Low-voltage fault arc identification and diagnosis method and device fused with multi-factor equivalence model

Abstract

The invention discloses a low-voltage fault arc identification and diagnosis method and device fusing a multi-factor equivalence model. The method comprises the following steps: extracting a characteristic parameter set of fault arcs generated by different source load devices under different space factors; establishing characteristic curves of different types of fault arcs; determining arc simulation parameters according to the characteristic curves of different types of fault arcs, inputting the arc simulation parameters into a fault arc true model reproduction device, and simulating a fault arc equivalent model under various space factors to generate fault arc signals of different fault types; extracting fault symptom features from each fault arc signal of different fault types, and counting distribution interval probabilities to form a fault feature database; and obtaining a signal to be diagnosed, extracting characteristic parameters, inputting the characteristic parameters into the fault characteristic database for matching, and diagnosing whether the arc is an early fault arc and a corresponding fault type. The method has the advantages of being simple in implementation method, high in identification and diagnosis precision and reliability, low in missing report and false report rate and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of low-voltage fault arc diagnosis, and in particular to a low-voltage fault arc identification and diagnosis method and device integrating a multi-factor equivalent model. Background Art

[0002] With the increasing complexity of power systems and the expansion of power consumption, the safe operation of power supply and consumption faces new challenges, so fault arc identification and diagnosis has become crucial. For low-voltage fault arc measurement and diagnosis, traditional analysis methods are usually used, such as visual inspection, thermal imaging technology, current and voltage waveform analysis, sound detection, gas detection, and instantaneous power outage method, but the above methods have the following problems:

[0003] 1. Traditional analysis methods usually diagnose by extracting a single fault arc feature from the power grid data of a specific area or historical fault records. However, on the one hand, due to the limited data source, it is impossible to cover the fault arc characteristics in different regions and changing environments; on the other hand, the fault arc is highly random and the inherent characteristics are difficult to fully extract. A single feature cannot accurately characterize the fault arc and cannot fully consider multi-dimensional influencing factors, resulting in an inability to fully characterize the dynamic change characteristics of the fault arc. Therefore, the actual identification accuracy and reliability of the traditional analysis method are not high, and the false alarm rate is high. With the widespread application of renewable energy and power electronic equipment, nonlinear loads and time-space 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 line materials of different electrical lines, the fault arc disaster mechanism is not the same, and the early signs are diverse. The arc occurrence process involves multiple physical field characteristics such as electric, magnetic, and thermal, which will be accompanied by high-frequency harmonics, non-periodic current / voltage fluctuations, and temperature fluctuations. The causes and signs of fault arc hazards are still unclear, and it is difficult to discover the early signs of fault arcs through traditional analysis methods, resulting in the inability to predict the occurrence of fault arcs in advance. Summary of the invention

[0005] The technical problem to be solved by the present invention is: in response to 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 with a simple implementation method, high identification and diagnosis accuracy and reliability, and low false alarm rate. The method can efficiently and accurately identify and diagnose early fault arcs, and fully explore the early fault signs and regular characteristics of fault arcs.

[0006] In order to solve the above technical problems, the technical solution proposed by the present invention is:

[0007] A low-voltage fault arc identification and diagnosis method integrating a multi-factor equivalent model comprises the following steps:

[0008] Extract characteristic parameter sets of fault arcs generated by different source load equipment under different spatial factors;

[0009] Establishing characteristic curves of different types of fault arcs according to the characteristic parameter set;

[0010] Determine arc simulation parameters according to the characteristic curves of the different types of fault arcs, input them into a pre-built fault arc true type reproduction device, and use the fault arc true type reproduction device to simulate the fault arc true type equivalent model of different fault types under multiple spatial factors, so as to simulate and generate fault arc signals of different fault types according to the arc simulation parameters, wherein the fault arc true type equivalent model is a simplified model for simulating and generating a fault arc with real fault arc characteristics, and the arc simulation parameters include discharge duration, discharge intensity and fault type;

[0011] Extracting fault symptom features characterizing the early symptom phenomenon of the fault arc from each fault arc signal of different fault types, and counting the distribution interval probability of the early symptom features of the fault arc to form a fault symptom feature database;

[0012] The real-time monitored signal to be diagnosed is obtained and the corresponding characteristic parameters are extracted. The characteristic parameters extracted in real time are input into the fault symptom feature database for matching, and it is diagnosed whether it is an early fault arc and the corresponding fault type is determined when it is diagnosed as an early fault arc.

[0013] Furthermore, the source-load equipment includes any one of photovoltaic arrays, energy storage devices, converters, and electric vehicles; the spatial factors include any one of insulation aging and damage, loose electrical connections, humid air, and high temperature of wires; the characteristic parameters of the fault arc include any one or more of the current, impedance, energy characteristics, and volt-ampere characteristics of the fault arc; the fault arc characteristic curve includes an energy characteristic curve and / or a volt-ampere 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 true reproduction device, it also includes multi-dimensional parameter observation of the fault arc signal generated by the fault arc true reproduction device to generate a fault arc signal including multiple different parameters, and the parameters include any one of the number of arcs, energy intensity, arc current, arc voltage, temperature, arc light, and arc sound.

[0014] Furthermore, the source-load equipment includes any one of photovoltaic arrays, energy storage devices, converters, and electric vehicles; the spatial factors include any one of insulation aging and damage, loose electrical connections, humid air, and high temperature of wires; the characteristic parameters of the fault arc include any one or more of the current, impedance, energy characteristics, and volt-ampere characteristics of the fault arc; the fault arc characteristic curve includes an energy characteristic curve and / or a volt-ampere characteristic curve; the fault types include series faults, parallel faults, and ground faults; the process in which the fault arc true-type reproduction device simulates the real fault arc characteristics according to the arc simulation parameters to generate fault arc signals of different fault types also includes multi-dimensional parameter observation of the fault arc signal generated by the fault arc true-type reproduction device to generate a fault arc signal including multiple different parameters, and the parameters include any one of the number of arcs, energy intensity, arc current, arc voltage, temperature, arc light, and arc sound.

[0015] Furthermore, a fixed electrode and a movable electrode are provided in the fault arc true-to-life reproduction device, wherein the fixed electrode is provided on a fixed slide, and the movable electrode is provided on a movable slide, the fixed slide is connected to the movable slide via a screw rod, and 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 the discharge duration can be controlled, and by controlling the load input on the load side, the fault arc simulation reproduction can be achieved for different fault types.

[0016] Furthermore, the early signs of the fault arc include any one or more of line high-frequency harmonics, non-periodic fluctuations of line electric energy, and abnormal line temperature rise. The non-periodic fluctuations of line electric energy include non-periodic fluctuations of line voltage and / or non-periodic fluctuations of line current. The fault symptom characteristics corresponding to the line high-frequency harmonics include any one or more of the number of harmonic components, harmonic distribution frequency bands, and harmonic amplitudes. The fault symptom characteristics corresponding to the non-periodic fluctuations of line electric energy include any one or more of fluctuation duration, fluctuation speed, and amplitude fluctuation variance. The fault symptom characteristics corresponding to the abnormal line temperature rise include heating duration and / or heating rate.

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

[0018] Pre-calculate the coupling degree parameters between various fault symptom features in the fault feature database, and set corresponding weight values ​​for various fault symptom features according to the calculation results of the coupling degree parameters, wherein the coupling degree parameters include any one or a combination of two or more of the coupling frequency, coupling probability and coupling degree;

[0019] When an early fault arc is diagnosed, the characteristic parameters extracted in real time are weighted and calculated using the corresponding weight values ​​to obtain a risk metric to evaluate the current disaster risk.

[0020] Furthermore, the coupling degree model is used to calculate the coupling degree parameters between the single factors of each fault symptom feature, the nonlinear dynamic model is used to calculate the coupling degree parameters between the two factors in each fault symptom feature, the NK model is used to calculate the coupling degree parameters between the multiple factors in each fault symptom feature, and the entropy weight method is used to perform weight allocation to determine the weight value of each fault symptom feature.

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

[0022] Fire dynamics modeling is carried out to statistically analyze the variation patterns of the flame characteristic distribution and smoke motion characteristic distribution of the fault arc generated by the fault arc true model reproduction device under different parameter input conditions, and the first relationship model between the fault arc parameters and the flame characteristic distribution is established, as well as the second relationship model between the fault arc parameters and the smoke motion characteristic distribution is established;

[0023] The first relationship model and the second relationship model are used to construct a corresponding relationship between fault arc parameters and critical conditions of electrical fires;

[0024] When an early fault arc is diagnosed, the characteristic parameters extracted in real time are compared with the critical condition of the electrical fire to determine whether the critical condition of the electrical fire is currently reached.

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

[0026] A low-voltage fault arc identification and diagnosis system integrating a multi-factor equivalent model comprises 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 above method.

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

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

[0029] 1. The present invention considers the multi-dimensional and multi-type source-load and spatial factors of the power system, extracts the characteristic parameter set of the fault arc generated by different source-load devices under different spatial factors, and uses the characteristic parameter set to establish the characteristic curves of different types of fault arcs, thereby controlling the fault arc true-type reproduction device to generate fault arcs of different fault types, and realizing the true equivalent reproduction of multi-type source-load and multi-spatial factor fault arcs, so that a large number of fault arc signals of different fault types can be fully simulated, and the problems of limited data sources and difficulty in covering fault arc characteristics in different regions and variable environments in traditional analysis methods can be solved; at the same time, by using the fault arc generated by the fault arc true-type reproduction device to extract the fault symptom characteristics and the corresponding distribution interval probability, a fault symptom feature database is established, and the database can be used to fully explore the early electrical symptom laws of the fault arc by considering multi-source-load and time-space factors, and explore the correlation between the early fault symptom characteristics and the characteristics, so that the actual early fault arc can be accurately and reliably identified by using the database, and the specific fault type can be diagnosed when the early fault arc is identified.

[0030] 2. The present invention further quantitatively calculates the multi-factor coupling measurement of the risk of arc fault fire, obtains the coupling frequency, coupling probability and coupling degree between the factors, and configures the weight of the comprehensive coupling degree parameters to calculate the fire risk measurement value. It can fully explore the rules of early electrical signs of arc faults such as line high-frequency harmonics, non-periodic fluctuations of current / voltage, and temperature fluctuations, effectively analyze the risk coupling complexity of early electrical signs of arc faults, and further explore the correlation between various early electrical sign factors and arc faults, so as to evaluate the risk of fire caused by arc faults in real time;

[0031] 3. The present invention further analyzes the fault arc data generated by the fault arc true reproduction device based on the fire dynamics modeling and analysis method, so as to fully explore the influence of the energy intensity of the fault arc on the electrical fire, and realize the establishment of the relationship model between the fault arc and the electrical fire. When an early fault arc is diagnosed, the characteristic parameters extracted in real time are compared with the critical conditions of the electrical fire, that is, it can be determined whether the critical conditions of the electrical fire are currently reached, so as to provide timely electrical fire warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is a schematic diagram of the implementation flow of the low-voltage fault arc identification and diagnosis method integrating a multi-factor equivalent model in this embodiment.

[0033] Figure 2 It is a schematic diagram of the implementation principle of the fault arc simulation reproduction in this embodiment.

[0034] Figure 3It is a schematic diagram of the structural principle of a fault arc true-type reproduction device used in a specific application embodiment of the present invention.

[0035] Figure 4 It is a schematic diagram of the classification of early signs and characteristics of arc faults and the principle of risk assessment of arc fault disasters in this embodiment.

[0036] Figure 5 It is a schematic diagram of the implementation principle of the critical condition identification of the fault arc disaster in this embodiment. DETAILED DESCRIPTION

[0037] The present invention is further described below in conjunction with the accompanying drawings and specific preferred embodiments, but the protection scope of the present invention is not limited thereby.

[0038] For ease of understanding, the relevant technical background of the present invention is first introduced by way of example.

[0039] Arcs are usually generated when a circuit is interrupted or an electrical device fails. When the circuit is interrupted, the current still flows between the interruption points, forming an arc. The key to this process is that under high voltage conditions, the current can pass through the insulating material and be maintained in the arc, resulting in 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, generating strong heat and light radiation. The arc requires certain current and voltage conditions to maintain, which depends on the arc length, circuit parameters (such as resistance, inductance) and material properties (such as the melting point and thermal conductivity of the insulating material). Under high current and voltage conditions, the arc can exist stably and release a large amount of energy, which may pose a serious threat to the power system.

[0040] Electric arcs are diverse and random. The diversity is mainly reflected in the fact that the degree of arc burning varies due to the voltage and current levels, load power, electrode materials, external environment and other working conditions. The current in the branch where the arc is located is affected by the downstream load impedance characteristics and is variable, making the time domain and frequency domain characteristics of the arc series current complex and diverse. The randomness of the arc is mainly manifested in the fluctuation of the arc burning degree, the non-fixed continuity, the unstable flashing of the electric spark, and the waveform burrs, spikes, flat shoulder duration and other distortion characteristics of the arc current without a fixed repetition pattern. The diversity and randomness of arc current are the main difficulties faced by arc fault detection.

[0041] The energy release of an arc exhibits complex nonlinear characteristics that depend on multiple factors of current and voltage. High temperature and high current density are generally associated with greater energy release, but the nonlinear characteristics of the arc make it more complicated. The energy release of an arc also exhibits dynamic changes in its development process. Different stages may have different energy release patterns, and there is a nonlinear relationship between the arc voltage and current. Although the volt-ampere characteristics of an arc may show a certain pattern under steady-state conditions, this characteristic may change significantly under transient conditions, such as the initial formation stage of the arc. For example, an increase in arc length usually leads to an increase in arc voltage, indicating that arc length has an important influence on its volt-ampere characteristics. In addition, the arc current will be different under different power, current and other working conditions, and the sampling waveform of the arc current will also be greatly different under different loads.

[0042] In summary, the fault arc is highly random and its inherent characteristics are difficult to extract comprehensively. In addition, the fault arc disaster mechanism is complex and its early signs are diverse.

[0043] The accuracy of traditional fault arc analysis methods depends on sufficient data sources and extracted arc features. However, the data sources in the existing technology are too limited, usually concentrated on power grid data and historical fault records in specific areas, resulting in insufficient geographical and conditional diversity. The final analysis results cannot fully represent the fault arc characteristics in different regions and changing environments, and may not be able to accurately reflect the actual situation and characteristics of the fault arc. It lacks the support of a global perspective and multi-dimensional data. In addition, traditional fault arc analysis methods are based on single or specific fixed features for identification, and fail to fully consider multi-dimensional influencing factors, resulting in incomplete analysis of energy and volt-ampere characteristics, and cannot fully explore the dynamic changes of fault arc characteristics. It is also difficult to accurately capture and analyze complex influencing factors, and therefore cannot accurately and reliably achieve early fault arc identification and diagnosis.

[0044] The present invention considers the multi-dimensional and multi-type source-load and spatial factors of the power system, extracts the characteristic parameter set of the fault arc generated by different source-load devices under different spatial factors, and uses the characteristic parameter set to establish the characteristic curves of different types of fault arcs, thereby describing the fault arc characteristics of different fault types. The fault arc true type reproduction device can realize the true equivalent reproduction of multi-type source-load and multi-spatial factor fault arcs, so that a large number of fault arc signals of different fault types can be fully simulated, and the problems of limited data sources and difficulty in covering fault arc characteristics in different regions and variable environments in traditional analysis methods can be solved; at the same time, by using the fault arc generated by the device to extract the fault symptom characteristics and the corresponding distribution interval probability, a fault symptom feature database is established, and the database can be used to fully explore the early electrical symptom laws of the fault arc by considering multi-source-load and time-space factors, and explore the correlation between the early fault symptom characteristics and the characteristics, so that the actual early fault arc can be accurately and reliably identified by using the database, and the specific fault type can be diagnosed when the early fault arc is identified.

[0045] The present invention will be further described below in conjunction with specific embodiments.

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

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

[0048] In this embodiment, if Figure 2 As shown, the source and load equipment include photovoltaic arrays, energy storage devices, converters, electric vehicles, etc., the spatial factors include insulation aging and damage, loose electrical connections, humid air, high wire temperature, etc., and the characteristic parameters of the fault arc include the current, impedance, energy characteristics, volt-ampere characteristics, etc. of the fault arc, which can be selected and configured according to actual needs. Furthermore, the 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 data set can be formed, which can be represented as a matrix X. By comprehensively analyzing the fault arcs generated by various types of source and load equipment under different spatial factors, more comprehensive and accurate information can be obtained, so that the behavior of the fault arc can be more comprehensively understood and the characteristics of different types of fault arcs can be accurately characterized.

[0050] There will be specific rules in the parameters of the fault arc, and mining this rule can help predict the changes in the fault arc. For example, there will be a correlation between the current in the fault arc and the arc length, so the change in the arc length can be predicted by the current in the fault arc. In this embodiment, the process of simulating the real fault arc characteristics by the fault arc true reproduction device to generate fault arc signals of different fault types also includes multi-dimensional parameter observation of the fault arc generated by the fault arc true reproduction device to generate a fault arc signal including a variety of different parameters, and realize multi-dimensional parameter observation of the fault arc, so that the characteristics of different types of fault arcs and the evolution trajectory of the arc state can be fully mined, so as to better understand the dynamic characteristics of the arc.

[0051] The above parameters may specifically include the number of arcs, energy intensity, arc current, arc voltage, temperature, arc light, arc sound, etc. Specifically, firstly, various fault arcs generated by photovoltaic arrays, energy storage devices, converters, electric vehicles and other source-load equipment under spatial factors such as insulation aging and damage, loose electrical connections, humid air, high wire temperature, etc. can be collected, and then the number of arcs, energy intensity, arc current, arc voltage, temperature, arc light, arc sound and other parameters in each fault arc can be observed 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 of different types of fault arcs according to the characteristic parameter set.

[0053] In this embodiment, the fault arc characteristic curve includes an energy characteristic curve, a volt-ampere characteristic curve, etc. The energy characteristic curve is used to describe the behavior characteristics of the fault arc at different energy levels. Based on this curve, it can help understand the behavior law of the fault arc under different energy states. The volt-ampere characteristic curve is a curve used to describe the change law of the voltage (U) and current (I) of the fault arc, that is, the IV curve. Based on this curve, the relationship between the voltage and current of the fault arc can be described. This embodiment extracts the characteristic parameter set of the current, impedance, energy characteristics, volt-ampere characteristics and other characteristics of the fault arc corresponding to each factor based on different source-load types, time scales and spatial factors, and then analyzes the data in the characteristic parameter set to establish the corresponding characteristic curves of different types of fault arcs, so as to form a simplified characterization method of the fault arc energy considering multiple factors.

[0054] In a specific application embodiment, principal component analysis, factor analysis, cluster analysis or multivariate 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 fault, parallel fault and ground fault, that is, the fault is simplified into three types: series fault, parallel fault and ground fault. It can be understood that, of course, more detailed fault type classification can be performed according to actual needs to further improve the identification precision.

[0056] Step 3. Fault arc simulation: arc simulation parameters are determined according to the characteristic curves of different types of fault arcs, and input into a pre-constructed fault arc true-type reproduction device. The fault arc true-type reproduction device simulates the true equivalent model of the fault arc of different fault types under various spatial factors, so as to simulate and generate fault arc signals of different fault types according to the arc simulation parameters. The fault arc true equivalent model is a simplified model for simulating a fault arc with real fault arc characteristics. The arc simulation parameters include discharge duration, discharge intensity, fault type, etc.

[0057] The data sources of traditional specific area power grid data or historical fault records are limited, and it is difficult to cover the fault arc characteristics in different areas and changing environments. After establishing the characteristic curves of different types of fault arcs, this embodiment uses the characteristic curves of different types of fault arcs. The arc simulation parameters of different types of fault arcs can be obtained from the characteristic curves of different types of fault arcs, such as discharge duration, discharge intensity, fault type, etc., and then the arc simulation parameters determined based on the characteristic curve are input into the pre-constructed fault arc true type reproduction device. The fault arc true type reproduction device simulates the fault arc true type equivalent model with real fault arc characteristics under various spatial factors, and the fault arc signals of different fault types can be generated. The fault arc true type equivalent model includes three types: series fault, parallel fault and ground fault. The simplified model of fault arc generation is established by simulating the three fault types respectively, and the real fault arc characteristics when considering various spatial factors under the three fault types are simulated. The fault arc true type reproduction device is equivalent to establishing a fault arc true type equivalent model. Based on the characteristic parameters of different types of fault arcs, it truly simulates the generation of various types of fault arcs, so that various types of fault arcs can be reproduced by considering multiple factors. When different input parameters are given (such as different spatial factors, fault types), the fault arc true type reproduction device can generate and output the corresponding fault arc true type, thereby achieving the goal of multi-factor arc true type equivalent reproduction, making it convenient and efficient to obtain a large number of different types of fault arc data, solving the problems of limited data sources in traditional analysis methods and difficulty in covering fault arc characteristics in different regions and changing environments.

[0058] The main causes of fault arcs include the following: (1) Aging of electrical lines, resulting in parallel fault arcs between phases. Long-term overload of lines, ultraviolet rays, smoky environments, etc. can all cause line aging; (2) Line insulation damage, resulting in parallel fault arcs between phases and fault arcs to ground; (3) Line disconnection, poor contact, etc., causing series fault arcs, such as loose connections of electrical connectors and terminal blocks, aging of sockets that lose elasticity, and poor contact of plugs.

[0059] In this embodiment, in combination with the causes of the above faults, an electric transmission device with a fixed slide and a movable slide is used as a fault arc true type reproduction device. Figure 3 As shown, based on this device, the distance between wires or between wires and switch elements, such as virtual connection, poor contact, insulation damage, and aging wires, can be simulated to simulate scenes such as aging cables, insulation damage cables, and poor contact plugs, and to reproduce various fault arcs. Figure 3 The fault arc true type reproducing device includes a fixed electrode and a movable electrode. The fixed electrode is arranged on a fixed slide, and the movable electrode is arranged on a movable slide. The fixed slide is connected to the movable slide by a screw rod. 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 are controlled to simulate the aging / insulation damaged wire to reproduce the discharge arc at a close distance. By controlling the load input on the load side, the fault arc simulation reproduction can be realized when the fault types are parallel (phase to phase), series (same phase), and grounding (phase to ground). A pressure sensor is also arranged at the fixed electrode. The process of arc generation is controlled by the pressure sensor. When the movable electrode gradually approaches the fixed electrode, the detection value of the pressure sensor is judged. When the value of the pressure sensor reaches the threshold and the line current is detected, the movable electrode starts to separate and move, that is, the electrode moves away from the fixed electrode according to the set speed and the predetermined displacement. In the fault arc true type reproducing device, a photoelectric sensor can be arranged on the screw rod, and a light shielding partition can be arranged between the movable slide and the movable electrode to assist in simulating the fault arc in various types of scenarios.

[0060] like Figure 2As shown, this embodiment considers the source load type (such as photovoltaic array, energy storage device, converter, electric vehicle) and spatial factors (such as insulation aging and damage, loose electrical connection, humid air, high temperature of wire) and other differentiated influencing factors, and considers different time scales (such as 50 milliseconds, 100 milliseconds, 200 milliseconds, 500 milliseconds), extracts the current, impedance, energy characteristics, volt-ampere characteristics and other characteristic parameter sets of the fault arc corresponding to different factors, so as to establish a mathematical model of the fault arc considering multiple factors, and then establish different types of fault arc energy characteristic curves and volt-ampere characteristic curves. Based on the characteristic curve, the fault arc energy representing multiple factors can be simplified, and then the arc simulation parameters such as discharge duration and discharge intensity are determined by using the characteristics represented by the curve, and then input into the above-mentioned fault arc true type reproduction device, so as to simulate the true type reproduction of fault arcs of different fault types (series, parallel and grounding), and generate various types of fault arcs. It can be understood that, of course, other structural types of fault arc true type reproduction devices can also be used according to actual needs to achieve different types of fault arc simulation reproduction.

[0061] Step 4, constructing a fault symptom database: extracting fault symptom features that characterize the early symptom phenomenon of the fault arc from each fault arc signal of different fault types, and statistically analyzing the distribution interval probability of the early symptom features of the fault arc to form a fault symptom feature database.

[0062] There will be specific electrical symptom patterns in the early stage of fault arcs, such as high-frequency harmonics of the line, non-periodic fluctuations of the line current / voltage, and abnormal changes in line temperature. If such electrical symptom patterns can be quantitatively observed, early fault arcs can be effectively identified, which is conducive to the corresponding fault handling in advance. In order to measure and classify the main early signs of fault arcs, this embodiment further establishes quantifiable indicators corresponding to each early sign phenomenon, such as the number of harmonic components, harmonic distribution frequency band and harmonic amplitude corresponding to high-frequency harmonics of the line; the fluctuation duration, fluctuation speed and amplitude fluctuation variance corresponding to non-periodic fluctuations of the line current / voltage; and the abnormal line temperature corresponding to the heating duration and heating rate.

[0063] This embodiment targets phenomena such as line high-frequency harmonics, non-periodic fluctuations in line current / voltage, and abnormal temperature changes in the line. By comprehensively considering early sign characteristics such as the number of arcs, energy intensity, arc current, arc voltage, temperature, arc light, and arc sound, and performing statistical analysis, the probability of the distribution interval corresponding to each early sign characteristic is statistically analyzed. The distribution interval probability is the probability of each early sign characteristic being distributed in which numerical intervals. Therefore, the probability of the early sign characteristics of different types of fault arcs being in each distribution interval can be analyzed. The analysis results of the early sign characteristics and the distribution interval probability can be used for real-time fault arc identification and diagnosis, so as to quickly and accurately identify whether there are early signs of fault arcs and the type of fault.

[0064] Step 5: Obtain the signal to be diagnosed monitored in real time and extract the corresponding characteristic parameters, input the extracted characteristic parameters in real time into the fault symptom feature database for matching, diagnose whether it is an early fault arc, and determine the corresponding fault type when 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, such as line high-frequency harmonics, non-periodic fluctuations of line current / voltage, and abnormal line temperature, as well as the probability of the distribution interval corresponding to each early symptom feature; when the signal to be diagnosed is monitored in real time, the corresponding characteristic parameters are extracted from the signal to be diagnosed, such as current, voltage, energy intensity, etc., and then input into the fault symptom feature database for matching, and according to the matching items, it can be determined whether there is an early fault arc and the corresponding fault type. For example, when a suspected fault arc signal is extracted from the signal to be diagnosed monitored in real time, feature extraction is performed. If the harmonic component can be extracted, the number of harmonic components, the harmonic distribution frequency band and the harmonic amplitude are counted, and 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 feature of the harmonic component extracted in real time falls into the distribution interval of the fault symptom feature of a certain type of fault arc in the fault symptom feature database, it is determined that there is an early fault arc, and then the specific fault type can be diagnosed according to the fault type corresponding to the matching item.

[0066] Step 6: Failure-caused disaster risk assessment

[0067] Step S601. Calculate the coupling parameters between various fault symptom features in the fault feature database in advance, and set corresponding weight values ​​for various fault symptom features according to the calculation results of the coupling parameters. The coupling parameters include coupling frequency, coupling probability, coupling degree, etc.;

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

[0069] Factors such as line high-frequency harmonics, current / voltage non-periodic Poisson and temperature anomalies are the key reasons for the formation of fault arc fire risk flow, such as Figure 4As shown, this embodiment further analyzes the connotation and classification of the coupling of the risk of arc fault fire by combining the identification of the early signs of the fault arc and the classification results of the fault arc, so as to fully explore the formation mechanism of the positive strong coupling of the fire risk and the coupling evolution mechanism of the fire risk factors, and uses System Dynamics (SD) to analyze the coupling of homogeneous factors, dual factors and multiple factors of the fire risk, and uses the NK model to quantitatively calculate the multi-factor coupling measurement of the fault arc fire risk, and obtain the coupling frequency, coupling probability and coupling degree between the factors. Since the disaster-causing factors and mechanisms of the early signs of the fault arc have certain complexity, in order to obtain the mutual influence relationship between the risk factors, this embodiment uses system dynamics to qualitatively analyze the coupling risk of homogeneous factors, dual factors and multiple factors of the early signs of the fault arc, and in view of the particularity and complexity of the disaster-causing law of the early signs of the fault arc, this embodiment calculates the coupling degree parameters between the single factor and the dual factors based on the coupling degree model and the nonlinear dynamics model, 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 model

[0071] The coupling degree model can quantitatively calculate the coupling degree between the various factors within the system, and reflect the mutual influence relationship between the factors and the degree of influence on the entire system through the calculation of the coupling degree. The modeling steps of the coupling degree model mainly include the following steps:

[0072] 1) Construct a coupling evaluation index system: First, a coupling 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 the expert scoring method (Delphi), the analytic hierarchy process (AHP), the structural entropy weight method (SWEM), etc., so as to obtain the influence of the factors corresponding to each index on the entire system;

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

[0074]

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

[0076]

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

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

[0079]

[0080] (2) Nonlinear dynamic model

[0081] Based on the self-evolution mechanism of complex systems, the nonlinear dynamic model can be used to analyze the mutual influence and interaction between various factors that cause early signs of fault arc disasters. The modeling steps of the nonlinear dynamic model mainly include constructing the nonlinear function of the system, constructing the evolution equation of the composite system, and the coupling function.

[0082] 1) Construct system nonlinear function:

[0083]

[0084] where x i Represents each element in the system, f is x i Since the stability of a nonlinear system is related to the characteristic roots of similar systems, the above formula is expanded according to the Taylor series approximation, and then an approximate expression after removing the highest order term can be obtained, where a and b represent the weight values ​​of each element:

[0085]

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

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

[0088]

[0089] Among them, A and B represent the evolution states of the two subsystems when they are coupled; V A 、V BRepresents the evolution rate of the subsystem; thus, the evolution rate formula of the entire system can be obtained through the functional relationship between the evolution rate of the system and the subsystem:

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

[0091] 3) Construction of coupling function

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

[0093]

[0094] Among them, α represents V A With V B The angle between the subsystems can reflect the degree of coupling between the subsystems. 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 a conflict between them; 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 a mutual promotion.

[0095] (3) NK model

[0096] The NK model mainly consists of two elements, N and K, where N represents the number of key elements in the system and K represents the number of relationships in the system, which is also an important factor affecting fitness. If the system contains N types of components and each type of component has n different situations, then the number of all possible combinations in the system is n. N If the components in the system are combined in a specific form, the system will take on the form of a network, and K∈(0,N-1). In order to realize the risk assessment of early signs of fault arc disasters, and fully explore the laws of fires formed by the mutual coupling of the line high-frequency harmonic factor subsystem, the line voltage / voltage non-periodic fluctuation factor subsystem, and the line temperature anomaly subsystem, it is necessary to quantitatively calculate the interactive information between the above three, that is, the more disasters caused by a certain coupling type, the greater the possibility of the occurrence of this type. At the same time, the larger the interactive information T value generated by the mutual coupling, the higher the coupling degree of the coupling type, the greater the mutual influence, and the greater the severity of the disaster consequences. In order to fully explore the laws of early electrical signs of fault arcs such as line high-frequency harmonics, current / voltage non-periodic fluctuations, and temperature fluctuations, this embodiment uses the NK model to calculate the coupling degree parameters between multiple factors, which can fully explore the mutual coupling relationship between various factors for the formation of fires, effectively analyze the risk coupling complexity of early electrical signs of fault arcs, and further explore the correlation between various early electrical sign factors and arc faults.

[0097] After calculating the coupling degree parameters between each fault symptom feature, the coupling degree parameters between single factors, double factors and multiple factors can be combined to set corresponding weight values ​​for each fault symptom feature. For example, if the coupling degree parameters between single factors, double factors and multiple factors can reflect that a certain fault symptom feature has a greater impact on the formation of fire, a larger weight value can be set accordingly. If the impact is small, a smaller weight value can be set accordingly. The weight value can be specifically configured using the structural entropy weight allocation method.

[0098] Furthermore, an index system for evaluating the disaster risk caused by early signs of fault arc can be constructed based on the characteristics of each fault symptom and the corresponding weight value. Experiments can also be carried out using a true-to-life fault arc reproduction device, and the index system can be corrected using the experimental data and results. Finally, the weights of each evaluation index can be calculated based on the calculation results of the risk coupling degree and the structural entropy weight method, and scoring criteria based on coupling probability and coupling frequency can be formulated based on the experimental results of the device, thereby forming a complete set of disaster risk evaluation models for early signs of fault arc. Based on this evaluation model, a fire risk metric value can be calculated for real-time risk conditions to evaluate the risk level of fire caused by fault arcs in real time.

[0099] In this embodiment, the fire risk measurement calculation method of structural entropy weight distribution can be used

[0100] Step 7: Electrical fire warning judgment

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

[0102] Step 702: constructing a corresponding relationship between fault arc parameters and critical conditions of electrical fires by using the first relationship model and the second relationship model;

[0103] Step 703: When an early fault arc is diagnosed, the characteristic parameters extracted in real time are compared with the critical condition of the electrical fire to determine whether the critical condition of the electrical fire is currently reached.

[0104] Factors such as the type of fault arc, energy intensity, and the number of durations are associated with electrical fires. Therefore, by exploring the critical conditions of electrical fires, low-voltage fault arc diagnosis can be achieved quickly and accurately, the reliability of fault diagnosis can be improved, and the problem of safe power control and efficient data interaction in low-voltage power distribution systems can be solved. This embodiment fully considers the impact of current / voltage fluctuations and temperature fluctuations on the early signs of arc faults. Based on thermodynamics and temperature modeling methods, it can explore the laws of early electrical signs of fault arcs and disaster-causing mechanisms. At the same time, by establishing a fire dynamics model, it can fully explore the correlation between energy intensity and fire occurrence, such as the impact of the number of durations of fault arcs on electrical fires, etc., and use the correlation between various factors and the occurrence of electrical fires to accurately determine the critical conditions of electrical fires.

[0105] In this embodiment, the flame characteristic distribution includes flame morphology distribution, heat radiation characteristics, temperature distribution in the flame, and flame burning rate, etc., and the smoke motion characteristic distribution includes smoke motion stage, smoke temperature distribution, and smoke motion rate, etc. In this embodiment, by statistically analyzing the flame characteristic distribution of the fault arc generated by the fault arc true type reproduction device under different parameter input conditions, a first relationship model between the fault arc parameters and the flame characteristic distribution is established to explore the influence of the fault arc type, arc energy intensity, and the number of continuous arcs on the flame morphology and heat transfer characteristics, and then obtain the change law of the fault arc parameters and the flame characteristic distribution; at the same time, statistically analyzing the change law of the smoke motion characteristic distribution of the fault arc generated by the fault arc true type reproduction device under different parameter input conditions, a second relationship model between the fault arc parameters and the smoke motion characteristic distribution is established to explore the influence of the fault arc type, arc energy intensity, and the number of continuous arcs on the smoke motion characteristics, and obtain the change law of the fault arc parameters and the smoke motion characteristics, and then establish the relationship between the fault arc parameters and the critical conditions of electrical fire on the basis of clarifying the critical points of the combustion flame characteristics and the smoke motion characteristics.

[0106] This embodiment analyzes the fault arc data generated by the fault arc true reproduction device based on the fire dynamics modeling and analysis method, so as to fully explore the influence of the energy intensity of the fault arc on the electrical fire, and realize the establishment of the relationship model between the fault arc and the electrical fire. When an early fault arc is diagnosed, the characteristic parameters extracted in real time are compared with the critical conditions of the electrical fire, that is, it can be determined whether the critical conditions of the electrical fire are currently reached, so as to provide timely electrical fire warning.

[0107] Arc fires usually originate from fault arcs in electrical systems, which are essentially a high-temperature discharge phenomenon. The three basic elements of arc fires are 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 insulating materials, cables, etc. The oxygen supply comes from oxygen in the air. This embodiment uses fire dynamics modeling and analysis to facilitate understanding of how the various elements interact with each other to lead to the development of arc fires. Secondly, combustion is the core process of arc fires. Combustion is a chemical reaction that usually requires sufficient heat source to bring the fuel to the ignition temperature. The arc itself is a high-temperature heat source that can ignite the surrounding combustible materials. The use of fire dynamics models can help understand the combustion process in arc fires, including fuel decomposition, oxygen supply, heat energy release, etc.

[0108] In the fire dynamics modeling and analysis method of this embodiment, various factors are considered, such as the nature of the fuel, oxygen supply, temperature, pressure, flow, etc. of the fire source, and the normal pressure fire model in the prior art is used to model indoor fires, and the computational fluid dynamics (CFD) model is used to model more complex fire scenes. These models can be used to predict the development process of the fire, including the temperature and location of the fire source, the generation and propagation of smoke, the spread rate of the fire, etc. Further based on the model analysis, the probability of arc fire, the heat release of the fire, the speed of fire propagation and other parameters can be evaluated, which is helpful to formulate fire risk management strategies and the safety design of electrical systems. In addition, based on the model analysis, the best fire control strategy can also be determined, such as automatic fire extinguishing system, smoke exhaust system, etc., to minimize the losses and hazards caused by arc fires.

[0109] In this embodiment, the Navier-Stokes equation can be used to process the fault arc data to fully explore the flow behavior of the arc fire, including the characteristics of smoke, flame and heat conduction, so as to optimize the propagation characteristics of the arc fire, the fire source temperature, the fire risk assessment and the fire control strategy, which is helpful to improve the design and safety of the electrical system, reduce the losses and hazards that may be caused by the arc fire, and improve the efficiency of fire prevention and control. The Navier-Stokes equation can be expressed as:

[0110]

[0111]

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

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

[0114]

[0115] Where Q is the thermal radiation power, σ is the Stefan-Boltzmann constant, A is the radiation surface area, T is the fire source temperature, and T 0 is the ambient temperature.

[0116] In addition, the heat conduction process characteristics of the fault arc can be described using the heat conduction equation:

[0117]

[0118] Where T is temperature, t is time, α is the thermal diffusion coefficient, is the Laplace operator.

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

[0120]

[0121] Here, ρ is the density and u is the velocity.

[0122] This embodiment combines models for various details of the fault arc process to accurately construct a fault arc model in a complex environment, and uses fire dynamics modeling and analysis methods to explore the correlation between the fault arc and electrical fire and the critical condition law, and obtains the influence of factors such as the fault arc energy intensity on the critical conditions of electrical fires.

[0123] like Figure 5As shown, in order to explore the critical conditions for fault arc disasters, this embodiment first uses a fault arc true-type reproduction device to generate different types of fault arcs and divide the experimental control groups according to the fault arc type (such as serial arc, parallel arc), arc energy intensity (such as low, medium, high) and the number of continuous arcs (such as the number of continuous arcs is between 10 and 100, divided into ten groups in total). For the arc generation schemes of all control groups, theoretical analysis, numerical simulation, reproduction experiment and result comparison are used to adjust the fault arc generation configuration parameters corresponding to all control groups to form a fault arc disaster critical experimental scheme library and parameter configuration set, and then the combustion flame characteristics and smoke movement characteristics are statistically analyzed according to the experimental parameter results. By statistically analyzing the fault arc under different parameter input conditions, the fault arc generation configuration parameters corresponding to all control groups are adjusted. The flame characteristic distribution of the fault arc generated by the true-type reproduction device, that is, the first relationship model between the fault arc parameters and the flame morphology distribution, thermal radiation characteristics, temperature distribution, burning rate, etc. is established to explore the influence of the fault arc type, arc energy intensity and the number of continuous arcs on the flame morphology and heat transfer characteristics, and then obtain the change law of the fault arc parameters and flame characteristic distribution; at the same time, the change law of the smoke movement characteristic distribution of the fault arc generated by the true-type reproduction device of the fault arc under different parameter input conditions is statistically analyzed, and the second relationship model between the fault arc parameters and the smoke movement stage, smoke temperature distribution and smoke movement rate is established. From the above-mentioned combustion flame and smoke movement characteristic statistical results, critical conditions are formed to determine whether the critical conditions of electrical fires are reached.

[0124] Specifically, in order to obtain the influence of fault arc type, arc energy intensity and the number of continuous arcs on flame morphology and heat transfer characteristics, the parameter set corresponding to the experimental program library for critical research on fault arc disasters is input one by one into a multi-factor fault arc true reproduction device, and the flame morphology distribution, thermal radiation characteristics, flame and temperature distribution and flame burning rate under different parameter input conditions are observed and recorded to establish a law model of fault arc parameters and flame characteristic distribution (first model); in order to obtain the influence of fault arc type, arc energy intensity and the number of continuous arcs on smoke movement characteristics, the parameter set corresponding to the experimental program library for critical research on fault arc disasters is input one by one into a multi-factor fault arc true reproduction device, and the smoke movement stage, smoke temperature distribution and smoke movement rate under different parameter input conditions are observed and recorded to establish a law model of fault arc parameters and smoke movement changes (second model), combined with the law model of fault arc parameters and flame characteristic distribution changes (first model), a comprehensive correspondence between fault arc parameter characteristics and critical conditions of electrical fire is formed.

[0125] This embodiment starts from the simplified representation of fault arc energy and the quantitative analysis of the multi-factor coupling of fire risk according to the characteristics of different source-load types, time scales and spatial factors. By considering the multi-factor fault arc mathematical model, it is able to fully explore the coupling evolution mechanism of various factors of the early signs of fault arc, reveal the relationship between the energy characteristics of fault arc and the volt-ampere change, and the laws of fault arc parameters and critical conditions of electrical fires, and solve the problems of unclear causes and symptom laws of multi-factor fault arc hazards and difficulty in reproduction in the prior art.

[0126] This embodiment further provides a low-voltage fault arc identification and diagnosis integrating a multi-factor equivalent model, including 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 above method.

[0127] It is understandable that the above method of this embodiment can be executed by a single device, such as a computer or server, etc., and can also be applied to a distributed scenario and completed by multiple devices in cooperation with each other. In the case of a distributed scenario, one of the multiple devices can only execute one or more steps in the above method of this embodiment, and multiple devices interact to complete the above method. The processor can be implemented in the form of a general-purpose CPU, a microprocessor, an application-specific integrated circuit, or one or more integrated circuits, etc., for executing related programs to implement the above method of this embodiment. The memory can be implemented in the form of a read-only memory ROM, a random access memory RAM, a static storage device, and a dynamic storage device. The memory can store an operating system and other applications. When the above method of this embodiment is implemented by 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, and when the computer program is executed by a processor, the above method is implemented.

[0129] Those skilled in the art will appreciate that the above-mentioned embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-readable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, may be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the functions in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 These computer program instructions can also be stored in a computer-readable memory that can guide a computer or other programmable data processing device to work in a specific way, so that the instructions stored in the computer-readable memory produce a product including an instruction device, which implements the functions specified in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide for implementing the process in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0130] The above is only a preferred embodiment of the present invention, and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment, it is not intended to limit the present invention. Therefore, any simple modification, equivalent change and modification made to the above embodiment according to the technical essence of the present invention without departing from the content of the technical solution of the present invention shall fall within the scope of protection of the technical solution of the present invention.

Claims

1. A low voltage fault arc identification and diagnosis method integrating a multi-factor equivalent model, characterized in that the steps include: Extract characteristic parameter sets of fault arcs generated by different source load equipment under different spatial factors; Establishing characteristic curves of different types of fault arcs according to the characteristic parameter set; Determine arc simulation parameters according to the characteristic curves of the different types of fault arcs, input them into a pre-built fault arc true type reproduction device, and use the fault arc true type reproduction device to simulate the fault arc true type equivalent model of different fault types under multiple spatial factors, so as to simulate and generate fault arc signals of different fault types according to the arc simulation parameters, wherein the fault arc true type equivalent model is a simplified model for simulating and generating a fault arc with real fault arc characteristics, and the arc simulation parameters include discharge duration, discharge intensity and fault type; Extracting fault symptom features characterizing the early symptom phenomenon of the fault arc from each fault arc signal of different fault types, and counting the distribution interval probability of the early symptom features of the fault arc to form a fault symptom feature database; The real-time monitored signal to be diagnosed is obtained and the corresponding characteristic parameters are extracted. The characteristic parameters extracted in real time are input into the fault symptom feature database for matching, and it is diagnosed whether it is an early fault arc and the corresponding fault type is determined when it is diagnosed as an early fault arc.

2. The low-voltage fault arc identification and diagnosis method integrating a multi-factor equivalent model according to claim 1 is characterized in that: The source-load equipment includes any one of photovoltaic arrays, energy storage devices, converters, and electric vehicles; the space factors include any one of insulation aging and damage, loose electrical connections, humid air, and high wire temperatures; the characteristic parameters of the fault arc include any one or more of the current, impedance, energy characteristics, and volt-ampere characteristics of the fault arc; The fault arc characteristic curve includes an energy characteristic curve and / or a volt-ampere characteristic curve, and 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 true reproduction device, it also includes multi-dimensional parameter observation of the fault arc signal generated by the fault arc true reproduction device to generate a fault arc signal including multiple different parameters, and the parameters include any multiple of the 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 integrating a multi-factor equivalent model according to claim 1 is characterized in that: The fault arc true-model reproduction device is provided with a fixed electrode and a movable electrode, wherein the fixed electrode is arranged on a fixed slide, and the movable electrode is arranged on a movable slide, the fixed slide is connected to the movable slide via a screw rod, and 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 the discharge duration can be controlled, and by controlling the input of the load on the load side, the fault arc simulation reproduction can be achieved for different fault types.

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

5. The low-voltage arc fault identification and diagnosis method integrating a multi-factor equivalent model according to claim 1 is characterized in that: When an early arc fault is diagnosed, the fault disaster risk assessment steps are also included, including: Pre-calculate the coupling degree parameters between various fault symptom features in the fault feature database, and set corresponding weight values ​​for various fault symptom features according to the calculation results of the coupling degree parameters, wherein the coupling degree parameters include any one or a combination of two or more of the coupling frequency, coupling probability and coupling degree; When an early fault arc is diagnosed, the characteristic parameters extracted in real time are weighted and calculated using the corresponding weight values ​​to obtain a risk metric to evaluate the current disaster risk.

6. The low-voltage fault arc identification and diagnosis method integrating a multi-factor equivalent model according to claim 5 is 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 in each fault symptom feature. The NK model is used to calculate the coupling degree parameters between multiple factors in each fault symptom feature. The entropy weight method is used to allocate weights to determine the weight value of each fault symptom feature.

7. The low-voltage arc fault identification and diagnosis method integrating a multi-factor equivalent model according to any one of claims 1 to 6, characterized in that: When an early fault arc is diagnosed, it also includes electrical fire early warning judgment steps, including: Fire dynamics modeling is carried out to statistically analyze the variation patterns of the flame characteristic distribution and smoke motion characteristic distribution of the fault arc generated by the fault arc true model reproduction device under different parameter input conditions, and the first relationship model between the fault arc parameters and the flame characteristic distribution is established, as well as the second relationship model between the fault arc parameters and the smoke motion characteristic distribution is established; The first relationship model and the second relationship model are used to construct a corresponding relationship between fault arc parameters and critical conditions of electrical fires; When an early fault arc is diagnosed, the characteristic parameters extracted in real time are compared with the critical condition of the electrical fire to determine whether the critical condition of the electrical fire is currently reached.

8. The low-voltage fault arc identification and diagnosis method integrating a multi-factor equivalent model according to claim 7 is characterized in that: The flame characteristic distribution includes any one or more of flame shape distribution, thermal radiation characteristics, temperature distribution in the flame and flame combustion rate, and the smoke motion characteristic distribution includes any one or more of smoke motion stage, smoke temperature distribution and smoke 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 configured to execute the computer program to perform the method according to 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, the method according to any one of claims 1 to 8 is implemented.

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