A method and system for detecting direct current series arc faults

Abstract

The application belongs to the technical field of power systems, and discloses a DC series arc fault detection method, which comprises the following steps: sampling loop current of a detected DC line to obtain a loop current sampling sequence, and performing band-pass filtering on the loop current sampling sequence to obtain a current high-frequency sequence; according to the current high-frequency sequence, calculating a peak-to-peak value and a fuzzy entropy value of the current high-frequency sequence within a time window, and calculating a time-frequency hybrid characteristic value according to the peak-to-peak value and the fuzzy entropy value; and judging whether a series arc fault occurs in the DC line according to the time-frequency hybrid characteristic value. Compared with a detection method based on time-domain quantities alone, the application has better anti-noise capability, can effectively improve the detection success rate of DC arc faults, and can reduce the overall fault detection calculation amount.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power systems, and particularly relates to a DC series arc fault detection method and system. BACKGROUND

[0002] Nowadays, the photovoltaic power generation, energy storage, new energy electric vehicles and other industries are developing rapidly, and the DC power supply system has been widely used. However, the series arc fault caused by the slight open circuit of the DC power supply loop threatens the safety of the system. The fault current of this kind of fault is smaller than the load current, and there is no obvious fault characteristic such as zero rest. The conventional fault detection and protection device is difficult to quickly isolate it. The heat quickly accumulates due to the long-term existence of the arc, which easily leads to equipment burning and personnel injury and other serious accidents.

[0003] Cattin V of CEA-liten Company in France uses arc sound signal to detect arc fault of electric vehicle battery pack, and uses signal time difference and triangulation to locate the fault. However, the detection method using arc sound signal has high requirements for sensor installation position and sensitivity, and is easily affected by external environment. Yao X of High Voltage and Power Electronics Laboratory of Ohio State University in the United States analyzes the change rate of DC arc current waveform within a certain time window, and proposes to use the change rate of current waveform within a certain time length to detect DC arc fault. Naidu M of Delphi Research Laboratories in the United States uses the change rate of current to detect arc fault to reduce the harm of arc fault. However, the detection method using the change rate of current signal may be affected by load change. Shekhar A of Delft University of Technology in the Netherlands uses voltage drop on the load side to detect DC series arc fault, and considers different types of loads.

[0004] However, the above methods are based on frequency domain calculation, so the anti-interference ability of fault detection is poor. The present application combines the peak-to-peak value feature in time domain with the fuzzy entropy feature in frequency domain, and proposes a DC series arc fault detection method with strong anti-interference ability and low algorithm operation amount. SUMMARY

[0005] The DC series arc fault detection method and system provided by the embodiments of the present application can solve the problem of poor anti-interference ability in the prior art.

[0006] The following presents a simplified summary of some aspects of the disclosure in order to provide a basic understanding of such aspects. This summary is not an extensive overview of the disclosure. It is not intended to identify key / critical elements of the embodiments or to delineate the scope of the embodiments. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later.

[0007] According to a first aspect of the embodiments of the present application, a DC series arc fault detection method is provided.

[0008] In one embodiment, the direct current series arc fault detection method comprises:

[0009] sampling loop current of the detected direct current line to obtain a loop current sampling sequence, and performing band-pass filtering processing on the loop current sampling sequence to obtain a current high-frequency sequence;

[0010] calculating a peak-to-peak value and a fuzzy entropy value of the current high-frequency sequence within a time window according to the current high-frequency sequence, and calculating a time-frequency hybrid feature value according to the peak-to-peak value and the fuzzy entropy value;

[0011] judging whether a series arc fault occurs in the direct current line according to the time-frequency hybrid feature value.

[0012] In one embodiment, the sampling rate of the loop current sampling is greater than or equal to 200 kHz, and the frequency of the band-pass filtering processing is 10 kHz to 100 kHz.

[0013] In one embodiment, calculating the peak-to-peak value of the current high-frequency sequence within the time window according to the current high-frequency sequence comprises: calculating the peak-to-peak value of the current high-frequency sequence within the time window according to the current high-frequency sequence through the following calculation formula:

[0014] I pp = I max -I min

[0015] In the formula, I pp is the peak-to-peak value of the current high-frequency sequence within the time window; I max is the maximum current value of the current high-frequency sequence within the time window; and I min is the minimum current value of the current high-frequency sequence within the time window.

[0016] In one embodiment, the direct current series arc fault detection method further comprises: before calculating the fuzzy entropy value, judging whether a suspected fault occurs in the direct current line according to the peak-to-peak value; in the case that the judgment result is that the suspected fault occurs in the direct current line, calculating the fuzzy entropy value again; and in the case that the judgment result is that the suspected fault does not occur in the direct current line, repeating the sampling of the loop current of the direct current line.

[0017] In one embodiment, the calculation formula of the fuzzy entropy value of the current high-frequency sequence is:

[0018] I fuz = lnφ m (r)-lnφ m+1 (r)

[0019]

[0020]

[0021] wherein I fuz is the fuzzy entropy value of the current high frequency sequence, m is the dimension, and φ m (r) is the relationship dimension under the m dimension, and φ m+1 (r) is the relationship dimension under the m+1 dimension, and N is the number of points of the current high frequency sequence {H(n)} = x(1), x(2), …, x(N), is the fuzzy similarity function.

[0022] In one embodiment, according to the peak-to-peak value and the fuzzy entropy value, the calculation formula for calculating the time-frequency hybrid feature value is as follows:

[0023] I mix = I pp I fuz

[0024] wherein I mix is the time-frequency hybrid feature value; I pp is the peak-to-peak value of the current high frequency sequence in the time window; I fuz is the fuzzy entropy value of the current high frequency sequence.

[0025] In one embodiment, according to the time-frequency hybrid feature value, the judgment on whether the series arc fault occurs in the DC line includes: comparing the time-frequency hybrid feature value with a predetermined threshold; and in the case that the time-frequency hybrid feature values of the current signals of multiple time windows are all greater than the predetermined threshold, judging that the series arc fault occurs in the DC line.

[0026] According to a second aspect of the embodiment of the present application, a DC series arc fault detection system is provided.

[0027] In one embodiment, the DC series arc fault detection system includes:

[0028] a sampling processing module, configured to sample the loop current of the DC line to be detected to obtain a loop current sampling sequence, and perform band-pass filtering processing on the loop current sampling sequence to obtain a current high frequency sequence;

[0029] a data calculation module, configured to calculate the peak-to-peak value and the fuzzy entropy value of the current high frequency sequence in a time window according to the current high frequency sequence, and calculate a time-frequency hybrid feature value according to the peak-to-peak value and the fuzzy entropy value;

[0030] a fault judgment module, configured to judge whether a series arc fault occurs in the DC line according to the time-frequency hybrid feature value.

[0031] In one embodiment, the sampling rate of the loop current sampling is greater than or equal to 200 kHz, and the frequency of the band-pass filtering processing is 10 kHz to 100 kHz.

[0032] In one embodiment, the data calculation module calculates the peak-to-peak value of the high-frequency current sequence in a time window according to the high-frequency current sequence, and the calculation formula is:

[0033] I pp = I max - I min

[0034] In the formula, I pp is the peak-to-peak value of the high-frequency current sequence in the time window, I max is the maximum current value of the high-frequency current sequence in the time window, and I min is the minimum current value of the high-frequency current sequence in the time window.

[0035] In one embodiment, the DC series arc fault detection system further comprises a suspected fault judgment module configured to judge whether a suspected fault occurs in the DC line according to the peak-to-peak value before calculating the fuzzy entropy value; when the suspected fault judgment module judges that a suspected fault occurs in the DC line, the data calculation module calculates the fuzzy entropy value; and when the suspected fault judgment module judges that a suspected fault does not occur in the DC line, the sampling processing module repeatedly samples the loop current of the DC line.

[0036] In one embodiment, the data calculation module calculates the fuzzy entropy value of the high-frequency current sequence according to the following formula:

[0037] I fuz = lnφ m (r) - lnφ m+1 (r)

[0038]

[0039]

[0040] In the formula, I fuz is the fuzzy entropy value of the high-frequency current sequence, m is a dimension, φ m (r) is a relationship dimension in the m dimension, φ m+1 (r) is a relationship dimension in the m+1 dimension, N is the number of points of the high-frequency current sequence {H(n)} = x(1), x(2), …, x(N), is a fuzzy similarity function.

[0041] In one embodiment, the data calculation module calculates the time-frequency mixed characteristic value according to the peak-to-peak value and the fuzzy entropy value, and the calculation formula is as follows:

[0042] I mix pp I fuz

[0043] In the formula, I mix is the time-frequency mixed characteristic value; I pp is the peak-to-peak value of the current high-frequency sequence in a time window; I fuz is the fuzzy entropy value of the current high-frequency sequence.

[0044] In one embodiment, the fault judgment module compares the time-frequency mixed characteristic value with a predetermined threshold value according to the time-frequency mixed characteristic value, and judges that the DC line has a series arc fault when the time-frequency mixed characteristic values of the current signals of multiple time windows are all greater than the predetermined threshold value.

[0045] The technical scheme provided by the embodiment of the application can have the following beneficial effects:

[0046] The embodiment of the application combines the time-domain characteristic represented by the peak-to-peak value and the frequency-domain characteristic represented by the fuzzy entropy value, takes the peak of the peak-to-peak value as the mark of the suspected series arc fault, and takes the product of the two characteristic values as the mixed characteristic value to judge whether the series arc fault occurs, so that the anti-noise capability is better than that of the detection method based on the time-domain value alone, the detection success rate of the DC arc fault is effectively improved, and the calculation amount of the overall fault detection is reduced.

[0047] It should be understood that the foregoing general description and the following detailed description are only exemplary and explanatory, and cannot limit the application. BRIEF DESCRIPTION OF DRAWINGS

[0048] The accompanying drawings, which are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the application and, together with the specification, serve to explain the principles of the application.

[0049] Figure 1 is a flowchart of a DC series arc fault detection method according to an exemplary embodiment;

[0050] Figure 2 is a structural schematic diagram of a DC series arc fault detection system according to an exemplary embodiment;

[0051] Figure 3 is a current signal sampling diagram according to an exemplary embodiment;

[0052] ​Figure 4 A filtered current signal plot according to an example embodiment;

[0053] Figure 5 A current signal peak-to-peak plot according to an example embodiment;

[0054] Figure 6 A current signal fuzzy entropy plot according to an example embodiment;

[0055] Figure 7 A current signal time-frequency hybrid feature plot according to an example embodiment. DETAILED DESCRIPTION

[0056] The following description and drawings are illustrative of the specific embodiments herein and are not intended to be limiting. Numerous specific details are described to provide a thorough understanding of the specific embodiments. However, in certain instances, well-known methods, procedures, components, and circuits have been omitted in order to avoid obscuring the concepts of the more important aspects. Some portions of the description may be presented in terms of algorithms or symbolic representations of operations on data stored as bits or binary digital signals within a memory. These algorithms describe and can be performed by hardware component(s) programmed to perform the algorithm, by special-purpose logic, or any combination thereof. Some embodiments can therefore be described in terms of components that are symbolic representations of tangible components of a computing device or as one or more machines configured to perform the algorithms. Any operations described herein can communicate information between various components. For example, drive units can receive information from a controller and cause movement of a component based on the information. Similarly, sensors can provide information to a controller based on the sensed information. Any operations described herein can be performed sequentially, in parallel, or in some other order. Any operations described herein can be performed by one or more machines configured to perform the operations. Any operations described herein can be performed by one or more machines configured to perform the operations.

[0057] The terms "longitudinal", "lateral", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer" and the like as used herein to indicate orientation or positional relationships based on the orientations or positional relationships shown in the drawings, are for purposes of this description only, and are not intended to indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore should not be construed as limiting the application. In the description of the description, unless otherwise specified and limited, the terms "mount", "connect", "connection" should be interpreted broadly, for example, can be mechanical connection or electrical connection, can be internal communication of two elements, can be direct connection, or indirect connection through intermediate medium, the specific meaning of the above terms can be understood by the person skilled in the art according to the specific circumstances.

[0058] In this paper, unless otherwise specified, the term "a plurality of" means two or more.

[0059] In this paper, the character " / " represents that the front and rear objects are a kind of "or" relationship. For example, A / B represents: A or B.

[0060] In this paper, the term "and / or" is a description of the association between objects, which means that there can be three kinds of relationships. For example, A and / or B, which means: A or B, or, A and B, three kinds of relationships.

[0061] It should be understood that although each step in the flowchart is displayed in sequence according to the direction of the arrow, these steps are not necessarily executed in sequence according to the direction of the arrow. Unless otherwise specified in this paper, the execution of these steps has no strict order limitation, and these steps can be executed in other order. Moreover, at least part of the steps in the figure can include multiple sub-steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily sequential, but can be executed with at least part of other steps or other steps or stages of sub-steps or stages in turn or alternately.

[0062] Each module in the device or system of the present application can be realized by software, hardware and their combination in whole or in part. The above modules can be embedded in or independent of the processor in the computer device in hardware form, or stored in the memory in the computer device in software form, so as to call and execute the operations corresponding to the above modules by the processor.

[0063] In the case of no conflict, the embodiments in the present application and the features in the embodiments can be combined with each other.

[0064] Figure 1An embodiment of the DC series arc fault detection method of the present application is shown.

[0065] In this alternative embodiment, the DC series arc fault detection method comprises:

[0066] In step S101, the loop current of the DC line under test is sampled to obtain a loop current sampling sequence, and the loop current sampling sequence is subjected to band-pass filtering to obtain a current high-frequency sequence.

[0067] In step S103, the peak-to-peak value and the fuzzy entropy value of the current high-frequency sequence within a time window are calculated according to the current high-frequency sequence, and a time-frequency hybrid feature value is calculated according to the peak-to-peak value and the fuzzy entropy value.

[0068] In step S105, it is determined whether a series arc fault occurs in the DC line according to the time-frequency hybrid feature value.

[0069] In one embodiment, the sampling rate of the loop current sampling is greater than or equal to 200 kHz, and the frequency of the band-pass filtering is 10 kHz to 100 kHz.

[0070] In one embodiment, calculating the peak-to-peak value of the current high-frequency sequence within a time window according to the current high-frequency sequence comprises: calculating the peak-to-peak value of the current high-frequency sequence within a time window according to the current high-frequency sequence by the following calculation formula:

[0071] I pp = I max - I min

[0072] In the formula, I pp is the peak-to-peak value of the current high-frequency sequence within a time window; I max is the maximum current value of the current high-frequency sequence within a time window; and I min is the minimum current value of the current high-frequency sequence within a time window.

[0073] In one embodiment, the DC series arc fault detection method further comprises: before calculating the fuzzy entropy value, determining whether a suspected fault occurs in the DC line according to the peak-to-peak value; in the case where the result of the determination is that a suspected fault occurs in the DC line, calculating the fuzzy entropy value again; and in the case where the result of the determination is that a suspected fault does not occur in the DC line, repeating the loop current sampling of the DC line.

[0074] Specifically, when determining whether a suspected fault occurs in the DC line according to the peak-to-peak value, the peak-to-peak value is compared with a predetermined threshold value, and in the case where the peak-to-peak value is greater than the predetermined threshold value, it is determined that a suspected fault occurs in the DC line.

[0075] In one embodiment, the calculation formula of the current high frequency sequence fuzzy entropy value is:

[0076] I fuz = lnφ m (r) - lnφ m+1 (r)

[0077] In the formula, I fuz is the current high frequency sequence fuzzy entropy value, m is the dimension, φ m (r) is the relationship dimension under m dimension, φ m+1 (r) is the relationship dimension under m+1 dimension;

[0078] The calculation formula of the relationship dimension is:

[0079]

[0080]

[0081] In the formula, N is the number of points of the current high frequency sequence {H(n)} = x(1), x(2), …, x(N), C i m (r) is the fuzzy similarity function;

[0082] The calculation formula of the fuzzy similarity function is:

[0083]

[0084] In the formula, is the similarity function between the vector X m (i) and the vector X m (j), and the calculation formula is:

[0085]

[0086] In the formula, r is a similarity tolerance parameter, which is defined as R times of the standard deviation of the original one-dimensional time sequence, that is, r = R x δ (δ is the standard deviation of the original data). is the maximum value of the difference between the corresponding elements of the two m-dimensional vectors X m (i) and X m (j), and the calculation formula is:

[0087]

[0088] In the formula, the m-dimensional vector X m (i) is constructed in the following manner:

[0089] X m(i) = [x(i), x(i+1), …, x(i+m-1)] - u(i)

[0090]

[0091] wherein, i = 1, 2, …, N-m+1, u(i) is the mean value.

[0092] In a specific application, the fuzzy entropy value is related to parameters m and r, and the parameter m is taken as 2 by comprehensively considering the sequence information and the calculation length. The parameter r is generally taken as 0.1-0.25SD, SD is the standard deviation of the signal sequence, r is too large to lose some information, and too small to obtain an ideal result, therefore, the present application takes r = 0.2SD.

[0093] In one embodiment, according to the peak-to-peak value and the fuzzy entropy value, the calculation formula for calculating the time-frequency hybrid feature value is:

[0094] I mix = I pp I fuz

[0095] In the formula, I mix is the time-frequency hybrid feature value; I pp is the peak-to-peak value of the current high-frequency sequence in the time window; and I fuz is the fuzzy entropy value of the current high-frequency sequence.

[0096] In one embodiment, according to the time-frequency hybrid feature value, the judgment of whether the series-arc fault occurs in the DC line includes: comparing the time-frequency hybrid feature value with a predetermined threshold; and in the case that the time-frequency hybrid feature values of the current signals of multiple time windows are all greater than the predetermined threshold, judging that the series-arc fault occurs in the DC line.

[0097] Correspondingly, Figure 2 An embodiment of the DC series-arc fault detection system of the present application is shown. In the optional embodiment, the DC series-arc fault detection system includes:

[0098] The sampling processing module 201 is configured to sample the loop current of the DC line to be detected to obtain a loop current sampling sequence, and perform a band-pass filtering processing on the loop current sampling sequence to obtain a current high-frequency sequence.

[0099] The data calculation module 203 is configured to calculate the peak-to-peak value and the fuzzy entropy value of the current high-frequency sequence in the time window according to the current high-frequency sequence, and calculate a time-frequency hybrid feature value according to the peak-to-peak value and the fuzzy entropy value.

[0100] The fault judgment module 205 is used for judging whether the series arc fault occurs in the DC line according to the time-frequency mixed characteristic value.

[0101] In order to facilitate the understanding of the above technical solutions of the present application, the above technical solutions of the present application are further described through specific application examples as follows.

[0102] Application example

[0103] According to the DC series arc fault detection method of the application example of the present application, the method comprises:

[0104] 1) The current of the monitored DC power supply line is sampled, the sampling frequency is 250 kHz, the time window of the algorithm is T = 5000t s = 20 ms; the DC line current signal sampling value is obtained as shown in Table 1, and the low-pass filtering is performed on the current signal, and the filtered current signal is as shown in Table 2. Figure 3 Figure 4

[0105] 2) It can be known that the load current of the line is about 8 A, and the peak-peak value threshold I pp-set = 0.06I = 0.48 A, in order to facilitate calculation, I pp-set = 0.5 A,

[0106] In order to determine the suspected fault, the peak-peak value of the data is calculated, the data is shown in Table 1, and the image is as shown in Table 2. Figure 5 pp = 1.495642 > I pp-set , so it is judged that the line is a suspected fault.

[0107] Table 1 Peak-peak value of current signal

[0108]

[0109]

[0110] 3) The fuzzy entropy value of the current signal is calculated, the data is shown in Table 2, and the image is as shown in Table 3. Figure 6

[0111] Table 2 Fuzzy entropy value of current signal

[0112]

[0113] The time-frequency mixed characteristic value threshold I pp-set of the current signal is calculated according to the peak-peak value threshold I mix-set of the line, I mix-set = 0.06I pp-set ​​​​=0.03A, multiplied by the peak-to-peak value to obtain a time-frequency mixed feature value, and the data of the time-frequency mixed feature value is shown in Table 3, and the image is shown in Figure 7

[0114] Table 3: Time-frequency mixed feature value of current signal

[0115]

[0116] In the application example, the number of time windows is α=5, and the mixed feature values of the current signals of the continuous 5 time windows from 0.21s to 0.31s all exceed the threshold value 0.03, and thus it is judged that the series arc fault occurs on the DC line.

[0117] It can be seen that the time domain feature represented by the peak-to-peak value and the frequency domain feature represented by the fuzzy entropy are combined together in the application, the peak of the peak-to-peak value is taken as the mark of the suspected series arc fault, and the product of the two feature values is taken as the mixed feature value to judge whether the series arc fault occurs, compared with the detection method based on the time domain value, the application has better anti-noise ability, can effectively improve the detection success rate of the DC arc fault and reduce the calculation amount of the overall fault detection.

[0118] The application is not limited to the structure described above and shown in the drawings, and various modifications and changes can be made without departing from the scope thereof. The scope of the application is only limited by the appended claims.​

Claims

1. A method for detecting DC series arc faults, characterized in that, include: The loop current of the DC line under test is sampled to obtain a loop current sampling sequence, and the loop current sampling sequence is bandpass filtered to obtain a high-frequency current sequence. Based on the high-frequency current sequence, calculate the peak-to-peak value and fuzzy entropy value of the high-frequency current sequence within the time window, and calculate the time-frequency hybrid feature value based on the peak-to-peak value and the fuzzy entropy value; Based on the time-frequency hybrid characteristic value, it is determined whether a series arc fault has occurred in the DC line; Before calculating the fuzzy entropy value, the peak-to-peak value is used to determine whether a suspected fault has occurred in the DC line; If the judgment result indicates that the DC line has a suspected fault, then the fuzzy entropy value is calculated. The formula for calculating the time-frequency mixing feature value based on the peak-to-peak value and the fuzzy entropy value is as follows: I mix＝ I pp I fuz In the formula, I mix For time-frequency mixed eigenvalues; I pp The peak-to-peak value of the high-frequency current sequence within the time window; I fuz The value is the fuzzy entropy of the high-frequency current sequence.

2. The DC series arc fault detection method according to claim 1, characterized in that, The sampling rate of the loop current sampling is greater than or equal to 200kHz, and the frequency of the bandpass filtering is 10kHz to 100kHz.

3. The DC series arc fault detection method according to claim 1, characterized in that, Calculating the peak-to-peak value of the high-frequency current sequence within a time window, based on the high-frequency current sequence, includes: Based on the high-frequency current sequence, the peak-to-peak value of the high-frequency current sequence within the time window is calculated using the following formula: I pp =I max -I min In the formula, I pp The peak-to-peak value of the high-frequency current sequence within the time window; I max I represents the maximum current value of the high-frequency current sequence within the time window. min The minimum current value of the high-frequency current sequence within the time window.

4. The DC series arc fault detection method according to claim 1, characterized in that, Also includes: If the judgment result is that no suspected fault has occurred in the DC line, the loop current sampling of the DC line is repeated.

5. The DC series arc fault detection method according to claim 1, characterized in that, The formula for calculating the fuzzy entropy value of the high-frequency current sequence is as follows: IN fuz =lnφ m (r)-lnφ m+1 (s) In the formula, I fuz Let m be the fuzzy entropy value of the high-frequency current sequence, and φ be the dimension. m (r) represents the relational dimension under dimension m, φ m+1 (r) represents the relational dimension in dimension m+1, and N is the number of points in the current high-frequency sequence {H(n)} = x(1), x(2), ..., x(N). This is a fuzzy similarity function.

6. The DC series arc fault detection method according to claim 1, characterized in that, Determining whether a series arc fault has occurred in the DC line based on the time-frequency hybrid characteristic value includes: The time-frequency hybrid feature value is compared with a predetermined threshold. If the comparison results show that the time-frequency mixed characteristic value of the current signal in multiple time windows is greater than a predetermined threshold, it is determined that a series arc fault has occurred in the DC line.

7. A DC series arc fault detection system, characterized in that, include: The sampling and processing module is used to sample the loop current of the DC line under test, obtain the loop current sampling sequence, and perform bandpass filtering on the loop current sampling sequence to obtain the high-frequency current sequence. The data calculation module is used to calculate the peak-to-peak value and fuzzy entropy value of the high-frequency current sequence within a time window based on the high-frequency current sequence, and to calculate the time-frequency mixed feature value based on the peak-to-peak value and the fuzzy entropy value. The fault diagnosis module is used to determine whether a series arc fault has occurred in the DC line based on the time-frequency mixed characteristic value. The suspected fault judgment module is used to judge whether a suspected fault has occurred in the DC line based on the peak-to-peak value before calculating the fuzzy entropy value. If the suspected fault judgment module determines that a suspected fault has occurred in the DC line, the data calculation module will then calculate the fuzzy entropy value. The calculation formula used by the data calculation module to calculate the time-frequency hybrid feature value based on the peak-to-peak value and the fuzzy entropy value is as follows: I mix＝ I pp I fuz In the formula, I mix For time-frequency mixed eigenvalues; I pp The peak-to-peak value of the high-frequency current sequence within the time window; I fuz The value is the fuzzy entropy of the high-frequency current sequence.

8. The DC series arc fault detection system according to claim 7, characterized in that, The sampling rate of the loop current sampling is greater than or equal to 200kHz, and the frequency of the bandpass filtering is 10kHz to 100kHz.

9. The DC series arc fault detection system according to claim 7, characterized in that, The data calculation module calculates the peak-to-peak value of the high-frequency current sequence within a time window using the following formula: I pp =I max -I min In the formula, I pp The peak-to-peak value of the high-frequency current sequence within the time window; I max I represents the maximum current value of the high-frequency current sequence within the time window. min The minimum current value of the high-frequency current sequence within the time window.

10. The DC series arc fault detection system according to claim 7, characterized in that, If the suspected fault judgment module determines that no suspected fault has occurred in the DC line, the sampling processing module will repeatedly sample the loop current of the DC line.

11. The DC series arc fault detection system according to claim 7, characterized in that, The data calculation module uses the following formula to calculate the fuzzy entropy value of the high-frequency current sequence: IN fuz =lnφ m (r)-lnφ m+1 (s) In the formula, I fuz Let m be the fuzzy entropy value of the high-frequency current sequence, and φ be the dimension. m (r) represents the relational dimension under dimension m, φ m+1 (r) represents the relational dimension in dimension m+1, and N is the number of points in the current high-frequency sequence {H(n)} = x(1), x(2), ..., x(N). This is a fuzzy similarity function.

12. The DC series arc fault detection system according to claim 7, characterized in that, When the fault judgment module determines whether a series arc fault has occurred in the DC line based on the time-frequency mixed feature value, it compares the time-frequency mixed feature value with a predetermined threshold. If the comparison results show that the time-frequency mixed characteristic values ​​of the current signals in multiple time windows are all greater than a predetermined threshold, it is determined that a series arc fault has occurred in the DC line.

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