Series fault arc identification method suitable for low-voltage alternating-current power distribution system

Abstract

The invention discloses a series fault arc identification method suitable for a low-voltage alternating-current power distribution system, which is different from a traditional fault arc detection method and comprises the following steps of: judging whether an arc fault occurs in a line or the line is in a normal state by using zero-sequence current as a detection signal; according to the method, the selected characteristic quantities accurately describe four stages of arc starting, arc burning, arc extinguishing and zero stopping of the fault arc, and a good fault arc detection effect is achieved for a low-voltage alternating-current power distribution system with complex scenes including multiple loads, multiple arc generation modes, multiple line topologies and the like.

Description

Technical Field

[0001] The present invention belongs to the technical field of electrical fault detection in low-voltage AC power distribution systems, and relates to a method for extracting features of zero-sequence current from three dimensions of time domain, frequency domain and signal disorder based on the physical process of a fault arc, and identifying a series fault arc using a threshold determination method. Background Art

[0002] According to data released by the Fire and Rescue Bureau of the Ministry of Emergency Management, from 2012 to 2021, a total of 1.324 million residential fires occurred nationwide, of which electrical fires accounted for 42.7%, and one of the main causes of electrical fires was fault arcs in the line. According to the line topology, fault arcs can be divided into series fault arcs and parallel fault arcs. When a parallel fault arc occurs, the line current will increase significantly, and traditional protection switches (such as miniature circuit breakers, residual current action protection switches, fuses, etc.) can cut off the line in time; when a series fault arc occurs, the loop current amplitude is lower than the current amplitude when the line is in normal state, and traditional protection switches cannot detect the series fault arc. As early as 1999, the United States formulated the UL1699 standard to guide the development of AC arc fault circuit breakers (Arc Fault Circuit Interrupter, AFCI). In 2013, the International Electrotechnical Commission also formulated the international standard (IEC62606-2013) for AC arc fault circuit breakers (Arc Fault Detection Device, AFDD). my country also began to implement the national standards GB 14287.4-2014 (Electrical Fire Monitoring System Part 4: Arc Fault Detectors) and GB / T31143-2014 (General Requirements for Arc Fault Protection Devices (AFDD)) in 2015.

[0003] In recent years, many researchers have conducted in-depth research on fault arc detection technology. However, due to line load, arc burning conditions, circuit topology and other reasons, fault arcs have the characteristics of concealment, complexity and uncertainty. It is still difficult to accurately identify series fault arcs in actual complex scenarios. Fault arc identification technology can be divided into three categories, namely, fault arc detection methods based on physical phenomena, fault arc detection methods based on time-frequency domain feature analysis, and fault arc detection methods based on artificial intelligence models. When a fault arc occurs, it is accompanied by obvious physical phenomena such as arc light, arc sound, thermal radiation and electromagnetic radiation. Therefore, some experts and scholars use these physical phenomena to detect fault arcs. However, this type of method requires knowing the location of the fault arc in advance, and is easily restricted and interfered by factors such as the sensor installation location and the external environment. It is not suitable for low-voltage AC power distribution systems. When a fault arc occurs in the line, the line current and voltage signals have time domain and frequency domain characteristics, and this type of signal is easy to measure. Therefore, some experts and scholars detect fault arcs by extracting the time-frequency domain characteristics of voltage and current signals. This type of method is simple and effective, with high engineering feasibility, but at the same time, some special loads have arc-like characteristics when the line is normal, which is prone to malfunction. With the application of artificial intelligence in various fields, some experts and scholars have also used machine learning methods to build fault arc identification models, and have achieved good results, but this method requires a lot of training data and preparation work, and some machine learning methods have high computational complexity. At present, the cost of making fault arc detection modules using this type of method is high, which is not suitable for widespread application in actual scenarios.

[0004] Chinese patent CN 105425118A discloses a multi-information fusion fault arc detection method and device, which extracts three time-frequency domain features from the current signal and establishes a fault arc detection model using the cerebellum model neural network. However, this method is only effective for training set data, and whether it is effective for unknown nonlinear loads remains to be verified. Similarly, Chinese patent CN 110702966A discloses a fault arc detection method based on a probabilistic neural network, which performs principal component analysis and dimensionality reduction processing on the current signal, extracts relevant feature quantities, and constructs a fault arc detection model using a probabilistic neural network. However, this method has not been tested for stability and generalizability, and whether it is still effective for fault arcs under a variety of special loads remains to be verified. In addition, although the use of neural network methods to construct fault arc detection models has good results under specific load types, this type of method has a large amount of calculation and low engineering feasibility.

[0005] Chinese patent CN 113176479A discloses a method for detecting series fault arcs in low-voltage distribution networks. The method measures the voltage at the head and end of the line, performs time-frequency domain analysis on the voltage difference signal, extracts relevant feature quantities, and uses a threshold determination method to detect fault arcs. The method is effective for specific lines and is feasible for simple engineering, but does not consider the impact of line loads. The protection only extends to the head end of the user side, and does not detect fault arcs on the user side line.

[0006] In summary, although the current fault arc detection method has a good detection effect under specific load types, it does not consider the influence of circuit topology and arcing mode at the same time, and there are still misjudgments and missed judgments when applied in actual distribution networks. Although the load type and circuit topology affect the loop current characteristics, their influence on the zero-sequence current is weak. The AC fault arc can be divided into four stages: arcing, arcing, arcing and zero rest. There is a large current fluctuation in the zero-sequence current during the arcing and arcing stages. In addition, the fault arc has a strong instability. The series connection of the line will cause the line parameters to change, and the change of the line parameters will inevitably affect the change of the electrical parameters. The instability of arcing aggravates the circuit noise, and the zero-sequence current has obvious ripples. Therefore, the mutation characteristics and ripple characteristics of the AC fault arc can be used to detect the fault arc. Although some special loads also have arc-like characteristics when the line is normal, the waveform of the zero-sequence current in the normal state of the line has certain regularities. On the contrary, when a fault arc occurs in the line, the instability of the fault arc will affect the regularity of the zero-sequence current waveform. Therefore, these characteristics can also be used to avoid interference and identify the fault arc. The fault arc is identified based on the changing characteristics of the zero-sequence current waveform when the fault arc occurs. It has a wider range of applications and can effectively detect fault arcs in complex scenarios such as multiple load types, multiple circuit topologies, and multiple arcing modes. The implementation process of this method is simple, the identification results are reliable, and the engineering feasibility is high. Summary of the invention

[0007] The purpose of the present invention is to provide a method for identifying series fault arcs in low-voltage AC power distribution systems, which uses zero-sequence current as a detection signal and uses pulse factor, adjacent pulse peak interval time, Shannon entropy and harmonic amplitude as a fault feature set to characterize arc characteristics. Experimental verification shows that the method can effectively detect arc faults in complex scenarios including multiple load types, multiple circuit topologies and multiple arcing modes, and the calculation process is simple and easy to implement in engineering.

[0008] The technical solution adopted by the present invention is as follows:

[0009] Step 1: Sample the zero-sequence current signals of different branches in the low-voltage AC power distribution system point by point at the sampling frequency f, or sample the zero-sequence current signals of the upper busbars of each branch point by point, and sample the zero-sequence current signals of the upper busbars of each branch point by point according to the time window length Ts Extract the corresponding detection signal x in the current analysis cycle respectively n (zero-sequence current), where n = 1, 2, ..., N;

[0010] Step 2: Calculate the pulse factor, adjacent pulse peak interval, Shannon entropy and harmonic amplitude of zero-sequence current in an analysis period;

[0011] Step 3: If the Shannon entropy is less than the preset threshold 1, execute step 4, otherwise execute step 5;

[0012] Step 4: If the pulse factor is less than the preset threshold 2, the line is marked as normal, otherwise, go to step 6;

[0013] Step 5: If the sum of the harmonic amplitudes is less than the preset threshold value 3, it is marked that a suspected arc fault occurs in the line, otherwise, step 7 is executed;

[0014] Step 6: If the interval between adjacent pulse peaks satisfies the integer multiple relationship of 10ms, the marked line is normal, otherwise, a suspected arc fault occurs in the marked line;

[0015] Step 7: If the pulse factor is less than the preset threshold value 2, the line is marked as normal, otherwise a suspected arc fault occurs in the line;

[0016] Step 8: Count the number of cycles in which suspected arc faults occur within q consecutive analysis cycles. If the number of cycles is greater than a preset threshold value 4, it is determined that an arc fault occurs in the line; otherwise, it is determined that the line is in a normal state.

[0017] Preferably, the sampling frequency f and the time window length T s 1MHz and 20ms respectively.

[0018] Preferably, the pulse factor is calculated as follows:

[0019] S11: Take the absolute value of the zero-sequence current within an analysis cycle to obtain the processed signal {|x1|,|x2|,…·…,|x N |};

[0020] S12: Determine the maximum value max(|x n |);

[0021] S13: Determine the average value of the processed signal;

[0022] S14: The impulse factor is the processing signal {|x1|,|x2|,………,|x N |} is the ratio of the maximum value to the average value.

[0023] The calculation method of the pulse factor can be expressed by the following formula:

[0024]

[0025] Among them, x n It represents the zero-sequence current signal within one analysis cycle, N is the number of zero-sequence current sampling points within one analysis cycle, and taking a sampling rate of 1 MHz and a time window of 20 ms as an example, N is equal to 20000.

[0026] Preferably, the steps for calculating the interval between adjacent pulse peaks are as follows:

[0027] S21: Take the absolute value of the zero-sequence current signal within an analysis cycle, and split the zero-sequence current within an analysis cycle into two parts, namely, {|x1|,|x2|,…··,|x N / 2 |} and {|x N / 2+1 |,|x N / 2+2 |,···,|x N |};

[0028] S22: Calculate the position of the maximum value of the first part and the position of the maximum value of the second part respectively;

[0029] S23: Determine the position difference of the maximum values ​​of the front and rear signals;

[0030] S24: The interval between adjacent pulse peaks is the product of the position difference and the interval between two adjacent sampling points. The interval between adjacent pulse peaks can be expressed by the following formula:

[0031] IT=(p2-p1)×t i

[0032] where p1 is the sequence {|x1|,|x2|,···,|x N / 2 |}, p2 is the position of the maximum value in the sequence {|x N / 2+1 |,|x N / 2+2 |,···,|x N |}, the position of the maximum value, t i is the time interval between two adjacent sampling points, that is, 1 / f. Taking a sampling rate of 1MHz as an example, t i =0.001ms.

[0033] Preferably, the calculation method of Shannon entropy is as follows:

[0034]

[0035] Preferably, the calculation steps of the sum of harmonic amplitudes are as follows:

[0036] S31: Perform Fourier transform on the zero-sequence current signal within an analysis period to obtain the amplitude of each harmonic, that is, {y 50 ,y100 ,y 150 ,y 200 ,···,y 500000};

[0037] S32: The sum of the harmonic amplitudes is the sum of the amplitudes of each harmonic, that is,

[0038]

[0039] Preferably, the determination of whether the intervals between adjacent pulse peaks satisfy the relationship of an integer multiple of 10 ms may be achieved by the following steps:

[0040] S41: Calculate the remainder between the interval between adjacent pulse peaks and 10ms;

[0041] S42: If the remainder is less than 0.03ms or greater than 9.96ms, it means that the intervals between adjacent pulse peaks satisfy the integer multiple relationship of 10ms; otherwise, it means that the intervals between adjacent pulse peaks do not satisfy the integer multiple relationship of 10ms.

[0042] Compared with the prior art, the present invention has the following beneficial technical effects:

[0043] The present invention uses zero-sequence current as a detection signal, selects electrical characteristics related to the arc starting, arc burning, arc extinction and zero-off stages of the fault arc, and can effectively reduce the influence of background noise signals on the characteristic signals of the fault arc. There are obvious current mutations in the arc starting and arc extinction stages of the fault arc, and the fault arc is easy to radiate energy to the external space during the arc burning stage, resulting in differences between the live line current and the neutral line current. The method is based on these physical characteristics unique to the fault arc. These physical processes unique to the arc fault are manifested as pulse peak characteristics and ripple characteristics in the zero-sequence current. In addition, the zero-sequence current shields the influence of the load type on the arc characteristics at the physical level through the mutual cancellation of the live line current and the neutral line current. Therefore, the method is less affected by the load type and circuit topology, and can be applied to low-voltage AC power distribution systems with more complex structures.

[0044] The present invention selects pulse factors to characterize the pulse peaks generated by the fault arc in the arc starting and arc extinguishing stages, selects the interval time between adjacent pulse peaks to avoid the interference of arc-like signals by using the unstable characteristics of the fault arc, selects Shannon entropy to characterize the ripple characteristics generated by the fault arc in the arc burning stage, and uses the regularity characteristics of arc-like signals to extract harmonic amplitudes and avoid interference signals. The above characteristics are closely related to the physical characteristics of the arc and the circuit characteristics, and the fault arc detection method established based on this has strong interpretability and applicability.

[0045] The present invention determines the threshold value of each characteristic quantity through data statistics, and then realizes the threshold value judgment. The method is simple to calculate and easy to implement in engineering, and can provide a reference for the development of arc fault protection switches. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0047] Figure 1 It is a fault arc test experimental platform used in the present invention;

[0048] Figure 2 It is the experimental circuit topology used in the present invention;

[0049] Figure 3 It is the zero-sequence current signal waveform under different line states described in the present invention;

[0050] Figure 4 is the zero-sequence current signal waveform under different load types described in the present invention;

[0051] Figure 5 is a flow chart of the arc fault detection algorithm described in the present invention;

[0052] Figure 6 It is the confusion matrix of the fault arc detection accuracy under different circuit topologies described in the present invention. DETAILED DESCRIPTION

[0053] The following is a description of exemplary embodiments of the present application in conjunction with the accompanying drawings, including various details of the embodiments of the present application to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present application. Similarly, for the sake of clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0054] In order to enable people in the technical field to better understand the scheme of the present invention, the technical scheme in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is only an embodiment of a part of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in the field without making creative work should fall within the scope of protection of the present invention.

[0055] like Figure 1As shown in FIG. 1 , the zero-sequence current signal at the installation position of the miniature circuit breaker (MCB) is collected at a sampling rate of 1 MHz and a time window of 20 ms. The collected zero-sequence current waveform is as follows: Figure 2 shown.

[0056] It should be noted that the present invention carries out series arc fault experiments under different experimental loads, different fault locations and different arcing modes. The experimental loads include: resistors (46, 23, 15.3, 11.5Ω), kettles (1400W), ovens (1500W), fluorescent lamps (180W), induction cookers (1700W), microwave ovens (1300W), hand drills (200W), switching power supplies (400W), halogen lamps (400W) and dimming lamps (1000W, 30, 60, 90, 120 degrees). The fault locations include main circuit faults, load branch 1 faults and load branch 2 normal, load branch 1 normal and load branch 2 faults, which can be generally equivalent to: Figure 3 Four circuit topologies are shown. Arc-generating methods include arc generator and carbonized cable, which simulate fault arcs caused by poor contact and insulation aging respectively.

[0057] Calculation of pulse factor: The pulse factor within an analysis period is calculated using the following formula: Figure 2 Taking the zero-sequence current waveform in as an example, the pulse factors in normal and fault conditions are 5.9 and 172 respectively.

[0058]

[0059] Among them, x n It represents the zero-sequence current signal within one analysis cycle, N is the number of zero-sequence current sampling points within one analysis cycle, and taking a sampling rate of 1 MHz and a time window of 20 ms as an example, N is equal to 20000.

[0060] Calculation of the interval between adjacent pulse peaks: Figure 4 As shown in the figure, under arc-like load, the zero-sequence current waveform in normal state also has pulse spikes, and the corresponding pulse factor is larger at this time. However, unlike the irregular pulse spikes generated by the fault arc, the pulse spikes during normal circuit operation are regular. Therefore, the interval time between adjacent pulse spikes is used to avoid arc-like signal interference, and its calculation method is shown in the following formula.

[0061] IT=(p2-p1)×t i

[0062] where p1 is the sequence {|x1|,|x2|,···,|x N / 2 |}, p2 is the position of the maximum value in the sequence {|x N / 2+1 |,|xN / 2+2 |,···,|x N |}, the position of the maximum value, t i It is the time interval between two adjacent sampling points, that is, 1 / f. Here, its specific value is 0.001ms.

[0063] Depend on Figure 4 It can be seen that when the dimming lamp load works normally, the interval time between adjacent pulse peaks is an integer multiple of 10ms, while the pulse peaks generated by the fault arc do not meet this characteristic in terms of interval time. Therefore, the interval time between adjacent pulse peaks can be used to avoid interference from arc-like signals.

[0064] Calculation of Shannon entropy: Fault arc has unstable characteristics, which will cause irregular ripple characteristics of zero-sequence current. Therefore, Shannon entropy is used to characterize the disorder degree of zero-sequence current signal. Its calculation method is as follows:

[0065]

[0066] Calculation of harmonic amplitude sum: In actual low-voltage AC power distribution systems, some nonlinear loads will generate harmonic currents, causing regular ripple characteristics in zero-sequence current, which in turn increases Shannon entropy and causes characteristic aliasing problems. Therefore, the harmonic amplitude sum is used to analyze the regular ripple contained in the zero-sequence current. Its calculation steps are as follows:

[0067] Perform Fourier transformation on the zero-sequence current to obtain the amplitude of each harmonic frequency, that is, {y 50 ,y 100 ,y 150 ,y 200 ,···,y 500000};

[0068] The amplitude of each harmonic frequency is summed to obtain the harmonic amplitude sum.

[0069]

[0070] The sum of harmonic amplitudes of three loads, namely, resistance (excluding power frequency ripple and pulse spikes), dimming lamp (including pulse spikes) and induction cooker (including power frequency ripple) under different working conditions are calculated. The results are shown in Table 1. It can be seen that the sum of harmonic amplitudes of the load containing power frequency ripple is larger, so this feature can effectively distinguish the load containing power frequency ripple.

[0071] Table 1 Harmonic amplitude and value under different experimental conditions

[0072] resistance Dimming lights induction cooker normal <0.03 <0.03 >0.29 Arc Fault <0.05 <0.06 <0.28

[0073] After determining the four characteristics, Figure 5 The algorithm shown detects arc faults. The specific steps are as follows:

[0074] Step 1: If the Shannon entropy is less than the preset threshold 1, execute S2, otherwise execute step 3;

[0075] Step 2: If the pulse factor is less than the preset threshold 2, the line is marked as normal, otherwise, go to step 4;

[0076] Step 3: If the sum of the harmonic amplitudes is less than the preset threshold value 3, it is marked that a suspected arc fault occurs in the line, otherwise, step 5 is executed;

[0077] Step 4: If the interval between adjacent pulse peaks satisfies the integer multiple relationship of 10ms, the marked line is normal, otherwise, a suspected arc fault occurs in the marked line;

[0078] Step 5: If the pulse factor is less than the preset threshold value 2, the line is marked as normal, otherwise a suspected arc fault occurs in the marked line;

[0079] Step 6: Count the number of cycles in which suspected arc faults occur within q consecutive analysis cycles. If the number of cycles is greater than a preset threshold value 4, it is determined that an arc fault occurs in the line; otherwise, it is determined that the line is in a normal state.

[0080] When determining whether the interval time between adjacent pulse spikes satisfies the integer multiple relationship of 10ms, it is first necessary to calculate the remainder between the interval time and 10ms. If the remainder is less than 0.03ms or greater than 9.97ms, it means that the interval time between adjacent pulse spikes satisfies the integer multiple relationship of 10ms. Otherwise, it means that the interval time between adjacent pulse spikes does not satisfy the integer multiple relationship of 10ms.

[0081] By analyzing 119,000 sets of data under different experimental conditions (including 80,000 sets of normal data and 39,000 sets of fault data), we determined Figure 5 The four thresholds in are shown in Table 2.

[0082] Table 2 Threshold value reference in fault arc detection algorithm

[0083] Preset threshold 1 Preset Threshold 2 Preset threshold 3 Preset threshold 4 Value reference 5.88 47.38 0.14 4

[0084] Both the international standard IEC 62606 and the national standard GB / T 31143 require that the arc fault protection switch interrupts the arc fault under the current of 63A within 120ms, so the value of q is 6.

[0085] The above algorithm is used to detect arc faults in complex scenarios including 16 loads, 4 circuit topologies and 2 arcing modes. The results are shown in Table 3 and Figure 6 As shown in Table 3 and Figure 6It can be seen that under circuit topologies A, B, C, and D, the detection accuracy of the method of the present invention is 99.6% (7654 / 7680), 99.4% (4135 / 4160), 99.6% (2072 / 2080), and 99.3% (3178 / 3200), respectively. Therefore, the method of the present invention can provide a reference for the development of arc fault protection switches.

[0086] Table 3 Arc detection accuracy under circuit topology A

[0087] Load Type normal(%) Arc generator (%) Carbonized cable (%) Resistors (5, 10, 15, 20A) 100(640 / 640) 99.7(638 / 640) 99.5(637 / 640) induction cooker 100(160 / 160) 96.9(155 / 160) 100(160 / 160) Dimming light (30, 60, 90, 120 degrees) 100(640 / 640) 99.8(639 / 640) 99.5(637 / 640) Switching Power Supply 100(160 / 160) 100(160 / 160) 100(160 / 160) oven 100(160 / 160) 99.4(159 / 160) 100(160 / 160) Halogen lamp 100(160 / 160) 97.5(156 / 160) 100(160 / 160) Kettle 100(160 / 160) 97.5(156 / 160) 100(160 / 160) Hand drill 100(160 / 160) 100(160 / 160) 100(160 / 160) Micro-wave oven 100(160 / 160) 100(160 / 160) 100(160 / 160) Fluorescent lamp 100(160 / 160) 99.4(159 / 160) 98.8(158 / 160)

[0088] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. A method for identifying series fault arcs in a low voltage AC power distribution system, characterized in that: Step 1: In the low-voltage AC power distribution system, the zero-sequence current signals of different branches are sampled point by point at a frequency f, or the zero-sequence current signals of the upper busbars of each branch are sampled point by point, and the zero-sequence current signals are sampled according to the time window length T. s Extract the corresponding detection signal x in the current analysis cycle respectively n is the zero-sequence current, where n=1,2,…,N; Step 2: Calculate the pulse factor, adjacent pulse peak interval, Shannon entropy and harmonic amplitude of zero-sequence current in an analysis period; Step 3: If the Shannon entropy is less than the preset threshold 1, execute step 4, otherwise execute step 5; Step 4: If the pulse factor is less than the preset threshold 2, the line is marked as normal, otherwise, go to step 6; Step 5: If the sum of the harmonic amplitudes is less than the preset threshold value 3, it is marked that a suspected arc fault occurs in the line, otherwise, step 7 is executed; Step 6: If the interval between adjacent pulse peaks satisfies the integer multiple relationship of 10ms, the marked line is normal, otherwise, a suspected arc fault occurs in the marked line; Step 7: If the pulse factor is less than the preset threshold value 2, the line is marked as normal, otherwise a suspected arc fault occurs in the line; Step 8: Count the number of cycles in which suspected arc faults occur within q consecutive analysis cycles. If the number of cycles is greater than a preset threshold value 4, it is determined that an arc fault occurs in the line; otherwise, it is determined that the line is in a normal state.

2. A method for identifying series fault arcs in a low voltage AC power distribution system according to claim 1, characterized in that: Signal sampling frequency f and time window length T s 1MHz and 20ms respectively.

3. A method for identifying series fault arcs in a low voltage AC power distribution system according to claim 1, characterized in that: The calculation steps of the pulse factor are as follows: S11: Take the absolute value of the zero-sequence current within an analysis cycle to obtain a processing signal {|x1|,|x2|,…,|xN|}; S12: Determine the maximum value max(|x n |); S13: Determine the average value of the processed signal; S14: The impulse factor is the processing signal {|x1|,|x2|,…,|x N |}The ratio of the maximum value to the average value; The pulse factor can be expressed as follows: Wherein, N is the number of zero-sequence current sampling points in one analysis cycle.

4. A method for identifying series fault arcs in a low voltage AC power distribution system according to claim 1, characterized in that: The calculation steps of the interval between adjacent pulse peaks are as follows: S21: Take the absolute value of the zero-sequence current signal within an analysis cycle, and split the zero-sequence current within an analysis cycle into two parts, namely, {|x1|,|x2|,…,|x N / 2 |} and {|x N / 2+1 |,|x N / 2+2 |,…,|x N |}; S22: Calculate the position of the maximum value of the first part and the position of the maximum value of the second part respectively; S23: Determine the position difference of the maximum values ​​of the front and rear signals; S24: The interval between adjacent pulse peaks is the product of the position difference and the interval between two adjacent sampling points; The interval time between adjacent pulse peaks can be expressed by the following formula: IT=(p2-p1)×t i where p1 is the sequence {|x1|,|x2|,…,|x N / 2 |}, p2 is the position of the maximum value in the sequence {|x N / 2+1 |,|x N / 2+2 |,…,|x N |}, the position of the maximum value, t i is the time interval between two adjacent sampling points, that is, 1 / f.

5. A method for identifying series fault arcs in a low voltage AC power distribution system according to claim 1, characterized in that: The calculation formula of the Shannon entropy is as follows:

6. A method for identifying series fault arcs in a low voltage AC power distribution system according to claim 1, characterized in that: The calculation steps of the harmonic amplitude sum are as follows: S31: Perform Fourier transform on the zero-sequence current signal within an analysis period to obtain the amplitude of each harmonic, that is, {y 50 ,y 100 ,y 150 ,y 200 ,…,y 500000 }; S32: The sum of harmonic amplitudes is the sum of the amplitudes of each harmonic; The sum of harmonic amplitudes can be expressed as follows:

7. A method for identifying series fault arcs in a low voltage AC power distribution system according to claim 1, characterized in that: Determining whether the interval between adjacent pulse peaks satisfies the integer multiple relationship of 10ms can be achieved through the following steps: S41: Calculate the remainder between the interval between adjacent pulse peaks and 10ms; S42: If the remainder is less than 0.03ms or greater than 9.97ms, it means that the intervals between adjacent pulse peaks satisfy the integer multiple relationship of 10ms; otherwise, it means that the intervals between adjacent pulse peaks do not satisfy the integer multiple relationship of 10ms.

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