A direct current arc detection method, system, medium, and device

Abstract

This invention discloses a method, system, medium, and device for detecting DC arc faults. The method includes: acquiring line current signals, performing feature processing to obtain feature values, and saving the average value of the features within a preset time period as initial feature values; filtering effective frequency bands whose noise floor values ​​meet set conditions based on the noise floor values ​​of each feature frequency band using a frequency band selection method; calculating arc energy values ​​to obtain dynamic arc energy and static arc energy; and detecting DC arc faults: when both dynamic arc energy and static arc energy exceed their respective thresholds, and the number of DC arc occurrences within a preset time period reaches a preset number, a DC fault arc is detected; otherwise, it is detected as normal. This invention is applied to DC fault arc circuit breakers, which can significantly improve the accuracy and reliability of DC arc detection and reduce the risk of false positives and false negatives in DC arc detection.

Description

Technical Field

[0001] This invention relates to a DC arc detection method, system, medium, and equipment, belonging to the field of photovoltaic DC arc detection technology. Background Technology

[0002] Photovoltaic power generation, as a new energy source, is particularly important in the global context of advocating green and sustainable energy. However, electrical fires caused by DC arc faults in photovoltaic systems are seriously affecting the safe operation of equipment and endangering people's lives. Because DC fault arcs do not exhibit the characteristic of extinction at zero crossing, their detection presents a significant challenge.

[0003] Traditional current protection devices and circuit breakers are completely incapable of detecting DC arcs. In recent years, DC arc detection has been widely developed. Methods exist for studying the electromagnetic characteristics of arc light, temperature, and sound, but these are often limited by cost and application scenarios. Other methods extract time-frequency domain features from the arc current for modeling, but these methods lack sufficient immunity to real-time lighting changes and interference from the surrounding power environment. They cannot select usable frequency bands from all characteristic frequency bands, nor can they model from both static and dynamic data dimensions, thus leading to misjudgments. Therefore, effectively selecting usable frequency bands and modeling from both static and dynamic perspectives is essential.

[0004] When equipment malfunctions and causes the inverter to shut down, it severely impacts photovoltaic power generation and increases maintenance costs. Therefore, there is an urgent need for a method suitable for photovoltaic power generation scenarios that can accurately detect DC fault arcs without misjudging the problem. Summary of the Invention

[0005] The purpose of this invention is to provide a method, system, medium, and device for detecting DC arcs. By selecting the frequency band of the processed current signal, the interference-affected Fast Fourier Transform frequency band is deleted, and the effective frequency band with stable noise floor is retained. Based on this, a joint judgment strategy for dynamic and static arc energy is proposed. When a DC arc simultaneously meets the conditions that both the dynamic and static arc energies exceed their respective thresholds and the number of occurrences within a preset time reaches a preset number, it is detected as a DC fault arc. The method of this invention significantly improves the accuracy of detection and anti-interference capability.

[0006] To achieve the above objectives, the present invention is implemented using the following technical solution.

[0007] In a first aspect, the present invention provides a method for detecting a DC electric arc, comprising:

[0008] The line current signal is collected, its characteristics are processed to obtain characteristic values, and the average value of the characteristics within a preset time period is saved as the initial characteristic value.

[0009] Based on the noise floor values ​​of each characteristic frequency band, the effective frequency bands that meet the set conditions are obtained by filtering through the frequency band selection method.

[0010] Based on the effective frequency band, the arc energy value is calculated to obtain the dynamic arc energy and the static arc energy;

[0011] Based on the dynamic and static arc energy, fault detection and judgment are performed on the DC arc:

[0012] When both dynamic arc energy and static arc energy exceed their respective thresholds, and the number of DC arc occurrences reaches a preset number within a preset time, it is detected as a DC fault arc.

[0013] Otherwise, the test is normal.

[0014] Furthermore, after the inverter starts up, the DC arc detection equipment starts working and performs feature processing on the features. The process involves using a current transformer to collect the current signal in the line and performing a Fast Fourier Transform (FFT) on the collected current signal to extract the corresponding spectral features.

[0015] Set a preset time as the time for updating the FFT feature benchmark. Then, calculate the average value of each FFT frequency band within the preset time and save it as the initial value of the benchmark FFT data.

[0016] Furthermore, different inverters have different noise floor values, which means that the FFT frequency bands affected by external interference in actual operation are different. In some cases, the amplitude of the FFT frequency bands is similar during normal operation and when a DC arc fault occurs. The FFT frequency bands that are severely interfered with will affect the judgment of DC arc faults.

[0017] Based on the normal FFT frequency band noise floor value, a frequency band selection method is used to filter and eliminate interference caused by differences in the noise floor characteristics of different inverters. The process is as follows: a detection threshold is set for each frequency band. Based on this, frequency bands in the inverter whose noise floor value exceeds the detection threshold are deleted, thereby obtaining effective frequency bands whose noise floor value meets the set conditions.

[0018] To eliminate the impact of differences in noise floor between different inverters, this invention employs a frequency band selection method. By statistically analyzing the noise floor distribution of various inverters, it was found that the amplitude of some FFT frequency bands remained at a relatively low normal level, while the amplitude of other FFT frequency bands was significantly higher than the normal value. Therefore, frequency bands with abnormally high amplitudes were identified as interference frequency bands and deleted, thereby effectively eliminating the interference of noise floor differences on arc detection.

[0019] Furthermore, the calculation of arc energy value is based on both dimensionless and dimensional indices to effectively distinguish between normal operating conditions and DC fault arc conditions.

[0020] Furthermore, the dimensionless index is obtained by calculating the ratio of the real-time FFT value to the reference FFT value. The reference FFT value is in a dynamic update state. The update method is as follows: within a preset time, it is determined whether the ratio of the average value of the FFT of each frequency band to the original reference FFT value is less than a preset multiple. When the condition is met, the average value of the FFT of each frequency band within the preset time is used to replace the original reference FFT value to complete the dynamic update of the reference value.

[0021] After obtaining the ratio of each FFT frequency band, the sum of the total ratios of all FFT frequency bands is calculated to characterize the arc energy, and the calculation result is defined as the dynamic arc energy.

[0022] Furthermore, the dimensional index is obtained by calculating the sum of all original FFT feature values. The sum is the absolute value of the real-time arc energy, which is used to directly characterize the absolute energy magnitude of the arc and is defined as the static arc energy.

[0023] The present invention innovatively proposes the concept of static and dynamic arc energy and their respective calculation methods, starting from two perspectives: dimensionless index and dimensional index. Static arc energy is used to characterize the absolute energy of the arc, while dynamic arc energy is used to characterize the change characteristics of the arc relative to a real-time reference, thereby comprehensively and accurately depicting the complex characteristics of DC arcs.

[0024] Furthermore, the specific process for detecting DC arcs is as follows:

[0025] Thresholds for dynamic arc energy and static arc energy are set separately. If both exceed the corresponding thresholds, it is judged as a suspected arc; if one of them does not meet the condition, it is judged as normal and the detection continues.

[0026] Within a preset time, suspected arcs that have already been identified are reconfirmed. If the number of suspected arcs reaches the preset number, they are finally detected as DC fault arcs, and a fault indication is issued. If the preset number is not reached, the current cached data is cleared, and subsequent judgments are performed.

[0027] In a second aspect, the present invention provides a DC arc detection system, comprising:

[0028] The feature processing module is used to acquire line current signals, perform feature processing, obtain feature values, and save the average value of the features within a preset time period as the initial feature value.

[0029] The frequency band selection module is used to filter effective frequency bands that meet the set conditions based on the noise floor values ​​of each characteristic frequency band using a frequency band selection method.

[0030] The arc energy calculation module is used to calculate the arc energy value based on the effective frequency band to obtain the dynamic arc energy and the static arc energy.

[0031] A DC arc detection module is used to detect and judge DC arcs based on the dynamic arc energy and static arc energy.

[0032] When both dynamic arc energy and static arc energy exceed their respective thresholds, and the number of DC arc occurrences reaches a preset number within a preset time, it is detected as a DC fault arc.

[0033] Otherwise, the test is normal.

[0034] Thirdly, the present invention provides a computer-readable storage medium having a computer program / instructions stored thereon, which, when executed by a processor, implements the steps of the DC arc detection method described in any of the first aspects.

[0035] Fourthly, the present invention provides a computer device, comprising:

[0036] Memory, used to store computer programs / instructions;

[0037] A processor for executing the computer program / instructions to implement the steps of the DC arc detection method described in any one of the first aspects.

[0038] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0039] 1. The DC arc detection method provided by this invention collects line current signals, performs feature processing to obtain feature values, and saves the average value of the features within a preset time as the initial feature value. Based on the noise floor value of each feature frequency band, a detection threshold is set for each frequency band, and the frequency band selection method proposed in this invention is used to filter and delete the FFT frequency bands that are interfered with in different inverters, obtaining effective frequency bands whose noise floor values ​​meet the set conditions, thereby eliminating the influence of differences in noise floor values ​​between different inverters on arc discrimination. Based on this, the arc energy value is calculated to obtain dynamic arc energy and static arc energy. DC arc fault detection and judgment are performed based on the dynamic arc energy and static arc energy: when both dynamic arc energy and static arc energy exceed their respective thresholds, and the number of suspected arc occurrences reaches a preset number within a preset time, it is detected as a DC fault arc; otherwise, it is detected as normal. This invention proposes a joint detection and judgment method for dynamic and static arc energy, ensuring that the DC arc that simultaneously meets both conditions is the final arc, significantly improving the accuracy and reliability of DC arc detection.

[0040] 2. The computer-readable storage medium and computer device provided by the present invention can execute the steps of the DC arc detection method provided by the present invention. Attached Figure Description

[0041] Figure 1 This is an overall flowchart of the DC arc detection method provided according to an embodiment of the present invention. Detailed Implementation

[0042] It should be noted that:

[0043] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations thereof. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0044] The term "and / or" simply describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. Additionally, the character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0045] Example 1

[0046] like Figure 1 As shown in the figure, this embodiment introduces a DC arc detection method, including:

[0047] The line current signal is collected, its characteristics are processed to obtain characteristic values, and the average value of the characteristics within a preset time period is saved as the initial characteristic value.

[0048] Based on the noise floor values ​​of each characteristic frequency band, the effective frequency bands that meet the set conditions are obtained by filtering through the frequency band selection method.

[0049] Based on the effective frequency band, the arc energy value is calculated to obtain the dynamic arc energy and the static arc energy;

[0050] The DC arc is detected and judged based on the dynamic arc energy and static arc energy.

[0051] When both dynamic arc energy and static arc energy exceed their respective thresholds, and the number of DC arc occurrences reaches a preset number within a preset time, it is detected as a DC fault arc.

[0052] Otherwise, the test is normal.

[0053] Furthermore, after the inverter starts up, the DC arc detection equipment starts working and performs feature processing on the features. The process involves using a current transformer to collect the current signal in the line and performing an M-point fast Fourier transform with an N-millisecond window on the current signal to extract the corresponding spectral features.

[0054] Set a preset time as the time for updating the FFT feature benchmark. Then, calculate the average value of each FFT frequency band within the preset time and save it as the initial value of the benchmark FFT data.

[0055] In this embodiment, N is set to 2 and M to 200, corresponding to a 2ms sliding window for performing a 200-point Fourier transform on the current signal; the preset time is set to 100ms, and a total of 200 FFT frequency bands are obtained under this window. Each frequency band has 50 transform values ​​within the preset time. The 50 values ​​of each frequency band within 100ms are summed and the average value is calculated to obtain 200 FFT average values, which are then saved as the initialization values ​​of the reference FFT data.

[0056] Furthermore, different inverters have different noise floor values, which means that the FFT frequency bands affected by external interference in actual operation are different. In some cases, the amplitude of the FFT frequency bands is similar during normal operation and when a DC arc fault occurs. The FFT frequency bands that are severely interfered with will affect the judgment of DC arc.

[0057] Based on the normal FFT frequency band noise floor value, a frequency band selection method is used to filter and eliminate interference caused by differences in the noise floor characteristics of different inverters. The process is as follows: a detection threshold is set for each frequency band. Based on this, frequency bands in the inverter whose noise floor value exceeds the detection threshold are deleted, thereby obtaining effective frequency bands whose noise floor value meets the set conditions.

[0058] In this embodiment, inverter A, inverter B, and 200 FFT feature frequency bands are set, wherein the preset detection threshold for each of the 200 FFT feature frequency bands is set to 1000.

[0059] If the amplitude of inverter A in the 1st, 2nd and 5th FFT frequency bands exceeds the detection threshold of 1000, then the 1st, 2nd and 5th frequency bands will be deleted, and DC arc fault judgment will be performed on the remaining 197 frequency bands.

[0060] If the amplitude of inverter B in the 10th, 30th, 60th, 150th and 200th FFT frequency bands exceeds the detection threshold of 1000, then the 10th, 30th, 60th, 150th and 200th frequency bands will be deleted, and DC arc fault judgment will be performed on the remaining 195 frequency bands.

[0061] This embodiment uses a frequency band selection method to delete different frequency bands that are interfered with by different inverters, thereby eliminating the influence of different noise floor values ​​of different inverters.

[0062] Furthermore, the calculation of arc energy value is based on both dimensionless and dimensional indices to effectively distinguish between normal operating conditions and DC fault arc conditions.

[0063] Furthermore, the dimensionless index is obtained by calculating the ratio of the real-time FFT value to the reference FFT value. The reference FFT value is in a dynamic update state. The update method is as follows: within a preset time, it is determined whether the ratio of the average value of the FFT of each frequency band to the original reference FFT value is less than a preset multiple. When the condition is met, the average value of the FFT of each frequency band within the preset time is used to replace the original reference FFT value, thereby completing the dynamic update of the reference value.

[0064] After obtaining the ratio of each FFT frequency band, the sum of the total ratios of all FFT frequency bands is calculated to characterize the arc energy, and the calculation result is defined as the dynamic arc energy.

[0065] Furthermore, the dimensional index is obtained by calculating the sum of all original FFT feature values. The sum is the absolute value of the real-time arc energy, which is used to directly characterize the absolute energy magnitude of the arc and is defined as the static arc energy.

[0066] In this embodiment, the preset multiple is set to 1.5. Within a preset time, when the ratio of the mean value of each frequency band FFT to the original reference FFT value is less than 1.5, the reference value is dynamically updated.

[0067] The original reference FFT frequency bands are set to 200. After filtering by the frequency band selection method, the number of valid FFT frequency bands is 190. After obtaining the ratio of each FFT frequency band, the sum of the total ratios of all FFT frequency bands is calculated to characterize the arc energy. If the ratio of each FFT frequency band is 2, the dynamic arc energy is set to 380.

[0068] In the 190 FFT bands, the sum of all original FFT feature values ​​is calculated. If the absolute value of each FFT band is 100, the static arc energy is set to 19000.

[0069] Furthermore, the specific process for detecting DC arcs is as follows:

[0070] Thresholds for dynamic arc energy and static arc energy are set separately. If both exceed the corresponding thresholds, it is judged as a suspected arc; if one of them does not meet the condition, it is judged as normal and the detection continues.

[0071] Within a preset time, suspected arcs that have already been identified are reconfirmed. If the number of suspected arcs reaches the preset number, they are finally detected as DC fault arcs, and a fault indication is issued. If the preset number is not reached, the current cached data is cleared, and subsequent judgments are performed.

[0072] In this embodiment, the threshold for dynamic arc energy is set to 400, and the threshold for static arc energy is set to 40000. If the dynamic arc energy is 500 and the static arc energy is 50000, both of which exceed the corresponding thresholds, then the detection result is a suspected arc.

[0073] The preset time is set to 200ms and the preset number of times is 5. If the number of times the electrostatic arc energy detection is suspected to be an arc is 6, which exceeds the preset number of 5, then the final detection is a DC fault arc, and a fault indication is issued; otherwise, if the number of times the suspected arc is detected is less than the preset number of 5, then the detection is normal, and the number of times the suspected arc is detected within the preset time of 200ms is also cleared.

[0074] Example 2

[0075] Based on the DC arc detection method described in Embodiment 1, this embodiment introduces a DC arc detection system, including:

[0076] The feature processing module is used to acquire line current signals, perform feature processing, obtain feature values, and save the average value of the features within a preset time period as the initial feature value.

[0077] The frequency band selection module is used to filter effective frequency bands that meet the set conditions based on the noise floor values ​​of each characteristic frequency band using a frequency band selection method.

[0078] The arc energy calculation module is used to calculate the arc energy value based on the effective frequency band to obtain the dynamic arc energy and the static arc energy.

[0079] A DC arc detection module is used to detect and judge DC arcs based on the dynamic arc energy and static arc energy.

[0080] When both dynamic arc energy and static arc energy exceed their respective thresholds, and the number of DC arc occurrences reaches a preset number within a preset time, it is detected as a DC fault arc.

[0081] Otherwise, the test is normal.

[0082] Example 3

[0083] Based on the DC arc detection method described in Embodiment 1, this embodiment introduces a computer-readable storage medium storing a computer program / instruction thereon. When the computer program / instruction is executed by a processor, it implements the steps of the DC arc detection method as described in any of Embodiment 1.

[0084] Example 4

[0085] Based on the DC arc detection method described in Embodiment 1, this embodiment provides a computer device, including:

[0086] Memory, used to store computer programs / instructions;

[0087] A processor is configured to execute the computer program / instructions to implement the steps of the DC arc detection method as described in any one of Embodiment 1.

[0088] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0089] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0090] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0091] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0092] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A method for detecting DC arc, characterized in that, include: The line current signal is collected, its characteristics are processed to obtain characteristic values, and the average value of the characteristics within a preset time period is saved as the initial characteristic value. Based on the noise floor values ​​of each characteristic frequency band, the effective frequency bands that meet the set conditions are obtained by filtering through the frequency band selection method. Based on the effective frequency band, the arc energy value is calculated to obtain the dynamic arc energy and the static arc energy; The DC arc is detected based on the dynamic arc energy and the static arc energy. When both dynamic arc energy and static arc energy exceed their respective thresholds, and the number of DC arc occurrences reaches a preset number within a preset time, it is detected as a DC fault arc. Otherwise, the test is normal.

2. The DC arc detection method according to claim 1, characterized in that, The feature processing involves using a current transformer to collect the current signal in the line, performing a fast Fourier transform on the collected current signal, and extracting the corresponding spectral features.

3. The DC arc detection method according to claim 1, characterized in that, The frequency band selection method is used to eliminate interference caused by differences in the noise floor characteristics of different inverters. The process is as follows: a detection threshold is set for each frequency band. Based on this, frequency bands in the inverter whose noise floor values ​​exceed the detection threshold are deleted, thereby obtaining effective frequency bands whose noise floor values ​​meet the set conditions.

4. The DC arc detection method according to claim 1, characterized in that, The calculation of the arc energy value is based on both dimensionless and dimensional indices, which is used to effectively distinguish between normal operating conditions and DC fault arc operating conditions.

5. The DC arc detection method according to claim 4, characterized in that, The dimensionless index is obtained by calculating the ratio of the real-time fast Fourier transform value to the reference fast Fourier transform value. The reference fast Fourier transform value is in a dynamic update state. The update method is as follows: within a preset time, it is determined whether the ratio of the mean of the fast Fourier transform of each frequency band to the original reference fast Fourier transform value is less than a preset multiple. When the condition is met, the mean of the fast Fourier transform of each frequency band within the preset time is used to replace the original reference fast Fourier transform value to complete the dynamic update of the reference value. After obtaining the ratio of each Fast Fourier Transform (FFT) segment, the sum of the total ratios of all FFT frequency bands is calculated to characterize the arc energy, and the calculation result is defined as the dynamic arc energy.

6. The DC arc detection method according to claim 4, characterized in that, The dimensional index is obtained by calculating the sum of all original fast Fourier transform eigenvalues. The sum is the absolute value of the real-time arc energy, which is used to directly characterize the absolute energy magnitude of the arc and is defined as the static arc energy.

7. The DC arc detection method according to claim 1, characterized in that, The specific process for detecting DC arc is as follows: Thresholds for dynamic arc energy and static arc energy are set separately. If both exceed the corresponding thresholds, it is judged as a suspected arc; if one of them does not meet the condition, it is judged as normal and the detection continues. Within a preset time, suspected arcs that have already been identified are reconfirmed. If the number of suspected arcs reaches the preset number, they are finally detected as DC fault arcs, and a fault indication is issued. If the preset number is not reached, the current cached data is cleared, and subsequent judgments are performed.

8. A DC arc detection system, characterized in that, include: The feature processing module is used to acquire line current signals, perform feature processing, obtain feature values, and save the average value of the features within a preset time period as the initial feature value. The frequency band selection module is used to filter effective frequency bands that meet the set conditions based on the noise floor values ​​of each characteristic frequency band using a frequency band selection method. The arc energy calculation module is used to calculate the arc energy value based on the effective frequency band to obtain the dynamic arc energy and the static arc energy. A DC arc detection module is used to detect and judge DC arcs based on the dynamic arc energy and static arc energy. When both dynamic arc energy and static arc energy exceed their respective thresholds, and the number of DC arc occurrences reaches a preset number within a preset time, it is detected as a DC fault arc. Otherwise, the test is normal.

9. A computer-readable storage medium having a computer program / instructions stored thereon, characterized in that, When the computer program / instruction is executed by the processor, it implements the steps of the DC arc detection method according to any one of claims 1 to 7.

10. A computer device, characterized in that, include: Memory, used to store computer programs / instructions; A processor for executing the computer program / instructions to implement the steps of the DC arc detection method according to any one of claims 1 to 7.

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