Waveform database-based arc fault detection device performance testing method and system

Abstract

The application discloses an arc fault detection device performance test method and system based on a waveform database, and belongs to the technical field of power fault detection device testing. The method comprises the following steps: establishing an arc waveform database and a data connection between the arc waveform database and a waveform generating device; establishing an electrical connection between the waveform generating device and a to-be-tested arc fault detection device; generating test waveform data based on the arc waveform database and according to user input and / or self-generation; receiving the test waveform data by the waveform generating device and generating a simulated arc signal based on the test waveform data; outputting the simulated arc signal to the to-be-tested arc fault detection device and obtaining a response result of the to-be-tested arc fault detection device, and generating performance test data. By establishing the arc waveform database and using the waveform generating device to automatically generate the simulated arc signal, the performance of the to-be-tested device can be tested in all directions, and a safe, efficient and low-cost standardized test means is provided for performance evaluation of the arc fault detection device.

Description

Technical Field

[0001] This application belongs to the field of power fault detection equipment testing technology, and relates to a performance testing method and system for an arc fault detection device based on a waveform database. Background Technology

[0002] An Arc Fault Detection Device (AFDD) is a protective electrical appliance that automatically cuts off power by identifying arc characteristics in a circuit. Its working principle and process can be summarized as follows: The AFDD collects current signals in the circuit in real time through a current sensor (such as a line transformer). After amplification and filtering, the waveform characteristics are analyzed by a microprocessor to distinguish between dangerous fault arcs and normal operating arcs. When a dangerous arc is confirmed, the device triggers an environmentally friendly electromagnetic circuit breaker or an integrated tripping mechanism to disconnect the circuit and prevent the arc from reaching a temperature that could cause a fire.

[0003] In AC and DC power systems, arc fault detection devices (AFDDs) are core components for preventing electrical fires and must comply with mandatory standards. The accuracy, safety, and compatibility of their performance testing and verification are crucial. However, existing AFDD detection technologies have many significant shortcomings, making them difficult to meet practical application requirements.

[0004] Traditional AFDD testing equipment employs a passive detection mode of "power supply + load + artificially induced fault," requiring the creation of a real fault arc. This not only poses high safety hazards such as fire and electric shock, but also makes it difficult to accurately control the fault intensity and development trend, resulting in extremely poor safety and controllability. Furthermore, its detection principle relies on specific power supply and load compatibility. Due to significant differences in AC and DC system power characteristics and load requirements, it typically only adapts to a single system. Covering dual scenarios requires purchasing two sets of dedicated equipment, which is costly and impractical. Simultaneously, the equipment needs to run a high-power load to simulate an arc, resulting in high energy consumption and significant losses, which does not align with the trend of green energy conservation. Moreover, the arc simulation effect depends on the load brand, parameters, and operating status, leading to highly random and inconsistent test results, failing to provide a stable reference for evaluating the performance of arc fault detection devices. In addition, the diverse loads in AC scenarios (motors, electronic equipment, etc.) lead to complex arc characteristics. In DC scenarios (such as 1500V photovoltaic systems), the high-frequency noise of weak arcs in high-voltage series and the millisecond-level response requirements of large current impacts in parallel further exacerbate the difficulty of detection. Most commercially available AFDD testing equipment simulates standard arcs through fault arc generators, which has low accuracy and cannot solve the above-mentioned core pain points.

[0005] In summary, there is an urgent need for a safe, controllable, AC / DC compatible, green, energy-saving, and experimentally consistent arc fault detection device performance testing and verification equipment to fill the existing technological gap. Summary of the Invention

[0006] To address the significant shortcomings of arc fault detection (AFDD) technologies in practical applications, which fail to meet real-world needs, this application aims to provide a performance testing method for AFDDs based on a waveform database. This method utilizes a waveform generator to actively generate arc signals, eliminating reliance on traditional power supplies and loads. It can output arc signals of different types, intensities, and frequencies, forming an arc database. A broadband constant current source is used for analog-to-digital conversion and amplification of the signals, outputting stable analog currents at different levels to the AFDD test sample, thereby accurately verifying the accuracy of the AFDD. To implement this performance testing method, a second objective of this application is to provide a performance testing system for AFDDs based on a waveform database, the specific scheme of which is as follows:

[0007] A performance testing method for an arc fault detection device based on a waveform database, comprising:

[0008] Establish an electric arc waveform database and its data connection with the waveform generator;

[0009] Establish an electrical connection between the waveform generator and the arc fault detection device under test;

[0010] Based on the aforementioned arc waveform database and according to user input and / or autonomously generated test waveform data;

[0011] The waveform generator receives the test waveform data and uses it to generate a simulated electric arc signal.

[0012] The simulated arc signal is output to the arc fault detection device under test and its response result is obtained to generate performance test data.

[0013] The establishment of the arc waveform database includes:

[0014] Obtain reference arc waveform data under various influencing factors and electrical parameter combinations, and store them as a reference dataset;

[0015] The influencing factors, electrical parameters and reference arc waveform data in the reference dataset are preprocessed, and then the correlation analysis algorithm is used to analyze the correlation between the reference arc waveform data and each influencing factor and / or electrical parameter to generate a waveform derivation model.

[0016] A predetermined number of influence factors, electrical parameters, and corresponding reference arc waveform data are selected from the reference dataset and stored as the arc waveform database; and / or

[0017] Input the set influence factor and / or electrical parameters, generate a derived arc waveform based on the waveform derivation model, and store it in the arc waveform database in association with the influence factor and / or electrical parameters;

[0018] The influencing factors include circuit layout and electromagnetic radiation intensity, temperature and humidity, and vibration data of the test environment;

[0019] The electrical parameters include the type and quantity of electrical appliances connected to the circuit within a set time period.

[0020] The above technical solution can directly obtain the corresponding test waveform data from the arc waveform database to generate a simulated arc signal, and complete the test of the arc fault detection device under test. It has strong controllability, does not require the operation of a high-power electrical load, is compatible with DC and AC detection, and has a wide range of applicable scenarios.

[0021] Furthermore, the performance testing method also includes:

[0022] Obtain basic circuit noise data corresponding to various circuit layouts and combinations of environmental electromagnetic radiation intensity, and store them as a basic circuit noise library.

[0023] Different circuit noise data are introduced during the test, and after being fused with the test waveform data, the data is output to the waveform generator.

[0024] The above technical solution can be used to expand the testing of the response of the arc fault detection device under test to the test waveform data in different electromagnetic radiation environments and circuit layouts, so as to make the measured performance data more accurate.

[0025] Furthermore, the performance testing method also includes:

[0026] Acquire the basic noise data of the arc fault detection device under test under various electromagnetic radiation intensities, temperature and humidity and vibration conditions, and store it as a basic noise database.

[0027] Different equipment basic noise data are introduced during the test, and after being fused with the test waveform data, the data is output to the waveform generator.

[0028] The above technical solution can incorporate the influence of the test environment on the arc fault detection device under test into the test waveform data, making the final test results more accurate and able to more comprehensively reflect the working performance of the arc fault detection device under test in different scenarios.

[0029] Furthermore, based on the aforementioned arc waveform database, test waveform data is generated autonomously, including:

[0030] The system obtains the applicable working scenario for the current arc fault detection device under test, acquires and stores common influencing factors and electrical parameter data in the above working scenario, automatically retrieves the corresponding influencing factor and electrical parameter data based on the user-input working scenario, and outputs the corresponding arc waveform data as test waveform data based on the arc waveform database; or

[0031] Obtain the detection range that the current arc fault detection device is compatible with, and select arc waveform data that is close to the detection range from the arc waveform database as test waveform data.

[0032] The above technical solution can quickly and accurately generate the test waveform data required for the current test, thereby improving test efficiency.

[0033] Furthermore, the response results of the arc fault detection device under test are obtained, and performance test data is generated, including:

[0034] Set and store the correlation between each response result of the arc fault detection device under test and each arc waveform characteristic;

[0035] Acquire the current test waveform data and confirm the arc waveform characteristics contained in the current test waveform data based on the waveform feature recognition algorithm to determine the expected response result;

[0036] The actual response results of the arc fault detection device under test are collected and compared with the expected response results. Based on the approximation between the two, the corresponding performance test score data is output.

[0037] The response result includes the response action type and the response time.

[0038] With the above technical solution, once the test waveform data is determined, the corresponding expected response result can be obtained. Based on the difference between the expected response result and the actual response result, the performance of the current arc fault detection device under test can be accurately evaluated, which is convenient and efficient.

[0039] Furthermore, the method of autonomously generating test waveform data based on the aforementioned arc waveform database also includes:

[0040] The performance test score data corresponding to each test waveform data is stored in association;

[0041] The correlation between the arc waveform characteristics contained in the statistical test waveform data and the performance test score data;

[0042] Based on the correlation between the above-mentioned arc waveform characteristics and performance test score data, arc waveform characteristics with performance test scores lower than the set value are selected as typical waveform characteristic data;

[0043] Test waveform data is generated by expanding upon the typical waveform feature data.

[0044] By using the above technical solutions, the test waveform can be focused on the part with lower performance test scores, highlighting the key points of the test and improving test efficiency.

[0045] A performance testing system for arc fault detection devices based on waveform databases, including...

[0046] The waveform data generation module includes an arc waveform database and a test waveform generation unit. The arc waveform database is configured to associate and store various influencing factors, electrical parameters and their corresponding arc waveform data. The test waveform generation unit is configured to generate test waveform data based on the arc waveform database and according to user input and / or autonomously.

[0047] A waveform generator is configured to be data-connected to the waveform data generation module and electrically connected to the arc fault detection device under test. It is configured to receive the test waveform data, generate a simulated arc signal, and output it to the arc fault detection device under test.

[0048] The performance test data generation module is configured to acquire the response results of the arc fault detection device under test and generate performance test data.

[0049] The waveform data generation module includes or is connected to a waveform database construction unit, which includes:

[0050] A reference dataset generation sub-unit is configured to obtain reference arc waveform data under various influencing factors and electrical parameter combinations, and is associated and stored as a reference dataset.

[0051] The derived model generation sub-unit is configured to preprocess the influencing factors, electrical parameters and reference arc waveform data in the reference dataset, and then use a correlation analysis algorithm to analyze the correlation between the reference arc waveform data and each influencing factor and / or electrical parameter to generate a waveform derived model.

[0052] The waveform data generation subunit is configured to select a predetermined number of influence factors, electrical parameters, and corresponding reference arc waveform data from the reference dataset and store them in the arc waveform database; and / or

[0053] Obtain the user-inputted set influence factors and / or electrical parameters, generate a derived arc waveform based on the waveform derivation model, and store it in the arc waveform database in association with the influence factors and / or electrical parameters;

[0054] The influencing factors include circuit layout and electromagnetic radiation intensity, temperature and humidity, and vibration data of the test environment;

[0055] The electrical parameters include the type and quantity of electrical appliances connected to the circuit within a set time period.

[0056] The above technical solution enables efficient, rapid, and accurate testing of the performance of the arc fault detection device under test, generating performance test data.

[0057] Furthermore, the performance testing system also includes a noise data fusion module, comprising:

[0058] The circuit basic noise generation unit is configured to acquire circuit basic noise data corresponding to various circuit layouts and combinations of environmental electromagnetic radiation intensity, and store them as a circuit basic noise library.

[0059] The equipment basic noise generation unit is configured to acquire the equipment basic noise data of the arc fault detection device under test under various electromagnetic radiation intensities, temperature and humidity and vibration conditions, and store it as an equipment basic noise library.

[0060] The noise data fusion unit is configured to introduce different circuit-based noise data during the test process based on user commands or user selection, fuse it with the test waveform data, and then output the fused data to the waveform generator.

[0061] Different equipment basic noise data are introduced during the test, and after being fused with the test waveform data, the data is output to the waveform generator.

[0062] The above technical solution can incorporate the influence of circuit layout and test environment on the arc fault detection device under test into the test waveform data, making the final test results more accurate.

[0063] Furthermore, the performance test data generation module includes:

[0064] The waveform feature-response result storage unit is configured to set and store the correlation between each response result of the arc fault detection device under test and each arc waveform feature;

[0065] The response result prediction unit is configured to acquire the current test waveform data and confirm the arc waveform characteristics contained in the current test waveform data based on the waveform feature recognition algorithm, and determine the expected response result.

[0066] The performance test score generation unit is configured to collect the actual response results of the arc fault detection device under test and compare them with the expected response results, and output the corresponding performance test score data based on the approximation between the two.

[0067] The response result includes the response action type and the response time.

[0068] Furthermore, the waveform generating device includes:

[0069] A digital-to-analog converter, configured to receive the test waveform generation data and convert it into an analog arc signal before outputting it;

[0070] A power amplifier configured to receive the analog arc signal and amplify and output it.

[0071] A filtering circuit is configured to filter noise interference in analog arc signals;

[0072] A voltage regulator circuit is configured to regulate and output the analog arc signal.

[0073] The control circuit is configured to receive test commands and adjust the output parameters of the simulated electric arc signal according to the commands.

[0074] The above technical solution can accurately convert test waveform data into simulated electric arc signals, ensuring the accuracy of test results.

[0075] This application includes at least one of the following beneficial effects:

[0076] Compared with the prior art, the beneficial effects of the present invention are:

[0077] (1) Safe and controllable: The test process does not require the creation of a real fault arc. The test is completed by actively generating an arc signal, which completely avoids the safety risks such as fire and electric shock caused by real faults. The test parameters and process can be precisely controlled, and the safety and controllability are significantly improved.

[0078] (2) Cost-free AC / DC compatible testing: Based on the active signal generation principle of the waveform generator, it gets rid of the dependence on traditional power supply and load, and does not require separate equipment for AC and DC scenarios, realizing true AC / DC compatible testing and greatly reducing testing costs;

[0079] (3) Green and energy-saving: No need to run high-power electrical loads, the energy consumption during the testing process is extremely low, the sustainability is strong, it is in line with the green and low-carbon technology development trend, and long-term use can significantly reduce energy consumption costs;

[0080] (4) Strong test consistency: By combining data acquisition and data input with waveform data derivation technology, high-fidelity multi-dimensional arc characteristics of various parameter electrical appliances when arc is generated are automatically generated, effectively avoiding the random differences in the generation of arcs by electrical appliances of different brands and parameters, ensuring that the test results are stable and consistent, and providing a reliable basis for the performance evaluation of AFDD test samples;

[0081] (5) Strong signal scalability: According to the test requirements, new arc waveform data can be generated based on impact factors and / or electrical parameters and incorporated into the arc waveform database, enriching the test scenarios. At the same time, it supports the analysis and identification of new data signals, enhancing the applicability and flexibility of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] Figure 1 is a schematic diagram of the performance test method of this application;

[0083] Figure 2 is a schematic structural diagram of the performance test system of this application.

[0084] Reference numerals: 100, waveform data generation module; 101, arc waveform database; 1011, waveform database construction unit; 1012, reference data set generation subunit; 1013, derivative model generation subunit; 1014, waveform data generation subunit; 102, test waveform generation unit; 200, waveform generating device; 201, digital-to-analog converter; 202, power amplifier; 203, filter circuit; 204, voltage stabilizing circuit; 205, control circuit; 300, performance test data generation module; 301, waveform feature-response result storage unit; 302, response result expectation unit; 303, performance test scoring generation unit. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0085] The following details the embodiments of this application, and the examples of the embodiments are shown in the drawings.

[0086] In the description of this specification, the description of reference terms "certain embodiments", "one embodiment", "some embodiments", "schematic embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the said embodiments or examples are included in at least one embodiment or example of this application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0087] A performance test method for an arc fault detection device based on a waveform database, as Figure 1 shown, mainly includes the following steps:

[0088] S100, establish a data connection between the arc waveform database 101 and the waveform generating device 200;

[0089] S200, establish an electrical connection between the waveform generating device 200 and the arc fault detection device to be tested;

[0090] S300, based on the arc waveform database 101 and according to user input and / or autonomously generate test waveform data;

[0091] S400, the test waveform data is received by the waveform generator 200 and a simulated electric arc signal is generated accordingly;

[0092] S500, the simulated arc signal is output to the arc fault detection device under test and its response result is obtained, generating performance test data.

[0093] In step S100 above, establishing the arc waveform database 101 includes:

[0094] S110, Obtain reference arc waveform data under various influencing factors and electrical parameter combinations, and store them as a reference dataset;

[0095] S120: The influencing factors, electrical parameters and reference arc waveform data in the reference dataset are preprocessed, and then the correlation analysis algorithm is used to analyze the correlation between the reference arc waveform data and each influencing factor and / or electrical parameter to generate a waveform derivation model.

[0096] S121, Select a predetermined number of influence factors, electrical parameters, and corresponding reference arc waveform data from the reference dataset and store them in the arc waveform database 101; and / or

[0097] S122, Input the set influence factor and / or electrical parameters, generate a derived arc waveform based on the waveform derivation model, and store it in the arc waveform database 101 in association with the influence factor and / or electrical parameters;

[0098] In step S110 above, the influencing factors include circuit layout and the electromagnetic radiation intensity, temperature, humidity, and vibration data of the test environment. Practice shows that the circuit layout, including the routing of the circuit and the placement of various electrical components, affects the circuit current. Furthermore, factors such as temperature, humidity, and electromagnetic radiation intensity in the test environment not only affect the circuit itself but also the arc fault detection device under test. For example, the permeability of the transformer core material changes with temperature, affecting the mutual inductance ratio and linearity.

[0099] The aforementioned electrical parameters include the type and quantity of electrical appliances connected to the circuit within a set time period.

[0100] In this embodiment, the arc waveform database 101 stores real-collected arc waveform data and derived arc waveform data, specifically including AC arc fault waveforms and DC arc fault waveforms. The AC arc fault waveforms cover sudden series arcs (3A-63A, resistive), closed series arcs (3A-63A, resistive), connected series arcs (3A-63A, resistive), current-limiting parallel arcs (75-500A, resistive), cutting parallel arcs (75-500A, resistive), grounding arcs (75-500A, resistive), and series arc waveforms corresponding to loads such as vacuum cleaners, switching power supplies, and air compressors. The DC arc fault waveforms include series arc waveforms.

[0101] In the actual use of the arc fault detection device, its detection accuracy is also affected by circuit noise and its own noise. In order to improve the accuracy of the arc fault detection device in recognizing arc waveforms, the performance testing method described in this application further includes the following in step S300:

[0102] S310 acquires the basic noise data of circuits under various circuit layouts and environmental electromagnetic radiation intensity combinations, and stores it as a basic noise library. In practice, a standard device (such as a resistive appliance) can be connected to the circuit, and then the basic noise data of each circuit can be obtained by measuring the circuit current data.

[0103] S311 introduces different circuit basic noise data during the test, and after fusing it with the test waveform data, outputs it to the waveform generator 200.

[0104] Furthermore, step S300 also includes:

[0105] S320: Obtain the basic noise data of the arc fault detection device under test under various electromagnetic radiation intensities, temperature and humidity and vibration conditions, and store it as a basic noise library.

[0106] S321 introduces different equipment basic noise data during the test, and outputs it to the waveform generator 200 after fusing it with the test waveform data.

[0107] The above scheme can incorporate the influence of the test environment on the arc fault detection device and circuit under test into the test waveform data, making the final test results more accurate and able to more comprehensively reflect the working performance of the arc fault detection device under test in different scenarios.

[0108] In step S300 of this embodiment, the user can manually input relevant parameters, such as working voltage, arc current, open-circuit voltage, arcing speed, decoupling network parameters, impedance network parameters, arc characteristic strength, and other specific arc characteristic parameters to generate test waveform data. Alternatively, the system can automatically generate test waveform data. Specifically, in step S300, generating test waveform data based on the arc waveform database 101 and automatically includes the following two methods:

[0109] S310: Obtain the applicable working scenario for the current arc fault detection device under test; acquire and associate common influencing factors and electrical parameter data in the above working scenario; automatically retrieve the corresponding influencing factors and electrical parameter data according to the working scenario input by the user; and search and output the corresponding arc waveform data as test waveform data based on the arc waveform database 101. Working scenarios include household power consumption scenarios, commercial lighting power consumption scenarios, etc.

[0110] S320: Obtain the detection range that the current arc fault detection device is adapted to, and select arc waveform data that is close to the detection range from the arc waveform database 101 as test waveform data.

[0111] The above technical solution can quickly and accurately generate the test waveform data required for the current test, thereby improving test efficiency.

[0112] In practical applications, arc fault detection devices from the same production batch often exhibit similar performance characteristics due to similar production time and conditions. Therefore, to improve testing efficiency and accuracy, in this embodiment, step S300, which involves generating test waveform data based on the arc waveform database 101, further includes:

[0113] S330, associated with the performance test score data corresponding to each test waveform data;

[0114] S331, the correlation between the arc waveform characteristics contained in the statistical test waveform data and the performance test score data;

[0115] S332, Based on the correlation between the above-mentioned arc waveform characteristics and performance test score data, select the arc waveform characteristics with a performance test score lower than the set value as typical waveform characteristic data;

[0116] S333, Test waveform data is generated by expanding the typical waveform feature data.

[0117] In step S331 above, the characteristics of the electric arc waveform include high-frequency oscillation, steep rising edges, and intermittent pulses. Methods for statistically analyzing the correlation between the electric arc waveform characteristics and performance test scores include correlation analysis, or it can be obtained through a combination of neural networks and attribution analysis.

[0118] The significance of steps S330-S333 is that, for AFDD samples in the same batch, the test waveform can be focused on the part with the lower performance test score, highlighting the key points of the test and improving the test efficiency.

[0119] Furthermore, in step S500, the response result of the arc fault detection device under test is obtained, and performance test data is generated, including:

[0120] S510 sets and stores the correlation between each response result of the arc fault detection device under test and each arc waveform characteristic. For example, when a steep rising edge is detected and the amplitude exceeds the set value, the circuit is turned off within a set time (the switching component in the AFDD test sample is disconnected).

[0121] S520: Acquire the current test waveform data and confirm the arc waveform characteristics contained in the current test waveform data based on the waveform feature recognition algorithm to determine the expected response result.

[0122] S530: Collect the actual response result of the arc fault detection device under test and compare it with the expected response result, and output the corresponding performance test score data based on the approximation between the two.

[0123] The response result includes the response action type and response time. The response action type includes, but is not limited to, shutting down the circuit and outputting alarm information.

[0124] Based on the above technical solution, once the test waveform data is determined, the corresponding expected response result can be obtained. Based on the difference between the expected response result and the actual response result, the performance of the current arc fault detection device under test can be accurately evaluated, which is convenient and efficient.

[0125] To implement the above performance testing method, this application also discloses a performance testing system for an arc fault detection device based on a waveform database, such as... Figure 2 As shown, it mainly includes a waveform data generation module 100, a waveform generator 200, and a performance test data generation module 300.

[0126] The waveform data generation module 100 includes an arc waveform database 101 and a test waveform generation unit 102. The arc waveform database 101 is configured to associate and store various influencing factors, electrical parameters, and their corresponding arc waveform data. The test waveform generation unit 102 is configured to generate test waveform data based on the arc waveform database 101 and according to user input and / or autonomously. As mentioned above, influencing factors include circuit layout and electromagnetic radiation intensity, temperature, humidity, and vibration data of the test environment. Electrical parameters include the type and quantity of electrical appliances connected to the circuit within a set time period. In practical applications, the arc waveform database 101 is configured in local storage or cloud storage, supporting fast local offline retrieval and remote data interaction with the cloud, adapting to multi-scenario testing needs.

[0127] In this embodiment of the application, the waveform data generation module 100 includes or is connected to a waveform database construction unit 1011, which specifically includes a reference dataset generation subunit 1012, a derived model generation subunit 1013, and a waveform data generation subunit 1014.

[0128] The reference dataset generation subunit 1012 is configured to acquire reference arc waveform data under various combinations of influencing factors and electrical parameters, and store it as a reference dataset. In practical applications, the reference dataset can be acquired by a cloud server, uploaded by users from various locations. The derivative model generation subunit 1013 is configured to preprocess the influencing factors, electrical parameters, and reference arc waveform data in the reference dataset, and then analyze the correlation between the reference arc waveform data and each influencing factor and / or electrical parameter using a correlation analysis algorithm to generate a waveform derivative model. In one embodiment, the waveform data generation subunit 1014 is configured to select a set number of influencing factors, electrical parameters, and corresponding reference arc waveform data from the reference dataset and store them as the arc waveform database 101. In another embodiment, the waveform data generation subunit 1014 is configured to acquire the set influencing factors and / or electrical parameters input by the user, generate a derivative arc waveform based on the waveform derivative model, and store it as a correlation with the influencing factors and / or electrical parameters in the arc waveform database 101.

[0129] The waveform generator 200 is configured to be data-connected to the waveform data generation module 100 and electrically connected to the arc fault detection device under test. It is configured to receive the test waveform data, generate a simulated arc signal, and output it to the arc fault detection device under test. Specifically, the waveform generator 200 includes a digital-to-analog converter 201, a power amplifier 202, a filter circuit 203, a voltage regulator circuit 204, and a control circuit 205.

[0130] The digital-to-analog converter 201 is configured to receive the test waveform data and convert it into an analog arc signal before outputting it. The power amplifier 202 is configured to receive the analog arc signal and amplify it before outputting it. The filter circuit 203 is used to filter noise interference in the waveform signal, ensuring signal purity and preventing noise from affecting test accuracy. The voltage regulator circuit 204 ensures the voltage stability of the output signal and prevents distortion of the analog arc signal due to voltage fluctuations. The control circuit 205 is responsible for receiving test commands and adjusting the signal output parameters according to the commands to achieve precise control of the test process. The above technical solution can accurately convert the test waveform data into an analog arc signal, ensuring the accuracy of the test results. When the waveform generator 200 receives the test waveform data output from the arc waveform database 101, it will sequentially complete the analog-to-digital conversion, signal amplification, filtering, and voltage regulation processes through its internal circuitry to accurately reproduce the analog arc fault signal in the database. Finally, it outputs a stable analog current to the AFDD test sample to complete the verification of the AFDD protection characteristics.

[0131] The performance test data generation module 300 is configured to acquire the response results of the arc fault detection device under test and generate performance test data.

[0132] In this embodiment of the application, the performance test data generation module 300 includes a waveform feature-response result storage unit 301, a response result prediction unit 302, and a performance test score generation unit 303.

[0133] The waveform feature-response result storage unit 301 is configured to set and store the correlation between each response result of the arc fault detection device under test and each arc waveform feature; the response result prediction unit 302 is configured to acquire the current test waveform data and, based on waveform feature recognition algorithms, such as time domain analysis, frequency domain analysis, or a trained neural network model, confirm the arc waveform features contained in the current test waveform data and determine the expected response result; the performance test score generation unit 303 is configured to collect the actual response result of the arc fault detection device under test and compare it with the expected response result, and output the corresponding performance test score data according to the approximation between the two. The response result includes the response action type and response time. For example, if the expected response time of the shutdown action is 10ms, then a score is assigned based on the difference between the actual response time and the expected response time.

[0134] In this embodiment of the application, the performance testing system further includes a noise data fusion module, specifically including: a circuit basic noise generation unit, a device basic noise generation unit, and a noise data fusion unit.

[0135] The circuit fundamental noise generation unit is configured to acquire circuit fundamental noise data corresponding to various circuit layouts and combinations of environmental electromagnetic radiation intensity, and store it as a circuit fundamental noise library. The equipment fundamental noise generation unit is configured to acquire equipment fundamental noise data corresponding to the arc fault detection device under test under various electromagnetic radiation intensities, temperature, humidity, and vibration conditions, and store it as an equipment fundamental noise library. The noise data fusion unit is configured to, based on user instructions or user selection, introduce different circuit fundamental noise data during the test, fuse it with the test waveform data, and output it to the waveform generator 200; and / or introduce different equipment fundamental noise data during the test, fuse it with the test waveform data, and output it to the waveform generator 200.

[0136] The above technical solution can incorporate the influence of circuit layout and test environment on the arc fault detection device under test into the test waveform data, making the final test results more accurate.

[0137] The specific workflow for this application is as follows:

[0138] (1) Data retrieval / generation: According to the test requirements, local test waveform data can be retrieved directly from the local storage unit of the arc waveform database 101, or remote test waveform data in the cloud server can be obtained through the cloud communication module; if the existing waveform cannot meet the test requirements, specific arc characteristic parameters can be input to generate new arc signals and store them.

[0139] (2) Signal transmission: The arc waveform database 101 transmits the selected or newly generated waveform data to the waveform generator 200.

[0140] (3) Signal processing: The waveform generator 200 processes the received waveform data in all aspects through internal filtering, voltage stabilization, amplification and other circuits and modules to accurately reproduce the simulated electric arc fault signal.

[0141] (4) Test and verification: The waveform generator 200 outputs the processed stable analog current to the test sample. By monitoring the response of the test sample, the protection characteristics are verified.

[0142] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A performance testing method for an arc fault detection device based on a waveform database, characterized in that, include: Establish an arc waveform database (101) and its data connection with the waveform generator (200); Establish an electrical connection between the waveform generator (200) and the arc fault detection device under test; Based on the aforementioned arc waveform database (101) and according to user input and / or autonomously generated test waveform data; The waveform generator (200) receives the test waveform data and uses it to generate a simulated electric arc signal; The simulated arc signal is output to the arc fault detection device under test and its response result is obtained to generate performance test data. The establishment of the arc waveform database (101) includes: Obtain reference arc waveform data under various influencing factors and electrical parameter combinations, and store them as a reference dataset; The influencing factors, electrical parameters and reference arc waveform data in the reference dataset are preprocessed, and then the correlation analysis algorithm is used to analyze the correlation between the reference arc waveform data and each influencing factor and / or electrical parameter to generate a waveform derivation model. A predetermined number of influence factors, electrical parameters, and corresponding reference arc waveform data are selected from the reference dataset and stored as the arc waveform database (101); and / or Input the set influence factor and / or electrical parameters, generate the derived arc waveform based on the waveform derivation model, and store it in the arc waveform database (101) in association with the influence factor and / or electrical parameters; The influencing factors include circuit layout and electromagnetic radiation intensity, temperature and humidity, and vibration data of the test environment; The electrical parameters include the type and quantity of electrical appliances connected to the circuit within a set time period.

2. The performance testing method for the arc fault detection device based on a waveform database according to claim 1, characterized in that, The performance testing method also includes: Obtain basic circuit noise data corresponding to various circuit layouts and combinations of environmental electromagnetic radiation intensity, and store them as a basic circuit noise library. Different circuit noise data are introduced during the test, and after being fused with the test waveform data, the data is output to the waveform generator (200).

3. The performance testing method for the arc fault detection device based on a waveform database according to claim 1, characterized in that, The performance testing method also includes: Acquire the basic noise data of the arc fault detection device under test under various electromagnetic radiation intensities, temperature and humidity and vibration conditions, and store it as a basic noise database. Different equipment basic noise data are introduced during the test, and after being fused with the test waveform data, the data is output to the waveform generator (200).

4. The performance testing method for the arc fault detection device based on a waveform database according to claim 2 or 3, characterized in that, Based on the aforementioned arc waveform database (101), test waveform data is generated autonomously, including: Obtain the applicable working scenario of the current arc fault detection device under test, acquire and associate the common influencing factors and electrical parameter data in the above working scenario, automatically retrieve the corresponding influencing factors and electrical parameter data according to the working scenario input by the user, and search and output the corresponding arc waveform data as test waveform data based on the arc waveform database (101); or Obtain the detection range that the current arc fault detection device is adapted to, and select arc waveform data that is close to the detection range from the arc waveform database (101) as test waveform data.

5. The performance testing method for the arc fault detection device based on a waveform database according to claim 1, characterized in that, Obtain the response results of the arc fault detection device under test and generate performance test data, including: Set and store the correlation between each response result of the arc fault detection device under test and each arc waveform characteristic; Acquire the current test waveform data and confirm the arc waveform characteristics contained in the current test waveform data based on the waveform feature recognition algorithm to determine the expected response result; The actual response results of the arc fault detection device under test are collected and compared with the expected response results. Based on the approximation between the two, the corresponding performance test score data is output. The response result includes the response action type and the response time.

6. The performance testing method for the arc fault detection device based on a waveform database according to claim 4, characterized in that, Based on the aforementioned arc waveform database (101) and autonomously generating test waveform data, it also includes: The performance test score data corresponding to each test waveform data is associated and stored. The correlation between the arc waveform characteristics contained in the statistical test waveform data and the performance test score data; Based on the correlation between the above-mentioned arc waveform characteristics and performance test score data, arc waveform characteristics with performance test scores lower than the set value are selected as typical waveform characteristic data; Test waveform data is generated by expanding upon the typical waveform feature data.

7. A performance testing system for an arc fault detection device based on a waveform database, characterized in that, include The waveform data generation module (100) includes an arc waveform database (101) and a test waveform generation unit (102). The arc waveform database (101) is configured to associate and store various influencing factors, electrical parameters and their corresponding arc waveform data. The test waveform generation unit (102) is configured to generate test waveform data based on the arc waveform database (101) and according to user input and / or autonomously. The waveform generator (200) is configured to be data-connected to the waveform data generation module (100) and electrically connected to the arc fault detection device under test. It is configured to receive the test waveform data, generate a simulated arc signal and output it to the arc fault detection device under test. The performance test data generation module (300) is configured to obtain the response results of the arc fault detection device under test and generate performance test data. The waveform data generation module (100) includes or is connected to a waveform database construction unit (1011), the waveform database construction unit (1011) including: Reference dataset generation sub-unit (1012) is configured to obtain reference arc waveform data under various influencing factors and electrical parameter combinations, and is associated and stored as a reference dataset; The derived model generation subunit (1013) is configured to preprocess the influence factors, electrical parameters and reference arc waveform data in the reference dataset, and then use the correlation analysis algorithm to analyze the correlation between the reference arc waveform data and each influence factor and / or electrical parameter to generate the waveform derived model. The waveform data generation subunit (1014) is configured to select a set number of influence factors, electrical parameters, and corresponding reference arc waveform data from the reference dataset and store them in the arc waveform database (101); and / or Obtain the user-inputted set influence factors and / or electrical parameters, generate a derived arc waveform based on the waveform derivation model, and store it in the arc waveform database in association with the influence factors and / or electrical parameters (101); The influencing factors include circuit layout and electromagnetic radiation intensity, temperature and humidity, and vibration data of the test environment; The electrical parameters include the type and quantity of electrical appliances connected to the circuit within a set time period.

8. The performance testing system for an arc fault detection device based on a waveform database according to claim 7, characterized in that, The performance testing system also includes a noise data fusion module, comprising: The circuit basic noise generation unit is configured to acquire circuit basic noise data corresponding to various circuit layouts and combinations of environmental electromagnetic radiation intensity, and store them as a circuit basic noise library. The equipment basic noise generation unit is configured to acquire the equipment basic noise data of the arc fault detection device under test under various electromagnetic radiation intensities, temperature and humidity and vibration conditions, and store it as an equipment basic noise library. The noise data fusion unit is configured to introduce different circuit-based noise data during the test process based on user instructions or user selection, fuse it with the test waveform data, and output it to the waveform generator (200), and / or Different equipment basic noise data are introduced during the test, and after being fused with the test waveform data, the data is output to the waveform generator (200).

9. The performance testing system for an arc fault detection device based on a waveform database according to claim 7, characterized in that, The performance test data generation module (300) includes: The waveform feature-response result storage unit (301) is configured to set and store the correlation between each response result of the arc fault detection device under test and each arc waveform feature; The response result prediction unit (302) is configured to acquire the current test waveform data and confirm the arc waveform features contained in the current test waveform data based on the waveform feature recognition algorithm, and determine the expected response result; The performance test score generation unit (303) is configured to collect the actual response results of the arc fault detection device under test and compare them with the expected response results, and output the corresponding performance test score data according to the approximation between the two. The response result includes the response action type and the response time.

10. The performance testing system for an arc fault detection device based on a waveform database according to claim 7, characterized in that, The waveform generating device (200) includes: A digital-to-analog converter (201) is configured to receive the test waveform generation data and convert it into an analog arc signal before outputting it. A power amplifier (202) is configured to receive the analog arc signal and amplify it before outputting it. The filter circuit (203) is configured to filter noise interference in the analog arc signal; A voltage regulator circuit (204) is configured to regulate and output the analog arc signal; The control circuit (205) is configured to receive test commands and adjust the output parameters of the simulated electric arc signal according to the commands.

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