A Method for Extracting Early Fault Characteristics of Elbow Heads in Ring Main Units

Abstract

The present invention discloses a method for extracting early fault characteristics of an elbow head of a ring main unit, which relates to the technical field of elbow head fault detection and includes the following steps: decomposing the fault current waveform through wavelet transform to judge whether an arc is generated; using various characteristics of the zero rest duration in the fault current waveform as a comprehensive criterion to jointly identify the early fault of the elbow head. The present invention uses the number of occurrences of the zero rest duration, the polarity effect, and the change trend characteristics of the time length as a comprehensive criterion to identify the early fault of the elbow head, and uses the early fault waveform data of other types measured in the full-scale test site and the full-scale test data of the early fault of the elbow head for identification to verify the effectiveness of the method, laying a foundation for realizing the online monitoring of the early fault of the elbow head.

Description

Technical Field

[0001] The present invention relates to the technical field of elbow head fault detection, and more specifically, to a method for extracting early fault characteristics of an elbow head of a ring main unit. Background Art

[0002] In a cable power supply system, a ring main unit power supply system with high reliability, high power supply quality, and low line loss is often used. The elbow head of the ring main unit is made of silicone rubber material. As a supporting accessory for the cable line, it is installed at the connection between the cable and the bushing, playing an insulating protection role to prevent the cable from discharging to the cabinet wall. Its failure has an important impact on the reliable operation of the ring main unit and the cable system.

[0003] The early failure of the elbow head of the ring main unit occurs in the fault transition period. During this stage, due to long-term operation, problems such as aging of insulating materials and decline in insulation performance occur in the equipment, resulting in some defects that are not sufficient to cause permanent faults. Due to reasons such as the environment where the equipment is located and the fault development mechanism, the arcs caused by these defects can often extinguish themselves. At the same time, the instantaneous arcs are not sufficient to trigger the protection action, and the equipment can still operate normally after the arc extinguishes. However, the damage caused by the arc to the insulating material cannot be restored by itself. As the number of arcs in the early fault increases, the performance of the insulating material continuously declines, and finally the equipment enters the permanent fault period.

[0004] Currently, the detection methods for arcs mainly include two categories. One category is achieved by detecting various signals such as sound, light, heat, and electricity synchronously generated during the combustion of the arc, and the other category is achieved by judging and identifying waveforms to detect the arc. From the on-site practical experience, the detection methods for signals such as the sound, light, heat, and electricity of the arc combustion have limited measurement ranges and only have good effects on specific faults of specific equipment. For distribution equipment with low cost, large quantity, and wide distribution, the method of judging and identifying waveforms can be better applied in practice.

[0005] The development process of the early fault of the elbow head is divided into three stages: "water belt approaching - water belt boiling - water belt drying up". The arc generation positions and intensities in different stages are different. Through the mechanism analysis, simulation calculation, and artificial test of the early fault of the elbow head of the ring main unit, it can be found that the early fault of the elbow head has the following characteristics: in the water belt approaching stage, the zero rest duration of the fault current waveform gradually decreases until it disappears and turns into a sine waveform; in the water belt boiling stage, there are multiple "no zero rest - there is zero rest - no zero rest" transitions in the fault current waveform, and the change trend of the zero rest duration is first increasing and then decreasing; in the water belt drying up stage, the fault current waveform changes from "no zero rest" to "there is zero rest", and the zero rest duration gradually increases.

[0006] Therefore, how to extract the early fault characteristics of the fault current based on the above characteristics and design an identification method for the early faults of elbow heads is a technical problem that needs to be solved urgently by those skilled in the art. Summary of the Invention

[0007] In view of this, the present invention provides a method for extracting early fault characteristics of elbow heads of ring main units, which solves the problems existing in the background technology.

[0008] In order to achieve the above object, the present invention provides the following technical solutions:

[0009] A method for extracting early fault characteristics of elbow heads of ring main units includes the following steps:

[0010] Decompose the fault current waveform by wavelet transform to judge whether an arc is generated;

[0011] Use various characteristics of the zero rest duration in the fault current waveform as a comprehensive criterion to jointly identify the early faults of elbow heads.

[0012] Optionally, judging whether an arc is generated includes the following steps:

[0013] Perform wavelet transform on the fault current waveform to obtain the basic signal and the high-frequency detail signal;

[0014] Analyze the high-order harmonic information in the high-frequency detail signal, and judge whether an arc is generated by comparing the energy intensities of the normal sinusoidal current and the fault current in different high-frequency bands.

[0015] Optionally, the specific process of performing wavelet transform on the fault current waveform is as follows:

[0016] Discretize the original waveform into , where n is a positive integer, and the discrete signal is solved by the Mallat algorithm;

[0017] Decompose the discrete signal into the basic signal and the detail signal through a low-pass filter and a high-pass filter, and further refine and decompose the obtained basic signal into higher-level basic signals and detail signals according to research needs. The final decomposition results are shown in formulas (1)-(2):

[0018] (1);

[0019] (2);

[0020] In the formulas: S represents the original signal, represents the decomposition to the nThe basic signal after the layer, indicating the detail signal after decomposition to the n layer.

[0021] Optionally, the db2 wavelet basis is adopted during wavelet transform.

[0022] Optionally, the basis for judging whether an arc is generated is the high-order harmonic signal within the frequency band, and the specific judgment method is as follows:

[0023] If the formula (3) is satisfied, that is, the normalized current peak value of the high-frequency detail signal within a half-cycle is higher than the corresponding value of the sine wave, and the current zero-crossing interruption phenomenon caused by the arc leads to a mutation of the high-frequency detail signal, it is considered that an arc occurs;

[0024] (3);

[0025] In the formula: represents the peak value of each half-cycle in the high-frequency detail signal of the 5th layer of wavelet decomposition of the fault current, is the corresponding peak value of the half-cycle of the fault current; represents the peak value of each half-cycle in the high-frequency detail signal of the 5th layer of wavelet decomposition of the sine wave under normal operating conditions, is the corresponding peak value of the half-cycle of the sine wave.

[0026] Optionally, the identification method for the early fault of the elbow head is as follows:

[0027] After judging that an arc occurs, taking the fault mutation point as the reference point, detect the current zero-crossing interruption flat shoulder in the fault current in the direction earlier and later than the reference point in time until the current zero-crossing interruption phenomenon disappears, and record the zero-crossing interruption duration and the number of zero-crossing interruptions;

[0028] Regarding the number of zero-crossing interruption flat shoulders during the occurrence and extinction process of one arc being greater than 4 times as one of the criteria for the early fault of the elbow head; meanwhile, judge whether the arc fault that occurs is an early fault of the elbow head by performing a quadratic function fitting on the zero-crossing interruption duration during one arc process.

[0029] Optionally, judge whether the arc fault that occurs is an early fault of the elbow head by performing a quadratic function fitting on the zero-crossing interruption duration during one arc process, specifically as follows:

[0030] During the water belt approaching stage, the current zero-crossing interruption duration continuously decreases, and the main trend of the curve is a straight line with a negative slope, and the first-order coefficient term is negative;

[0031] During the water belt drying stage, the current zero-crossing interruption duration continuously increases, and the main trend of the curve is a straight line with a positive slope, and the first-order coefficient term is positive;

[0032] During the water belt boiling stage, the overall trend of the current zero rest duration is a downward-opening parabola, and the quadratic coefficient term is negative;

[0033] The zero rest duration at the end stage during the water belt boiling stage is less than that at the middle stage, and the zero rest duration at the end stage during the water belt drying stage is greater than that at the middle stage;

[0034] Therefore, by judging the fitting result parameters of a section of current zero rest duration and the relationship between the zero rest durations at the end stage and the middle stage, the corresponding fault stage is jointly judged; among them, Equation (4) is used to identify the water belt approaching stage, Equation (5) is used to identify the water belt boiling stage, and Equation (6) is used to identify the water belt drying stage:

[0035] (4);

[0036] (5);

[0037] (6);

[0038] In the formula: A , B are the parameters obtained by fitting the current zero rest duration, corresponding to the quadratic coefficient term and the linear coefficient term respectively; represents the zero rest duration at the middle stage, represents the zero rest duration at the end stage.

[0039] Optionally, it further includes: comparing the zero rest durations of the positive and negative half-cycles, and using the absence of polarity effect as the judgment basis for the early fault of the elbow head.

[0040] From the above technical solutions, it can be seen that compared with the prior art, the present invention discloses a method for extracting the early fault characteristics of the elbow head of a ring main unit. By wavelet decomposing the fault current waveform, calculating the normalized current peak value of the half-cycle high-frequency detail signal in the frequency band and comparing it with the sine waveform to judge the occurrence of the arc; then, taking the number of zero rest flat shoulders of the continuous fault current, the difference between the positive and negative half-cycles, and the fitting parameters of the zero rest duration in different stages as the fault identification features, the early fault of the elbow head can be accurately identified, laying a foundation for further realizing the online monitoring of the early fault of the elbow head, timely discovering and replacing the defective elbow head, and is of great significance for improving the operation reliability of the distribution network.

[0041] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained according to the provided drawings.

[0042] Figure 1 It is a flowchart of the method for extracting early fault characteristics of the elbow head of the ring main unit provided by the present invention;

[0043] Figure 2 It is a schematic diagram of the wavelet decomposition waveform process provided by the present invention;

[0044] Figure 3 It is the wavelet decomposition result of the current signal provided by the present invention. Specific embodiments

[0045] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0046] The embodiments of the present invention disclose a method for extracting early fault characteristics of the elbow head of the ring main unit, including the following steps:

[0047] Decompose the fault current waveform through wavelet transform to judge whether an arc is generated;

[0048] Use various characteristics of the zero rest duration in the fault current waveform as a comprehensive criterion to jointly identify the early faults of the elbow head.

[0049] Next, the technical solution of the present invention will be elaborated in detail through the Figure 1 specific process shown.

[0050] I. Basis for fault feature recognition

[0051] When an alternating current arc starts, the voltage will generate an arc ignition spike, and when the arc extinguishes, an arc extinction spike will be generated. At the same time, the arc current will have a short zero rest flat shoulder when passing through zero. Compared with the sine waveform, a large number of high-order harmonics will be generated during the zero rest flat shoulder period and at the moments of the arc ignition spike and the arc extinction spike of the arc current. Therefore, the specific method for judging whether an arc is generated given in this embodiment is as follows:

[0052] Perform wavelet transform on the fault current waveform to obtain the basic signal and high-frequency detail signals; analyze the high-order harmonic information in the high-frequency detail signals, and judge whether an arc is generated by comparing the energy intensities of the normal sinusoidal current and the fault current in different high-frequency bands.

[0053] Based on the mechanism analysis of the early faults of elbow joints, the equivalent test results and the full-scale test results, the arc in the early faults of elbow joints occurs in the air gap between the elbow joint and the bushing. The arc combustion environment is relatively stable and less affected by external factors. Therefore, in the artificial test results, the arc will continue to burn for more than two power frequency cycles after it appears. The number of continuous zero-rest flat shoulders in the fault current waveform can be used as one of the bases for identifying the early faults of elbow joints.

[0054] Through the mechanism analysis, simulation calculation and artificial test of the early faults of elbow joints, it is found that the fault current waveform can be roughly divided into three stages, and at the same time, the zero-rest duration of the fault current waveform has certain characteristics in each stage: in the stage when the water belt approaches, the zero-rest duration gradually decreases until it disappears; in the stage when the water belt boils, there are multiple "no zero-rest - there is zero-rest - no zero-rest" transitions, and the change trend of the zero-rest duration is first increasing and then decreasing; in the stage when the water belt dries up, the fault current waveform changes from "no zero-rest" to "there is zero-rest", and the zero-rest duration gradually increases. Therefore, by fitting the zero-rest flat shoulder time of the current, the development trend of the zero-rest flat shoulder time can be shown, and this feature can be used as one of the bases for identifying the early faults of elbow joints.

[0055] In addition, the materials on both sides of the arc in the early faults of elbow joints are conductive rubber and water belt or water belt and water belt. The material characteristics of the anode and cathode on both sides of the arc are not very different. Therefore, in the fault current waveform, zero-rest flat shoulders will appear in both the positive and negative half-cycles, and the duration will not show obvious differences. The characteristics of the collision between the branch and the overhead bare conductor and the early faults of the pin insulator on the distribution line are significantly different from this. Therefore, the number of zero-rest flat shoulders and the duration characteristics in the positive and negative half-cycles can be used as one of the bases for identifying the early faults of elbow joints.

[0056] To sum up, the characteristic information that can be used to identify the early faults of elbow joints includes: the mutation intensity of the decomposed high-order harmonic signal, the zero-rest time difference between the positive and negative half-cycles, the number of continuous zero-rest flat shoulders in the current waveform, and the fitting result of the zero-rest flat shoulder duration.

[0057] II. Wavelet Decomposition

[0058] In the field of arc waveform analysis, the generally traditional method is Fourier transform. However, this method can only reflect the characteristics of the waveform signal from the global features and it is difficult to observe the local features of the waveform. Wavelet transform takes into account the advantages of the improved Fourier algorithm in extracting the typical characteristics of the waveform and also has the advantage of being able to refine the multi-scale detailed features of the waveform in different frequency bands. Therefore, in this embodiment, wavelet transform is used to decompose the fault current waveform, analyze the high-order harmonics caused by the ignition, extinction, and zero rest of the arc, and use the high-order harmonic current intensity shown in the decomposition result as the feature for identifying the arc.

[0059] The schematic diagram of wavelet transform for decomposing the waveform is as Figure 2 shown, and the specific process is as follows:

[0060] Discretize the original waveform into , where n is a positive integer, and the discrete signal is solved using the Mallat algorithm;

[0061] Pass the discrete signal through a low-pass filter and a high-pass filter to decompose it into a basic signal and a detail signal. If the research requirements are not met, the obtained basic signal is further refined and decomposed into higher-level basic signals and detail signals. The final decomposition results are shown in Equations (1)-(2):

[0062] (1);

[0063] (2);

[0064] In the equations: S represents the original signal, represents the basic signal after being decomposed to the n th layer, represents the detail signal after being decomposed to the n th layer.

[0065] III. Discrimination of Feature Information

[0066] When performing wavelet decomposition, the wavelet basis used for decomposition will affect the decomposition result. After comprehensively considering the support length, order of vanishing moments, regularity, and symmetry of each wavelet, the db2 wavelet basis is adopted for wavelet decomposition in this embodiment. At the same time, if the wavelet decomposition order is too small, sufficient fine high-frequency detail signals cannot be obtained; if the wavelet decomposition order is too large, the high-frequency detail signals will be significantly affected by noise. Therefore, when selecting the wavelet decomposition order in this embodiment, the normal sinusoidal current waveform obtained in the true model test, and the fault current waveform segments with current zero rest and flat shoulder phenomena in the water belt approaching stage, water belt boiling stage, and water belt drying stage are respectively subjected to 4, 5, and 6 layers of wavelet decomposition. It is found that the high-frequency detail signals of the 5th layer obtained by using 5-layer decomposition are significantly different from those of other layers. Therefore, the 5-layer wavelet decomposition result is used as the identification criterion in this embodiment. The decomposition results of the current waveforms with zero rest and flat shoulder are similar. Therefore, taking the decomposition result obtained in the water belt approaching stage as an example in this embodiment, the decomposition results of the sinusoidal current waveform and the current waveform with zero rest and flat shoulder are as Figure 3 shown, where (a) is the wavelet decomposition result of the sinusoidal current, and (b) is the wavelet decomposition result of the current in the water belt approaching stage.

[0067] From Figure 3 the shown results, it can be seen that due to the existence of the zero rest and flat shoulder phenomenon in the arc current, a large amount of high-order harmonic information is generated in different frequency bands after wavelet decomposition. Combining Figure 3 the shown decomposition results, since the current signal amplitude in the frequency band is large and the clutter is small, and the high-order harmonic signal corresponding to the arc is significantly prominent. Therefore, in this embodiment, considering the amplitude height and vanishing order of each decomposition result comprehensively, the high-order harmonic signal in the frequency band is used as the basis for judging the arc occurrence of the fault current. The specific judgment method is as follows:

[0068] If the formula (3) is satisfied, that is, the normalized current peak value of the high-frequency detail signal within a half-cycle is higher than the corresponding value of the sine wave, and the current zero rest phenomenon caused by the arc causes a mutation in the high-frequency detail signal, it is considered that an arc occurs;

[0069] (3);

[0070] In the formula: represents the peak value of each half-cycle in the 5th layer high-frequency detail signal of the fault current wavelet decomposition, is the corresponding half-cycle peak value of the fault current; represents the peak value of each half-cycle in the 5th layer high-frequency detail signal of the sine wave wavelet decomposition under normal operating conditions, is the corresponding half-cycle peak value of the sine wave.

[0071] Table 1 lists the maximum normalized current peak value of the half-cycle high-frequency detail signal and the corresponding zero rest duration in the frequency band after wavelet decomposition of the fault current waveform in the three stages of the early fault of the elbow head calculated according to Equation (3). It can be seen from Table 1 that the longer the zero rest duration, the more severe the distortion of the current waveform, the greater the mutation of the decomposed high-frequency signal, and the greater the normalized current peak value of the half-cycle high-frequency detail signal, which can be used to detect the occurrence of an arc.

[0072] Table 1 Three stages of early faults and corresponding values of sinusoidal current

[0073]

[0074] Next, after judging the occurrence of an arc, taking the fault mutation point as the reference point, detect the current zero rest flat shoulder in the fault current in the direction earlier and later than the reference point in time until the zero rest phenomenon disappears, and record the zero rest duration and the number of zero rest occurrences.

[0075] Generally speaking, in the early fault of the elbow head, the number of zero rest flat shoulders during the occurrence and extinction of a single arc is greater than 4 times. Therefore, this rule can be used as one of the criteria for the early fault of the elbow head; at the same time, by fitting the quadratic function of the zero rest duration during a single arc process to judge whether the arc fault that occurred is an early fault of the elbow head, the fitting results of the zero rest duration shown in Table 2 are obtained.

[0076] Table 2 Fitting results of zero rest duration

[0077]

[0078] Furthermore, by fitting the quadratic function of the zero rest duration during a single arc process to judge whether the arc fault that occurred is an early fault of the elbow head, specifically:

[0079] In the stage when the water belt is approaching, the zero rest duration of the current continuously decreases, the main trend of the curve is a straight line with a negative slope, and the first-order coefficient term is negative;

[0080] In the stage when the water belt is drying up, the zero rest duration of the current continuously increases, the main trend of the curve is a straight line with a positive slope, and the first-order coefficient term is positive;

[0081] In the stage when the water belt is boiling, the overall trend of the zero rest duration of the current is a parabola opening downward, and the second-order coefficient term is negative;

[0082] In the stage when the water belt is boiling, the zero rest duration at the end stage is less than that in the middle stage, and in the stage when the water belt is drying up, the zero rest duration at the end stage is greater than that in the middle stage;

[0083] Therefore, by jointly judging the fitting result parameters of the current zero rest duration and the relationship between the zero rest durations in the late and middle stages, the corresponding fault stage can be determined. Among them, Equation (4) is used to identify the stage when the water belt is approaching, Equation (5) is used to identify the stage when the water belt is boiling, and Equation (6) is used to identify the stage when the water belt is drying up:

[0084] (4);

[0085] (5);

[0086] (6);

[0087] In the formula: A and B are the parameters obtained after fitting the current zero rest duration, corresponding to the quadratic coefficient term and the linear coefficient term respectively; represents the zero rest duration in the middle stage, represents the zero rest duration in the late stage.

[0088] In addition, it can be seen from the change trend of the zero rest duration that in the early failure of the elbow head, the arc occurs between materials with similar properties, without the participation of metal, and there is no obvious difference in the zero rest duration between the positive and negative half cycles. Therefore, by comparing the zero rest durations of the positive and negative half cycles, the absence of polarity effect can be used as the judgment basis for the early failure of the elbow head.

[0089] IV. Verification of Test Data

[0090] To verify the accuracy of the method of the present invention, in this embodiment, tree branches touching overhead bare conductors, early failures of distribution line pin insulators, and cable creepage tests are carried out in the same full-scale test site. The measured fault waveform data and the fault waveform data obtained from the full-scale test of the elbow head carried out in this embodiment are jointly used to verify the practicability of the proposed identification method. The fault current waveform acquisition methods for various tests are the same as those of the equivalent test and full-scale test of the early failure of the elbow head carried out in this embodiment.

[0091] The waveform recording device has now developed to a relatively mature stage and can perform high-precision online monitoring on the power supply lines of the distribution network. In practical applications, the current information at the fault location can be obtained through the waveform recording devices on both sides of the fault location. The power supply distance of the cable power supply system in the 10 kV distribution network is generally within 3 km to 5 km, so there is no need to consider the attenuation of traveling waves during transmission.

[0092] The resistance of a tree itself is relatively large. When a tree branch touches an overhead bare conductor, the resulting arc represents a typical high-resistance grounding fault, which is similar to the characteristics of the high-resistance grounding fault shown in the early-stage elbow fault due to the relatively large resistance of the water tape. However, the main characteristic of the early-stage tree-touching-line fault is that during the separation process between the tree branch and the conductor, the arc gradually elongates, and the zero-rest flat-shoulder time of the fault current gradually increases. But the characteristics of the early-stage tree-touching-line fault do not include the trend that the zero-rest duration of the fault current first increases and then decreases during the water-tape boiling stage in the early-stage elbow fault.

[0093] The early-stage fault of the pin insulator in the distribution line occurs in rainy and foggy weather. Moisture seeps into the cracks of the insulator, causing a self-extinguishing arc. The electrodes on both sides of the arc are aluminum conductors and electrolyte, so its fault waveform has an obvious polarity effect, and the arcing probability in the positive half-cycle is higher than that in the negative half-cycle. In the early-stage elbow fault, the electrodes on both sides of the arc are conductive rubber and water tape or water tape and water tape, and their material characteristics are similar, so the fault current waveform does not have a polarity effect, and there is no obvious difference in the zero-rest duration between the positive and negative half-cycles.

[0094] The cable operates in a cable well. Due to the relatively high humidity, the cable may be affected by moisture, resulting in water ingress at the cable body and cable accessories such as intermediate joints, causing creepage. This type of fault is affected by the generation of organic substances at high temperatures in cross-linked polyethylene insulation and the narrow space. There are a large number of unstable transient characteristics in the fault current waveform, which is significantly different from the early-stage elbow fault current waveform.

[0095] By inputting 168 pieces of fault current waveform data obtained from tree branches touching overhead bare conductors, early-stage faults of pin insulators in distribution lines, cable creepage, and full-scale elbow tests, using the Figure 1 shown process, all 32 pieces of full-scale elbow early-stage fault test data were successfully identified, verifying the effectiveness of the early-stage elbow fault identification method proposed in this paper.

[0096] V. Summary

[0097] In this embodiment, a feature extraction method for early-stage elbow faults is designed by comprehensively analyzing the mechanism, simulation calculation, and artificial test results. First, the fault current waveform is decomposed by wavelet, and the normalized current peak value of the half-cycle high-frequency detail signal in the frequency band is calculated and compared with the sine waveform to determine the occurrence of the arc. Then, the continuous zero-rest flat-shoulder times of the fault current, the difference between the positive and negative half-cycles, and the fitting parameters of the zero-rest duration in different stages are used as fault identification features, and the overall identification method process is designed. Finally, combined with the early-stage fault current waveforms of other types in the test, the effectiveness of the designed method is verified.

[0098] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. For the same or similar parts among the various embodiments, reference can be made to each other.

[0099] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for extracting early fault characteristics of an elbow head of a ring main unit, characterized in that, It includes the following steps: Decompose the fault current waveform through wavelet transform to judge whether an arc is generated. The specific steps are as follows: perform wavelet transform on the fault current waveform to obtain the basic signal and high-frequency detail signal; analyze the high-order harmonic information in the high-frequency detail signal, and judge whether an arc is generated by comparing the energy intensities of the normal sinusoidal current and the fault current in different high-frequency bands; Use various characteristics of the zero rest duration in the fault current waveform as a comprehensive criterion to jointly identify the early faults of the elbow head; Among them, the judgment basis for whether an arc is generated is the high-order harmonic signal in the D5 frequency band. The specific judgment method is as follows: If the formula (3) is satisfied, that is, the normalized current peak value of the high-frequency detail signal within a half cycle is higher than the corresponding value of the sine wave, and the current zero rest phenomenon caused by the arc causes a mutation in the high-frequency detail signal, it is considered that an arc occurs; Where: I imax (D5) represents the peak value of each half-cycle in the 5th layer high-frequency detail signal of the fault current wavelet decomposition, and I imax (S) is the corresponding peak value of the half-cycle of the fault current; I smax (D5) represents the peak value of each half-cycle in the 5th layer high-frequency detail signal of the sine wave wavelet decomposition under normal operating conditions, and I smax (S) is the corresponding peak value of the half-cycle of the sine wave; The identification method for the early faults of the elbow head is as follows: After judging that an arc occurs, taking the fault mutation point as the reference point, detect the current zero rest flat shoulder in the fault current in the direction earlier and later than the reference point in time until the current zero rest phenomenon disappears, and record the zero rest duration and the number of zero rest occurrences; Taking the number of zero rest flat shoulder occurrences greater than 4 times during one arc appearance and extinction process as one of the criteria for the early faults of the elbow head; at the same time, judge whether the arc fault that occurs is an early fault of the elbow head by quadratic function fitting of the zero rest duration during one arc process.

2. The method for extracting early fault characteristics of an elbow head of a ring main unit according to claim 1, characterized in that, The specific process of performing wavelet transform on the fault current waveform is as follows: Discretize the original waveform S(t) into S(n), where n is a positive integer, and the discrete signal S(n) is solved by the Mallat algorithm; Decompose the discrete signal S(n) into the basic signal and the detail signal through a low-pass filter and a high-pass filter, and further refine and decompose the obtained basic signal into higher-level basic signals and detail signals according to research needs. The final decomposition results are shown in formulas (1)-(2): S = S1 + D1 = S2 + D1 + D2 = … (1); S(n) = S n + D1 + D2 + … + D n (2); Where: S represents the original signal, S n represents the basic signal after being decomposed to the nth layer, D n represents the detail signal after being decomposed to the nth layer.

3. A method for extracting early fault characteristics of an elbow head of a ring main unit according to claim 1, characterized in that, The db2 wavelet basis is used during wavelet transform.

4. A method for extracting early fault characteristics of an elbow head of a ring main unit according to claim 1, characterized in that, Judge whether the arc fault that occurs is an early fault of the elbow head by quadratic function fitting of the zero rest duration during one arc process. Specifically: During the water belt approaching stage, the current zero rest duration continuously decreases, and the main trend of the curve is a straight line with a negative slope, and the first-order coefficient term is negative; During the water belt drying stage, the current zero rest duration continuously increases, and the main trend of the curve is a straight line with a positive slope, and the first-order coefficient term is positive; During the water belt boiling stage, the overall trend of the current zero rest duration is a parabola opening downward, and the second-order coefficient term is negative; The zero rest duration at the end stage during the water belt boiling stage is less than the zero rest duration at the middle stage, and the zero rest duration at the end stage during the water belt drying stage is greater than the zero rest duration at the middle stage; Therefore, jointly judge the corresponding fault stage by judging the fitting result parameters of a section of the zero rest duration and the relationship between the zero rest durations at the end stage and the middle stage; among them, formula (4) is used to identify the water belt approaching stage, formula (5) is used to identify the water belt boiling stage, and formula (6) is used to identify the water belt drying stage: B<0 (4)； A<0,t middle >t late (5); B>0,t middle <t late (6); where: A and B are the parameters obtained by fitting the current zero rest duration, corresponding to the quadratic coefficient term and the linear coefficient term respectively; t middle represents the medium-term zero rest duration, and t late represents the late-term zero rest duration.

5. A method for extracting early fault characteristics of an elbow head of a ring main unit according to claim 1, characterized in that, Further included are: Comparing the zero rest durations of the positive and negative half-cycles, and taking the absence of polarity effect as the basis for judging the early faults of the elbow head.

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