Series fault arc detection method and system based on multi-criterion fusion

Abstract

The invention relates to a series fault arc detection method and system based on multi-criterion fusion, and the method comprises the steps: building a first experiment scene without interference and a second experiment scene with interference, and carrying out the detection of a series fault arc in the first experiment scene and the second experiment scene, simulating to obtain a plurality of first normal working conditions, a plurality of first fault working conditions, a plurality of second normal working conditions and a plurality of second fault working conditions by respectively changing a circuit load, a circuit topology and an arc generation mode in the experimental circuit; collecting circuit data generated under the first normal working condition, the first fault working condition, the second normal working condition and the second fault working condition; and training the machine learning model based on the circuit data to obtain a trained machine learning model for detecting whether a fault arc is generated in the circuit. According to the invention, the detection precision of the machine learning model on various fault arcs is improved by simulating various complex circuits, and the problem of how to accurately detect the fault arcs generated in the circuit is solved.

Description

Technical Field

[0001] The present application relates to the technical field of electrical safety detection, and particularly to a series fault arc detection method and system based on multi-criterion fusion. Background Art

[0002] With the continuous improvement of the electrification level of modern society, electrical equipment is increasingly widely used in production and life, and the potential hazards of electrical fires are becoming increasingly prominent. Among them, fault arcs, as one of the key factors causing electrical fires, seriously threaten people's lives and property safety. When a series fault arc occurs, the circuit is equivalently connected to a time-varying nonlinear load, and at this time, the fault current is less than the load current during the normal operation of the electrical system. This characteristic makes it difficult for traditional protection switches to detect it in a timely and effective manner, thus increasing the risk of electrical fires.

[0003] Existing detection schemes mainly focus on preprocessing current or voltage signals, then extracting fault arc features and constructing a feature set, and finally making classification judgments based on prior knowledge or machine learning methods. However, these schemes have many limitations. On the one hand, the applicable load types of existing schemes are limited and it is difficult to cover various load types common in real distribution systems. In actual electrical systems, there are a wide variety of loads, and the electrical characteristics of different loads vary greatly, which makes it difficult for a single detection scheme to accurately detect fault arcs under all load conditions. On the other hand, fault arc features are easily affected by various factors such as load type, circuit topology, arcing mode, and circuit noise, resulting in dynamic changes. Some circuit noises are similar to fault arc features, which easily lead to feature aliasing, making the detection model prone to misoperation under unknown working conditions. Moreover, existing features are difficult to effectively distinguish fault arcs from interference, resulting in the accuracy and reliability of detection being affected.

[0004] Currently, no effective solution has been proposed for how to accurately detect fault arcs generated in a circuit in the related art. Summary of the Invention

[0005] Embodiments of the present application provide a series fault arc detection method and system based on multi-criterion fusion to at least solve the problem of how to accurately detect fault arcs generated in a circuit in the related art.

[0006] In a first aspect, embodiments of the present application provide a series fault arc detection method based on multi-criterion fusion, and the method includes:

[0007] Construct a first experimental scenario and a second experimental scenario, where the first experimental scenario is a circuit scenario without interference, and the second experimental scenario is a circuit scenario with interference;

[0008] In the first experimental scenario, several first normal conditions and first fault conditions are simulated by changing the circuit load, circuit topology, and arcing mode in the experimental circuit;

[0009] In the second experimental scenario, several second normal conditions and second fault conditions are simulated by changing the circuit load, circuit topology, and arcing mode in the experimental circuit;

[0010] Collect the circuit data generated under the first normal condition, the first fault condition, the second normal condition, and the second fault condition;

[0011] Train a machine learning model based on the circuit data to obtain a trained machine learning model, where the machine learning model is used to detect whether a fault arc occurs in the circuit.

[0012] In some embodiments, training a machine learning model based on the circuit data to obtain a trained machine learning model includes:

[0013] Extract the fault arc feature parameters of the fault arc from the circuit data;

[0014] Perform feature reconstruction on the fault arc feature parameters to reduce the fuzziness of the fault features caused by circuit noise interference, and obtain the reconstructed fault arc feature parameters;

[0015] Train a machine learning model based on the circuit data and the reconstructed fault arc feature parameters to obtain a trained machine learning model.

[0016] In some embodiments, extracting the fault arc feature parameters of the fault arc from the circuit data includes:

[0017] Decompose the current signal of the circuit data using the mother wavelet coif1 to obtain a preprocessed signal;

[0018] Extract the fault arc feature parameters from the preprocessed signal based on two dimensions of single period and multi-period to obtain the single-period feature parameters and multi-period feature parameters of the fault arc.

[0019] In some embodiments, performing feature reconstruction on the fault arc feature parameters to reduce the fuzziness of the fault features caused by circuit noise interference and obtain the reconstructed fault arc feature parameters includes:

[0020] Use the Teager energy operator to enhance the mutation features in the zero-sequence current coupling signal of the circuit data to obtain the fault arc feature parameters with enhanced features;

[0021] Utilize the periodic symmetry characteristic of the AC power supply voltage to calculate the ratio of the fault arc characteristic parameters after the characteristic enhancement within the front and back 10 ms time windows, so as to obtain the reconstructed fault arc characteristic parameters.

[0022] In some embodiments, training the machine learning model based on the circuit data and the reconstructed fault arc characteristic parameters, and obtaining the trained machine learning model includes:

[0023] Construct a machine learning model based on the decision tree algorithm, wherein the machine learning model uses the information gain ratio as the measurement criterion for feature selection, and by calculating the information gain ratios of different features, select the features with the maximum information gain ratio for decision tree node splitting;

[0024] Input the circuit data and the reconstructed fault arc characteristic parameters into the machine learning model, determine the network weights of the model, and obtain the trained machine learning model.

[0025] In some embodiments, collecting the circuit data generated under the first normal condition, the first fault condition, the second normal condition, and the second fault condition includes:

[0026] Collect the circuit data generated under the first normal condition, the first fault condition, the second normal condition, and the second fault condition, wherein the circuit data includes current signals, arc voltage signals, and zero-sequence current coupling signals.

[0027] In some embodiments, building the second experimental scenario includes:

[0028] Set up a bypass interference circuit to build the second experimental scenario on the basis of the first experimental scenario, wherein the bypass interference circuit uses an AC / AC converter and an AC / DC inverter as loads to simulate and generate harmonic interference and voltage fluctuation interference.

[0029] In some embodiments, the method includes:

[0030] The circuit loads include resistors, halogen lamps, kettles, ovens, induction cookers, microwave ovens, vacuum cleaners, electric drills, switching power supplies, fluorescent lamps, and dimming lamps;

[0031] The circuit topologies include single-load circuits, multi-load circuits, parallel connection of small-power fault branches and large-power normal branches, and parallel connection of small-power normal branches and large-power fault branches;

[0032] The arc generation methods include arcing by pulling and arcing by carbonized wires.

[0033] In some embodiments, the method includes:

[0034] If the machine learning model detects a faulty arc in the circuit, the protection switch is triggered, and the circuit is cut off through the protection switch.

[0035] In a second aspect, an embodiment of the present application provides a series fault arc detection system based on multi-criterion fusion. The system includes a data acquisition module and a model training module;

[0036] The data acquisition module is used to build a first experimental scenario and a second experimental scenario. Among them, the first experimental scenario is a circuit scenario without interference, and the second experimental scenario is a circuit scenario with interference;

[0037] The data acquisition module is used to simulate and obtain a number of first normal conditions and first fault conditions in the first experimental scenario by changing the circuit load, circuit topology, and arc generation method in the experimental circuit;

[0038] The data acquisition module is used to simulate and obtain a number of second normal conditions and second fault conditions in the second experimental scenario by changing the circuit load, circuit topology, and arc generation method in the experimental circuit;

[0039] The data acquisition module is used to collect the circuit data generated under the first normal condition, the first fault condition, the second normal condition, and the second fault condition;

[0040] The model training module is used to train the machine learning model according to the circuit data to obtain a trained machine learning model, where the machine learning model is used to detect whether a faulty arc occurs in the circuit.

[0041] Compared with the related technology, an embodiment of the present application provides a series fault arc detection method and system based on multi-criterion fusion. Among them, the method builds a first experimental scenario without interference and a second experimental scenario with interference. In the first experimental scenario and the second experimental scenario, a number of first normal conditions and first fault conditions, as well as second normal conditions and second fault conditions, are simulated by changing the circuit load, circuit topology, and arc generation method in the experimental circuit; the circuit data generated under the first normal condition, the first fault condition, the second normal condition, and the second fault condition are collected; the machine learning model is trained based on the circuit data to obtain a trained machine learning model for detecting whether a faulty arc occurs in the circuit, realizing covering complex circuits with various load types and circuit topologies through the built experimental scenarios, simulating the circuit data of complex circuits under different conditions, and enabling the machine learning model to comprehensively and accurately learn the influence of various factors on the faulty arc in the circuit based on the circuit data, significantly improving the accuracy of faulty arc detection in various scenarios, and solving the problem of how to accurately detect the faulty arc generated in the circuit. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The drawings described herein are provided to further understand the present application and form a part of the present application. The schematic embodiments of the present application and their descriptions are used to explain the present application and do not unduly limit the present application. In the drawings:

[0043] Figure 1 is a flowchart of the steps of the series fault arc detection method based on multi-criterion fusion according to an embodiment of the present application;

[0044] Figure 2 is a schematic flowchart of feature reconstruction according to an embodiment of the present application;

[0045] Figure 3 is a comparison schematic diagram of the pulse factor ratio before and after reconstruction according to an embodiment of the present application;

[0046] Figure 4 is a comparison schematic diagram of the zero-sequence current Shannon entropy ratio before and after reconstruction according to an embodiment of the present application;

[0047] Figure 5 is a comparison schematic diagram of the D1 energy ratio before and after reconstruction according to an embodiment of the present application;

[0048] Figure 6 is a schematic internal structure diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0049] In order to make the objectives, technical solutions and advantages of the present application more clear and understandable, the present application will be described and explained below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments provided in the present application without making creative efforts fall within the scope of protection of the present application.

[0050] Obviously, the drawings in the following description are only some examples or embodiments of the present application. For those of ordinary skill in the art, without making creative efforts, the present application can also be applied to other similar scenarios based on these drawings. In addition, it can also be understood that although the efforts made in this development process may be complex and lengthy, for those of ordinary skill in the art related to the content disclosed in the present application, some design, manufacturing or production changes based on the technical content disclosed in the present application are only conventional technical means and should not be understood as the content disclosed in the present application being insufficient.

[0051] Reference to "embodiment" in this application means that the specific features, structures, or characteristics described in connection with the embodiment can be included in at least one embodiment of this application. The phrase appears in various places in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. It is explicitly and implicitly understood by those of ordinary skill in the art that the embodiments described in this application can be combined with other embodiments without conflict.

[0052] Unless otherwise defined, the technical terms or scientific terms involved in this application shall have the ordinary meaning understood by those with ordinary skills in the technical field to which this application belongs. The words such as "a", "an", "one kind", "the" and the like involved in this application do not indicate a quantity limitation and can represent a singular or plural number. The terms "include", "comprise", "have" and any variations thereof involved in this application are intended to cover non-exclusive inclusion; for example, a process, method, system, product or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may further include unlisted steps or units, or may further include other steps or units inherent to these processes, methods, products or devices. The words such as "connect", "be connected", "couple" and the like involved in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The "multiple" involved in this application refers to two or more. "And / or" describes the association relationship of associated objects and indicates that three relationships may exist. For example, "A and / or B" may represent: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally represents an "or" relationship between the front and rear associated objects. The terms "first", "second", "third", etc. involved in this application are only used to distinguish similar objects and do not represent a specific order for the objects.

[0053] An embodiment of this application provides a series fault arc detection method based on multi-criterion fusion. Figure 1 It is a step flowchart of the series fault arc detection method based on multi-criterion fusion according to the embodiment of this application, as Figure 1 shown, and this method includes the following steps:

[0054] Step S102, build a first experimental scenario and a second experimental scenario, where the first experimental scenario is a circuit scenario without interference, and the second experimental scenario is a circuit scenario with interference;

[0055] Specifically, in step S102, a first experimental scenario without interference is set up, and on the basis of the first experimental scenario, a second experimental scenario is set up by setting up a bypass interference circuit. The bypass interference circuit uses an AC / AC converter and an AC / DC inverter as loads to simulate harmonic interference and voltage fluctuation interference.

[0056] It should be noted that the bypass interference circuit uses an AC / AC converter and an AC / DC inverter as loads. The output frequency adjustment range of the AC / AC converter should cover the common frequency fluctuation range in the actual electrical system, and the amplitude adjustment accuracy should reach ±1V; the DC output voltage adjustment range of the AC / DC inverter is 98V - 242V, and the current adjustment range is 1A - 500A. By precisely adjusting these parameters, various complex interferences such as harmonic interference and voltage fluctuation interference are simulated. The simulation of these interferences truly restores the situation where the fault arc characteristics are annihilated due to interference in the actual operating environment, providing more comprehensive, true, and reliable experimental data for the training of the machine learning model for subsequent fault arc detection.

[0057] In step S104, in the first experimental scenario, by changing the circuit load, circuit topology, and arcing method in the experimental circuit, a number of first normal operating conditions and first fault conditions are simulated.

[0058] It should be noted that to ensure that various load characteristics in the actual electrical system can be comprehensively simulated in the first experimental scenario, the selection of the circuit load covers both typical loads such as resistors and halogen lamps required by the standard for testing, and also includes common loads such as kettles, ovens, induction cookers, microwave ovens, vacuum cleaners, electric drills, switching power supplies, fluorescent lamps, and dimming lamps widely used in real distribution systems. For the resistive load, by setting different resistance values, the electrical characteristics under different power consumption scenarios are precisely simulated; for the dimming lamp, its conduction angle can be flexibly changed (such as 30°, 60°, 90°, 120°, 180°) to simulate the electrical behavior under different brightness adjustment states, so as to comprehensively and accurately reproduce the operating conditions of various actual electrical appliances in the circuit.

[0059] Furthermore, it should be noted that representative circuit topologies are designed, including but not limited to single-load circuits, multi-load circuits, parallel connections of low-power fault branches and high-power normal branches, and parallel connections of low-power normal branches and high-power fault branches. The single-load circuit is mainly used to deeply study the characteristics of fault arcs under single-load conditions; the multi-load circuit simulates the complex scenarios of multiple electrical appliances operating simultaneously in households or small commercial premises; the two topologies of parallel connections of low-power fault branches and high-power normal branches, and parallel connections of low-power normal branches and high-power fault branches focus on exploring the effects of the mutual influence between branches with different powers on the characteristics of fault arcs. During the design process of each topology, the stability and reliability of the circuit operation are ensured, and at the same time, precise control of circuit parameters is achieved.

[0060] Moreover, it should be noted that real arc faults are simulated through arc generation methods. Mainly two methods, arc-pulling arc generation and carbonized wire arc generation, are used to simulate the fault situations in actual electrical systems. During the arc-pulling arc generation process, by precisely controlling the electrode material (such as selecting electrodes of different materials like copper and aluminum), the electrode spacing (which can be finely adjusted within the range of 0.1 - 10 mm), and the contact pressure (monitored and adjusted in real time using a high-precision pressure sensor), the generation process of fault arcs when the line is in poor contact is realistically simulated; for carbonized wire arc generation, insulated wires with different degrees of aging are selected, and local carbonization is achieved by methods such as heating or chemical corrosion to simulate the fault arcs caused by line insulation aging.

[0061] Step S106, in the second experimental scenario, by changing the circuit load, circuit topology, and arc generation method in the experimental circuit, a number of second normal operating conditions and second fault conditions are simulated.

[0062] It should be noted that to ensure that various load characteristics in the actual electrical system can be comprehensively simulated in the second experimental scenario, the selection of circuit loads covers typical loads such as resistors and halogen lamps required by the standard for testing, and also includes common loads widely used in real distribution systems, such as kettles, ovens, induction cookers, microwave ovens, vacuum cleaners, electric drills, switching power supplies, fluorescent lamps, and dimmable lamps. For resistive loads, by setting different resistance values, the electrical characteristics under different power consumption scenarios are precisely simulated; for dimmable lamps, the conduction angle can be flexibly changed (such as 30°, 60°, 90°, 120°, 180°) to simulate the electrical behavior under different brightness adjustment states, so as to comprehensively and accurately reproduce the operating conditions of various actual electrical devices in the circuit.

[0063] It should be further explained that the representative circuit topology structures are designed, including but not limited to single-load circuit, multi-load circuit, small-power fault branch in parallel with large-power normal branch, and small-power normal branch in parallel with large-power fault branch. The single-load circuit is mainly used to deeply study the characteristics of the fault arc under a single load condition; the multi-load circuit simulates the complex scenario of multiple electrical appliances running simultaneously in a home or small commercial place; the two topological structures of small-power fault branch in parallel with large-power normal branch and small-power normal branch in parallel with large-power fault branch focus on exploring the role of mutual influence between different power branches on the characteristics of the fault arc. In the design process of each topological structure, the stability and reliability of the circuit operation are ensured, and the precise control of the circuit parameters is achieved.

[0064] It should be further explained that the real arc fault is simulated by arcing. The two main methods of arcing and arcing with carbonized wire are used to simulate the fault conditions in the actual electrical system. In the arcing process, by precisely controlling the electrode material (such as electrodes made of different materials such as copper and aluminum), the electrode spacing (which can be finely adjusted within the range of 0.1-10mm) and the contact pressure (using high-precision pressure sensors for real-time monitoring and adjustment), the generation process of the fault arc when the line contact is poor is truly simulated; the arcing with carbonized wire is achieved by selecting insulated wires with different degrees of aging, and using heating or chemical corrosion to locally carbonize them, so as to simulate the fault arc caused by the aging of the line insulation.

[0065] Step S108, collecting circuit data generated under a first normal operating condition, a first fault operating condition, a second normal operating condition, and a second fault operating condition;

[0066] Step S108 specifically collects circuit data generated under the first normal working condition, the first fault working condition, the second normal working condition and the second fault working condition, wherein the circuit data includes current signals, arc voltage signals and zero-sequence current coupling signals. It should be noted that, in addition to the circuit data, in each working condition simulation experiment, it is also necessary to record in detail the environmental parameters such as ambient temperature and humidity during each experiment, because these environmental factors may have a subtle but non-negligible effect on the electrical characteristics, to ensure the integrity and traceability of the collected data; and strictly screen and preprocess the collected data to eliminate abnormal data caused by equipment failure or experimental operation errors, so as to construct a high-quality and representative data set.

[0067] Step S108 preferably includes the following steps:

[0068] Step S1081, in the offline analysis mode, a high-precision oscilloscope is selected for data acquisition. The sampling rate of the oscilloscope is set to 10 MHz, and the time window is set to 20 ms to ensure that the current, arc voltage, and zero-sequence current coupling signals can be synchronously and accurately acquired. Before sampling, the oscilloscope is strictly calibrated to ensure the accuracy of the measurement accuracy; during the acquisition process, the acquired data is real-time transmitted to the computer through a high-speed data transmission interface, and with the help of specially developed data storage software, the data is classified and stored according to the experimental conditions (including load type, circuit topology, arcing method, etc.) for subsequent in-depth analysis.

[0069] Step S1082, in the online detection mode, a high-performance microprocessor TMS320F28335 undertakes the data acquisition task. The microprocessor integrates a high-speed A / D conversion module internally, which can quickly and accurately convert analog signals into digital signals. By optimizing the data acquisition program, it is ensured that the current and zero-sequence current coupling signals can still be stably and reliably acquired in a complex electromagnetic environment.

[0070] Step S110, based on the circuit data, the machine learning model is trained to obtain a trained machine learning model, where the machine learning model is used to detect whether a fault arc occurs in the circuit.

[0071] Step S110 specifically includes the following steps:

[0072] Step S1101, extract the fault arc characteristic parameters of the fault arc from the circuit data;

[0073] Specifically, in Step S1101, the mother wavelet coif1 is used to decompose the current signal of the circuit data to obtain a preprocessed signal; based on two dimensions of single cycle and multi-cycle, the fault arc characteristic parameters are extracted from the preprocessed signal to obtain the single-cycle characteristic parameters and multi-cycle characteristic parameters of the fault arc.

[0074] It should be noted that in view of the fact that the waveform characteristics of the current signal are extremely vulnerable to interference from the circuit load and circuit topology, the mother wavelet coif1 is used in the present invention to decompose the current signal by one layer. During the decomposition process, by optimizing the wavelet decomposition parameters (such as reasonably selecting the decomposition layer number, accurately setting the threshold, etc.), it is ensured that the 50 Hz background signal and low-frequency noise signals can be effectively removed, and the characteristic information related to the fault arc can be retained to the greatest extent. The first-layer detail coefficients are used as the preprocessed signal for subsequent fault arc characteristic extraction, and the characteristic parameter extraction is carried out from two dimensions of single cycle and multi-cycle.

[0075] Further, it should be noted that the single-cycle characteristic parameters include D1 energy, D1 Shannon entropy, D1 range, pulse factor, interval time, pulse peak standard deviation, zero-sequence current standard deviation, and zero-sequence current Shannon entropy.

[0076] Taking the D1 energy as an example, the first-level detail coefficients d of the preprocessed signal j (j = 1, 2, …, N / 2) are traversed one by one, and the cumulative calculation is performed according to the formula to obtain the D1 energy value within this period, where N is the number of sampling points within one current period.

[0077] Taking the pulse factor as an example, first find the maximum value max(|p j |) of the zero-sequence current coupling signal p within one unit current period, and then calculate its ratio to the average absolute value j , that is, the pulse factor to accurately characterize the pulse characteristics of the current signal within one period. This accurately characterizes the pulse characteristics of the current signal within one period.

[0078] In addition, the D1 Shannon entropy The D1 range C r = max(d j ) - min(d j ), the interval time (f s is the sampling frequency), the standard deviation of pulse spikes (x1 represents the maximum value in the sequence p j (j = 1, 2, …, N), x2 represents the second maximum value in the sequence p j (j = 1, 2, …, N), and so on, m is the number of pulse spikes participating in the calculation), the standard deviation of zero-sequence current The zero-sequence current Shannon entropy The pulse characteristics of the current signal within one period are accurately characterized by the above single-period characteristic parameters.

[0079] Furthermore, it should be noted that the multi-period characteristic parameters include the standard deviation of D1 energy, the standard deviation of D1 Shannon entropy, the standard deviation of D1 range, the standard deviation of pulse factor, and the standard deviation of zero-sequence current Shannon entropy. Taking the standard deviation of D1 energy as an example, first calculate the D1 energy within multiple adjacent current periods q is the number of calculation periods, and then calculate the standard deviation according to the formula , where is the average value of D1 energy in multiple periods, and the calculation methods of other multi-period characteristic parameters are similar. Through the above multi-period characteristic parameters, the fluctuation of D1 energy in multiple periods can be effectively reflected, providing more comprehensive and accurate characteristic information for fault arc detection.

[0080] Step S1102: Perform feature reconstruction on the fault arc characteristic parameters to reduce the fuzziness of fault characteristics caused by circuit noise interference, and obtain the reconstructed fault arc characteristic parameters;

[0081] Specifically, in step S1102, the Teager energy operator is used to enhance the mutation characteristics in the zero-sequence current coupling signal of the circuit data, and the fault arc characteristic parameters after feature enhancement are obtained; by utilizing the periodic symmetry characteristic of the AC power supply voltage, the ratio of the fault arc characteristic parameters after feature enhancement within the front and rear 10 ms time windows is calculated to obtain the reconstructed fault arc characteristic parameters.

[0082] It should be noted that in view of the problem that the boundary between the normal and fault states becomes unclear after some fault characteristics are interfered by circuit noise, the present invention introduces the Teager energy operator to process the zero-sequence current coupling signal. For the discrete zero-sequence current coupling signal {x1, x2,..., x j , …, x N}, the energy value of each sampling point is calculated according to the formula . By performing point-by-point calculation on the zero-sequence current coupling signal, the mutation characteristics in the signal are effectively enhanced.

[0083] Furthermore, it should be noted that by utilizing the periodic symmetry characteristic of the low-voltage AC power supply voltage u s (t) (that is, the electrical signals in the first 10 ms and the last 10 ms are symmetric), the fault arc feature reconstruction is carried out (calculating the ratio of the fault arc characteristic parameters within the front and rear 10 ms time windows). In the normal state, the fault arc characteristic parameters extracted from the electrical signals in the front and rear parts are at the same order of magnitude, and their ratio fluctuates within a small range near 1; while when a series fault arc occurs, due to the unstable characteristics of the fault arc, the fault arc characteristic parameters extracted from the electrical signals in the front and rear parts show obvious differences, and their ratio fluctuates significantly near 1. Based on this characteristic, the fault arc characteristic parameters are reconstructed by the characteristic ratio (single-cycle characteristic parameters - pulse factor ratio, zero-sequence current Shannon entropy ratio, D1 energy ratio, D1 Shannon entropy ratio, D1 range ratio, etc., and corresponding multi-cycle characteristic parameters - D1 energy ratio standard deviation, D1 Shannon entropy ratio standard deviation, D1 range ratio standard deviation, pulse factor ratio standard deviation, zero-sequence current Shannon entropy ratio standard deviation). Through this characteristic ratio reconstruction, the distinguishability of the characteristics under different working conditions is significantly improved, laying a solid foundation for the subsequent machine learning model to accurately detect fault arcs. Figure 2 is a schematic flow diagram of the feature reconstruction according to the embodiment of the present application. As Figure 2 shown, it is necessary to first reconstruct the single-cycle characteristic parameters and then reconstruct the corresponding multi-cycle characteristic parameters;

[0084] Figure 3 is a schematic comparison diagram of the pulse factor ratio before and after reconstruction according to the embodiment of the present application, Figure 4 is a schematic comparison diagram of the zero-sequence current Shannon entropy ratio before and after reconstruction according to the embodiment of the present application, Figure 5Schematic diagram of the comparison of D1 energy ratio before and after reconstruction according to an embodiment of the present application. As Figures 3 to 5 shown, the dark dots are the features after reconstruction, and the light dots are the features before reconstruction. It can be seen that the distribution of the feature parameters after reconstruction has the advantage of being more concentrated, which can improve the discrimination from noise.

[0085] Step S1103: Train the machine learning model based on the circuit data and the reconstructed fault arc feature parameters to obtain a trained machine learning model.

[0086] Specifically, in step S1103, a machine learning model based on the decision tree algorithm is constructed. Among them, the machine learning model uses the information gain ratio as the measurement standard for feature selection. By calculating the information gain ratios of different features, the feature with the largest information gain ratio is selected for decision tree node splitting; the circuit data and the reconstructed fault arc feature parameters are input into the machine learning model to determine the network weights of the model, and a trained machine learning model is obtained.

[0087] It should be noted that a decision tree is used as the classifier of the machine learning model (the depth of the decision tree can be less than 10). When constructing the decision tree, the information gain ratio is used as the measurement standard for feature selection. By calculating the information gain ratios of different features, the feature with the largest information gain ratio is selected for node splitting to optimize the classification performance of the decision tree. Based on the reconstructed fault arc feature parameters and the training data, the relevant network weights of the classification rules and thresholds of the model are determined (for example, when the pulse factor ratio exceeds the set threshold T1 and the standard deviation of the D1 energy ratio is lower than the set threshold T2, the decision tree determines that a fault arc has occurred). The relevant network weights of the classification rules and thresholds need to be trained and optimized according to a large amount of experimental data to ensure the accuracy and reliability of the detection.

[0088] After step S110, the method includes step S111. If the machine learning model detects a fault arc in the circuit, the protection switch is triggered, and the circuit is cut off through the protection switch.

[0089] It should be noted that the detection result of the detection device is used as the trigger signal for the action of the fault arc protection switch. When a fault arc is detected, the protection switch should cut off the circuit within the set time; according to the fault arc features extracted in the detection method and the set detection algorithm, the action logic of the protection switch is optimized to improve the action accuracy and reliability of the protection switch, so that the protection switch can act in a timely and accurate manner according to the detection result, effectively cut off the faulty circuit, and ensure the safety of the electrical system.

[0090] Through the above steps in the embodiments of the present application, the following beneficial technical effects can be achieved:

[0091] 1. Comprehensive consideration of system factors: It can highly simulate various load types, complex circuit topologies, different arcing methods, and circuit noise in a real electrical system. By comprehensively considering these factors, the obtained data is more representative, providing a solid and reliable basis for in-depth research on the characteristics of fault arcs and accurate detection of fault arcs.

[0092] 2. Improvement of detection accuracy: Through innovative fault arc feature reconstruction technology, the distinguishability of features under different working conditions is significantly enhanced. This technology effectively reduces the false operation rate of the detection model under unknown working conditions, greatly improving the accuracy and reliability of fault arc detection, and providing a more effective technical means for preventing electrical fires.

[0093] 3. Engineering application value: It has a wide range of engineering application prospects and can be directly applied to low-voltage AC distribution systems. At the same time, it provides important technical references for the development of fault arc protection switches, which has important practical significance for ensuring the safe and stable operation of electrical systems.

[0094] It should be noted that the steps shown in the above process or the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. And although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order from that here.

[0095] The embodiment of the present application provides a series fault arc detection device based on multi-criterion fusion. This device is specifically designed for series fault arc detection and integrates a data acquisition unit, a signal processing unit, a feature extraction and analysis unit, and a decision-making unit. Each unit works together to achieve fast and accurate detection of fault arcs.

[0096] Data acquisition unit: High-precision current sensors and zero-sequence current sensors are used to ensure accurate acquisition of current and zero-sequence current coupling signals. These sensors have good electromagnetic compatibility and can work stably in a complex electromagnetic environment, effectively avoiding the influence of external interference on the acquired signals. The acquired analog signals are quickly converted into digital signals through a high-speed A / D conversion module, providing a high-quality data basis for subsequent signal processing and analysis.

[0097] Signal processing unit: Perform preprocessing operations such as filtering and amplification on the acquired digital signals. Digital filters, such as Butterworth filters, are used to design appropriate filtering parameters according to the frequency characteristics of the signals, effectively removing high-frequency noise and low-frequency interference signals, and improving the purity of the signals; the signals are gain-adjusted through an amplifier to ensure that the signal amplitude meets the requirements of subsequent processing, further improving the signal quality.

[0098] Feature extraction and analysis unit: Implement the above-mentioned fault arc feature extraction and reconstruction algorithms at the hardware or software level. For example, utilize the high-speed computing capabilities of field-programmable gate arrays (FPGAs) or digital signal processors (DSPs) to quickly calculate various feature parameters and perform feature reconstruction. In this way, provide accurate and reliable feature information for the decision-making unit to ensure that the detection device can identify fault arcs in a timely and accurate manner.

[0099] Decision-making unit: Input the feature parameters output by the feature extraction and analysis unit into the trained decision tree classifier, and determine whether a fault arc has occurred based on the output result of the classifier. If a fault arc is detected, the decision-making unit immediately triggers the alarm system and simultaneously controls the circuit cutting unit to act, quickly cutting off the faulty circuit to achieve real-time protection of the electrical system and effectively prevent the occurrence of electrical fires.

[0100] The embodiment of the present application provides a series fault arc detection system based on multi-criterion fusion, and the system includes a data acquisition module and a model training module;

[0101] Data acquisition module, used to build a first experimental scenario and a second experimental scenario, where the first experimental scenario is a circuit scenario without interference, and the second experimental scenario is a circuit scenario with interference;

[0102] Data acquisition module, used to simulate and obtain a number of first normal working conditions and first fault working conditions by changing the circuit load, circuit topology, and arc generation method in the experimental circuit in the first experimental scenario;

[0103] Data acquisition module, used to simulate and obtain a number of second normal working conditions and second fault working conditions by changing the circuit load, circuit topology, and arc generation method in the experimental circuit in the second experimental scenario;

[0104] Data acquisition module, used to collect circuit data generated under the first normal working condition, the first fault working condition, the second normal working condition, and the second fault working condition;

[0105] Model training module, used to train a machine learning model based on the circuit data to obtain a trained machine learning model, where the machine learning model is used to detect whether a fault arc occurs in the circuit.

[0106] It should be noted that the above-mentioned each module can be a functional module or a program module, and can be implemented either by software or by hardware. For the modules implemented by hardware, the above-mentioned each module can be located in the same processor; or the above-mentioned each module can also be located in different processors in any combination form.

[0107] This embodiment also provides an electronic device, including a memory and a processor. A computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any of the above method embodiments.

[0108] Optionally, the above electronic device may further include a transmission device and an input / output device. The transmission device is connected to the above processor, and the input / output device is connected to the above processor.

[0109] It should be noted that the specific examples in this embodiment may refer to the examples described in the above embodiments and optional implementation manners, and will not be elaborated herein.

[0110] In addition, in combination with the above-described series fault arc detection method based on multi-criterion fusion in the embodiments, an embodiment of the present application can provide a storage medium to implement. A computer program is stored on the storage medium; when the computer program is executed by a processor, it implements any one of the above-described series fault arc detection methods based on multi-criterion fusion.

[0111] In one embodiment, a computer device is provided. The computer device may be a terminal. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected through a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it implements a series fault arc detection method based on multi-criterion fusion. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the housing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0112] In one embodiment, Figure 6 is a schematic internal structure diagram of an electronic device according to an embodiment of the present application, as Figure 6 shown, an electronic device is provided. The electronic device may be a server, and its internal structure diagram may be as Figure 6As shown. The electronic device includes a processor, a network interface, an internal memory, and a non-volatile memory connected by an internal bus. Among them, the non-volatile memory stores an operating system, computer programs, and a database. The processor is used to provide computing and control capabilities. The network interface is used to communicate with external terminals through a network connection. The internal memory is used to provide an environment for the operation of the operating system and computer programs. The computer program, when executed by the processor, implements a method for detecting series fault arcs based on multi-criterion fusion. The database is used to store data.

[0113] Those skilled in the art can understand that Figure 6 the structure shown in is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the electronic device to which the solution of this application is applied. The specific electronic device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0114] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the various embodiments provided in this application can include non-volatile and / or volatile memories. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or an external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0115] Those skilled in the art should understand that the technical features of the above-described embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as within the scope described in this specification.

[0116] The above-described embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the appended claims.

Claims

1. A series fault arc detection method based on multi-criterion fusion, characterized in that, The method includes: Constructing a first experimental scenario and a second experimental scenario, where the first experimental scenario is a circuit scenario without interference, and the second experimental scenario is a circuit scenario with interference; In the first experimental scenario, by changing the circuit load, circuit topology, and arcing method in the experimental circuit, a number of first normal operating conditions and first fault conditions are simulated; In the second experimental scenario, by changing the circuit load, circuit topology, and arcing method in the experimental circuit, a number of second normal operating conditions and second fault conditions are simulated; Collecting circuit data generated under the first normal operating condition, the first fault condition, the second normal operating condition, and the second fault condition; Training a machine learning model based on the circuit data to obtain a trained machine learning model, where the machine learning model is used to detect whether a fault arc occurs in the circuit.

2. The method according to claim 1, characterized in that, Training a machine learning model based on the circuit data to obtain a trained machine learning model includes: Extracting fault arc characteristic parameters of the fault arc from the circuit data; Performing feature reconstruction on the fault arc characteristic parameters to reduce the fuzziness of fault features caused by circuit noise interference, and obtaining the reconstructed fault arc characteristic parameters; Training a machine learning model based on the circuit data and the reconstructed fault arc characteristic parameters to obtain a trained machine learning model.

3. The method according to claim 2, wherein Extracting fault arc characteristic parameters of the fault arc from the circuit data includes: Decomposing the current signal of the circuit data using the mother wavelet coif1 to obtain a preprocessed signal; Extracting fault arc characteristic parameters from the preprocessed signal based on two dimensions of single period and multi-period to obtain the single-period characteristic parameters and multi-period characteristic parameters of the fault arc.

4. The method according to claim 2, wherein Performing feature reconstruction on the fault arc characteristic parameters to reduce the fuzziness of fault features caused by circuit noise interference, and obtaining the reconstructed fault arc characteristic parameters includes: Using the Teager energy operator to enhance the mutation characteristics in the zero-sequence current coupling signal of the circuit data to obtain the fault arc characteristic parameters after feature enhancement; Utilizing the periodic symmetry characteristic of the AC power supply voltage to calculate the ratio of the fault arc characteristic parameters after feature enhancement within a 10 ms time window before and after to obtain the reconstructed fault arc characteristic parameters.

5. The method according to claim 2, wherein Training a machine learning model based on the circuit data and the reconstructed fault arc characteristic parameters to obtain a trained machine learning model includes: Constructing a machine learning model based on the decision tree algorithm, where the machine learning model uses the information gain ratio as a measure for feature selection, and by calculating the information gain ratio of different features, selects the feature with the maximum information gain ratio for decision tree node splitting; Inputting the circuit data and the reconstructed fault arc characteristic parameters into the machine learning model to determine the network weights of the model, and obtaining a trained machine learning model.

6. The method according to claim 1, wherein Collecting circuit data generated under the first normal operating condition, the first fault condition, the second normal operating condition, and the second fault condition includes: Collect the circuit data generated under the first normal condition, the first fault condition, the second normal condition, and the second fault condition, where the circuit data includes current signals, arc voltage signals, and zero-sequence current coupling signals.

7. The method according to claim 1, characterized in that Building the second experimental scenario includes: Setting up a bypass interference circuit to build the second experimental scenario on the basis of the first experimental scenario, where the bypass interference circuit uses an AC / AC converter and an AC / DC inverter as loads to simulate harmonic interference and voltage fluctuation interference.

8. The method according to claim 1, wherein The method includes: The circuit loads include resistors, halogen lamps, kettles, ovens, induction cookers, microwave ovens, vacuum cleaners, electric drills, switching power supplies, fluorescent lamps, and dimmable lamps; The circuit topologies include single-load circuits, multi-load circuits, parallel connections of small-power fault branches and large-power normal branches, and parallel connections of small-power normal branches and large-power fault branches; The arc generation methods include arc generation by arcing and arc generation by carbonized wires.

9. The method according to claim 1, wherein The method includes: If the machine learning model detects a fault arc in the circuit, trigger the protection switch to cut off the circuit through the protection switch.

10. A series fault arc detection system based on multi-criterion fusion, characterized in that, The system includes a data acquisition module and a model training module; The data acquisition module is used to build the first experimental scenario and the second experimental scenario, where the first experimental scenario is a circuit scenario without interference, and the second experimental scenario is a circuit scenario with interference; The data acquisition module is used to simulate and obtain a number of first normal conditions and first fault conditions in the first experimental scenario by changing the circuit loads, circuit topologies, and arc generation methods in the experimental circuit; The data acquisition module is used to simulate and obtain a number of second normal conditions and second fault conditions in the second experimental scenario by changing the circuit loads, circuit topologies, and arc generation methods in the experimental circuit; The data acquisition module is used to collect the circuit data generated under the first normal condition, the first fault condition, the second normal condition, and the second fault condition; The model training module is used to train the machine learning model according to the circuit data to obtain a trained machine learning model, where the machine learning model is used to detect whether a fault arc occurs in the circuit.

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