three fault types, simulating the realistic fault arc characteristics considering various spatial factors under each fault type. The fault arc simulation device is equivalent to establishing a fault arc simulation model. Based on the characteristic parameters of different types of fault arcs, it realistically simulates the generation of various types of fault arcs, enabling the simulation of various types of fault arcs by considering multiple factors. When given different input parameters (such as different spatial factors and fault types), the fault arc simulation device can generate corresponding fault arc simulation models, thereby achieving the goal of multi-factor arc simulation. This allows for convenient and efficient acquisition of a large amount of fault arc data of different types, solving the problems of limited data sources and difficulty in covering fault arc characteristics in different regions and changing environments in traditional analysis methods.

[0058] The main causes of fault arcs include the following: (1) Aging of electrical circuits, leading to parallel fault arcs between phases. Overloading of the circuit for a long time, ultraviolet radiation, smoke environment, etc. can all cause the circuit to age; (2) Damage to the insulation of the circuit, leading to parallel fault arcs between phases and fault arcs to ground; (3) Fault arcs caused by circuit breaks, poor contact, etc., such as loose connections of electrical joints, terminals, etc., aging of sockets that lose elasticity and poor contact with plugs, etc.

[0059] This embodiment, considering the causes of the above-mentioned faults, uses an electric drive device with a fixed slide and a movable slide as a fault arc simulation device, such as... Figure 3 As shown, this device simulates various electrical phenomena, including loose connections, poor contact, insulation damage, and distances between aging wires, whether between wires or between wires and switching elements. This allows for the reproduction of various fault arcs, such as those associated with aging cables, cables with damaged insulation, and plugs with poor contact. (See also...) Figure 3 This fault arc simulation device includes a fixed electrode and a moving electrode. The fixed electrode is mounted on a fixed slide, and the moving electrode is mounted on a moving slide. The fixed and moving slides are connected by a lead screw. The moving electrode is connected to the load side. By adjusting the distance and time between the fixed and moving electrodes, the discharge intensity and duration can be controlled to simulate the close proximity of aging / insulation-damaged conductors and reproduce the discharge arc. By controlling the load on the load side, fault arc simulation can be achieved for different fault types, including parallel (phase-to-phase), series (same phase), and grounding (phase-to-ground). A pressure sensor is also installed at the fixed electrode. The pressure sensor controls the arc generation process. As the moving electrode gradually approaches the fixed electrode, the pressure sensor reading is checked. When the pressure sensor reading reaches a threshold and detects line current, the moving electrode begins to separate, moving away from the fixed electrode at a set speed and predetermined displacement. Further modifications to the fault arc simulation device could include arranging a photoelectric sensor on the lead screw and a light-shielding partition between the moving slide and the moving electrode to assist in simulating fault arcs in various scenarios.

[0060] like Figure 2As shown, this embodiment considers differentiated influencing factors such as source-load type (e.g., photovoltaic array, energy storage device, converter, electric vehicle) and spatial factors (e.g., insulation aging and damage, loose electrical connections, air humidity, high wire temperature), and also considers different time scales (e.g., 50 milliseconds, 100 milliseconds, 200 milliseconds, 500 milliseconds). It extracts characteristic parameter sets such as current, impedance, energy characteristics, and volt-ampere characteristics of the fault arc corresponding to different factors to establish a multi-factor fault arc mathematical model. This model then establishes energy characteristic curves and volt-ampere characteristic curves for different types of fault arcs. Based on these characteristic curves, the energy of the fault arc due to multiple factors can be simplified. The arc simulation parameters such as discharge duration and discharge intensity are then determined using the characteristics represented by these curves and input into the aforementioned fault arc simulation device. This allows for the simulation of fault arcs of different fault types (series, parallel, and grounding), generating various types of fault arcs. It is understood that other structural types of fault arc simulation devices can also be used to achieve the simulation of different types of fault arcs according to actual needs.

[0061] Step 4: Construction of Fault Symptom Database: Extract fault symptom features that characterize the early signs of fault arcs from various fault arc signals of different fault types, and statistically analyze the distribution interval probabilities of the early signs of fault arcs to form a fault symptom feature database.

[0062] Early-stage fault arcs exhibit specific electrical symptoms, such as high-frequency harmonics, aperiodic fluctuations in line current / voltage, and abnormal line temperature. Quantitative observation of these electrical symptoms can effectively identify early-stage fault arcs, facilitating timely fault handling. To measure and classify the main early symptoms of fault arcs, this embodiment further establishes quantifiable indicators for each early symptom, such as the number of harmonic components, harmonic distribution frequency bands, and harmonic amplitudes corresponding to high-frequency harmonics; the duration, velocity, and amplitude variance corresponding to aperiodic fluctuations in line current / voltage; and the duration and rate of temperature increase corresponding to abnormal line temperature.

[0063] This embodiment addresses phenomena such as high-frequency harmonics in power lines, aperiodic fluctuations in line current / voltage, and abnormal temperature changes in power lines. It comprehensively considers and statistically analyzes early warning characteristics such as the number of arcs, energy intensity, arc current, arc voltage, temperature, arc light, and arc sound. The probability of each early warning characteristic within its corresponding distribution interval is calculated. This distribution interval probability represents the probability that each early warning characteristic falls within a specific numerical range. Therefore, the probability of early warning characteristics of different types of fault arcs falling within each distribution interval can be analyzed. Furthermore, the analysis results of early warning characteristics and distribution interval probabilities can be used for real-time fault arc identification and diagnosis, enabling rapid and accurate identification of the presence of early fault arc symptoms and the type of fault.

[0064] Step 5: Obtain the real-time monitored signal to be diagnosed and extract the corresponding feature parameters. Input the real-time extracted feature parameters into the fault symptom feature database for matching, diagnose whether it is an early fault arc, and determine the corresponding fault type when it is diagnosed as an early fault arc.

[0065] In this embodiment, the fault symptom feature database stores early symptom features such as the number of arcs, energy intensity, arc current, arc voltage, temperature, arc light, and arc sound when different fault types occur, including line high-frequency harmonics, non-periodic fluctuations in line current / voltage, and abnormal line temperature. It also stores the probability of the distribution intervals corresponding to each early symptom feature. When a signal to be diagnosed is detected in real time, the corresponding feature parameters, such as current, voltage, and energy intensity, are extracted from the signal and then input into the fault symptom feature database for matching. Based on the matching items, it can be determined whether an early fault arc exists and the corresponding fault type. For example, when a suspected fault arc signal is extracted from the real-time monitored signal to be diagnosed, feature extraction is performed. If harmonic components can be extracted, the number of harmonic components, the frequency band of harmonic distribution, and the amplitude of harmonic components are counted. The extracted harmonic component features are matched with the features of each harmonic component in the fault symptom feature database. If the match is successful, that is, the features of the harmonic components extracted in real time fall into the distribution range of the fault symptom features of a certain type of fault arc in the fault symptom feature database, then it is determined that there is an early fault arc. Then, based on the fault type corresponding to the matching item, the specific fault type can be diagnosed.

[0066] Step 6: Fault-induced disaster risk assessment

[0067] Step S601. Calculate the coupling degree parameter between each fault symptom feature in the fault feature database in advance, and set the corresponding weight value for each fault symptom feature according to the calculation result of the coupling degree parameter. The coupling degree parameter includes coupling frequency, coupling probability and coupling degree, etc.

[0068] Step S602. When an early fault arc is diagnosed, the feature parameters extracted in real time are weighted using the corresponding weight values ​​to obtain a risk metric value to assess the current disaster risk level.

[0069] High-frequency harmonics in the circuit, non-periodic Poisson current / voltage, and abnormal temperatures are key factors contributing to the formation of fault arc fire risk currents. Figure 4As shown, this embodiment further analyzes the connotation and classification of fire risk coupling in fault arc fires by combining the identification of early symptoms of fault arcs and the classification results of fault arcs. This aims to fully explore the formation mechanism of strong positive coupling in fire risk and the evolution mechanism of fire risk factor coupling. System Dynamics (SD) is used to analyze the coupling of homogeneous factors, two-factor coupling, and multi-factor coupling in fire risk. Simultaneously, the NK model is used to quantitatively calculate the multi-factor coupling measure of fault arc fire risk, obtaining the coupling frequency, coupling probability, and coupling degree between factors. Due to the complexity of the disaster-causing factors and mechanisms of early symptoms of fault arcs, this embodiment uses system dynamics to qualitatively analyze the homogeneous factor coupling risk, two-factor coupling risk, and multi-factor coupling risk of early symptoms of fault arcs in order to obtain the mutual influence relationships between risk factors. Furthermore, considering the particularity and complexity of the disaster-causing laws of early symptoms of fault arcs, this embodiment calculates the coupling degree parameters between single and two factors based on the coupling degree model and the nonlinear dynamics model, respectively, and then uses the NK model to calculate the coupling degree parameters (coupling frequency, coupling probability, and coupling degree) between multiple factors. The specific principles of each model are as follows:

[0070] (1) Coupling Degree Model

[0071] Coupling degree models can quantitatively calculate the degree of coupling between various factors within a system, reflecting the mutual influence between factors and their impact on the entire system. The modeling steps for coupling degree models mainly include the following:

[0072] 1) Construct a coupling degree evaluation index system: First, a coupling degree evaluation index system that can describe the overall characteristics of the system should be constructed, and the weights of each index should be calculated using methods such as expert scoring (Delphi), analytic hierarchy process (AHP), and structural entropy weighting (SWEM) to obtain the degree of influence of each index on the entire system.

[0073] 2) Construct the utility function: Let u i , (i = 1, 2, 3, ..., n) are the order parameters of the system, u ij Let X be the j-th index of the i-th order parameter, and X ij ,(i,j=1,2,3,...,n) is u ij The value of α. ij β represents the upper limit of the j-th index of the i-th order parameter. ij Let represent the lower limit value of the j-th index of the i-th order parameter. Then, the power function of each coupling index can be expressed as:

[0074]

[0075] 3) Construction of the coupling function: Drawing on the concept of capacity coupling and the coupling coefficient model from physics, the degree of coupling between two or more factors can be quantitatively calculated. The calculation formula is as follows:

[0076]

[0077] Among them, C ij This represents the degree of coupling between factors i and j.

[0078] The formula for the coupling degree between three or more factors is as follows:

[0079]

[0080] (2) Nonlinear dynamics model

[0081] Based on the self-evolution mechanism of complex systems, nonlinear dynamic models can be used to analyze the interactions and effects among various factors contributing to early signs of electric arc faults. The modeling steps of nonlinear dynamic models mainly include three aspects: constructing the system's nonlinear function, constructing the evolution equations of the complex system, and constructing the coupling degree function.

[0082] 1) Constructing the system's nonlinear function:

[0083]

[0084] Where x i Representing the various elements in the system, f is x i The nonlinear function. Since the stability of a nonlinear system is related to the eigenvalues ​​of similar systems, the above formula is approximately expanded using a Taylor series, resulting in an approximate expression after removing the highest-order term, where a and b represent the weights of each element:

[0085]

[0086] 2) Construction of evolution equations for composite systems

[0087] Suppose there are two subsystems f(w) in the system. a ) and f(w b ) have an interactive relationship, and f(w) a ) and f(w b As the dominant part of the system, its evolution process is described by systems theory as follows:

[0088]

[0089] Where A and B represent the respective evolution states of the two subsystems when they are coupled; V A V BThis represents the evolution rate of the subsystem; therefore, the formula for the evolution rate of the entire system can be derived from the functional relationship between the evolution rates of the system and the subsystems:

[0090] V = f(V) A V B (7)

[0091] 3) Construction of the coupling function

[0092] Specifically, the coupling degree function can be constructed according to the following formula:

[0093]

[0094] Where α represents V A With V B The angle between the two subsystems can reflect the degree of coupling between them. When α∈(-π / 2,0), it means that the evolution of the whole system is at a low level, the coupling between the subsystems is low and there is mutual conflict; when α∈(0,π / 2), it means that the evolution of the whole system is at a high level, the coupling between the subsystems is high and there is mutual promotion.

[0095] (3) NK model

[0096] The NK model primarily comprises two elements: N and K. N represents the number of key elements in the system, and K represents the number of relationships within the system, which is also a significant factor influencing fitness. If the system contains N classes of members, and each class has n distinct possible combinations, then the total number of possible combinations in the system is n. N If the components in a system are combined in a specific form, the system will exhibit a network structure, where K∈(0,N-1). To achieve early-stage fault arc risk assessment and fully explore the patterns of fires caused by the coupling of the line high-frequency harmonic factor subsystem, the line voltage / voltage aperiodic fluctuation factor subsystem, and the line temperature anomaly subsystem, it is necessary to quantitatively calculate the interaction information between these three factors. That is, the more disasters caused by a certain coupling type, the greater the probability of that type occurring. Simultaneously, the larger the interaction information T value generated by mutual coupling, the higher the degree of coupling of that coupling type, the greater the mutual influence, and the more severe the resulting disaster consequences. To fully explore the patterns of early electrical fault arc symptoms such as line high-frequency harmonics, current / voltage aperiodic fluctuations, and temperature anomalies, this embodiment uses the NK model to calculate the coupling degree parameters between multiple factors. This allows for a thorough exploration of the mutual coupling relationships between various factors in fire formation, effectively analyzing the risk coupling complexity of early electrical fault arc symptoms, and further exploring the correlation between early electrical symptoms and arc faults.

[0097] After calculating the coupling degree parameters between various fault symptom characteristics, corresponding weight values ​​can be assigned to each fault symptom characteristic by combining the coupling degree parameters between single factors, two factors, and multiple factors. For example, if the coupling degree parameters between single factors, two factors, and multiple factors all reflect that a certain fault symptom characteristic has a significant impact on fire formation, then a larger weight value can be assigned accordingly; conversely, if the impact is relatively small, a smaller weight value can be assigned. The weight values ​​can be configured using the structural entropy weight allocation method.

[0098] Furthermore, a disaster risk assessment index system for early signs of electric arc faults can be constructed based on the characteristics of each fault symptom and their corresponding weight values. Experiments can also be conducted using a fault arc simulation device, and the index system can be corrected using experimental data and results. Finally, the weights of each evaluation index are calculated based on the risk coupling degree and the structural entropy weight method. Based on the experimental results of the device, scoring rules based on coupling probability and coupling frequency are formulated, thereby forming a complete disaster risk assessment model for early signs of electric arc faults. Based on this evaluation model, fire risk measurement values ​​can be calculated for real-time risk conditions to assess the degree of risk of fire caused by electric arc faults in real time.

[0099] In this embodiment, a fire risk measurement calculation method based on structural entropy weight allocation can be used.

[0100] Step 7: Electrical Fire Early Warning Judgment

[0101] Step 701. Statistically analyze the variation patterns of flame characteristic distribution and flue gas motion characteristic distribution of the fault arc generated by the fault arc simulation device under different parameter input conditions, and establish a first relationship model between fault arc parameters and flame characteristic distribution, and a second relationship model between fault arc parameters and flue gas motion characteristic distribution.

[0102] Step 702. Construct the correspondence between fault arc parameters and critical conditions for electrical fires using the first relational model and the second relational model;

[0103] Step 703. When an early fault arc is diagnosed, the feature parameters extracted in real time are compared with the electrical fire critical conditions to determine whether the electrical fire critical conditions have been met.

[0104] Factors such as the type, energy intensity, and duration of fault arcs are correlated with electrical fires. Therefore, by exploring the critical conditions for electrical fires, low-voltage fault arc diagnosis can be achieved quickly and accurately, improving the reliability of fault diagnosis and solving the challenges of power safety control and efficient data exchange in low-voltage power distribution systems. This embodiment fully considers the impact of current / voltage fluctuations and temperature anomalies on early signs of arc faults. Based on thermodynamic and temperature modeling methods, it can explore the patterns of early electrical signs of fault arcs and their disaster-causing mechanisms. Simultaneously, by establishing a fire dynamics model, it can fully explore the correlation between energy intensity and fire occurrence, such as the impact of the duration of fault arcs on electrical fires. By utilizing the correlation between various factors and the occurrence of electrical fires, the critical conditions for electrical fires can be accurately determined.

[0105] In this embodiment, the flame characteristic distribution includes flame morphology distribution, thermal radiation characteristics, temperature distribution within the flame, and flame combustion rate, while the smoke motion characteristic distribution includes smoke motion stage, smoke temperature distribution, and smoke motion rate. This embodiment establishes a first relationship model between fault arc parameters and flame characteristic distribution by statistically analyzing the flame characteristic distribution of fault arcs generated by the fault arc simulation device under different parameter inputs. This model aims to uncover the influence of fault arc type, arc energy intensity, and the number of continuous arcs on flame morphology and heat transfer characteristics, thereby obtaining the variation law of fault arc parameters and flame characteristic distribution. Simultaneously, it statistically analyzes the variation law of smoke motion characteristic distribution of fault arcs generated by the fault arc simulation device under different parameter inputs, establishing a second relationship model between fault arc parameters and smoke motion characteristic distribution. This model aims to uncover the influence of fault arc type, arc energy intensity, and the number of continuous arcs on smoke motion characteristics, obtaining the variation law of fault arc parameters and smoke motion. Furthermore, based on clarifying the critical points of combustion flame characteristics and smoke motion characteristics, it establishes the relationship between fault arc parameters and critical conditions for electrical fires.

[0106] This embodiment analyzes the fault arc data generated by the fault arc simulation device based on fire dynamics modeling and analysis methods. This fully explores the influence of the energy intensity of the fault arc on electrical fires and establishes a model of the relationship between fault arcs and electrical fires. When an early fault arc is diagnosed, the feature parameters extracted in real time are compared with the critical conditions for electrical fires. This allows for a determination of whether the critical conditions for electrical fires have been met, thus enabling timely early warning of electrical fires.

[0107] Arc fires typically originate from faulty arcs in electrical systems, and are essentially a high-temperature discharge phenomenon. The three basic elements of an arc fire are the arc source, fuel, and oxygen supply. The arc source is usually an arc fault in electrical equipment, while the fuel is a combustible substance, such as insulation materials or cables. The oxygen supply comes from the air. This embodiment utilizes fire dynamics modeling and analysis to facilitate understanding how these elements interact, leading to the development of an arc fire. Secondly, combustion is the core process of an arc fire; combustion is a chemical reaction that typically requires a sufficient heat source to bring the fuel to its ignition temperature. The electric arc itself is a high-temperature heat source that can ignite surrounding combustible materials. Using a fire dynamics model helps to understand the combustion process in an arc fire, including fuel decomposition, oxygen supply, and heat release.

[0108] This embodiment considers various factors in its fire dynamics modeling and analysis method, such as fuel properties, oxygen supply, and the temperature, pressure, and flow of the ignition source. It uses existing atmospheric pressure fire models to model indoor fires and computational fluid dynamics (CFD) models to model more complex fire scenarios. These models can be used to predict the fire's development process, including the temperature and location of the ignition source, smoke generation and propagation, and the fire's spread rate. Further model analysis can assess parameters such as the probability of arc fires, heat release, and fire propagation speed, which helps in developing fire risk management strategies and safe electrical system designs. Furthermore, model analysis can determine optimal fire control strategies, such as automatic fire suppression systems and smoke extraction systems, to minimize the losses and hazards caused by arc fires.

[0109] In this embodiment, the Navier-Stokes equations can be used to process fault arc data to fully explore the flow behavior of arc fires, including the characteristics of smoke, flame, and heat conduction. This facilitates the optimization of arc fire propagation characteristics, ignition source temperature, fire risk assessment, and fire control strategies, contributing to improved electrical system design and safety. Ultimately, this reduces potential losses and hazards from arc fires and enhances fire prevention and control efficiency. The Navier-Stokes equations can be expressed as:

[0110]

[0111]

[0112] Where ρ is density, u is velocity vector, P is pressure, and μ is dynamic viscosity. It is the gravity vector, T is the temperature, α is the thermal diffusivity, and c is the temperature vector. p It is the specific heat capacity under constant pressure. It is a heat source.

[0113] Thermal radiation is one of the main energy transfer mechanisms in a fire. The thermal radiation from the fire source heats surrounding fuel and objects, causing them to ignite or burn. The intensity of thermal radiation is related to the temperature of the fire source and its distance, and can be described by the Stefan-Boltzmann law:

[0114]

[0115] Where Q represents thermal radiation power, σ is the Stefan-Boltzmann constant, A is the radiation surface area, T is the temperature of the fire source, and T0 is the ambient temperature.

[0116] Furthermore, the characteristics of the heat conduction process in a fault arc can be described using the heat conduction equation:

[0117]

[0118] Where T is temperature, t is time, and α is the thermal diffusivity. It is the Laplace operator.

[0119] The mass conservation equation for a fire caused by a fault arc process describes the flow of gas and smoke, and the corresponding equation is:

[0120]

[0121] Where ρ is density and u is velocity.

[0122] This embodiment, by combining models of various details of the fault arc process, can accurately construct a fault arc model in complex environments. It utilizes fire dynamics modeling and analysis methods to explore the correlation between fault arcs and electrical fires, as well as the laws governing critical conditions, and obtains the influence of factors such as fault arc energy intensity on the critical conditions of electrical fires.

[0123] like Figure 5As shown, in order to explore the critical conditions for disaster caused by fault arcs, this embodiment first uses a fault arc simulation device to generate different types of fault arcs according to the type of fault arc (e.g., serial arc, parallel arc), arc energy intensity (e.g., low, medium, high), and the number of continuous arcs (e.g., between 10 and 100 continuous arcs, divided into ten groups), and then divides them into experimental control groups. For the arc generation schemes of all control groups, theoretical analysis, numerical simulation, reproduction experiments, and result comparison are adopted to adjust the fault arc generation configuration parameters corresponding to all control groups, forming a fault arc disaster-causing critical experimental scheme library and parameter configuration set. Then, based on the experimental parameter results, the combustion flame characteristics and flue gas motion characteristics are statistically analyzed. By statistically analyzing the fault arcs under different parameter input conditions, The distribution of flame characteristics of the fault arc generated by the true-type reproduction device involves establishing a first relationship model between fault arc parameters and flame morphology distribution, thermal radiation characteristics, temperature distribution, and combustion rate. This model aims to explore the influence of fault arc type, arc energy intensity, and the number of continuous arcs on flame morphology and heat transfer characteristics, thereby obtaining the variation law of fault arc parameters and flame characteristic distribution. Simultaneously, the variation law of flue gas motion characteristics distribution of the fault arc generated by the true-type reproduction device under different parameter input conditions is statistically analyzed. A second relationship model is established between fault arc parameters and flue gas motion stage, flue gas temperature distribution, and flue gas motion rate. Based on the statistical results of the above combustion flame and flue gas motion characteristics, critical conditions are formed to determine whether the critical conditions for electrical fire have been reached.

[0124] Specifically, to obtain the influence of fault arc type, arc energy intensity, and number of continuous arcs on flame morphology and heat transfer characteristics, the parameter set corresponding to the fault arc-induced disaster criticality research experimental scheme library is input one by one into a multi-factor fault arc true-type reproduction device. The flame morphology distribution, thermal radiation characteristics, flame and temperature distribution, and flame combustion rate under different parameter input conditions are observed and recorded to establish a model of the variation law of fault arc parameters and flame characteristic distribution (first model). To obtain the influence of fault arc type, arc energy intensity, and number of continuous arcs on flue gas movement characteristics, the parameter set corresponding to the fault arc-induced disaster criticality research experimental scheme library is input one by one into a multi-factor fault arc true-type reproduction device. The flue gas movement stage, flue gas temperature distribution, and flue gas movement rate under different parameter input conditions are observed and recorded to establish a model of the law of fault arc parameters and flue gas movement changes (second model). Combined with the model of the variation law of fault arc parameters and flame characteristic distribution (first model), a comprehensive correspondence between fault arc parameter characteristics and electrical fire critical conditions is formed.

[0125] This embodiment, based on the characteristics of different source-load types, time scales, and spatial factors, starts from the simplified characterization of fault arc energy and the quantitative analysis of multi-factor coupling of fire risk. By considering the mathematical model of fault arc with multiple factors, it can fully explore the coupling evolution mechanism of various factors in the early signs of fault arc, reveal the energy characteristics of fault arc and the relationship between volt-ampere changes, the law of fault arc parameters and the critical conditions of electrical fire, and solve the problems of unclear causes and difficult reproduction of multi-factor fault arc hazards in the prior art.

[0126] This embodiment further provides a low-voltage fault arc identification and diagnosis method that integrates a multi-factor equivalent model, including a processor and a memory. The memory is used to store a computer program, and the processor is used to execute the computer program to perform the method described above.

[0127] It is understood that the method described in this embodiment can be executed by a single device, such as a computer or server, or it can be applied to a distributed scenario where multiple devices cooperate to complete the task. In a distributed scenario, one of the multiple devices may execute only one or more steps of the method described in this embodiment, and the multiple devices interact to complete the method. The processor can be implemented using a general-purpose CPU, microprocessor, application-specific integrated circuit, or one or more integrated circuits, and is used to execute relevant programs to implement the method described in this embodiment. The memory can be implemented using read-only memory (ROM), random access memory (RAM), static storage devices, and dynamic storage devices. The memory can store the operating system and other applications. When the method described in this embodiment is implemented through software or firmware, the relevant program code is stored in the memory and called and executed by the processor.

[0128] This embodiment further provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described above.

[0129] Those skilled in the art will understand that the above embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-readable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create an implementation for the process. Figure 1 One or more processes and / or boxes Figure 1 The computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The functions specified in one or more boxes. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0130] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the invention. Therefore, any simple modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention should fall within the protection scope of the present invention.

Claims

1. A method for identifying and diagnosing low-voltage fault arcs by integrating a multi-factor equivalent model, characterized in that the steps include... include: Extract the set of characteristic parameters of fault arcs generated by different source load devices under different spatial factors; Based on the set of characteristic parameters, characteristic curves for different types of fault arcs are established; Based on the characteristic curves of the different types of fault arcs, the arc simulation parameters are determined and input into a pre-constructed fault arc simulation device. The fault arc simulation device simulates equivalent models of fault arcs of different fault types under various spatial factors to generate fault arc signals of different fault types according to the arc simulation parameters. The equivalent model of the fault arc is a simplified model for simulating fault arcs with real fault arc characteristics. The arc simulation parameters include discharge duration, discharge intensity, and fault type. Fault symptom features that characterize the early signs of fault arcs are extracted from the fault arc signals of different fault types, and the distribution interval probabilities of the early signs of fault arcs are statistically analyzed to form a fault symptom feature database. The system acquires the real-time monitored signals to be diagnosed and extracts the corresponding feature parameters. The extracted feature parameters are then input into the fault symptom feature database for matching to diagnose whether it is an early fault arc and, if it is diagnosed as an early fault arc, to determine the corresponding fault type.

2. The low-voltage fault arc identification and diagnosis method based on a multi-factor equivalent model according to claim 1, characterized in that, The source and load devices include any of the following: photovoltaic arrays, energy storage devices, converters, and electric vehicles. The spatial factors include any of the following: insulation aging and damage, loose electrical connections, humid air, and high temperature of the conductors. The characteristic parameters of the fault arc include any one or more of the following: fault arc current, impedance, energy characteristics, and volt-ampere characteristics. The fault arc characteristic curve includes an energy characteristic curve and / or a current-voltage characteristic curve. The fault types include series faults, parallel faults, and ground faults. In the process of simulating and generating fault arc signals of different fault types by the fault arc simulation device, the process also includes multi-dimensional parameter observation of the fault arc signals generated by the fault arc simulation device to generate fault arc signals with multiple different parameters, including any combination of the following parameters: number of arcs, energy intensity, arc current, arc voltage, temperature, arc light, and arc sound.

3. The low-voltage fault arc identification and diagnosis method based on a multi-factor equivalent model according to claim 1, characterized in that, The fault arc simulation device is equipped with a fixed electrode and a movable electrode. The fixed electrode is set on a fixed slide, and the movable electrode is set on a movable slide. The fixed slide and the movable slide are connected by a lead screw. The movable electrode is connected to the load side. By adjusting the distance and time between the fixed electrode and the movable electrode, the discharge intensity and discharge duration can be controlled. By controlling the load input on the load side, the fault arc simulation can be reproduced for different fault types.

4. The low-voltage fault arc identification and diagnosis method based on a multi-factor equivalent model according to claim 1, characterized in that, The early signs of the fault arc include any one or more of the following: line high-frequency harmonics, line power non-periodic fluctuations, and line temperature rise anomalies. The line power non-periodic fluctuations include line voltage non-periodic fluctuations and / or line current non-periodic fluctuations. The fault sign characteristics corresponding to the line high-frequency harmonics include any one or more of the following: the number of harmonic components, the harmonic distribution frequency band, and the harmonic amplitude. The fault sign characteristics corresponding to the line power non-periodic fluctuations include any one or more of the following: fluctuation duration, fluctuation speed, and amplitude fluctuation variance. The fault sign characteristics corresponding to the line temperature rise anomalies include the temperature rise duration and / or the temperature rise rate.

5. The low-voltage fault arc identification and diagnosis method based on a multi-factor equivalent model according to claim 1, characterized in that, When an early-stage fault arc is diagnosed, the process also includes a fault-causing risk assessment step, including: The coupling degree parameter between each fault symptom feature in the fault feature database is calculated in advance, and a corresponding weight value is set for each fault symptom feature according to the calculation result of the coupling degree parameter. The coupling degree parameter includes any one or a combination of two or more of coupling frequency, coupling probability and coupling degree. When an early-stage fault arc is diagnosed, the feature parameters extracted in real time are weighted using the corresponding weight values ​​to obtain a risk metric value to assess the current disaster risk level.

6. The low-voltage fault arc identification and diagnosis method based on a multi-factor equivalent model according to claim 5, characterized in that, The coupling degree model is used to calculate the coupling degree parameters between single factors of each fault symptom feature. The nonlinear dynamic model is used to calculate the coupling degree parameters between two factors of each fault symptom feature. The NK model is used to calculate the coupling degree parameters between multiple factors of each fault symptom feature. The entropy weight method is used to assign weights to determine the weight values ​​of each fault symptom feature.

7. The low-voltage fault arc identification and diagnosis method based on a multi-factor equivalent model according to any one of claims 1 to 6, characterized in that, When an early fault arc is diagnosed, the procedure also includes an electrical fire early warning judgment step, including: Fire dynamics modeling was carried out, and the variation laws of flame characteristic distribution and smoke motion characteristic distribution of fault arc generated by the fault arc simulation device under different parameter input conditions were statistically analyzed. The first relationship model between fault arc parameters and flame characteristic distribution and the second relationship model between fault arc parameters and smoke motion characteristic distribution were established respectively. The first relational model and the second relational model are used to construct a correspondence between fault arc parameters and critical conditions for electrical fires. When an early fault arc is diagnosed, the feature parameters extracted in real time are compared with the electrical fire critical conditions to determine whether the electrical fire critical conditions have been met.

8. The low-voltage fault arc identification and diagnosis method based on a multi-factor equivalent model according to claim 7, characterized in that, The flame characteristic distribution includes any one or more of flame morphology distribution, thermal radiation characteristics, temperature distribution in the flame, and flame combustion rate; the flue gas motion characteristic distribution includes any one or more of flue gas motion stage, flue gas temperature distribution, and flue gas motion rate.

9. A low-voltage fault arc identification and diagnosis device integrating a multi-factor equivalent model, comprising a processor and a memory, wherein the memory is used to store a computer program, characterized in that, The processor is used to execute the computer program to perform the method as described in any one of claims 1 to 8.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 8.

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